(upbeat music) >> From theCUBE studios in Palo Alto, in Boston bringing you data-driven insights from theCUBE and ETR. This is braking analysis with Dave Vellante. >> Database is the heart of enterprise computing. The market is both exploding and it's evolving. The major force is transforming the space include Cloud and data, of course, but also new workloads, advanced memory and IO capabilities, new processor types, a massive push towards simplicity, new data sharing and governance models, and a spate of venture investment. Snowflake stands out as the gold standard for operational excellence and go to market execution. The company has attracted the attention of customers, investors, and competitors and everyone from entrenched players to upstarts once in the act. Hello everyone and welcome to this week's Wikibon CUBE Insights powered by ETR. In this breaking analysis, we'll share our most current thinking on the database marketplace and dig into Snowflake's execution. Some of its challenges and we'll take a look at how others are making moves to solve customer problems and try to get a piece of the growing database pie. Let's look at some of the factors that are driving market momentum. First, customers want lower license costs. They want simplicity. They want to avoid database sprawl. They want to run anywhere and manage new data types. These needs often are divergent and they pull vendors and technologies in different direction. It's really hard for any one platform to accommodate every customer need. The market is large and it's growing. Gardner has it at around 60 to 65 billion with a CAGR of somewhere around 20% over the next five years. But the market, as we know it is being redefined. Traditionally, databases have served two broad use cases, OLTP or transactions and reporting like data warehouses. But a diversity of workloads and new architectures and innovations have given rise to a number of new types of databases to accommodate all these diverse customer needs. Many billions have been spent over the last several years in venture money and it continues to pour in. Let me just give you some examples. Snowflake prior to its IPO, raised around 1.4 billion. Redis Labs has raised more than 1/2 billion dollars so far, Cockroach Labs, more than 350 million, Couchbase, 250 million, SingleStore formerly MemSQL, 238 million, Yellowbrick Data, 173 million. And if you stretch the definition of database a little bit to including low-code or no-code, Airtable has raised more than 600 million. And that's by no means a complete list. Now, why is all this investment happening? Well, in a large part, it's due to the TAM. The TAM is huge and it's growing and it's being redefined. Just how big is this market? Let's take a look at a chart that we've shown previously. We use this chart to Snowflakes TAM, and it focuses mainly on the analytics piece, but we'll use it here to really underscore the market potential. So the actual database TAM is larger than this, we think. Cloud and Cloud-native technologies have changed the way we think about databases. Virtually 100% of the database players that they're are in the market have pivoted to a Cloud first strategy. And many like Snowflake, they're pretty dogmatic and have a Cloud only strategy. Databases has historically been very difficult to manage, they're really sensitive to latency. So that means they require a lot of tuning. Cloud allows you to throw virtually infinite resources on demand and attack performance problems and scale very quickly, minimizing the complexity and tuning nuances. This idea, this layer of data as a service we think of it as a staple of digital transformation. Is this layer that's forming to support things like data sharing across ecosystems and the ability to build data products or data services. It's a fundamental value proposition of Snowflake and one of the most important aspects of its offering. Snowflake tracks a metric called edges, which are external connections in its data Cloud. And it claims that 15% of its total shared connections are edges and that's growing at 33% quarter on quarter. This notion of data sharing is changing the way people think about data. We use terms like data as an asset. This is the language of the 2010s. We don't share our assets with others, do we? No, we protect them, we secure or them, we even hide them. But we absolutely don't want to share those assets but we do want to share our data. I had a conversation recently with Forrester analyst, Michelle Goetz. And we both agreed we're going to scrub data as an asset from our phrasiology. Increasingly, people are looking at sharing as a way to create, as I said, data products or data services, which can be monetized. This is an underpinning of Zhamak Dehghani's concept of a data mesh, make data discoverable, shareable and securely governed so that we can build data products and data services that can be monetized. This is where the TAM just explodes and the market is redefining. And we think is in the hundreds of billions of dollars. Let's talk a little bit about the diversity of offerings in the marketplace. Again, databases used to be either transactional or analytic. The bottom lines and top lines. And this chart here describe those two but the types of databases, you can see the middle of mushrooms, just looking at this list, blockchain is of course a specialized type of database and it's also finding its way into other database platforms. Oracle is notable here. Document databases that support JSON and graph data stores that assist in visualizing data, inference from multiple different sources. That's is one of the ways in which adtech has taken off and been so effective. Key Value stores, log databases that are purpose-built, machine learning to enhance insights, spatial databases to help build the next generation of products, the next automobile, streaming databases to manage real time data flows and time series databases. We might've missed a few, let us know if you think we have, but this is a kind of pretty comprehensive list that is somewhat mind boggling when you think about it. And these unique requirements, they've spawned tons of innovation and companies. Here's a small subset on this logo slide. And this is by no means an exhaustive list, but you have these companies here which have been around forever like Oracle and IBM and Teradata and Microsoft, these are the kind of the tier one relational databases that have matured over the years. And they've got properties like atomicity, consistency, isolation, durability, what's known as ACID properties, ACID compliance. Some others that you may or may not be familiar with, Yellowbrick Data, we talked about them earlier. It's going after the best price, performance and analytics and optimizing to take advantage of both hybrid installations and the latest hardware innovations. SingleStore, as I said, formerly known as MemSQL is a very high end analytics and transaction database, supports mixed workloads, extremely high speeds. We're talking about trillions of rows per second that could be ingested in query. Couchbase with hybrid transactions and analytics, Redis Labs, open source, no SQL doing very well, as is Cockroach with distributed SQL, MariaDB with its managed MySQL, Mongo and document database has a lot of momentum, EDB, which supports open source Postgres. And if you stretch the definition a bit, Splunk, for log database, why not? ChaosSearch, really interesting startup that leaves data in S-3 and is going after simplifying the ELK stack, New Relic, they have a purpose-built database for application performance management and we probably could have even put Workday in the mix as it developed a specialized database for its apps. Of course, we can't forget about SAP with how not trying to pry customers off of Oracle. And then the big three Cloud players, AWS, Microsoft and Google with extremely large portfolios of database offerings. The spectrum of products in this space is very wide, with you've got AWS, which I think we're up to like 16 database offerings, all the way to Oracle, which has like one database to do everything not withstanding MySQL because it owns MySQL got that through the Sun Acquisition. And it recently, it made some innovations there around the heat wave announcement. But essentially Oracle is investing to make its database, Oracle database run any workload. While AWS takes the approach of the right tool for the right job and really focuses on the primitives for each database. A lot of ways to skin a cat in this enormous and strategic market. So let's take a look at the spending data for the names that make it into the ETR survey. Not everybody we just mentioned will be represented because they may not have quite the market presence of the ends in the survey, but ETR that capture a pretty nice mix of players. So this chart here, it's one of the favorite views that we like to share quite often. It shows the database players across the 1500 respondents in the ETR survey this past quarter and it measures their net score. That's spending momentum and is shown on the vertical axis and market share, which is the pervasiveness in the data set is on the horizontal axis. The Snowflake is notable because it's been hovering around 80% net score since the survey started picking them up. Anything above 40%, that red line there, is considered by us to be elevated. Microsoft and AWS, they also stand out because they have both market presence and they have spending velocity with their platforms. Oracle is very large but it doesn't have the spending momentum in the survey because nearly 30% of Oracle installations are spending less, whereas only 22% are spending more. Now as a caution, this survey doesn't measure dollar spent and Oracle will be skewed toward the big customers with big budgets. So you got to consider that caveat when evaluating this data. IBM is in a similar position although its market share is not keeping up with Oracle's. Google, they've got great tech especially with BigQuery and it has elevated momentum. So not a bad spot to be in although I'm sure it would like to be closer to AWS and Microsoft on the horizontal axis, so it's got some work to do there. And some of the others we mentioned earlier, like MemSQL, Couchbase. As shown MemSQL here, they're now SingleStore. Couchbase, Reddis, Mongo, MariaDB, all very solid scores on the vertical axis. Cloudera just announced that it was selling to private equity and that will hopefully give it some time to invest in this platform and get off the quarterly shot clock. MapR was acquired by HPE and it's part of HPE's Ezmeral platform, their data platform which doesn't yet have the market presence in the survey. Now, something that is interesting in looking at in Snowflakes earnings last quarter, is this laser focused on large customers. This is a hallmark of Frank Slootman and Mike Scarpelli who I know they don't have a playbook but they certainly know how to go whale hunting. So this chart isolates the data that we just showed you to the global 1000. Note that both AWS and Snowflake go up higher on the X-axis meaning large customers are spending at a faster rate for these two companies. The previous chart had an end of 161 for Snowflake, and a 77% net score. This chart shows the global 1000, in the end there for Snowflake is 48 accounts and the net score jumps to 85%. We're not going to show it here but when you isolate the ETR data, nice you can just cut it, when you isolate it on the fortune 1000, the end for Snowflake goes to 59 accounts in the data set and Snowflake jumps another 100 basis points in net score. When you cut the data by the fortune 500, the Snowflake N goes to 40 accounts and the net score jumps another 200 basis points to 88%. And when you isolate on the fortune 100 accounts is only 18 there but it's still 18, their net score jumps to 89%, almost 90%. So it's very strong confirmation that there's a proportional relationship between larger accounts and spending momentum in the ETR data set. So Snowflakes large account strategy appears to be working. And because we think Snowflake is sticky, this probably is a good sign for the future. Now we've been talking about net score, it's a key measure in the ETR data set, so we'd like to just quickly remind you what that is and use Snowflake as an example. This wheel chart shows the components of net score, that lime green is new adoptions. 29% of the customers in the ETR dataset that are new to Snowflake. That's pretty impressive. 50% of the customers are spending more, that's the forest green, 20% are flat, that's the gray, and only 1%, the pink, are spending less. And 0% zero or replacing Snowflake, no defections. What you do here to get net scores, you subtract the red from the green and you get a net score of 78%. Which is pretty sick and has been sick as in good sick and has been steady for many, many quarters. So that's how the net score methodology works. And remember, it typically takes Snowflake customers many months like six to nine months to start consuming it's services at the contracted rate. So those 29% new adoptions, they're not going to kick into high gear until next year, so that bodes well for future revenue. Now, it's worth taking a quick snapshot at Snowflakes most recent quarter, there's plenty of stuff out there that you can you can google and get a summary but let's just do a quick rundown. The company's product revenue run rate is now at 856 million they'll surpass $1 billion on a run rate basis this year. The growth is off the charts very high net revenue retention. We've explained that before with Snowflakes consumption pricing model, they have to account for retention differently than what a SaaS company. Snowflake added 27 net new $1 million accounts in the quarter and claims to have more than a hundred now. It also is just getting its act together overseas. Slootman says he's personally going to spend more time in Europe, given his belief, that the market is huge and they can disrupt it and of course he's from the continent. He was born there and lived there and gross margins expanded, do in a large part to renegotiation of its Cloud costs. Welcome back to that in a moment. Snowflake it's also moving from a product led growth company to one that's more focused on core industries. Interestingly media and entertainment is one of the largest along with financial services and it's several others. To me, this is really interesting because Disney's example that Snowflake often puts in front of its customers as a reference. And it seems to me to be a perfect example of using data and analytics to both target customers and also build so-called data products through data sharing. Snowflake has to grow its ecosystem to live up to its lofty expectations and indications are that large SIS are leaning in big time. Deloitte cross the $100 million in deal flow in the quarter. And the balance sheet's looking good. Thank you very much with $5 billion in cash. The snarks are going to focus on the losses, but this is all about growth. This is a growth story. It's about customer acquisition, it's about adoption, it's about loyalty and it's about lifetime value. Now, as I said at the IPO, and I always say this to young people, don't buy a stock at the IPO. There's probably almost always going to be better buying opportunities ahead. I'm not always right about that, but I often am. Here's a chart of Snowflake's performance since IPO. And I have to say, it's held up pretty well. It's trading above its first day close and as predicted there were better opportunities than day one but if you have to make a call from here. I mean, don't take my stock advice, do your research. Snowflake they're priced to perfection. So any disappointment is going to be met with selling. You saw that the day after they beat their earnings last quarter because their guidance in revenue growth,. Wasn't in the triple digits, it sort of moderated down to the 80% range. And they pointed, they pointed to a new storage compression feature that will lower customer costs and consequently, it's going to lower their revenue. I swear, I think that that before earnings calls, Scarpelli sits back he's okay, what kind of creative way can I introduce the dampen enthusiasm for the guidance. Now I'm not saying lower storage costs will translate into lower revenue for a period of time. But look at dropping storage prices, customers are always going to buy more, that's the way the storage market works. And stuff like did allude to that in all fairness. Let me introduce something that people in Silicon Valley are talking about, and that is the Cloud paradox for SaaS companies. And what is that? I was a clubhouse room with Martin Casado of Andreessen when I first heard about this. He wrote an article with Sarah Wang, calling it to question the merits of SaaS companies sticking with Cloud at scale. Now the basic premise is that for startups in early stages of growth, the Cloud is a no brainer for SaaS companies, but at scale, the cost of Cloud, the Cloud bill approaches 50% of the cost of revenue, it becomes an albatross that stifles operating leverage. Their conclusion ended up saying that as much as perhaps as much as the back of the napkin, they admitted that, but perhaps as much as 1/2 a trillion dollars in market cap is being vacuumed away by the hyperscalers that could go to the SaaS providers as cost savings from repatriation. And that Cloud repatriation is an inevitable path for large SaaS companies at scale. I was particularly interested in this as I had recently put on a post on the Cloud repatriation myth. I think in this instance, there's some merit to their conclusions. But I don't think it necessarily bleeds into traditional enterprise settings. But for SaaS companies, maybe service now has it right running their own data centers or maybe a hybrid approach to hedge bets and save money down the road is prudent. What caught my attention in reading through some of the Snowflake docs, like the S-1 in its most recent 10-K were comments regarding long-term purchase commitments and non-cancelable contracts with Cloud companies. And the companies S-1, for example, there was disclosure of $247 million in purchase commitments over a five plus year period. And the company's latest 10-K report, that same line item jumped to 1.8 billion. Now Snowflake is clearly managing these costs as it alluded to when its earnings call. But one has to wonder, at some point, will Snowflake follow the example of say Dropbox which Andreessen used in his blog and start managing its own IT? Or will it stick with the Cloud and negotiate hard? Snowflake certainly has the leverage. It has to be one of Amazon's best partners and customers even though it competes aggressively with Redshift but on the earnings call, CFO Scarpelli said, that Snowflake was working on a new chip technology to dramatically increase performance. What the heck does that mean? Is this Snowflake is not becoming a hardware company? So I going to have to dig into that a little bit and find out what that it means. I'm guessing, it means that it's taking advantage of ARM-based processes like graviton, which many ISVs ar allowing their software to run on that lower cost platform. Or maybe there's some deep dark in the weeds secret going on inside Snowflake, but I doubt it. We're going to leave all that for there for now and keep following this trend. So it's clear just in summary that Snowflake they're the pace setter in this new exciting world of data but there's plenty of room for others. And they still have a lot to prove. For instance, one customer in ETR, CTO round table express skepticism that Snowflake will live up to its hype because its success is going to lead to more competition from well-established established players. This is a common theme you hear it all the time. It's pretty easy to reach that conclusion. But my guess is this the exact type of narrative that fuels Slootman and sucked him back into this game of Thrones. That's it for now, everybody. Remember, these episodes they're all available as podcasts, wherever you listen. All you got to do is search braking analysis podcast and please subscribe to series. Check out ETR his website at etr.plus. We also publish a full report every week on wikinbon.com and siliconangle.com. You can get in touch with me, Email is David.vellante@siliconangle.com. You can DM me at DVelante on Twitter or comment on our LinkedIn posts. This is Dave Vellante for theCUBE Insights powered by ETR. Have a great week everybody, be well and we'll see you next time. (upbeat music)

>> Live from Las Vegas, it's theCUBE. Covering Adobe Summit 2019. Brought to you by Adobe. >> Welcome back everyone, live CUBE coverage here in Las Vegas for Adobe Summit 2019 I'm John Furrier. With Jeff Frick. Our next guest is Jeff Allen, Senior Director Product Marketing, Adobe. Jeff, welcome to theCUBE, thanks for joining us. >> Thank you. Nice to be here. >> So day one is kind of winding down, big, great keynote, laid out the platform product's working together, lot of data, lots of data conversations. >> Yeah, exciting day. Excited to have Adobe Analytics in the mix with that, you saw the four clouds we talked about, Analytics Cloud is one of them and really kind of core to everything we do at Adobe, right? In fact, even in the Creative Cloud side, Document Cloud side, our customers have to be able to measure what they're doing and so, data is obviously key to that. >> Tapping the data across the different applications and now clouds - It's interesting - it's a whole new grail, people have been trying to do for how many years? >> Forever, from the beginning. >> And it's always been that holy grail, where is it? Now some visibility is starting to get to see into the benefits of horizontal scale, diverse data, contextual workloads, >> Absolutely, yeah. >> This is a big deal. >> It is a big deal. >> Explain why it's impacting. >> It's funny. Our culture now expects data right? We measure everything. Our kids are taught to measure things, even something as simple as likes on, my kids, they argue about whether the picture mom posted of them or the other one got more likes, right? So we kind of have hardwired our society around measurement, and now of course, marketing has always been a measurement-heavy discipline, and so, it's just absolutely core to what we're doing. >> And we had a historic moment, we've been doing theCUBE, it's our 10th season, a lot of events. >> Congratulations. >> And we had a guest come on here, that we've never had before, the title was Marketing CIO, it was one of your customers at MetLife >> Interesting, yeah. >> But this brings the question of, of the confluence of you know, the factions coming together. IT, creative, marketing, where the tech, measurement, data. >> Yeah, totally. >> Data processing, information systems, kind of an IT concept now being driven and married in with the business side. >> Absolutely. >> This is really the fundamental thing. >> I started my career marketing to CIOs, in fact, I've spent most of my career marketing to the CIO organization, right, and about 7 years ago, I came over to Adobe to market to marketing, right? And I used to say, "You know I kind of like marketing to this guy, I understand him better," right? Because I know how marketers think a lot better than CIOs, I had to go learn how they thought. But it's amazing how the tech explosion has happened in MarTech and AdTech, all of these vendors here at this event, this is just a piece of our industry, right? There's thousands of companies serving marketing organizations, and so, all of a sudden, the tech stack looks more crazy than even what many CIOs manage, and so it doesn't surprise me at all that organizations, you're talking to organizations that have a CIO/CMO hybrid role. >> Jeff, I'm curious how the landscape is changing, because all the talk here is about experiences, right? And the transaction is part of the experience, but it's not the end game, in fact, it's just a marker on a journey that hopefully lasts a long time. How does that change kind of the way that you look at data, the way customers are looking at data, you know, how the KPIs are changing, and what they're measuring, and the value of the different buckets of data as it's no longer about getting to that transaction, boom, ship the product, and we're done. >> Yeah, so I look after Adobe Analytics, and Adobe Analytics was the first component we acquired in this business, right? Experience Cloud, started with the acquisition of a company called Omniture back in 2009, was an analytics company, primarily web and mobile app analytics, and it has grown since then, to measure many more things. And we've seen our category with analytics that we've addressed move from web analytics to a broader view of digital analytics, right? The digital parts of marketing to all of marketing, the rest of marketing said, "Hey, we need measurements too. We need tools." And then it clicked out another broader click to this idea of experience, right? Because everybody has a stake in experience, and experience is all wrapped around people and how people move through experiences with your brand, so that's where we sit today, is really helping organizations measure experiences, and that spans every person in the organization. >> Talk about the dynamic between how the old way of thinking was shifting to this new way, and specifically, the old way was "I'm a database guy. I've got operational databases and analytical databases," you know, and that was it. You know, relational, unstructured, you know, kind of quadrants. Now, it's kind of, you have (laughs) it's not about databases, it's about data. So you have operational data, which is the analytical data now >> Yeah. >> So you have now, this new dynamic, it's not about the databases anymore >> Absolutely. >> It's about the data itself. >> It's not about, I would say, it's not about the stores of data, right? It's about really getting the insights out of the data, and you know, for the longest time, in my career, uh, you went to CIO, the CIO organization and there was a BI team there, and you would ask them for data, and they could go to the main frame, they could go to these big IT systems, and you know, in 30 days, they could email you back a .csv file, or even before that meeting, give you a .zip file or something with the .csv file on it. And then you got to go see if you could even get it to open on your laptop and get it into Excel and start to manipulate it. And those days don't work. >> And then you go get your root canal right after. It's a painful process. >> What if the data - today that data is trying to understand, "Hey I got a guy that just checked into the hotel. He's standing in front of me, I need to know if he had a bad experience the last time he checked in with us, so I know if I need to give him an upgrade. And you can't go down to I.T. real quick and ask them to take 30 days to get that data and then crunch the data all to find out. Customers need to know, and in the experience business, immediately this person just walked into the hotel and we need to give them a good experience, we blew it last time for them. That's what the experience business wants out of data. >> One of the questions we had with Anjul, who runs engineering on the platform side, was around the rise of prominence of streaming data, how is that impacting the analytics piece, because, you know, if you want the flow, this is a key part of probably your side of the business. Can you comment, what's your reaction to that - streaming trend? >> We've been talking about streaming for a while. CIO, this isn't a new thing, we were streaming applications, right, 10 years ago, 15 years ago, but really in the story I just shared, right? The idea of going down and waiting in this asynchronous process with data, the experience business can't handle that, so streaming data is really implying that, as it's coming in, we're processing it, and learning from it, and getting that out into the systems and the people that can take action, instantaneously. >> Talk about the dynamic that customers have around, traditional silos within their organization, you know, that guy runs the database and data for that department, that person runs the data over there, and if this vision is to be, is to be, is to come true, you have to address all the data, you got to know what's out there you got to have data about the data, you got to know in real time, and these are important concepts. How does a company get through that struggle, to break down those kind of existing organizational structures? >> It's a cultural shift, I mean, who has a desktop publishing team anymore in their organization, right? Everyone does desktop publishing, that is how data is too. Everyone's got to be comfortable with data, they have to be conversing around data, and everyone needs access to data. So, that's, you know, that's what is happening in our industry, the analytics industry, is that we're democratizing that data, and getting it everybody's hands, but it's not enough to give them charts and graphs, they have to be able to manipulate that and make it apply to their part of the business, so they can make a decision, and go, and so, that shift in how people think about data, as it's not part of your - it's part of everyone's job, as opposed to being a specialized, siloed job. >> I'm just curious to get your take, a lot of conversations here about you know, Adobe, using their own products, eating your own dog food, drinking your own champagne, whatever analogy (laughs) you like to use. And when you see the DDOM, right, the Data-Driven Operating Model, on the screen, in the keynote, with the CEO, and he says, "Basically everyone at this company is running their business off of these dashboards, that's got to be pretty, pretty, uh, profound for a guy like you who is helping feed those things. >> It's cool. I like to talk about what I call the modern measurement team. The modern measurement team is no longer that centralized data team, right, or that centralized BI team, but every single function, right, under CIO. Every one of the CEO's directs, has their own data team. You go look around and you see that in every single function, there is a sophisticated data team. They have the best tools in the industry, they have the smartest people they can find, they have PhDs on staff, and that's not enough. So, these teams now have to get that out to every constituent in their organization. And that's what we're trying to do at Adobe, that's what we're seeing our best customers do as well, is trying to inform every decision anybody makes. >> And that's where machine learning really shines. You get high quality data on the front end, with the semantic data pipeline capability, get that into the machine learning, help advance, automate, that seems to be the trend. >> Yeah. Yeah, look the insights that you can get from the data, the ability to predict with rich data, it sounds - prediction sounds like - invention used to sound like this novel thing, right, and then you realize, we're inventing things all the time, that's not so - that's just creativity. Well, the same thing is happening with AI and ML, is we're able to predict things with good statistical modeling, with pretty strong, uh, reliability around those models. >> The keynote had great content, I liked how you guys did a lot things really well, you had the architectural slides, platform was a home run, how you guys evolved as a business, see you laid that out nicely, but one of the things I liked, not that obvious, unless you go to a lot of events like we do, everyone says "The journey of the customer", I mean, it's a, it's become a cliche, you guys actually mapped specific things to the journey piece that fit directly into the Adobe set of products and technologies, and the platform. It's interesting, so the word journey has become, actually something you can look at, see some product, see some - a pathway to get some value. >> There's definitely a risk if the word journey, becomes like "Big Data" and all these cliche terms, you know, that means everything, so it comes to mean nothing. But for us, journey, and as marketers especially, journey is just naturally understanding where did I interact with this person, and what did that lead to along the way, right? And so, customer journey, is absolutely core to data analytics. >> All the hype markets, cloud washing, until Amazon shows them how it's done, everyone else kind of follows, you guys are doing it here with journey, one of the things that came out was a journey IQ. I didn't really catch that. Can you take a minute to explain? >> So we have a couple of things. We have something called Segment IQ, Attribution IQ, and now we have even introduced Journey IQ. And when you see that IQ moniker on one of our, kind of our super umbrella features - that means that we're applying AI and ML, right, and Sensei is involved. So we're using powerful data techniques, and we're also wrapping it with a really simple user experience. So Journey IQ starts to break down the customer journey in terms that a normal person, without a PhD, without knowing statistical methods, or advanced mathematics, can leverage those techniques to get really powerful insights. And that's specifically around the customer journey. >> So the IQ is a marker that you guys use to indicate some extra intelligence coming out of the Adobe, from the platform. >> Yeah, yeah, if we're going to democratize data, right, we have to democratize data science as well, right? And so, a big part of what we're doing at Adobe Analytics is really simplifying the user experience, right? So I don't say, Do you want to run a regression model against this to answer your question? We just say Click this button to analyze. Right? So it's a simple user experience, behind the scenes, we can run these powerful models for the customer, and give them back valuable insights. So, Journey IQ is specifically taking things like cohorts, and introducing cohort analysis into the experience, making it simple to do powerful things with cohorts. >> What's the pitch to a customer when you go to one and talk about all this complicated tech and kind of new, operationalized business models around the way you guys are rolling it out, when they just want to ask you, "Hey Jeff, I care about customer experiences." So, bottom line me. What's the pitch? >> How can you possibly address your customer's needs if you don't know what they think. Right? What they need? So, at the end of the day, the great thing about working with customers, like most businesses do, is customers are happy to tell you where you're getting it right, and where you're getting it wrong, right? And that's all over the data. So all you have to do is develop a culture of using data to make decisions, and 9 times out of 10, if you have the right data, and people are using the data to make decisions, they are going to make the right calls and get it right for your customer. And when they don't, they're using opinions and they're going to get it wrong all the time. >> Or, bad data, could be hearsay. >> Or you course correct, or that wasn't - you know, make an adjustment. Right? Again, based on the data. >> Exactly, yeah. >> You're in product marketing, which is a unique position, because you have to look back into the engineering organization, and look out to the customers, so you're, you're in a unique position. What's the customer trend look like right now? What are some of the things you're hearing from the market basket of customers that you talk to? Generally, their orientation towards data? Where are they on the progress bar? What is the state of the market on the landscape of the customer, what patterns are you seeing? >> Good question. So there's a lot of - there's a lot of, um, anxiety around where do I have pockets of data that I'm not able to leverage, and how do I bring that together, so when we tell a platform story, like you heard us tell today, customers are really excited about that, because they know, they've known forever. I mean, this isn't a new problem, like, data silos have been around as long as data has. So, the idea of being able to bring this data into a central place, and do powerful things with it, that's a big point of stress for our customers. And they know, like, "Hey, I have dark spots in my customer experience, that I lose the customer." For example, if I'm heavily oriented around digital, let's say, um, I'm a retailer, and I see a customer, I acquire them through advertising channels, they come through an experience on my website, and they buy the product. Success. I ship the product to them, and then they return it in the retail store. The digital team might not see that return. >> So they might think it was successful. >> They think it was successful. So what do they do? They go take more money and spend it in the ad channel, where that person originated. When in reality, if they could look at the data over time, and incorporate this other channel data, of in-store returns, the picture might look very different. >> So basically, basically. >> It's those dark spots that customers are really needing. >> So getting access to more diverse data, gives you better visibility into what's happening contextually, to open up those blind spots. >> Exactly. Yup. It's just that, adding resolution to a photo. >> Love this conversation, obviously we're data-driven as well on theCUBE, we're sharing the data out there. This interview is data as well. >> Fantastic. >> Jeff, final question for you - for the folks that couldn't make it here, what's the - how would you summarize the show this year, what's the vibe, what's the top story here, what's the big story that needs to be told from Adobe Summit? >> We're just a day in, there a lot, there's a lot to do still, right? We still have two more solid days of this show. But you know, the big themes are going to be around data, they are going to be optimizing the experience for your customers, and what's really amazing is how many customers are here, telling their stories. That's the thing, I wish everybody in your audience could experience by coming here, because there is 300 breakout sessions that feature our customers talking. All of our sessions on main stage, we bring customers out, and we learn from them. That's the best part of my job, is seeing how customers do that. >> Some of the best marketing, you let the customers do the talking, and they're doing innovative things. They're not just your standard, typical, testimonials, they're actually doing - I mean, Best Buy, what a great example that was. >> Cool brand - we work with some of the coolest brands in the world, so, fascinating, brilliant people. >> Marketing, at scale, with data. Good job, Jeff, thanks for coming on, appreciate it. >> Thank you. >> Jeff Allen, here inside theCUBE with Adobe. I'm John Furrier with Jeff Frick. Stay with us for more Day 1 coverage after this short break. Stay with us.

(upbeat music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBE conversation. >> Hello everyone, welcome to Palo Altos Cube Studios for CUBE Conversation. I'm John Furrier, co-host of theCUBE. We're with Kevin Ackroyd, CEO of Cision, CUBE Alumni. He's been on before. Building one of the most compelling companies that's disrupting and changing the game in Comms, advertising, PR, with Cloud technologies. Kevin, great to see you again, thanks for coming in. >> Likewise John, It's really good to be back. >> So, we haven't chatted in two years. You've been busy. Our last conversation was the beginning of 2017. Cision's done a lot of interesting things. You've got a lot of M and A under your belt. You're putting this portfolio together with Cloud technologies. Really been interesting. I really got to say I think you cracked the code on I think a new reality, a new economic reality. Also new capabilities for comms folks. Congratulations. >> Thank you, it's been a fun ride. >> So give us the update. So two years since we talked, how many deals, companies have you bought? What's the headcount, what's the revenue? Give us an update. >> In the four years, 12 acquisitions, seven of which have happened since I've been here. Up to 4,500 employees in over 40 countries. Customer count has grown to over 50,000 customers globally. Revenue's kind of gone from 500s to just shy of 800 million. A lot of leadership changes, and as you just mentioned, pretty seismic change, finally. We've certainly been the catalyst and the cattle prod for that seismic change around tech, data, measurement and analytics finally becoming mature and adopted inside this line of business like the Chief Communication Officer, the earn media folks. To say that they were not tech savvy a few years ago would be an understatement. So, a lot's been going on. >> Yeah, and certainly the trend is your friend, in my opinion, for you. But I think the reality is not yet upon people's general mindset. It's coming quickly, so if you look at some of the big trends out there. Look at fake news, look at Facebook, look at the Google effect. Elizabeth Warren wants to break up Big Tech, Amazon. Cloud computing, in that time period that you were, prior to just going to Cision, you had Oracle Cloud, done a lot of great things on the Marketing Cloud side. But the timing of Cloud computing, the timing of how media has changed. There's not many journalists anymore. We had Andy Cunningham, a legendary industry veteran, formerly of Cunningham Communications. He did the PR for Steve Jobs. You said, there's no more journalists, a few left, but you got to tell your story direct to the consumer. >> You do. >> This is now a new marketing phenomenon. This is a tailwind for you at Cision because you guys, although put these cubbies together, have a unique vision around bringing brand value advertising at PR economics. >> Yeah, that's a good way to put it. >> Tell us the vision of Cision and specifically the shift that's happening. Why are you guys important? What wave are you riding? >> So, there's a couple shifts, John. You and I have talked about this in previous programs There's this shift of the line of business, having to work in a whole bunch of non-integrated point solutions. The CFO used to live in 17 different applications from 17 vendors. That's all squished together. Now I buy from one Cloud platform, right, from Oracle or SAP. Same thing happened in Human Capital Management. 22 things squished into the Cloud, one from Workday, right. Same thing happened, you had 25 different things for sales and service. That all squished together, into one CRM in the Cloud, I buy from Salesforce, right. And our last rodeo, the early part of this stack, it was me and Adobe battling it out for the right to go squish the entire the LUMAscape into a marketing cloud, right, so there could be one ring to rule them all for the CMO. So, it happens in every single category. It just hasn't had over here, happened on the earned media side and the Chief Communications Officer. So, bringing the tech stack so that now we are for the CCO what Adobe is for the CMO what Salesforce is for the CRO, Workday is for the CHRO. That has to happen. You can't do, you can't manage it this way without sophisticated tech, without automation, without integration, you can't do it. The second thing that had to happen, especially in marketing and advertising, they all figured out how to get revenue credit. Advertising was a slow single-digit CAGR industry for 50 years. And then something happened. After 5% CAGR for 50 years, and then something happened over the next 10 years. Digital paid went from like 15 billion to 150 billion. And what happened is that old, I know half my advertising is wasted on this one half. That went bye-bye. Now I know immediately, down to the page, down the ad unit, down to this, exactly what worked, right. When I was able to put Pixels on ads, John, you'd go to that page, Pixel would go on you, It would follow you around If you ended up putting something in the e-commerce shop that ad got credit. I'm not saying that's right, I'm just saying that's how the entire-- >> But that's how the infrastructure would let you, allowed you, it enabled you to do that. Then again, paid advertising, paid search, paid advertising, that thing has created massive value in here. >> Massive value. But my buyer, right, so the person that does the little ad on the most regional tech page got credit. My buyer that got Bob Evans, the Cloud King, to write an article about why Microsoft is going to beat AWS, he's a credible third party influencer, writing objectively. That article's worth triple platinum and has more credibility than 20,000 Microsoft sales reps. We've never, until Cision, well let's Pixel that, let's go figure out how many of those are the target audience. Let's ride that all the way down to the lead form that's right. Basically it's super simple. Nobody's ever tracked the press releases, the articles or any of the earned media content, the way people have tracked banner ads or e-commerce emails. Therefore this line of business never get revenue credit. It stayed over here in the OpEx pile where things like commerce and advertising got dumped onto the revenue pile. Well, you saw the crazy investment shift. So, that's really the more important one, is Comms is finally getting quantified ROI and business's attribution like their commerce and advertising peers for the first time ever in 2018 via what Cision's rolled out. That's the exciting piece. >> I think, I mean, I guess what I hear you saying is that for the first time, the PR actually can be measured, similar to how advertising >> You got it. >> Couldn't be measured then be measured. Now PR or communications can be measured. >> They get measured the same way. And then one other thing. That ad, that press release, down to the business event. This one had $2 million dollars of ad spend, this one had no ad spend. When it goes to convert, in CRM or it goes to convert on a website, this one came from banner ad, this one came from credible third party content. Guess which one, not only had zero ad spend instead of $2 million in ad spend. Guess which one from which source actually converts better. It's the guy that chose to read credible third-party article. He's going to convert in the marketing system way better that somebody who just clicked on the ad. >> Well certainly, I'm biased-- >> So all the way down the funnel, we're talking about real financial impact based on capturing earned media ID, which is pretty exciting. >> Well, I think the more exciting thing is that you're basically taking a value that is unfunded quote by the advertising firm, has no budget basically, or thin budgets, trying to hit an organic, credible outlet which is converting in progression to a buyer, an outcome. That progression is now tracked. But let's just talk about the economics because you're talking about $2 million in spend, it could be $20 million. The ratio between ad spend and conversion to this new element you mentioned is different. You're essentially talking about the big mega trend, which is organic content. Meaning connecting to sources. >> That's right. >> That flow. Of course, we believe and we, at the Cube, everyone's been seeing that with our business. Let's talk about that dynamic because this is not a funded operationalized piece yet, so we've been seeing, in the industry, PR and comms becoming more powerful. So, the Chief Communication Officer isn't just rolling out press releases, although they have to do that to communicate. You've got medium posts now, you've got multiple channels. A lot of places to put the story. So the Chief Communication Officer really is the Chief Storyteller Officer, Not necessarily the CMO. >> Emphatically. >> The Martech Stack kind of tracking. So talk about that dynamic. How is the Chief Communication Officer role change or changing? Why is that important and what should people be thinking about, if they are a Chief Communication Officer? >> You know, it's interesting. There's a, I'm just going to call it an actual contradiction on this front. When you and I were getting out of our undergrad, 7 out of 10 times that CCO, the Chief Communication Officer, worked for the CEO and 30% of time other. Yet the role was materially narrow. The role has exploded. You just said it pretty eloquently. This role has really exploded and widened its aperture. Right now though 7 out of 10 of them actually do work for the CMO, which is a pretty interesting contradiction. And only 30% of them work for the CEO. Despite the fact that from an organizational stand point, that kind of counter intuitive org move has been made. It doesn't really matter because, so much of what you just said too, you was in marketing's purview or around brand or around reputation or around telling the story or around even owning the key assets. Key assets isn't that beautiful Budweiser frog commercial they played on Super Bowl anymore. The key assets are what's getting done over in the communications, in part. So, from a storytelling standpoint, from an ownership of the narrative, from a, not just a product or a service or promotion, but the whole company, the whole brand reputation, the goodwill, all of that is comms. Therefore you're seeing comms take the widest amount of real estate around the boardroom table than they've ever had. Despite the fact that they don't sit in the chair as much. I mentioned that just because I find it very interesting. Comms has never been more empowered, never had a wider aperture. >> But budget wise, they're not really that loaded up with funding. >> And to my earlier point, it's because they couldn't show. Super strategic. Showing ROI. >> So, showing ROI is critical. >> Not the quality of clippings. >> It was the Maslow of Hierarchy of Needs if you can just show me that I put a quarter in and I got a dollar out. Like the ads and the e-commerce folks do. It simply drives the drives me. >> So take us through some of those analytics because people who know about comms, the old school comms people who are doing this, they should really be thinking about what their operation is because, can I get an article in the Wall Street Journal? Can Silicon Angle write about us? I've got to get more clippings. That tend to be the thing. Did we get the press release out on time? They're not really tied into some of the key marketing mix pieces. They tend to be kind of a narrow scope. Those metrics were pretty clear. What are the new metrics? What's the new operational playbook.? >> Yeah, we call those Vanity Metrics. I cared about theoretical reach. Hey, Yahoo tells me I reached 222 billion people, so I plug in 222 billion people. I reached more people than there are on the planet with this PR campaign. I needed to get to the basic stuff like how many people did I actually reach, number one. But they don't, they do theoretical reach. They work in things like sentiment. Well, I'm going to come up with, 100 reporters wrote about me. I'm going to come up with, how many of them I thought were positive, negative, neutral. Sentiment analysis, they measure number of reporters or hits versus their competitors and say, Proctor and Gamble rolled out this diaper product, how did I do this five days? How much did Proctor and Gamble diapers get written about versus Craft diapers versus Unilever's. Share a voice. Not irrelevant metrics. But not metrics the CEO and the CFO are going to invest in. >> Conversion to brand or sales, those kind of things? >> They never just never existed. Those never existed. Now when we can introduce the same exact metrics that the commerce and the ad folks do and say, I can tell you exactly how many people. I can tell you exactly who they were, demographic, firmographic, lifestyle, you name it. I can tell you who the audience is you're reaching. I can tell you exactly what they do. When those kind of people read those kind of articles or those kind of people read those kind of press releases, they go to these destinations, they take these behaviors. And because I can track that all the way down to whatever that success metric is, which could be a lead form if I'm B2B for pipe. It could be a e-commerce store from B2C. It could be a rating or review or a user generation content gourd. It could be a sign up and register, if I'm trying to get database names. Whatever the business metric is. That's what the commerce and the ad people do all day every day. That's why they are more funded than ever. The fact that press releases, articles, tweets, blogs, the fact that the earned media stuff has never been able to do those things is why they just continue to suffer and have had a real lack of investment prices going on for the last 20 year. >> Talk about the trend around-- >> It's simple stuff. >> I know, if you improve the ROI, you get more budget. >> It really is that simple. >> That's been the challenge. I think PR is certainly becoming, comms is becoming more powerful. People know I talk about it all the time. I think comms is the new CMO I think command and control and organic content work together in the organic. We've seen it first hand in our business. But, it's an issue of tech savviness and also vision. A lot of people just are uncomfortable shifting to the new realities. >> That's for sure. >> What are some of the people tech savvy look at when they look at say revamping comms platform or strategy versus say old school? >> I'll give you two answers on that, John. Here is one thing that is good for us, that 7 out of 10 to the CCOs work for the CMO. Because when I was in this seat starting to light that fire under the CMO for the first time, which was not that long ago, and they were not tech savvy, and they were not sophisticated. They didn't know how to do this stuff either. That was a good 10 year journey to get the CMO from not sophisticated to very sophisticated. Now they're one of the more sophisticated lines of business in the world. But that was a slog. >> So are we going to see a Comms Stack? Like Martech, ComTech. >> ComTech is the decision communication Cloud, is ComTech. So we did it. We've built the Cloud stack. Again like I said, just like Adobe has the tech stack for marketing, Cision has the tech stack for comms, and we've replicated that. But because the CCO works for the CMO and the CMO's already been through this. Been through this with Ad Techs, been through this with MarTech, been through this with eCommerce, been through this with Web. You know, I've got a three or four year sophistication path this time just because >> The learnings are there >> The company's already done it everywhere else. The boss has already done it everywhere else. >> So the learnings are there from the MarTech so it's a pretty easy leap to take? >> That's exactly right. >> It's just-- >> How CommTech works is shocking. Incredibly similar to how MarTech and AdTech work. A lot of it is the same technology, just being applied different. >> That's good news >> So, the adoption curve for us is a fantastic thing. It's a really good thing for us that 70% of them work for CMOs because the CMO is the most impatient person on the planet, to get this over because the CMO is sick of doing customer journeys or omni channel across just paid and owned. They recognize that the most influential thing to influence you, it's not their emails, it's not their push notifications, It's not their ads. It's recognizing which credible third-party content you read, getting them into that, so that they're influencing you. >> It's kind of like Google PageRank in the old days. This source is more relevant than that one, give it more weight. >> And now all of a sudden if I have my Cision ID, I can plug in the more weight stuff under your profile. I want to let him go across paid and owned too, I materially improve the performance of the paid and owned because I'm putting in the really important signal versus what's sitting over there in the DMP or the CDP, which is kind of garbage. That's really important. >> I really think. >> I thinks you've got a home run here. I think you've really cracked the code on this. I think you are absolutely right on the money with comms and CommsTech. I see it all the time. In my years of experiences, it's so obvious. Then again, the tailwind is that they've been through the MarTech. The question I have for you is cultural shift. That's a big one. So, I'm out evangelizing all the time about the CUBE Cloud and some of the things we're doing. I run into the deer in the headlights on one side, what do you mean? And then people like, I believe, I totally understand. The believers and the non believers. What's the cultural shift? Because some chief comms op, they're very savvy, progressive, we've got to make the shift. How do they get the ship to turn? What are some of the cultural challenges? >> And boy is that right. I felt the same thing, getting more doing it with the CMO. A lot of people kept their head in the sand until they got obsoleted. They didn't know. Could they not see the train coming? They didn't want to see the train coming. Now you go look at the top 100 CMOs in the world today. Pretty different bunch than who those top 100 CMOs were 10 years ago. Really different bunch. History's repeating itself over here too. You've got the extremely innovative CCOs that are driving that change and transformation. You've got the deer in the headlight, okay, I know I need to do this, but I'm not sure how, and you do have your typical, you know, nope, I've got my do not disturb sign and police tape over my office. I won't even let you in my door. I don't want to hear about it. You've got all flavors. The good news is we are well past the half point where the innovators are starting actually to deploy and show results, the deer in the headlights are starting to innovate, and these folks are at least opening up the door and taking down some tape. >> Is there pressure on the agency side now? A lot of agencies charge a lot of monthly billings for these clients, the old school thing. Some are trying to be progressive and do more services. Have you seen, with the Cision Cloud and things that you're doing, that you're enabling, those agencies seem to be more productive? >> Yes. >> Are the client's putting pressure on those agencies so they see more value? Talk about the agency dynamic. >> That's also a virtuous cycle too, right? That cycle goes from, it's a Bell Curve. At the beginning of the bell curve, customers have no clue about the communications. They go to their agencies for advice. So, you have to educate the agencies on how to say nice things about you. By the time you're at the Bell Curve, the client's know about the tech or they've adopted the tech, and the agencies realize, oh, I can monetize the hell out of this. They need strategy and services and content and creative and campaign. This is yet another good old fashioned >> High gross profit. >> A buck for the tech means six bucks for me as the service agency. At the bottom, over here, I'll never forget this when we did our modern marketing experiences, Erik, the CMO of Clorox said, hey, to all you agencies out there, now that we're mature, you know, we choose our our agency based on their fluency around our tech stack. So it goes that violently and therefore, the agencies really do need to try to get fluent. The ones that do, really reap rewards because there is a blatant amount of need as the line of business customer tries to get from here to here. And the agency is the is the very first place that that customer is going to go to. >> So, basically the agency-- >> The customer has first right of refusal to go provide these services and monetize them. >> So, the agency has to keep up. >> They certainly do. >> Because, if the game gets changed by speed, it's accelerated >> If they keep up, yup. >> Value is created. If they don't have their running shoes on, they're out. >> If they keep up and they stay fluent, then they're going to be great. The last thing back in the things. We've kind of hit this. This is one of those magic points I've been talking about for 20 years. When the CFO or the CEO or the CMO walk down to the CCOs office and say, where are we on this, 'cause it's out in the wild now, there are over 1200 big brands doing this measurement, Cision ID, CommsTech stuff. It's getting written about by good old fashioned media. Customer says, wow, I couldn't do this for 50 years, now I am, and look what I just did to my Comms program. That gets read. The world's the same place as it always has been. You and I read that. We go down to our comms department and say, wow, I didn't know that was possible, where are we on this? So the Where Are We On This wave is coming to communications, which is an accelerant. >> It's an accountability-- >> Now it's accountability, and therefore, the urgency to get fluent and changed. So now they're hiring up quantums and operations and statisticians and database people just like the marketers did. The anatomy of a communications department is starting to like half science half art, just like happened in marketing. Whereas before that, it was 95% art and 5% science. But it's getting to be 50/50. >> Do you have any competition? >> We have, just like always. >> You guys pretty much have PR Newswire, a lot of big elements there. >> We do. >> You've got a good foothold. >> This is just an example. Even though Marketo is part of Adobe, giant. And Eloqua is part of Oracle, giant and Pardot is part of Salesforce. You've got three goliaths in marketing automation. Hubspot's still sticking around. PeerPlay, marketing Automation. You can just picture it. CRM giants, Microsoft and Salesforce have eaten the world Zendesk's still kicking around. It's a little PeerPlay. That equivalent exists. I have nobody that's even one fifth as big as I am, or as global or complete. But I do have some small, point specific solution providers. They're still hanging out there. >> The thing is, one, first you're a great leader. You've seen the moving on the marking tech side. You've got waves of experience under your belt. But I think what's interesting is that like the Web 1.0, having websites and webpages, Web 2.0 and social networks. That was about the first generation. Serve information, create Affiliate programs, all kind of coded tracking. You mentioned all that. I over-simplified it, but you get the idea. Now, every company needs a new capability. They need to stand up media infra structure. What does that mean? They're going to throw a podcast, they're going to take their content, put them into multiple channels. That's a comms function. Now comms is becoming the new CMO-like capability in this earned channel. So, your Cloud becomes that provisioning entity for companies to stand up capabilities without waiting. Is that the vision? >> You've nailed it. And that is one of the key reasons why you have to have a tech stack. That's a spot on one, another one. Early in my career, the 20 influences that mattered, they were all newspaper reporters or TV folks. There was only 20 of them. I had a Rolodex. so I could take each one of them out for a three Martini lunch, they'd write something good about me. >> Wish is was that easy now. >> Now, you have thousands of influencers across 52 channels, and they change in real time, and they're global in nature. It's another example of where, well, if you don't automate that with tech and by the way. >> You're left behind. >> If you send out digital content they talk back to you in real time. You have to actually not only do influencer identification, outreach and curation, you've got to do real time engagement. >> There's no agility. >> There's none. >> Zero agility. >> None, exactly. >> There's no like Dev Ops mindset in there at all. >> Then the speed with which, it's no longer okay for comms to call the agency and say, give me a ClipBook, I've got to get it to my CEO by Friday. That whole start the ClipBook on Tuesday, I've got to have the ClipBook, the physical ClipBook on the CEO as an example. Nope, if I'm not basically streaming my senior executives in real time, curated and analyzed as to what's important and what it means, I can't do that without a tech stack. >> Well, Andy Cunningham was on the Cube. >> This whole thing has been forced to get modernized by cloud technology and transformation >> Andy Cunningham, a legend in the comms business who did all Steve Jobs comms, legend. She basically said on The Cube, it's not about waiting for the clips to create the ClipBook, create your own ClipBook and get it out there. Then evaluate and engage. This is the new command and control with digital assets. >> Now, it's become the real-time, curated feed that never stops. It sure as hell better not. Because comms is in trouble if it does. >> Well this is a great topic. But let's have you in this, I can go deep on this. I think this is a really important shift, and you guys are the only ones that are on it at this level. I don't think the Salesforce and the Adobe yet, I don't think they're nimble enough to go after this wave. I think they're stuck on their wave and they're making a lot of money. >> You know John, paid media and owned media. The Google Marketing Cloud, that SAP Marketing Cloud, Adobe, Oracle, Salesforce Marketing Clouds. They don't do anything in earned. Nothing. This is one of the reasons I jumped because I knew this needed to happen. But, you know, they're also chasing much bigger pots of money. Marketing and Advertising is still a lot more money. We're working on it to grow the pie for comms. But, bottom line is, they're chasing the big markets as I was at Oracle. And they're still pretty much in a violent arms race against each other. Salesforce is still way more focused on what Adobe's doing. >> You're just on a different wave. >> So, we're just over here doing this, building a billion dollar cloud leader, that is mission critical to everyone of their customers. They're going to end up being some pretty import partners to us, because they've been too focused on the big arms race against each other, in paid and owned and have not had the luxury to even go here. >> Well I think this wave that you're on is going to be really big. I think they don't see it, in my opinion, or can't get there. With the right surfboard, to use a surfing analogy, there's going to be a big wave. Thanks for sharing your insights. >> Absolutely. >> While you're here, get the plug in for Cision. What's going on, what's next? What's the big momentum? Get the plug in for the company. What are you guys still going to do? >> Plugin for the company. The company has acquired a couple of companies in January. You might see, one of which is Falcon. Basically Falcon is one of the big four in the land of Hootsuite, Sprinklr, Spredfast. Cloud companies do this. Adobe has Creative Cloud, Document Cloud, Parking Cloud. Salesforce has Sales Cloud, Service Cloud, Marketing Cloud. Cision has just become a multi cloud company. We now have the Cision Social Cloud and the Cision Communications Cloud. And we're going to go grab a couple hundred million dollars of stuff away from Sprinklr, Hootsuite and collapse social into this. Most of social is earned as well. So, look for a wing spread, into another adjacent market. I think that's number one. Then look for publishing of the data. That's probably going to be the most exciting thing because we just talked about, again our metrics and capabilities you can buy But, little teaser. If we can say, in two months here's the average click through on a Google ad, YouTube ad, a banner ad, I'll show it to you on a Blog, a press release, an article. Apples to apples. Here is the conversion rate. If I can start becoming almost like an eMarketer or publisher on what happens when people read earned, there's going to be some unbelievable stats and they're going to be incredibly telling, and it's going to drive where are we on that. So this is going to be the year. >> It's a new digital advertising format. It's a new format. >> That's exactly right. >> It's a new digital advertising format. >> And its one when the CEO understands that he or she can have it for earned now, the way he's had it for marketing and advertising, that little conversation walking down the hall. In thousands of companies where the CCO or the VP of PR looks up and the CEO is going where are we on that? That's the year that that can flip switches, which I'm excited about. >> Every silo function is now horizontally connected with data, now measured, fully instrumented. The value will be there and whoever can bring the value gets the budget. That's the new model. Kevin Ackroyd, CEO of Cision, changing the game in the shift around the Chief Communications Officer and how that is becoming more tech savvy. Really disrupting the business by measuring earned media. A big wave that's coming. Of course, it's early, but it's going to be a big one. Kevin, thanks for coming on. >> My pleasure, John, thank you. >> So, CUBE conversation here in Palo Alto Thanks for watching. >> Thanks John. (upbeat music)

>> Narrator: Live from Las Vegas, it's The Cube. Covering IBM Think 2018. Brought to you by IBM. >> Hello everyone and welcome to The Cube here at IBM Think in Las Vegas, the Mandalay Bay. I'm John Furrier, the host of The Cube. We're here in this Cube studio as a set for IBM Think. My next guest is Jennifer Shiin who's the founder of 8 Path Solutions. Twitter handle Jenn, J-S-H-I-N. Great to see you. Thanks for joining me. >> Yeah, happy to be here. >> I'm glad you stopped by. I wanted to get your thoughts. You're thought leader in the industry. You've been on multiple Cube panels. Thank you very much. And also Cube alumni. You know, IBM with the data center of the value proposition. The CEO's up on the stage today saying you got data, you got blockchain and you got AI, which is such the infrastructure of the future. And AI is the software of the future, data's at the middle. Dave and I were talking about that as the innovation sandwich. The data is being sandwiched between blockchain and AI, two super important things. And she also mentioned Moore's law. Faster, smaller, cheaper. Every 6 months doubling in speed and performance. And then Metcalfe's law, which is more of a network effect. Kind of teasing out token economics. You see kind of where the world's going. This is an interesting position from IBM. I like it. Is it real? >> Well it sounds very data sciency, right? You have the economics part, you have the networking. You have all these things in your plane. So I think it's very much in line with what you would expect if data science actually sustains (mumbles), which thankfully it has. >> Yeah. >> And I think the reality is you know, we like to boil things down into nice, simple concepts but in the real world when you're actually figuring it all out its going to be multiple effects. It's going to be, you know a lot of different things that interact. >> And they kind of really tease out their cloud strategy in a very elegant way. I mean they essentially said, 'Look we're into the cloud and we're not going to try to.' They didn't say it directly, but they basically said it. We're not going to compete with Amazon head-to-head. We're going to let our offerings to do the talking. We're going to use data and give customers choice with multi cloud. How does that jive for you? How does that work because at the end of the day I got to have business logics. I need applications. >> Yes. >> You know whether its blockchains, cryptocurrency or apps. The killer app's now money. >> Yep. >> If no one's making any money. >> Sure. >> No commerce is being done. >> Right. I mean I think it makes sense. You know, Amazon has such a strong hold in the infrastructure part, right? Being able to store your data elsewhere and have it be cloud. I don't think that was really IBM's core business. You know, a lot of I think their business model was built around business and business relationships and these days, one of the great things about all these data technologies is that one company doesn't have to do all of it, right? You have partnerships and actually partners so that you know, one company does AI. You partner with another company that has data. And that way you can actually both make money, right? There's more than enough work to go around and that much you can say having worked in data science teams right? If I can offload some of my work to different divisions, fantastic. That'd be great. Saves us time. You get to market faster. You can build things quicker. So I think that's one of the great things about what's happening with data these days, right? There's enough work to get around. >> And it's beautiful too because if you think about the concept that made cloud great is DevOps. Blockchain is an opportunity to use desensualization to take away a lot of inefficiencies. AI is also an automation opportunity to create value. So you got inefficiencies on block chains side and AI to create value, your thoughts and reaction to where that's going to go. You know, in light of the first death on a Uber self-driving car. Again, historic yesterday right? And so you know, the reality is right there. We're not perfect. >> Yeah. >> But there's a path. >> Well so most of its inefficiency out there. It's not the technology. It's all the people using technology, right? You broke the logic by putting in something you shouldn't have put in that data set, you know? The data's now dirty because you put in things that you know, the developer didn't think you'd put in there. So the reality is we're going to keep making mistakes and there will be more and more opportunities for new technologies to help you know, cheer that up. >> So I was talking to Rob Thomas, GM of the analytics team. You know Rob, great guy. He's smart. He's also an executive but he knows the tech. He and I were talking about this notion of data containers. So with Kubernetes now front and center as an orchestration layer for cloud and application workloads, IBM has an interesting announcement with this cloud private approach. Where data is the central thing in this. Because you've got things like GDPR out there and the regulatory environment not going to get any easier. You got blockchain crypto. That's a regulatory nightmare. We know a GDBR. That's a total nightmare. So this is happening, right? So what should customers be doing, in your experience? Customers are scratching their head. They don't want to make a wrong bet, but they need good data, good strategy. They need to do things differently. How do they get the best out of their data architecture knowing that there's hurdles and potential blockers in front of them? >> Well so I think you want to be careful of what you select. and how much are you going to be indebted to that one service that you selected, right? So if you're not sure yet maybe you don't want to invest all of your budget into this one thing you're not sure is going to be what you really want to be paying for a year or two, right? So I think being really open to how you're going to plan for things long term and thinking about where you can have some flexibility, whereas certain things you can't. For instance, if you're going to be in an industry that is going to be you know, strict on regulatory requirements right? Then you have less wiggle room than let's say an industry where that's not going to be an absolute necessary part of your technology. >> Let me ask you a question and being kind of a historian you know, what say one year is seven dog years or whatever the expression is in the data space. It just seems like yesterday that Hadoop was going to save the world. So that as kind of context, what is some technologies that just didn't pan out? Is the data link working? You know, what didn't work and what replaced it if you can make an observation? >> Well, so I think that's hard because I think the way I understood technology is probably not the way everyone else did right? I mean, you know at the end of the day it just is being a way to store data right? And just being able to use you know, more information store faster, but I'll tell you what I think is hilarious. I've seen people using Hadoop and then writing sequel queries the same way we did like ten plus years ago, same inefficiencies and they're not leveling the fact that it's Hadoop. Right? They're treating it like I want to create eight million tables and then use joins. So they're not really using the technology. I think that's probably the biggest disappointment is that without that knowledge sharing, without education you have people making the same mistakes you made when technology wasn't as efficient. >> I mean if you're a hammer, everything else is like a nail I guess if that's the expression. >> Right. >> On the exciting side, what are you excited about in technology right now? What are you looking at that's a you know, next 20 mile stare of potential goodness that could be coming out of the industry? >> So I think anytime you have better science, better measurements. So measurement's huge, right? If you think about media industry, right? Everyone's trying to measure. I think there was an article that came out about some of YouTube's failure about measurement, right? And I think in general like Facebook is you know, very well known for measurement. That's going to be really interesting to see, right? What methodologies come out in terms of how well can we measure? I think another one will be say, target advertising right? That's another huge market that you know, a lot of companies are going after. I think what's really going to be cool in the next few years is to see what people come up with, right? It's really the human ingenuity of it, right? We have the technology now. We have data engineers. What can we actually build? And how are we going to be able to partner to be able to do that? >> And there's new stacks that are developing. You think about the ecommerce stack. It's a 30 year old stack. AdTech and DNS and cookiing, now you've got social and network effects going on. You mentioned you know, the Metcalfe's law. So with all that, I want to get just your personal thoughts on blockchain. Beyond blockchain, token economics because there are a lot people who are doing stuff with crypto. But what's really kind of pointing as a mega trands standpoint is a new class of desensualized application developers are coming in. >> Right. >> Okay. They're dealing with data now on a desensualized basis. At the heart of that is the token economics, which is changing some of the business model dynamics. Have you seen anything? Your thoughts on token economics? >> So I haven't seen it from the economics standpoint. I've seen it from more of the algorithms and that standpoint. I actually have a good friend of mine, she's at Yale. And she actually runs the, she's executive director of their corporate law center. So I hear some from her on the legal side. I think what's really interesting is there's all these different arenas. Legal being a very important component in blockchain. As well as, from the mathematical standpoint. You know when I was in school way back when, we studied things like hash keys and you know, RSA keys and so from a math standpoint that's also a really cool aspect of it. So I think it's probably too early to say for sure what the economics part is going to actually look like. I think that's going to be a little more longterm. But what is exciting about this, is you actually see different parts of businesses, right? Not just the financial sector but also the legal sector and then you know say, the math and algorithms and you know. Having that integration of being able to build cooler things for that reason. >> Yeah the math's certainly exciting. Machine learning, obviously that's well documented. The growth and success of what, and certainly the interests are there. You seeing Amazon celebrating all the time. I just saw Werner Vogels, the CTO. Talking about another SageMaker, a success. They're looking at machine learning that way. You got Google with TensorFlow. You've got this goodness in these libraries now that are in the community. It's kind of a perfect storm of innovation. What's new in the ML world that developers are getting excited about that companies are harnessing for value? You seeing anything there? Can you share some commentary on the current machine learning trends? >> So I think a lot of companies have gotten a little more adjusted to the idea of ML. At the beginning everyone was like, 'Oh this is all new.' They loved the idea of it but they didn't really know what they were doing, right? Right now they know a little bit more. I think in general everyone thinks deep learning is really cool, neural networks. I think what's interesting though is everyone's trying to figure out where's the line. What's the different between AI versus machine learning versus deep learning versus neural networks. I think it's a little bit fun for me just to see everyone kind of struggle a little bit and actually even know the terminology so we can have a conversation. So I think all of that, right? Just anything related to that you know, when do you TensorFlow? What do you use it for? And then also say, from Google right? Which parts do you actually send through an API? I mean that's some of the conversations I've been having with people in the business industry, like which parts do you send through an API. Which parts do you actually have in house versus you know, having to outsource out? >> And that's really kind of your thinking there is what, around core competencies where people need to kind of own it and really build a core competency and then outsource where its more a femoral invalue. Is there a formula, I guess to know when to bring it in house and build around? >> Right. >> What's your thoughts there? >> Well part of it, I think is scalability. If you don't have the resources or the time, right? Sometimes time. If you don't have the time to build it in house, it does make sense actually to outsource it out. Also if you don't think that's part of your core business, developing that within house do you're spending all that money and resources to hire the best data scientists, may not be worth it because in fact the majority of your actual sales is with the sale department. I mean they're the ones that actually bring in that revenue. So I think it's finding a balance of what investment's actually worth it. >> And sometimes personnel could leave and you could be a big problem, you know. Someone walks about the door, gets another job because its a hot commodity to be. >> That's actually one of the big complaints I've heard is that we spend all this time investing in certain young people and then they leave. I think part of this is actually that human factor. How do you encourage them to stay? >> Let's talk about you. How did you get here? School? Interests? Did you go off the path? Did you come in from another vector? How did you get into what you're doing now and share a little bit about who you are? >> Yeah so I studied economics, mathematics, creative writing as an undergrad and statistics as a grad student. So you know, kind of perfect storm. >> Natural math, bring it all together. >> Yeah but you know its funny because I actually wrote about and talked about how data is going to be this big thing. This is like 2009, 2010 and people didn't think it was that important, you know? I was like next three to five years mathematicians are going to be a hot hire. No one believed me. So I ended up going, 'Okay well, the economy crashed.' I was in management consulting in finance, private equity hedge funds. Everyone swore like, if you do this you're going to be set for life, right? You're on the path. You'll make money and then the economy crashed. All the jobs went away. And I went, 'Maybe not the best career choice for me.' So I did what I did at companies. I looked at the market and I went, 'Where's their growth?' I saw tech had growth and decided I'm going to pick up some skills I've never had before, learn to develop more. I mean in the beginning I had no idea what an application development process was, right? I'm like, 'What does that mean to actually develop an application?' So the last few years I've really just been spending, just learning these things. What's really cool though is last year when my patents went through and I was able to actually able to launch something with Box at their keynote. That was really awesome. >> Awesome. >> So I became a long way from I think, have the academic knowledge to being able to apply it and then learn the technologies and then developing the technologies, which is a cool thing. >> Yeah and that's a good path because you came in with a clean sheet of paper. You didn't have any dogma of waterfall and all the technologies. So you kind of jumped in. Did you use like a cloud to build on? Was it Amazon? Was it? >> Oh that's funny too. Actually I do know Legacy's technology quite well because I was in corporate America before. Yeah, so like Sequel. For instance like when I started working data science, funny enough we didn't call it data science. We just called it like whatever you call it, you know. There was no data science term at that point. You know we didn't have that idea of whether to use R or Python. I mean I've used R over ten years, but it was for statistics. It was never for like actual data science work. And then we used Sequel in corporate America. When I was taking data it was like in 2012. Around then, everyone swore that no, no. They're going to programmers. Got to know programming. To which, I'm like really? In corporate America, we're going to have programmers? I mean think about how long it's going to take to get someone to learn any language and of course, now everyone's learning. It's on Sequel again right? So. >> Isn't it fun to like, when you see someone on Facebook or Linkdin, 'Oh man data's a new oil.' And then you say, 'Yeah here's a blog post I wrote in 2009.' >> Right. Yeah, exactly. Well so funny enough Ginni Rometty today was saying about exponential versus linear and that's one of the things I've been saying over the last year about because you know, you want exponential growth. Because linear anyone can do. That's a tweet. That's not really growth. >> Well we value your opinion. You've been great on The Cube. Great to help us out on those panels, got a great view. What's going on with your company? What are you working on now? What's exciting you these days? >> Yeah so one of the cool things we worked on, it's very much in line with what the IBM announcement was, so being smarter, right? So I developed some technology in the photo industry, digital assent management as well as being able to automate the renaming of files, right? So you think you probably a picture on your digital camera you never moved over because you, I remember the process. You open it, you rename it, you saved it. You open the next one. Takes forever. >> Sometimes its the same number. I got same version files. It's a nightmare. >> Exactly. So I basically automated that process of having all of that automatically renamed. So the demo that I did I had 120 photos renamed in less than two minutes, right? Just making it faster and smarter. So really developing technologies that you can actually use every day and leverage for things like photography and some cooler stuff with OCR, which is the long term goal. To be able to allow photographers to never touch the computer and have all of their clients photos automatically uploaded, renamed and sent to the right locations instantly. >> How did you get to start that app? Are you into photography or? >> No >> More of, I got a picture problem and I got to fix it? >> Well actually its funny. I had a photographer taking my picture and she showed me what she does, the process. And I went, 'This is not okay. You can do better than this.' So I can code so I basically went to Python and went, 'Alright I think this could work,' built a proof of concept and then decided to patent it. >> Awesome. Well congratulations on the patent. Final thoughts here about IBM Think? Overall sentiment of the show? Ginni's keynote. Did you get a chance to check anything out? What's the hallway conversations like? What are some of the things that you're hearing? >> So I think there's a general excitement about what might be coming, right? So a lot of the people who are here are actually here to, I think share notes. They want to know what everyone else is doing, so that's actually great. You get to see more people here who are actually interested in this technology. I think there's probably some questions about alignment, about where does everything fit. That seems to be a lot of the conversation here. It's much bigger this year as I'm sure you've noticed, right? It's a lot bigger so that's probably the biggest thing I've heard like there's so many more people than we expected there to be so. >> I like the big tent events. I'm a big fan of it. I think if I was going to be critical I would say, they should do a business event and do a technical one under the same kind of theme and bring more alpha geeks to the technical one and make this much more of a business conversation because the business transformation seems to be the hottest thing here but I want to get down in the weeds, you know? Get down and dirty so I would like to see two. That's my take. >> I think its really hard to cater to both. Like whenever I give a talk, I don't give a really nerdy talk to say a business crowd. I don't give a really business talk to a nerdy crowd, you know? >> It's hard. >> You just have to know, right? I think they both have a very different sensibility, so really if you want to have a successful talk. Generally you want both. >> Jennifer thanks so much for coming by and spending some time with The Cube. Great to see you. Thanks for sharing your insights. Jennifer Shin here inside The Cube at IBM Think 2018. I'm John Furrier, host of The Cube. We'll be back with more coverage after this short break.

>> Welcome back everybody. Jeff Frick here with theCube. We're in the Palo Alto studio talking about customer journeys today. And we're really excited to have professional, who's been doing this for a long time, he's Jeff Weidener, he's an Information Management Professional at this moment in time, and still, in the past and future, Jeff Welcome. >> Well thank you for having me. >> So you've been playing in the spheres for a very long time, and we talked a little bit before we turned the cameras on, about one of the great topics that I love in this area is, the customer, the 360 view of the customer. And that the Nirvana that everyone says you know, we're there, we're pulling in all these data sets, we know exactly what's going on, the person calls into the call center and they can pull up all their records, and there's this great vision that we're all striving for. How close are we to that? >> I think we're several years away from that perfect vision that we've talked about, for the last, I would say, 10, 10 to 15 years, that I've dealt with, from folks who were doing catalogs, like Sears catalogs, all the way to today, where we're trying to mix and match all this information, but most companies are not turning that into actionable data, or actionable information, in any way that's reasonable. And it's just because of the historic kind of Silo, nature of all these different systems, I mean, you know, I keep hearing about, we're gonna do it, all these things can tie together, we can dump all the data in a single data lake and pull it out, what are some of the inhibitors and what are some of the approaches to try to break some of those down? >> Most has been around getting that data lake, in order to put the data in its spot, basically try and make sure that, do I have the environment to work in? Many times a traditional enterprise warehouse doesn't have the right processing power, for you, the individual, who wants to do the work, or, doesn't have the capacity that'll allow you to just bring all the data in, try to ratify it. That's really just trying to do the data cleansing, and trying to just make some sense of it, cause many times, there aren't those domain experts. So I usually work in marketing, and on our Customer 360 exercise, was around, direct mail, email, all the interactions from our Salesmaker, and alike. So, when we look at the data, we go, I don't understand why the Salesmaker is forgetting X, of that behavior that we want to roll together. >> Right. >> But really it's finding that environment, second is the harmonization, is I have Bob Smith and Robert Smith, and Master Data Management Systems, are perhaps few and far between, of being real services that I can call as a data scientist, or as a data worker, to be able to say, how do I line these together? How can I make sure that all these customer touchpoints are really talking about the same individual, the company, or maybe just the consumer? >> Right. >> And finally, it is in those Customer 360 projects getting those teams to want to play together, getting that crowdsourcing, either to change the data, such as, I have data, as you mentioned around Chat, and I want you to tell me more about it, or I want you to tell me how I can break it down. >> Right, right. >> And if I wanna make changes to it, you go, we'll wait, where's your money, in order to make that change. >> Right, right. >> And there's so many aspects to it, right. So there's kind of the classic, you know, ingest, you gotta get the data, you gotta run it through the processes you said did harmonize it to bring it together, and then you gotta present it to the person who's in a position at the moment of truth, to do something with it. And those are three very very different challenges. They've been the same challenges forever, but now we're adding all this new stuff to it, like, are you pulling data from other sources outside of the system of record, are you pulling social data, are you pulling other system data that's not necessarily part of the transactional system. So, we're making the job harder, at the same time, we're trying to give more power to more people and not just the data scientists. But as you said I think, the data worker, so how's that transformation taking place where we're enabling more kind of data workers if you will, that aren't necessarily data scientists, to have the power that's available with the analytics, and an aggregated data set behind them. >> Right. Well we are creating or have created the wild west, we gave them tools, and said, go forth and make, make something out of it. Oh okay. Then we started having this decentralization of all the tools, and when we finally gave them the big tools, the big, that's quote unquote, big data tools, like the process, billings of records, that still is the wild west, but at least we're got them centralized with certain tools. So we were able to do at least standardize on the tool set, standardize on the data environment, so that at least when they're working on that space, we get to go, well, what are you working on? How are you working on that? What type of data are you working with? And how do we bring that back as a process, so that we can say, you did something on Chat Data? Great! Bob over here, he likes to work with that Chat data. So that, that exposure and transparency because of these centralization data. Now, new tools are adding on top of that, data catalogs, and putting inside tools that will make it so that you actually tell, that known information, all-in-one wiki-like interface. So we're trying to add more around putting the right permissions on top of that data, cataloging them in some way, with these either worksheets, or these information management tools, so that, if you're starting to deal with privacy data, you've got a flag, from, it's ingest all the way to the end. >> Right. >> But more controls are being seen as a way that a business is improving its maturity. >> Yeah. Now, the good news bad news is, more and more of the actual interactions are electronic. You want it going to places, they're not picking up the phone as much, as they're engaging with the company either via web browser or more and more a mobile browser, a mobile app, whatever. So, now the good news is, you can track all that. The bad news is, you can track all that. So, as we add more complexity, then there's this other little thing that everybody wants to do now, which is real-time, right, so with Kafka and Flink and Spark and all these new technologies, that enable you to basically see all the data as it's flowing, versus a sampling of the data from the past, a whole new opportunity, and challenge. So how are you seeing it and how are you gonna try to take advantage of that opportunity as well as address that challenge in your world. >> Well in my data science world, I've said, hey, give me some more data, keep on going, and when I have to put on the data sheriff hat, I'm now having to ask the executives, and our stakeholders, why streaming? Why do you really need to have all of this? >> It's the newest shiny toy. >> New shiny toy! So, when you talk to a stakeholder and you say, you need a shiny toy, great. I can get you that shiny toy. But I need an outcome. I need a, a value. And that helps me in tempering the next statement I give to them, you want streaming, so, or you want real time data, it's gonna cost you, three X. Are you gonna pay for it? Great. Here's my shiny toy. But yes, with the influx of all of this data, you're having to change the architecture and many times IT traditionally hasn't been able to make that, that rapid transition, which lends itself to shadow IT, or other folks trying to cobble something together, not to make that happen. >> And then there's this other pesky little thing that gets in the way, in the form of governance, and security. >> Compliance, privacy and finally marketability, I wanna give you a, I want you to feel that you're trusting me, in handling your data, but also that when I respond back to you, I'm giving you a good customer experience so called, don't be creepy. >> Right, right. >> Lately, the new compliance rule in Europe, GDPR, a policy that comes with a, well, a shotgun, that says, if there are violations of this policy, which involves privacy, or the ability for me to be forgotten, of the information that a corporation collects, it can mean four percent of a total company's revenue. >> Right. >> And that's on every instance, that's getting a lot of motivation for information governance today. >> Right. >> That risk, but the rules are around, trying to be able to say, where did the data come from? How did the data flow through the system? Who's touched that data? And those information management tools are mostly the human interaction, hey what are you guys working on? How are you guys working on it? What type of assets are you actually driving, so that we can bring it together for that privacy, that compliance, and workflow, and then later on top of that, that deliverability. How do you want to be contacted? How do you, what are the areas that you feel, are the ways that we should engage with you? And of course, everything that gets missed in any optimization exercise, the feedback loop. I get feedback from you that say, you're interested in puppies, but your data set says you're interested in cats. How do I make that go into a Customer 360 product. So, privacy, and being, and coming at, saying, oh, here's an advertisement for, for hippos and you go, what do you know about me that I don't know? >> Wrong browser. >> So you chose Datameer, along the journey, why did you choose them, how did you implement them, and how did they address some of these issues that we've just been discussing? >> Datameer was chosen primarily to take on that self-service data preparational layer from the beginning. Dealing with large amounts of online data, we move from from taking the digital intelligence tools that are out there, knowing about browser activities, the cookies that you have to get your identity, and said, we want the entire feed. We want all of that information, because we wanna make that actionable. I don't wanna just give it to a BI report, I wanna turn it into marketing automation. So we got the entire feed of data, and we worked on that with the usual SQL tools, but after a while, it wasn't manageable, by either, all of the 450 to 950 columns of data, or the fact that there are multiple teams working on it, and I had no idea, what they were able to do. So I couldn't share in that value, I couldn't reuse, the insights that they could have. So Datameer allowed for a visual interface, that was not in a coding language, that allowed people to start putting all of their work inside one interface, that didn't have to worry about saving it up to the server, it was all being done inside one environment. So that it could take not only the digital data, but the Salesforce CRN data, marry them together and let people work with it. And it broadened on the other areas, again allowing it that crowdsourcing of other people's analytics. Why? Mostly because of the state we are in around IT, is an inability to change rapidly, at least for us, in our field. >> Right. >> That my, the biggest problem we had, was there wasn't a scheduler. We didn't have the ability to get value out of our, on our work, without having someone to press the button and run it, and if they ran it, it took eight hours, they walked away, it would fail. And you had no, you had to go back and do it all over again. >> Oh yeah. >> So Datameer allows us to have that self-service interface, that had management that IT could agree upon, to let us have our own lab environment, and execute our work. >> So what was the results, when you suddenly give people access to this tool? I mean, were they receptive, did you have to train them a lot, did some people just get it and some people just don't, they don't wanna act on data, what was kind of the real-world results of rolling this out, within the population? Real-world results allowed us to get ten million dollars in uplift, in our marketing activities across multiple channels. >> Ten million dollars in uplift? How did you measure that? >> That was measured through the operating expenses, by one not sending that work outside, some of the management, of the data, is being, was sent outside, and that team builds their own models off of them, we said, we should be able to drink our own champagne, second, it was on the uplift of a direct mail and email campaign, so having a better response rate, and generally, not sending out a bunch of app store messages, that we weren't needing too. And then turning that into a list that could be sent out to our email and direct mail vendors, to say, this is what we believe, this account or contact is engaged with on the site. Give those a little bit more context. So we add that in, that we were hopefully getting and resonating a better message. >> Right. >> In, and where did you start? What was the easiest way to provide an opportunity for people new to this type of tooling access to have success? >> Mostly it was trying to, was taking pre-doctored worksheets, or already pre-packaged output, and one of the challenges that we had were people saying well I don't wanna work in a visual language, while they're users of tools like Tableau or Clicks, and others that are happy to drag-and-drop in their data, many of the data workers, the tried-and-true, are saying, I wanna write it in SQL. >> Mm hm. >> So, we had to give at least that last mile, analytical data set to them, and say, okay. Yeah, go ahead and move it over to your SQL environment, move it over into the space that you feel comfortable and you feel confident to control, but let' come on back and we'll translate it back to, this tool, we'll show you how easy it was, to go from, working with IT, which would take months, to go and doing it on yourself, which would take weeks, and the processing and the cost of your Siloed, shadowed IT environment, will go down in days. We're able to show them that, that acceleration of time to market of their data. >> What was your biggest surprise? An individual user, an individual use case, something that really you just didn't see coming, that's kind of a pleasant, you know the law of unintended consequences on the positive side. >> That's was such a wide option, I mean honestly, beginning back from the data science background, we thought it would just be, bring your data in, throw it on out there, and we're done. We went from, maybe about 20 large datasets of AdTech and Martech, and information, advertising, technology, marketing technology, data, to CRMM formation, order activity, and many other categories, just within marketing alone, and I think perhaps, the other big ah-ha moment was, since we brought that in, of other divisions data, those own teams came in, said, hey, we can use this too. >> Right. >> The adoption really surprised me that it would, you would have people that say, oh I can work with this, I have this freedom to work with this data. >> Right right. >> Well we see it time and time again, it's a recurring theme of all the things we cover, which is, you know a really, big piece of the innovation story, is giving, you know, more people access to more data, and the tools to actually manipulate it. So that you can unlock that brain power, as opposed to keeping it with the data scientists on Mahogany Row, and the super-big brain. So, sounds like that really validates that whole hypothesis. >> I went through reviewing hands-on 11 different tools, when I chose Datameer. This was everything from, big name companies, to small start-up companies, that have wild artificial intelligence slogans in their marketing material, and we chose it mostly because it had the right fit, as an end-to-end approach. It had the scheduler, it had the visual interface, it had the, enough management and other capabilities that IT would leave us alone. Some of the other products that we were looking at gave you, Pig-El-Lee to work with data, will allow you to schedule data, but they never came all together. And for the value we get out of it, we needed to have something altogether. >> Right. Well Jeff, thanks for taking a few minutes and sharing your story, really appreciate it, and it sounds like it was a really successful project. >> Was! >> All right. He's Jeff Weidener, I'm Jeff Frick, you're watching theCube from Palo Alto. Thanks for watching.

>> Announcer: From the Computer History Museum, in the heart of Silicon Valley, it's theCUBE. Covering Food IT: Fork to Farm, brought to you by Western Digital. >> Hey welcome back everybody Jeff Frick here with theCUBE. We're at the Food IT show at the Computer History Museum here in Mountain View, California. Really an amazing show, 350 people, all kind of pieces of the spectrum from academia to technology, to start-ups to Yamaha. Who thought Yamaha was into food tech, I didn't think that. To start-ups and we're really excited to have two of the partners form the Mixing Bowl and the Better Food Ventures, Brita Rosenheim and Seana Day welcome. >> Thank you. >> Thanks Jeff. >> So first off, congratulations on the event, what are your impressions? you guys been doing this for a couple years now I think. Bigger, badder, better? >> No I think this is great. We've has a fantastic turn out and the content's always very interesting and the interaction between the audience and the speakers is fantastic. >> Yeah, we just finished up a panel, IoT, Internet of Tomatoes, so there's always some great conversations really going. >> I think we're talking about that later this afternoon. >> Oh fantastic. >> It is interesting right, because all the big megatrends of cloud and we cover these in tech infrastructure all the time and big data and sensors and IoT and drones and these things. Really, all being brought to bare in agriculture from everything from producing the food to eating the food to the scraps that we don't eat I guess. >> No, you're spot on, some of the big macro challenges are what's driving a lot of the innovation. As you said food scraps, but waste is a major challenge. Labor, certainly here in California is something that we've seen a lot of innovation around solving some of those labor pain points. Certainly sort of environmental sustainability and resource management, you know, how are we using water, how are we using our inputs. Those are a lot of big themes that are driving interest in this sector and driving investment. >> Right so you guys are talking about some of the investments, like you guys put on a show, but you also have an investment arm, so you're looking for new technologies that play in this space correct? >> Yeah, Better Food Ventures makes early stage, seed investments so really kind of, not ideation stage, but pretty close after that. So working with entrepreneurs and really helping them, nurture them, and grow into hopefully successful companies. We've made 12 investments so far, I think seven of them have stepped up to priced equity so. >> Excellent, and you guys have brought this architecture landscape of the innovation. We won't share this on camera because it's way too many names for you to see, but obviously you can go online. >> Seana: It's available for download on our website MixingBowlHub.com. >> It's fascinating, there are literally what, a dozen categories and many firms within each category per side, so I wonder if you can give us a little bit more color on this landscape. I had no idea, the level of innovation that's happening in the food tech space, you just don't think about it probably if you're not in the industry. >> I'll let Seana kick off, between Seana and I, we cover Fork to Farm, so Seana covers from the farm, all the way through distribution and the area that I focus on, distribution all the way to consumer consumption. So we have a nice harmony there. We'll start at the beginning with Seana. >> Looking at over 3,000 companies. >> Jeff: 3,000? >> 3,000 between the two of our sort of database's. My coverage area is really infield technologies, hardware, software, applications. So anything from sensors, drones, soil moisture, weather, crop management, farm management software, all the way through as Brita said, distribution. So looking at supply chain management, logistics, trading platforms, collaboration platforms, so there's a lot going on. Every time, I roll out one of these technology landscapes. I'm always adding categories, which is sort of representative of the way that the market is evolving. I think that there is a lot of interesting stuff happening now in the post-harvest part of this market that more investors are starting to pay attention to. We've heard of that more today's even as well. Technologies that are focused on minimizing waste in the supply chain, making things more efficient helping shorten that supply chain so that we've got fresher food. More local options for consumers. >> I've been tracking the space for the last six or seven years, and to echo Seana's point on every time you put a new map out, you know we're thinking about different categories I mean every single year you've looked at it, the ecosystem has changed so much in terms of even how you categorize or even think of the different innovations that are shaping the space. I focus on, the way I look at my map is from in-home media consumption, discovery, so media, marketing, advertising, all the way through eCommerce, so both the B2B and B2C eCommerce platforms, all the way through restaurant and retail. So grocery, delivery, hyper-local marketing and the like. >> So can you explain the crazy success of these little, event handling, short food videos that are just taking the internet by storm? It's fascinating right? >> Yeah, BuzzFeed's tasty. >> Media consumption is really something to see. >> Yeah, I think BuzzFeed really took the traditional food media category by surprise. They really created the new, literally, video content for consumption that is extremely addicting, short, it makes everything seem approachable. It's kind of the bite-size version of the Food Network and I find myself. >> Off the chart right? >> You can't stop. Whether I'll make it or not you know, like the twirling potato and. (Brita chuckling) >> So the other, the sub-theme for this years conference is Fork to Farm and I'm just curious right. Because we've seen consumerization of IT impact all the different industries that we cover. It is really the end user at the end point that's driving the innovation back upstream. I wonder if you could speak to kind of the acceleration of that trend over time. Or is it relatively recent or you know there's some specific catalyst that you've seen as you've studied the market that has really driven an acceleration of that? >> Seana: Do you want to start with consumer and then we'll get back into the grower side of that? >> Yeah, I mean, I think you've seen kind of the long evolution since my web grocer cosmos of 10, 15 years ago and you know, people thinking, I'm never going to buy food online really don't have that trust level and you know kind of eCommerce in general, mobile technology in general has changed the consumers expectation and purchase and consumption patterns, period, for all other goods, so we've gotten to a point where there is a level of trust of if something is going to come to you in the mail there's just an expected level of trust or you can send it back. So that's kind of lent itself to this food category. I think in one way, that's been an overall industry shift in terms of the changing expectations of the consumer. You want to push a button, you've got your shoes, your lipstick you know your dog toys at the push of a button, why not your food. So the problem with that is food is very different it's has to be hot or cold, you have the cold chain speed, the manual labor involved. Just kind of the cost infrastructure is totally different than sending a box of lipstick and makeup to a consumer so I think you've seen a tremendous amount of funding in this on-demand delivery category a ton of different Uber for this, Uber for that, around the food space. Meal kits, but I think the reality of running those businesses have proven to be very difficult in terms of making the costs work out in terms of a business model so. >> Don't they all know why Van failed? They all probably too young to miss the Webvan and AT&T. >> Yeah, that being said, there's some opportunity there it's just about getting to the right scale. So obviously Amazon just bought Whole Foods last week I think there is room for a brick and mortar approach here but there, I think on-demand delivery's not going away in the food category, so who can actually deliver that because the consumer's not going to say, oh the business model doesn't make sense, I don't want this anymore. They just don't want to pay for it. Somebody has to figure out a way to. >> Oh that other pesky little detail About. And Seana it used to be if we make it they will eat right? I guess that doesn't hold true anymore. >> Well, you know it's a different adoption dynamic in the grower part of the technology adoption curve the consumers tend to pick things up more quickly than the traditional Ag player, Ag stake holder, the growers have been a little bit more tentative in terms of trying to figure out what kinds of technologies actually work. They're all of a sudden confronted with this idea of data overload. All of a sudden, you go from having no data to more data than you know what to do with. That's driving some of these adoption dynamics. People really trying to figure out what works, what business models are sustainable in agriculture and I know unsustainable from a resource standpoint. But just, will that business be around in six to nine to 12 months to support the technology that's in the field. So it's been a little slower I would say, on the production agriculture and grower side in terms of that uptake, but you know the other challenge that I think we face in terms of those models is really the flow of data. The flow of information is still very silo'd and in order to get the kind of decision support tools and the supply chain efficiencies that we're looking for in the food system, we really need to figure out how to integrate those data sources better. What's coming out of the field, what's happening in the mid-stream processing, and then what's happening on the supply chain and logistics side before you get to that consumer who's demanding it. But there's a lot of stages of information that need to harmonize before we can really have a more optimized system. >> Right, and are you seeing within the data side specifically some of the traditional players, like Tableau and clearly there's been a lot of activity in big data for awhile we've been going to Hadoop Summit and Hadoop World for ever and ever, are those people building Ag specific solutions or are there new players that really see the specific opportunity and better position to build you know the analytics to enable the use of that data? >> I think the big IT incumbents are looking at this very, very carefully. But there's are a lot of nuances to agriculture that are different from some of the other vertical industries and there's been a lot of observing from the sidelines down there, less from the deployment of actual technologies. Until people really understand how this market is starting to shake out. I think IBM and some of those big tech players are definitely on the fringes here, but I think again, we've got this challenge of how do you actually deliver value to growers. So, you've got all this data and you can crunch all this data how do you present that in a way that a grower can make a better decision about their operation. And oh, by the way, does the grower trust that data. That sort of is the challenge that I think we're still in the early innings in terms of of how that. It will come, but we're still in the early innings. >> Which is always the case right, to go from kind of an intuition, we've always done it this way, you know, like three generations of grandfathers that have worked this land too, you know here's the data, you can micro-optimize for this, that and the other and really take a different approach. >> I's say one of the challenges both on the Ag side, but also even on the food side, that there's a lot of start-ups that you meet with that are all about big data, big data, but big data really needs to be big data. So the incumbents are really the only ones that are in the position to crunch that amount of data. You can't actually get the insights when you don't have scale so there's a tremendous amount of companies that have a really interesting, innovative, approach to collecting data, to how you can use it and all they need is scale. That's virtually impossible unless they're acquired by or have a partnership with, which isn't going to happen a larger incumbent so big data, you really need a tremendous amount of data points to actually get to something that's useful. >> Alright, well, Seana and Brita thanks for taking a few min utes again, where can people go to get the pretty download it's a lot of data on this thing. >> It's MixingBowlHub.com so that's available both the AdTech landscape and the Food Tech landscape. >> Alright great, well again thanks, for inviting us to the show, really great show and congrats to you both for pulling it off. >> Thank you very much. >> Thanks very much. >> Alright, Brita, Seana, I'm Jeff you're watching theCUBE we're at FoodIT in the Computer Science Museum in Mountain View, California. We'll be back after the short break. Thanks for watching.

>> Narrator: Live, from San Jose, in the heart of Silicon Valley, it's The Cube. Covering, DataWorks Summit, 2017. Brought to you by Hortonworks. >> Welcome back to The Cube. We are live on day two of the DataWorks Summit. From the heart of Silicon Valley. I am Lisa Martin, my co-host is George Gilbert. We're very excited to be joined by our next guest from DataTorrent, we've got Nathan Trueblood, VP of Product, hey Nathan. >> Hi. >> Lisa: And, the man who gave me my start in high tech, 12 years ago, the SVP of Marketing, Jeff Bettencourt. Welcome, Jeff. >> Hi, Lisa, good to see ya. >> Lisa: Great to see you, too, so. Tell us about the SVP of Marketing, who is DataTorrent, what do you guys do, what are doing in the big data space? >> Jeff: So, DataTorrent is all about real time streaming. So, it's really taken a different paradigm to handling information as it comes from the different sources that are out there, so you think, big IOT, you think, all of these different new things that are creating pieces of information. It could be humans, it could be machines. Sensors, whatever it is. And taking that in realtime, rather than putting it traditionally just in a data lake and then later on coming back and investigating the data that you stored. So, we started about 2011, started by some of the early founders, people that started Yahoo. And, we're pioneers in Hadoop with Hadoop yarn. This is one of the guys here, too. And so we're all about building realtime analytics for our customers, making sure that they can get business decisions done in realtime. As the information is created. And, Nathan will talk a little bit about what we're doing on the application side of it, as well. Building these hard application pipelines for our customers to assist them to get started faster. >> Lisa: Excellent. >> So, alright, let's turn to those realtime applications. Umm, my familiarity with DataTorrent started probably about five years ago, I think, where it was, I think the position is, I don't think that there was so much talk about streaming but it was like, you know, realtime data feed, but, now we have, I mean, streaming is sort of center of gravity. Sort of, appear to big data. >> Nathan: Yeah. >> So, tell us how someone whose building apps, should think about the two solution categories how they compliment each other and what sort of applications we can build now that we couldn't build before? >> So, I think the way I look at it, is not so much two different things that compliment each other, but streaming analytics and realtime data processing and analytics is really just a natural progression of where big data has been going. So, you know, when we were at Yahoo and we're running Hadoop in scale, you know, first thing on the scene was just simply the ability to produce insight out of a massive amount of data. But then there was this constant pressure, well, okay, now we've produced that insight in a day, can you do it in an hour? You know, can you do it in half an hour? And particularly at Yahoo at the time that Ah-mol, our CTO and I were there, there was just constant pressure of can you produce insight from a huge volume of data more quickly? And, so we kind of saw at that time, two major trends. One, was that we were kind of reaching a limit of where you could go with the Hadoop and batch architecture at that time. And so a new approach was required. And that's what really was sort of, the foundation of the Apache Apex project and of DataTorrent the company, was simply realizing that a new approach was required because the more that Yahoo or other businesses can take information from the world around them and take action on that as quickly as possible, that's going to make you more competitive. So I'd look at streaming as really just a natural progression. Where, now it's possible to get inside and take action on data as close to the time of data creation as possible and if you can do that, then, you're going to be competitive. And so we see this coming across a whole bunch of different verticals. So that's how I kind of look at the sort of it's not too much complimentary, as a trend in where big data is going. Now, the kinds of things that weren't possible before this, are, you know, the kinds of applications where now you can take insight whether it's from IOD or from sensors or from retail, all the things that are going on. Whereas before, you would land this in a data lake, do a bunch of analysis, produce some insight, maybe change your behavior, but ultimately, you weren't being as responsive as you could be to customers. So now what we are seeing, why I think the center of mass is moved into realtime and streaming, is that now it's possible to, you know, give the customer an offer the second they walk into a store. Based on what you know about them and their history. This was always something that the internet properties were trying to move towards, but now we see, that same technology is being made available across a whole bunch of different verticals. A whole bunch of different industries and that's why you know, when you look at Apex and DataTorrent, we're involved not only in things like adtech, but in industrial automation and IOT, and we're involved in, you know, retail and customer 360 because in every one of these cases, insurance, finance, security and fraud prevention, it's a huge competitive advantage if you can get insight and make a decision, close to the time of the data creation. So, I think that's really where the shift is coming from. And then the other thing I would mention here, is that a big thrust of our company, and of Apache Apex and this is, so we saw streaming was going to be something that every one was going to need. The other thing we saw from our experience at Yahoo, was that, really getting something to work at a POC level, showing that something is possible, with streaming analytics is really only a small part of the problem. Being able to take and put something into production at scale and run a business on it, is a much bigger part of the problem. And so, we put into both the Apache Apex problem as well as into our product, the ability to not only get insight out of this data in motion, but to be able to put that into production at scale. And so, that's why we've had quite a few customers who have put our product, in production at scale and have been running that way, you know, in some cases for years. And so that's another sort of key area where we're forging a path, which is, it's not enough to do POC and show that something is possible. You have to be able to run a business on it. >> Lisa: So, talk to us about where DataTorrent sits within a modern data architecture. You guys are kind of playing in a couple of, integrated in a couple of different areas. What goes through what that looks like? >> So, in terms of a modern data architecture, I mean part of it is what I just covered in that, we're moving sort of from a batch to streaming world where the notion of batch is not going away, but now when you have something, you know a streaming application, that's something that's running all the time, 24/7, there's no concept of batch. Batch is really more the concept of how you are processing data through that streaming application so, what we're seeing in the modern data architecture, is that, you know, typically you have people taking data, extracting it and eventually loading it into some kind of a data lake, right? What we're doing is, shifting left of the data lake. You know, analyzing information when it's created. Produce insight from it, take action on it, and then, yes, land it in the data lake, but once you land it in the data lake, now, all of the purposes of what you're doing with that data have shifted. You know, we're producing insight, taking action to the left of the data lake and then we use that data lake to do things, like train your you know, your machine learning model that we're then going to use to the left of the data lake. Use the data lake to do slicing and dicing of your data to better understand what kinds of campaigns you want to run, things like that. But ultimately, you're using the realtime portion of this to be able to take those campaigns and then measure the impacts you're having on your customers in realtime. >> So, okay, cause that was going to be my followup question, which is, there does seem to be a role, for a historical repository for richer context. >> Nathan: Absolutely. >> And you're acknowledging that. Like, did the low legacy analytics happen first? Then, store up for a richer model, you know, later? >> Nathan: Correct. >> Umm. So, there are a couple things then that seem to be like requirements, next steps, which is, if you're doing the modeling, the research model, in the cloud, how do you orchestrate its distribution towards the sources of the realtime data, umm, and in other words, if you do training up in the cloud where you have, the biggest data or the richest data. Is DataTorrent or Apex a part of the process of orchestrating the distribution and coherence of the models that should be at the edge, or closer to where the data sources are? >> So, I guess there's a couple different ways we can think about that problem. So, you know we have customers today who are essentially providing into the streaming analytics application, you know, the models that have been trained on the data from the data lake. And, part of the approach we take in Apex and DataTorrent, is that you can reload and be changing those models all of the time. So, our architecture is such that it's full tolerant it stays up all the time so you can actually change the application and evolve it over time. So, we have customers that are reloading models on a regular basis, so that's whether it's machine learning or even just a rules engine, we're able to reload that on a regular basis. The other part of your question, if I understood you, was really about the distribution of data. And the distribution of models, and the distribution of data and where do you train that. And I think that you're going to have data in the cloud, you're going to have data on premises, you're going to have data at the edge, again, what we allow customers to do, is to be able to take and integrate that data and make decisions on it, regardless kind of where it lives, so we'll see streaming applications that get deployed into the cloud. But they may be synchronized in some portion of the data, to on premises or vis versa. So, certainly we can orchestrate all of that as part of an overall streaming application. >> Lisa: I want to ask Jeff, now. Give us a cross section of your customers. You've got customers ranging from small businesses, to fortune 10. >> Jeff: Yep. >> Give us some, kind of used cases that really took out of you, that really showcased the great potential that DataTorrent gives. >> Jeff: So if you think about the heritage of our company coming out of the early guys that were in Yahoo, adtech is obviously one that we hit hard and it's something we know how to do really really well. So, adtech is one of those things where they're constantly changing so you can take that same model and say, if I'm looking at adtech and saying, if I applied that to a distribution of products, in a manufacturing facility, it's kind of all the same type of activities, right? I'm managing a lot of inventory, I'm trying to get that inventory to the right place at the right time and I'm trying to fill that aspect of it. So that's kind of where we kind of started but we've got customers in the financial sector, right, that are really looking at instantaneous type of transactions that are happening. And then how do you apply knowledge and information to that while you're bringing that source data in so that you can make decisions. Some of those decisions have people involved with them and some of them are just machine based, right, so you take the people equation out. We kind of have this funny thing that Guy Churchward our CEO talks about, called the do loop and the do loop is where the people come in and how do we remove people out of that do loop and really make it easier for companies to act, prevent? So then if you take that aspect of it, we've got companies like in the publishing space. We've got companies in the IOT space, so they're doing interview management, stuff like that, so, we go from very you know, medium sized customers all the way up to very very large enterprises. >> Lisa: You're really turning up a variety of industries and to tech companies, because they have to be these days. >> Nathan: Right, well and one other thing I would mention, there, which is important, especially as we look at big data and a lot of customer concern about complexity. You know, I mentioned earlier about the challenge of not just coming up with an idea but being able to put that into production. So, one of the other big ares of focus for DataTorrent, as a company, is that not only have we developed platform for streaming analytics and applications but we're starting to deliver applications that you can download and run on our platform that deliver an outcome to a customer immediately. So, increasingly as we see in different verticals, different applications, then we turn those into applications we can make available to all of our customers that solve business problems immediately. One of the challenges for a long time in IT is simply how do you eliminate complexity and there's no getting away from the fact that this is big data in its complex systems. But to drive mass adoption, we're focused on how can we deliver outcomes for our customers as quickly as possible and the way to do that is by making applications available across all these different verticals. >> Well you guys, this has been so educational. We wish you guys continued success, here. It sounds like you're really being quite disruptive in an of yourselves, so if you haven't heard of them, DataTorrent.com, check them out. Nathan, Jeff, thanks so much for giving us your time this afternoon. >> Great, thanks for the opportunity. >> Lisa: We look forward to having you back. You've been watching The Cube, live from day two of the DataWorks Summit, from the heart of Silicon Valley, for my co-host George Gilbert, I'm Lisa Martin, stick around, we'll be right back. (upbeat music)

>> (Announcer) Live from San Jose, in the heart of Silicon Valley, it's theCUBE covering DataWorks Summit 2017, brought to you by Hortonworks. >> Hi everybody, this is George Gilbert, Big Data and Analytics Analyst with Wikibon. We are wrapping up our show on theCUBE today at DataWorks 2017 in San Jose. It has been a very interesting day, and we have a special guest to help us do a survey of the wrap-up, George Chow from Simba. We used to call him Chief Technology Officer, now he's Technology Fellow, but when we was explaining the different in titles to me, I thought he said Technology Felon. (George Chow laughs) But he's since corrected me. >> Yes, very much so >> So George and I have been, we've been looking at both Spark Summit last week and DataWorks this week. What are some of the big advances that really caught your attention? >> What's caught my attention actually is how much manufacturing has really, I think, caught into the streaming data. I think last week was very notable that both Volkswagon and Audi actually had case studies for how they're using streaming data. And I think just before the break now, there was also a similar session from Ford, showcasing what they are doing around streaming data. >> And are they using the streaming analytics capabilities for autonomous driving, or is it other telemetry that they're analyzing? >> The, what is it, I think the Volkswagon study was production, because I still have to review the notes, but the one for Audi was actually quite interesting because it was for managing paint defect. >> (George Gilbert) For paint-- >> Paint defect. >> (George Gilbert) Oh. >> So what they were doing, they were essentially recording the environmental condition that they were painting the cars in, basically the entire pipeline-- >> To predict when there would be imperfections. >> (George Chow) Yes. >> Because paint is an extremely high-value sort of step in the assembly process. >> Yes, what they are trying to do is to essentially make a connection between downstream defect, like future defect, and somewhat trying to pinpoint the causes upstream. So the idea is that if they record all the environmental conditions early on, they could turn around and hopefully figure it out later on. >> Okay, this sounds really, really concrete. So what are some of the surprising environmental variables that they're tracking, and then what's the technology that they're using to build model and then anticipate if there's a problem? >> I think the surprising finding they said were actually, I think it was a humidity or fan speed, if I recall, at the time when the paint was being applied, because essentially, paint has to be... Paint is very sensitive to the condition that is being applied to the body. So my recollection is that one of the finding was that it was a narrow window during which the paint were, like, ideal, in terms of having the least amount of defect. >> So, had they built a digital twin style model, where it's like a digital replica of some aspects of the car, or was it more of a predictive model that had telemetry coming at it, and when it's an outside a certain bounds they know they're going to have defects downstream? >> I think they're still working on the predictive model, or actually the model is still being built, because they are essentially trying to build that model to figure out how they should be tuning the production pipeline. >> Got it, so this is sort of still in the development phase? >> (George Chow) Yeah, yeah >> And can you tell us, did they talk about the technologies that they're using? >> I remember the... It's a little hazy now because after a couple weeks of conference, so I don't remember the specifics because I was counting on the recordings to come out in a couples weeks' time. So I'll definitely share that. It's a case study to keep an eye on. >> So tell us, were there other ones where this use of real-time or near real-time data had some applications that we couldn't do before because we now can do things with very low latency? >> I think that's the one that I was looking forward to with Ford. That was the session just earlier, I think about an hour ago. The session actually consisted of a demo that was being done live, you know. It was being streamed to us where they were showcasing the data that was coming off a car that's been rigged up. >> So what data were they tracking and what were they trying to anticipate here? >> They didn't give enough detail, but it was basically data coming off of the CAN bus of the car, so if anybody is familiar with the-- >> Oh that's right, you're a car guru, and you and I compare, well our latest favorite is the Porche Macan >> Yes, yes. >> SUV, okay. >> But yeah, they were looking at streaming the performance data of the car as well as the location data. >> Okay, and... Oh, this sounds more like a test case, like can we get telemetry data that might be good for insurance or for... >> Well they've built out the system enough using the Lambda Architecture with Kafka, so they were actually consuming the data in real-time, and the demo was actually exactly seeing the data being ingested and being acted on. So in the case they were doing a simplistic visualization of just placing the car on the Google Map so you can basically follow the car around. >> Okay so, what was the technical components in the car, and then, how much data were they sending to some, or where was the data being sent to, or how much of the data? >> The data was actually sent, streamed, all the way into Ford's own data centers. So they were using NiFi with all the right proxy-- >> (George Gilbert) NiFi being from Hortonworks there. >> Yeah, yeah >> The Hortonworks data flow, okay >> Yeah, with all the appropriate proxys and firewall to bring it all the way into a secure environment. >> Wow >> So it was quite impressive from the point of view of, it was life data coming off of the 4G modem, well actually being uploaded through the 4G modem in the car. >> Wow, okay, did they say how much compute and storage they needed in the device, in this case the car? >> I think they were using a very lightweight platform. They were streaming apparently from the Raspberry Pi. >> (George Gilbert) Oh, interesting. >> But they were very guarded about what was inside the data center because, you know, for competitive reasons, they couldn't share much about how big or how large a scale they could operate at. >> Okay, so Simba has been doing ODBC and JDBC drivers to standard APIs, to databases for a long time. That was all about, that was an era where either it was interactive or batch. So, how is streaming, sort of big picture, going to change the way applications are built? >> Well, one way to think about streaming is that if you look at many of these APIs, into these systems, like Spark is a good example, where they're trying to harmonize streaming and batch, or rather, to take away the need to deal with it as a streaming system as opposed to a batch system, because it's obviously much easier to think about and reason about your system when it is traditional, like in the traditional batch model. So, the way that I see it also happening is that streaming systems will, you could say will adapt, will actually become easier to build, and everyone is trying to make it easier to build, so that you don't have to think about and reason about it as a streaming system. >> Okay, so this is really important. But they have to make a trade-off if they do it that way. So there's the desire for leveraging skill sets, which were all batch-oriented, and then, presumably SQL, which is a data manipulation everyone's comfortable with, but then, if you're doing it batch-oriented, you have a portion of time where you're not sure you have the final answer. And I assume if you were in a streaming-first solution, you would explicitly know whether you have all the data or don't, as opposed to late arriving stuff, that might come later. >> Yes, but what I'm referring to is actually the programming model. All I'm saying is that more and more people will want streaming applications, but more and more people need to develop it quickly, without having to build it in a very specialized fashion. So when you look at, let's say the example of Spark, when they focus on structured streaming, the whole idea is to make it possible for you to develop the app without having to write it from scratch. And the comment about SQL is actually exactly on point, because the idea is that you want to work with the data, you can say, not mindful, not with a lot of work to account for the fact that it is actually streaming data that could arrive out of order even, so the whole idea is that if you can build applications in a more consistent way, irrespective whether it's batch or streaming, you're better off. >> So, last week even though we didn't have a major release of Spark, we had like a point release, or a discussion about the 2.2 release, and that's of course very relevant for our big data ecosystem since Spark has become the compute engine for it. Explain the significance where the reaction time, the latency for Spark, went down from several hundred milliseconds to one millisecond or below. What are the implications for the programming model and for the applications you can build with it. >> Actually, hitting that new threshold, the millisecond, is actually a very important milestone because when you look at a typical scenario, let's say with AdTech where you're serving ads, you really only have, maybe, on the order about 100 or maybe 200 millisecond max to actually turn around. >> And that max includes a bunch of things, not just the calculation. >> Yeah, and that, let's say 100 milliseconds, includes transfer time, which means that in your real budget, you only have allowances for maybe, under 10 to 20 milliseconds to compute and do any work. So being able to actually have a system that delivers millisecond-level performance actually gives you ability to use Spark right now in that scenario. >> Okay, so in other words, now they can claim, even if it's not per event processing, they can claim that they can react so fast that it's as good as per event processing, is that fair to say? >> Yes, yes that's very fair. >> Okay, that's significant. So, what type... How would you see applications changing? We've only got another minute or two, but how do you see applications changing now that, Spark has been designed for people that have traditional, batch-oriented skills, but who can now learn how to do streaming, real-time applications without learning anything really new. How will that change what we see next year? >> Well I think we should be careful to not pigeonhole Spark as something built for batch, because I think the idea is that, you could say, the originators, of Spark know that it's all about the ease of development, and it's the ease of reasoning about your system. It's not the fact that the technology is built for batch, so the fact that you could use your knowledge and experience and an API that actually is familiar, should leverage it for something that you can build for streaming. That's the power, you could say. That's the strength of what the Spark project has taken on. >> Okay, we're going to have to end it on that note. There's so much more to go through. George, you will be back as a favorite guest on the show. There will be many more interviews to come. >> Thank you. >> With that, this is George Gilbert. We are DataWorks 2017 in San Jose. We had a great day today. We learned a lot from Rob Bearden and Rob Thomas up front about the IBM deal. We had Scott Gnau, CTO of Hortonworks on several times, and we've come away with an appreciation for a partnership now between IBM and Hortonworks that can take the two of them into a set of use cases that neither one on its own could really handle before. So today was a significant day. Tune in tomorrow, we have another great set of guests. Keynotes start at nine, and our guests will be on starting at 11. So with that, this is George Gilbert, signing out. Have a good night. (energetic, echoing chord and drum beat)

(soft music) (soft music) >> Narrator: Live from Las Vegas. It's the Cube. Covering Oracle Modern Customer Experience 2017. Brought to you by Oracle. >> Okay welcome back everyone. We're live in Las Vegas. This is the Cube. SiliconAngles flagship program. We got out to the events and extract the (mumbles). Been here two full days of wall to wall coverage. I'm John Furrier. My cohost Peter Burris. Peter really good to see Oracle really move from modern marketing experience, the old show name, to a cleaner broader canvas called Modern CX. Which is modern customer experience. And you startin to see the new management which took the baton from the old management. Kevin Akeroyd. Andrea Ward who did a lot of work. I mean they basically did a ton of acquisitions. We talked last year if you remember. Look they have a data opportunity and we spelled it right out there and said if they can leverage that data horizontally and then offer that vertical specialism with differentiation, they could have their cake and eat it too. Meaning the pillars of solutions in a digital fabric with data. That's what they did. They essentially did it. >> Yeah they did. And it's been, it was a. We came here hoping that that's what we would see and that's what we saw John. Oracle not only has access to a lot of data but a lot of that first person data that really differentiates the business. Information about your finances. Information about your customers. Information about orders. That's really, really crucial data. And it's not easy to get. And if you could build a a strategy for your customers that says let's find ways of bringing in new sources of data. Leveraging that data so that we can actually help you solve and serve your customers better. You got a powerful story. That's a great starting point. >> And one of the things that I would observe here is that this event, the top story was that Mark Hurd came down and talked to the customers in the keynote. And also made a cameo visit to the CMO, some which they had separately. But really kind of basically putting it transparently out there. Look we got all this technology. Why are we spending all of this technology and effort to get a one percent conversion rate on something that happens over here. Let's look at it differently. And I think the big story here is that Oracle puts the arc to the future. Which I think is a very relevant trajectory. Certainly directionally correct using data and then figuring out your process and implementing it. But really looking at it from a people perspective and saying if you can use the data, focus your energies on that data to get new things going. And not rely on the old so much. Make it better but bring in the new. >> I think that's the one thing that we need to see more from Oracle in all honesty. At shows, this show, and shows like this. Is that and we asked the question to a couple quests. What exactly is modern marketing? Technology can allow a company to do the wrong things faster and cheaper. And in some cases that's bad. In marketing that's awful. Because more of the wrong thing amplifies the problem. That's how you take down a brand. You can really annoy the hell out of your customers pretty quickly. >> Well I think you made that point interesting I thought. On that just to reiterate that, validate that, and amplify. Is that if you focus more on serving the business as a marketer versus now it's about the customer. Okay which is why I like the CX and I know you do too. You can create enterprise value through that new way. Versus hey look what team. I'm helping you out with some leads and whatever. Support, content. Marketing now owns the customer relationship. >> Well marketers talk about a persona all the time John. They say what's the persona? It's a stylized type of customer, and now with data we can make it increasingly specific. Which is very, very powerful. I think Oracle needs to do the same thing with the marketing function. What is that marketing function persona that Oracle is, it's self driving to. Driving it's customers to. And trying to lead the industry into. So I would personally like to see a little bit more about what will be the role of marketing in the future. What exactly is the modern. What exactly is modern marketing? What is the road map that Oracle has, not just for delivering the technology, but for that customer transformation that they talk about so much. It's clear that they have an idea. I'd like to see a little bit more public. Cause I think a lot of marketers need to know where they're going to end up. >> I was a bit skeptical coming in here today. I was a little nervous and skeptical. I like the team though, the people here. But I wasn't sure they were going to be able to pull this off as well as they did. I'd give them a solid letter grade of an A on this event. Not an A plus because I think there's some critical analysis that's worth addressing in my opinion. In my opinion Oracle's missing some things. It's not their fault. They're only going as fast as they can. Not to get into your perspective too, but here's my take. They don't know how to deal with video. That came up as technical issue. But Jay -- >> But nobody really does. >> But nobody really does. And that's just again because we're in the video business it jumped out at me. But Jay Baer was on. Who's hosted the CMO Summit. And he's out there too like us. Content is a big thing. And I haven't heard a lot about the content equation in the marketing mix. So if you look at the modern marketing mix, content is data. And content is instrumental as a payload for email marketing. And we're in the content business so we know a lot about the engagement side of it. So I just don't see a lot of the engagement conversations that are happening around content. Don't see that dots connecting. >> And I think you're right. I think you're right John. And part of the reason is, and again I think Oracle needs to do a better job at articulating what this means. From our perspective, it's my perspective but you agree with me. I'll put words in your mouth. Is that marketing has to be a source of value to customers. Well what do customers find valuable? They find information in easily digestible, consumable chunks as they go on their journey. What are those chunks? Those chunks, in fact, are content. So to tie this back and show how crucial this is. At the end of the day, consumers, businesses need to learn about your brand. Need to learn about next best action. All that other stuff. In consumable interesting, valuable chunks. And it ultimately ends up looking like content. So your absolutely right to talk about how this all comes together and show how, that content is the mechanism by which a lot of this value's actually going to be delivered. Is really crucial. >> And now to give the praise sandwich, as we say in positive coaching alliance, two positives and then the critical analysis in the middle. That's the praise sandwich. So to give them some praise around the criticism. I will say that Oracle validates for me, and this is why I think they got a good strategy. That there's no silver bullet in marketing. Okay there's no silver bullet. This product will get you more engagement. This will do that. They do show that data is going to be an instruble part of creating a series of collections of silver bullets. Of bullets if you will. To create that value. And I think that's the key. And then the second praise is, this is kind of nuance in their analysis. But the third party data support, is a big deal in my mind. I want to expand more on that. I want to learn more about it. Because when you have the first party data, which is very valuable, and access to more data sources. That becomes increasingly interesting. So the extensibility for getting content data or other data can come in through third party. I think that opens the door for Oracle to innovate on the area we gave the criticism on. So I think that's a positive trend. I think that's a good outlook on having the ability to get that third party data. >> Yeah but it's also going to be one of the places where Oracle is going to have to compete very, very aggressively with some other leaders who are a little bit more oriented towards content. At least some of their marketing clients are a little bit more content oriented. I'm comfortable Oracle will get there because let's face it. At the end of the day, marketing's always done a pretty good job of created, creative, using data to figure out what creative to use or create is nice. Very important. But what we're really talking about is customer experience. Will the customer get something out of every interaction? And while content's crucial to that the end result is ultimately, is the customer successful? And Oracle is showing a better play for that. So I'll give you, I like the way you did it on the grading. I'll give them a B plus. But I'm not disagreeing with you. I think we saw A talent here. We saw an A minus story. And they're a year in. So there's still some work that needs to be done, but it's clearly -- >> Why you weighted as a B plus >> I give them an A on vector. And where they're going. >> I would agree with that. >> And the feedback that we've gotten from the customers walking the show floor. There's a lot of excitement. A lot of positive energy. The other thing that I would say -- >> Oh the band. I'd give the band, the band was a B minus. (Peter laughs) Yeah that takes it. That's going to kill the curve. >> What was the band last night? >> I don't even remember. We missed the good one, I know that. We had dinner so we came late. It was a good band. It wasn't like, it wasn't like Maroon 5 or One Republic. Or Imagine Dragons or U2. >> Or one of the good ones. Sting. C minus. But the other thing that I think is really important is at least it pertains to modern customer experience. Is that they are, they are absolutely committed to the role the data's going to play. And we talked about that right at the front. But they are demonstrating a deep knowledge of how data and data integration and data flows are really going to impact the way their customers businesses operate. And I think that there were a couple of, I'll give a really high point and one that I want to hear more about in terms of the interviews we had. Great high point was one, we talked a lot about data science and how data science technologies are being productized. And that we heard, for example, that Oracle's commitment to it's marketplace is that they are going to insure that their customers can serve their customer's customers with any request within 130 milliseconds anywhere in the world. That's a very, very powerful statement that you can only really make if you're talking about having an end to end role over, or influence -- >> Like we commented, that's a good point. Like we commented that this end to end architecture is going to be fundamental. If you read the tea leaves and look at other things happening, like at Mobile World Congress. Intel I think is a bellwether on this with 5G. Cause they have to essentially create this overlay for connectivity as well as network transformation to do autonomous vehicles. To do smart cities. To smart homes. All these new technologies. It's an end to end IPR (mumbles). It's connected devices. So they're super smart to have this connected data theme which I think's relevant. But the other one, Ron Corbusier's talked about this evolution. And I find some of these, and I want to get your reaction to this statement. So Ron was kind of like, "oh it's an evolution. "We've seen this movie before." Okay great. But when you talk to Marta Feturichie, who was a customer from Royal Phillips. >> Peter: Great interview. >> She's head of CRM. Now she's doing some other stuff. So okay. What does CRM mean? So if you think evolution. What the customers are doing. Time Warner and Royal. It's interesting. Certain things are becoming critical infrastructure and other things are becoming more dynamic and fluid. So if you believe in evolution, these are layers of innovation. So stuff can be hardened as critical infrastructure, say like email marketing. So I think that what's happening here is you start to see some hardening of some critical infrastructure, aka marketing technology. MarTech (mumbles). Maybe some consolidation. AdTech kind of comes together. Certain things are going to be hardened and platformized. >> Let's take the word hardened and change it cause I know what you mean. Let's say it's codified. Now why is that, why is that little distinction a little bit interesting is because the more codified it gets, the more you can put software on it. The more you can put software on it the more you can automate it. And now we're introducing this whole notion of the adaptive intelligence. Where as we start to see marketing practices and processes become increasingly codified. What works, what doesn't work? What should we do more of? What should we do less of? Where should we be spending out time and innovating? Versus where should we just be doing it because it's a road activity at this point in time. That's where introducing this adaptive intelligence technology becomes really interesting. Because we can have the adaptive technology elements handle that deeply codified stuff where there really is not a lot of room for invention. And give the more interesting ongoing, customer engagement, customer experience -- >> Right on. And I think we should challenge Oracle post event and keep an eye on them on this adaptive intelligence app concept. Because that is something that they should ride to the sunset cause that is just a beautiful positioning. And if they can deliver the goods on that, they say they have it. We'll expand on that. That's going to give them the ability to churn out a ton of apps and leverage the data. But to the codified point you're making, here's my take. One of the things that I hear from customers in marketing all the time is a lot of stuff if oh yeah mobile first all that stuff. But still stuff's web presence based. So you got all these coded URL's. You got campaigns running ten ways from Sunday. DNS is not built to be adaptive and flexible. So it's okay to codify some of those systems. And say, "look we just don't tinker with these anymore." They're locked and loaded. You build on top of it. Codify it. And make that data the enabling technology from that. >> Peter: Without it become new inflexible (mumbles). >> Yeah I can't say, "Hey let's just tweak the hardened infrastructure "to run an AB test on a campaign." Or do something. No, no. You set this codified systems. You harden them. You put software on top of them. And you make it a subsystem that's hardened. And that's kind of what I mean. That's where the market will go because let's face it. The systems aren't that intelligent to handle a lot of marketing. >> Peter: They're still computers. >> They're still computers. People are running around just trying to fix some of this spaghetti code in marketing. And as the marketing department gets more IT power. Hey you own it. They're owning now. Be afraid what you wish for you might get it. So now they own the problem. So I think Oracle on the surfaces side has a huge opportunity to do what they did with Time Warner. Come into the market and saying, "Hey we got that for you." And that's what Hurd's kind of subtle message was on his keynote. Hey we're IT pros, but by the way you don't need to be in the IT business to do this. We fix your problems and roll out this -- >> We're going to talk to you in your language. And your language is modern customer experience. Which is one of the reasons why they've got to be more aggressive. And stating what they mean by that. >> And we have all the data in our data cloud. And all the first party data in our Oracle database. >> Right, right exactly right. >> That system of record becomes the crown jewel. Oracle has a lock spec on the table. You think it's a lock spec? >> Uh no. And that's exactly why I think they need to articulate where this is all going a little bit. They have to be a leader in defining what the future of marketing looks like so they can make it easier for people to move forward. >> Alright putting you on the spot. What do you think a modern marketing looks like? And organization. >> We talked about this and the answer that I gave, and I'll evolve it slightly, cause we had another great guest and I thought about it a little bit more is. A brand continuously and always delivers customer value. Always. And one of the -- >> Kind of cliche-ish. >> Kind of cliche-ish. >> Dig into it. >> But modern marketing is focused on delivering customer value. >> How? >> If they're deliver - well for example when the customer has a moment in a journey of uncertainty. Your brand is first is first to the table with that content that gets them excited. Gets them comfortable. >> Lot of progression. >> Makes them feel ready to move forward. That your, and well I'll make another point in a second. And I would even say that we might even think about a new definition of funnel. At the risk of bringing up that old artifact. Historical funnel went to the sale. Now we can actually start thinking about what's that funnel look like to customer success. >> Well there's two funnel dynamics that are changing. This is important, I think. This is going to be one of those moments where wow the Cube actually unpacked a major trend and I believe it to be true. The vertical funnel has collapsed. And now the success funnel is not >> Peter: It's not baked. >> Not big. It's decimated from this perspective of if the sale is the end game of the funnel, pop out that's over. Your point is kind of like venture funding for starter. That's when the start line begins. So here it's, okay we got a sale. But now we have instrumentation to take it all the way through the life cycle. >> And you know John. That's a great way of thinking about it. That many respects when you, when you introduce a customer to a new solution that has complex business implications that you are jointly together making an investment in something. And you both have to see it through. >> I mean sales guys put investment proposal on the -- >> That's exactly right. And so I think increasingly. So I would say modern marketing, modern marketing comes down to customer success. A prediction I'll make for next year is that this session is called, you know we'll call it the modern marketing modern customer experience show. But the theme is going to be customer success. >> Heres what I'm going to do. Here's what we're going to do this year Peter. We're going to, we will, based upon this conversation which we're riffing in real time as we analyze and summarize the event. We, I will make it my mission. And you're going to work with me on this as a directive. We're going to interview people, we're going to pick people that are truly modern marketing executives. >> Peter: That's great. >> We're going to define a simple algorithm that says this is what we think a modern marketing executive looks like. And we're going to interview them. We're going to do a story on them. And we're going to start to unpack because I think next year. We should be coming here saying, "we actually did our work on this." We figured out that a modern marketing organization and an executive behave and look this way. >> Right I think it's a great idea. So I'll give you one more thought. Cause I know you'll like this one too. Doug Kennedy. The partner. The conversation that we had. >> Very good. >> Talking about clearly a grade A executive. Seven weeks into the job. But that is going to be, you know for this whole thing to succeed he's got a lot of work in front of him. It's going to be very interesting to see how over the course of time this show and other Oracle shows evolve. >> I have a lot of partner experience. You do too. He's got a zillion years under his belt. He's a pro. He did not have any deer in the headlights look for seven weeks on the job. He's been there. He's done that. He knows the industry. He's seen the cycles of change. He's ridden waves of innovation up and down. And I think Oracle has a huge opportunity with his new program. And that is Oracle knows how to make money. Okay Oracle knows how to price things. They know how to execute on the sales side and go to market. And partners relationships are grounded in trust. And profitability. I would say profitability first and trust second. And it's kind of a virtuous circle. >> But John they've got to start getting grown in customer experience right? >> John: Yeah, yep. >> And that's not, it's doable but it's going to be a challenge. >> Well we talk about swim lanes with his interview, and I thought that was interesting. If you look at a center for instance, Deloy, PWC and all the different players. They're picking their swim lanes where their core competency is. And that's what he was basically saying. They're going to look for core competency. Now I think they're not there yet. The major SI's and potential partners. So he's going to have to put the spec out and put the bar there and say this is what we got to do. But you got to make the channel serve the customer. It has to be profitable. And it has to be relevant. And the only dangerous strategy I would say is the co-selling thing is always dicey. >> Especially if one has customer experience as a primary. >> It requires equilibrium in the ecosystem. >> You got it, you got it. >> It isn't there. >> And also it's a multi-partner go to market. It's not just one or two now. >> So he's going to have to really spread the love at the same time have hardened rules. Stick to his knitting on that one. Okay Peter final word. What do you, bottom line the show. Encapsulate the show into a bumper sticker. >> Well we heard Amazon released today. Google released today. Beat their numbers. Two companies that are trying to build an ecosystem from their core of the cloud. And the question is. Is Oracle who has customers with applications and with that first person data. Are they going to be able to cloudify, sorry for using that word, but are they going to be able to gain that trust that this new operating model they're really committed to for the future. Before Amazon and Google can create applications to their platform. Because Oracle has the end to end advantage right now. And in the world where digital's important. Speed's important. The fidelity of the data's important. The customer experience is important. That end to end has a window of opportunity. >> And I would also add two other companies reported, Microsoft and Intel and missed. So you have Amazon and Google. New guard, newer guard. Old guard Intel, Microsoft. Oracle is considered old guard even though they have some modernization going on from CX and the cloud. But Oracle is cloud a hundred percent in the cloud. Their SAP, for instance, is going multi-class. So the wild card in all this is, if the multi-cloud game evolves. >> Think end to end. End to end. Because that has advantages. When you're talking data, one of the things that Jack Brookwood said. He said, "you know why we can hit that 150 millisecond target?" >> Cause you don't have to move the data around. >> Cause sometimes we don't have to move the data around. >> This can be very interesting. And this going to be fun to watch and participate in. Of course the Cube will covering Oracle, well we'll be there again this year. We don't have the exacts specifics on that, but certainly if your interested in checking us out. Were siliconangle.com. Peter's research is at wikibon.com as well as SiliconANGLE on the front page. SiliconAngle.tv has all the videos. And well will be documenting and following the modern marketing experience with people and companies. And documenting that on the Cube and SiliconANGLE. So that's a wrap from day two at Oracle Modern CX. Thanks for watching. (electronic music)

>> Narrator: Live from Las Vegas, it's theCUBE, covering Oracle Modern Customer Experience 2017, brought to you by Oracle. (bright, lively music) >> Hello and welcome back to a CUBE coverage of Oracle's Modern Customer Conference here at the Mandalay Bay in Las Vegas. I'm John Furrier with SiliconANGLE, theCUBE, with my co-host this week, Peter Burris, head of research at Wikibon.com, part of SiliconANGLE Media, and our next guest is Austin Miller, Product Marketing Director for Oracle Marketing Cloud. Welcome to theCUBE conversation. >> Thank you very much for having me. >> This coveted post-launch spot. >> Yeah, we have a lunch coma kicking in, but no, seriously, you have a really tough job because you're seeing the growth of the Platform Play, right, really robust horizontal platform, but how you got here through some really smart acquisitions but handled well, and integrated, we covered that last year. You guys are seeing some nice tailwinds with some momentum certainly around the expectations of what the customers want. >> Yeah, I think that one of the best things when we start thinking about, to your point, product integration, it's also the way that we are talking to our customers about how they can use the products together. It's not really enough just to have maybe one talk to another, but unless we prove out the use cases, you don't get the utilization, and I think this year what we've really seen is getting those use cases to actually start getting some traction in the field. >> So this integrated marketing idea seems to be the reality that everyone wants. >> Where are we on that progress bar, because this seems to be pretty much unanimous with customers, the question is how to get there, the journey, and the heroes that are going to drive and the theme of the conference. But the reality is this digital transformation is being forced for business change. >> Austin: Absolutely. >> And marketing is part of that digital fabric. >> I think that one of the most interesting things about this is if you look at kind of the history of when did the stacks start becoming actually part of the story, it was at a point where we didn't really necessarily even have the capabilities to do it. As a result many marketers who thought they were maybe buying into a stack approach got a little bit burned. I think now we are actually at that place where that value is not only something that they can see inherently and say "oh, I'd like all these applications to talk together," but it's actually feasible, it's something that they're going to be able to use, and they can be optimistic about, frankly. >> Where are they getting burned, you mentioned that, from buying into a full stack of software for a point solution, is that kind of what you meant? >> No, I think that in the marketing realm, when you're talking to marketers, it is very easy to think about all the horrible things that they have to deal with on a daily basis, all these problems. And the reality is that oftentimes you've had to have this conversation with them that says, you know, there are not going to be easy answers to hard problems. There are usually hard answers to hard problems. We can help alleviate some of that friction, especially when we start talking about data silos or things about interoperability, so being able to not just have integration, but pre-built function within these particular platforms, but realistically, it just wasn't something that we necessarily in the market in general were able to deliver on until somewhat recently. >> So, I am very happy that I heard you use the word "use cases," especially at a launch, because that's been one of the biggest challenges of both marketing technology when we think about big data, there's been such a focus on the technology, getting the technology right, and then the use cases and how it changed the way the business or the function did things, kind of either did or didn't happen. Talk about how a focus in use case is actually getting people to emphasize the outcomes, and how Oracle is helping people then turn that into technology decisions. >> This may sound almost counterintuitive, but in reality the way that use cases we see helping us the most is that it really helps spur about the organizational changes that we need in order to actually have some of this happen, 'cause it's very easy to say, "we have all this technology marketer and you should be using it all," but if you don't actually prove it out and how that's going to impact let's say the way that they're creating their marketing messages, on even a kind of not exciting basis, like how are you creating your emails, how are you creating your mobile messaging, how are you doing your website, and then start talking about those in actual use cases, it's very hard for people to organize their organizations around this kind of transformation. They need something tangible to hold onto. >> And the old way with putting things in buckets, >> Austin: Exactly. >> Right, so so hey we got one covered, move on to the next one ... >> Peter: Or by channels even. We got an email solution, or we got a web solution and as the customer moves amongst these different mechanisms, or engages differently with these mechanisms, the data then becomes, we've talked a lot about this, becomes the integration point, and that as you said affects a significant change on how folks think about organizing, but what do you think are going to be some of the big use cases if people are going to be ... you're providing advice and counsel to folks on the 2017. >> Yeah, so I think that talking about marketing-specific use cases is really important, especially when we start thinking about how am I using my first-party data that I may have within a particular channel. And I'm using that to contextually change the way I'm communicating to somebody on another channel. But if we kind of take that theme, and we think about let's not just expand it to marketing but let's really talk about customer experience, because as a customer, I go in-store, I go on email, I go on your mobile app, I don't view those as different things. That's just my experience with your brand. And even as we start getting to maybe some of the service things, am I calling a call center? The way that we're really thinking about marketing is not only bringing all this information across our traditional marketing channels, but how are we helping marketers drive organizational change beyond the traditional bounds of even their own marketing department into service, into sales, into on-store, because in reality that's where kind of the next step is. It's not just about, to your point, promotional emails. It's about how are we bringing this experience across the full spectrum. >> So it's really how is first-person data going to drive the role of marketer differently, the tasks of marketing as a consequence, and therefore how we institutionalize that work. >> Absolutely, and I think that you can see this in the investments that we've made in the ODC, Oracle Data Cloud. It's first step, let's start thinking about how we can start moving around on first-party data, that'll be a nice starting point, but then afterwards, how are we taking third-party data let's say from offline purchases, starting to incorporate that and that store's third-party data, 'cause then we really start getting to that simultaneously good experience or at least consistent experience across digital, across in-store, we start piecing together, but we really need to start at that baseline. >> A lot of people have been talking about the convergence of adtech and martech for years, and we had a CUBE alumni on our CUBE many years ago, when the Big Data movement started to happen, and he was a visionary, revolutionary kind of guy, Jeff Hammerbacher, the founder of Cloudera, who's now doing some pioneering work in New York City around science. He's since left Cloudera. But he said on theCUBE what really bothered him was some of the brightest minds in the industry were working on using data and put an ad in the right place. And he was being kind of critical of, use it for cooler things, but we look at what's happening on martech side, when you have customer experience, that same kind of principle of predictive thinking around how to use an asset can be applied to the customer journey, so now you bring up the question of A.I. If you broaden the scope of adtech and martech to say all things consumer, in any context, at any given time, you got to have an A.I. or machine learning approach to put the right thing at the right place at the right time that benefits the user >> Austin: It's not scalable. That's the reality of it. To you point, if you're going to start thinking about this across all these different channels, including advertising as well, the idea of being able to do these on a one-off basis, from a manual perspective, it's completely untenable, you're completely correct, but to that point, where you're talking about the best minds in the industry maybe dedicated to figuring out, "if I put a little target here, am I going to get somebody to click on that ad one time, or how am I placing it," that is very much the way that we were at the very beginning parts of marketing technology, where it was bash and blast messaging, how can we just kind of get the clicks and the engagement, and how do we send out >> John: spray and pray >> Exactly. And now I think that we are getting to a much more nuanced understanding of the way that we advertise because it's much more reliant on context, it's not just how can I get my stuff in front of somebody's eyeballs, it's how am I placing it when they're actually showing some sort of intention for maybe the products I already have. >> Adaptive intelligence is interesting to me because what that speaks to is, one, being adapted to a real time, not batch, spray and pray and the old methodology of database-driven things, no offense to the main database cache at Oracle, but it's a system of record, but now new systems of data are available, and that seems to be the key message here, that the customer experience is changing, multiple channels, that's omnichannel, there needs to be ... everyone's looking for the silver bullet. They think it's A.I., augmented intelligence or artificial intelligence. How do you see that product roadmap looking, because you're going to need to automate, you're going to need to use software differently to handle literally real time. >> Completely. I think that this is a really important distinction about the way that we view A.I. and how it factors into marketing technology and the way that I think a lot of people in the industry do. I think that once again this theme of there aren't easy answers to hard problems, it is very pleasant to think that I'm just going to have one product that's going to solve everything, from when I should send my next email, to if there's clean water in this particular area in a third-world country, and that's just something that maybe sounds nice, but it's not necessarily something that's actually tangible. The way that we view A.I. is it's something that's going to be embedded and actually built into each of these different functions so that we can do the mission-critical things on the actual practical level, and kind of make it real for marketers, make it something that's isn't just "oh, buy this and it will solve all your problems." >> So I'm going to ask you the question, the old adage, "Use the right tool for the right job, and if you're a hammer everything looks like a nail." A lot of people use email marketing that way, they're using it for notifications when in reality that's not the expectation of the consumer, some are building in a notification engine separate from email. All that stuff's kind of under the covers, in the weeds, but the bigger question to you is, I want to get your insight on this because you're talking to customers all the time, is as customers as you said need to change organizationally, they're essentially operationalizing this modern era of CX, customer experience, so it's a platform-based concept which pretty much everyone agrees on, but we're in the early innings of operationalizing this >> Austin: Oh yeah. >> So how do you see that evolving and what do you want customers to do to be set up properly if they're coming in for the first inning of their journey, or even if they're midstream with legacy stuff? >> I think that that's a really good perspective, because you don't want to necessarily force people to go through excruciating organizational change in preparation if we're in maybe the first inning, but it is really just about setting up the organization to adjust as realistically we get into the middle innings and into the later innings. And really the kind of beginning foundation of this is understanding that these arbitrary almost like tribal distinctions between who owns what channel, who's the email marketer, or who's the mobile person, they need to be broken down, and start thinking about things instead of these promotional blasts to your point, or even maybe reactionary notifications. How is this contributing to the number of times your brand is touching me in a day, or the way that I'm actually communicating, so I think that it's an interesting kind of perspective of how we were organizationally set up for that, but the short answer is that A.I. is going to fundamentally change the way that marketers are operating. It's not going to fundamentally change maybe everything that they're doing or it's not going to be replacing it. It's going to be a complementary role that they need to be ready to adjust to. >> So you are, you're in product, product management. >> Austin: Product marketing >> Product marketing. So you are at that interface between product and marketing, both moving more towards agile. How are you starting to use data differently and how would you advise folks like you in other businesses not selling software that might not have the same digital component today but might have a comparable digital component in the future, what would you tell them to do differently? >> So, I think that the first step is to actually have an honest assessment of what we have and what we don't have. I think that there's a lot of people who like to kind of close their eyes or maybe plug their ears and just sort of continue down the path of least resistance. >> Peter: Give me ... >> Oh, an honest assessment of what kind of data we do have today, what kind of data we might actually need, and then most importantly, is that actually feasible data to get. Because you can't >> you can wish it but you can't get it >> You can wave a magic wand and say these are the numbers that I need on this particular maybe interest level of these particular ... >> John: The fatal flaw is hoping that you're going to get data that you never get, or is ungettable. >> Or, this is really something that I think a lot, would resonate more with marketers is that we have now set up all these different points of interaction that are firehoses of data spraying it at me, I may be able to retroactively look at it and maybe garner some kind of insight, but there's just no real way for me to take that and make it actionable right away. It is a complete mess of data in a lot of these organizations. >> And that's where A.I. comes in. >> Austin: Absolutely. It's able to automate that, reaction ... >> Peter: Triage at a bare minimum. >> Correct >> So the first starts with data. What would be the second thing? >> So it's data, presume that you're going to need help on the triage and organizing that data. Is there a third thing? >> I would say that you're going down the right path with the steps there, but once again, we're all talking about these concepts that do require a great deal of specialization and a lot of actual understanding of the way we're dealing with data. So honest assessment is definitely that first part, but then do I have the actual people that I need in order to actually take action on this? Because it is a specialized kind of role that really hasn't traditionally been within marketing organizations. >> I know you guys have a big account-based, focus-account-based marketing, you know, doing all kinds of things, but I'm a person, I'm not a company, so that's a database saying "hey, what company do you work for?" And all the people who work for that company and their target list. I'm a person. I'm walking around, I've got a wearable, I might be doing a retail transaction, so the persona base seems to be the rage and seems to be the center and we heard from Mark Hurd's keynote, that's obviously his perspective and others as well so it's not like a secret, but how do you take it to the next level? An account base could help there too, but you need to organize around the person, and that seems to open up the identity question of okay, how do I know it's John? >> I think that goes beyond just personal taste, but into what does this person actually do at this company, because I can go in and give a headspinning presentation to maybe a C-level executive and say, "look at all this crazy stuff you can do," and meanwhile the guy who might be making the buying decision at the end of the table's looking at that and being like, "there's no way we can do that, we don't have the personnel to do that, there's no chance," and you have already dissension from the innards of the actual people who are making the buying decisions. The vision can't be so big that it resonates with no one. And you need to understand on a persona level what is actually resonated with them. 'Cause feasibility is a very important thing to our end user, and we need to actually incorporate that into our messaging, so it's not just so pie-in-the-sky visioning. >> I did a piece of research, sorry John, I did a piece of research a number of years ago that looked at the impact of selling mainly to the CIO. And if you sell successfully to the CIO, you can probably guarantee nine months additional time before the sale closes. >> Austin: Yeah. Because the CIO says "this is a great idea," and then everybody in the organization who's now responsible for doing it says "hold on, don't put this in my KPIs while I take a look at it and what it really means and blah blah blah. Don't make me responsible for this stuff." You just added nine months. >> Absolutely. I even have a very minute example for something that we rolled out. This was a great learning opportunity. Because we rolled out a feature called multi-variant testing. It's not important what exactly it is for the purposes of this, but basically it's the idea of you can take one email and eight versions of it, test it, and then send out the best one. Sounds great, right? I'm an executive, I'm like boy, I'm going to get every last ounce of revenue from my emails, I'm only going to send out the best content. If you don't pitch that right, the end user, all they hear is wait, the thing that I do one of, I have to create eight of now? Am I going to get to see my kids ever again? That's just the way you have to adjust ... >> And seven of 'em are going to be thrown away. I'm going to be called a failure. >> Exactly. So it's just not something that you can take for granted because marketers have a variety of different roles and a variety of firm responsibilities. >> And compound that with everything's going digital. >> Exactly. >> So (mumbles) Austin, great to have you on theCUBE. Spend the last minute though, I'd like you just to share for the last minute, what's the most important thing happening here at #ModernCX besides the simplicity of the messaging of modern era of customer expectations, experiences, all that's really awesome, but what should people know about that aren't here, watching. >> I'd just say that the one thing that at least resonates most with me, and this is once again coming from a product and sort of edging on marketing, is that the things that we've been talking about with not only A.I. but even just simple things like having systems that are communicating to each other, they're actually real and we're seeing that as real. You can actually see them working together in products and serving up experiences to customers that we're even doing now as part of the sales process and saying "hey, this is how you would actually do this," as opposed to just "here's our Chinese menu of different options. Pick what you want and then we can just kind of serve it up." Because I think that there's something that's very heartening to maybe marketers who have a little bit of, I don't know, doubt about whether or not this is real. It is real, it's here today, and we're able to execute on it. >> And that's the integration of a multi-product and technology solution. >> I would almost say that it's slightly different from that though, in terms of, it's not just integration of these pieces, it's integration that's pre-built, so we actually have it pre-built together and then we also have these tremendous, new, innovative features and functionality that are coming with those integrations. It's not just portability, it's actual use cases. >> Would you say that it's as real as the data? >> It's as real as the data. I think that that's ... >> If you have the data, then you can do what you need to do. >> That's a very, a very good point. >> Austin Miller, Product Marketing Director at Oracle Marketing Cloud. Thanks for sharing the data here on theCUBE where we're agile, agile marketing is the focus. I'm John Furrier, Peter Burris. More coverage from day one at Mandalay Bay for Oracle Modern Customer Experience show. We'll be right back with more after this short break. (bright, lively music)

>> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're in downtown San Francisco at Twitter's world headquarters at the Data Privacy Day, a full day event of sessions and breakout sessions really talking about privacy. Although privacy is dead in 1999 get over it, not really true and certainly a lot of people here beg to differ. We're excited to have our next guest Jules Polonetsky, excuse me, CEO of Future of Privacy Forum. Welcome. >> Thank you, great to be here. Exciting times for data, exciting times for privacy. >> Yeah, no shortage of opportunity, that's for sure. The job security and the privacy space is pretty high I'm gathering after a few of these interviews. >> There's a researcher coming up with some new way we can use data that is both exciting, curing diseases, studying genes, but also sometimes orwellian. Microphones are in my home, self-driving cars, and so, getting that right is hard. We don't have clear consensus over whether we want the government keeping us safe by being able to catch every criminal, or not getting into our stuff because we don't trust them >> Right. [Jules] - So challenging times. [Jeff] - So, before we jump into it, Future Privacy Forum, kind of a little bit about the organization, kind of your mission... [Jules] - We're eight years old at the Future Privacy Forum, we're a think tank in Washington, D.C. Many of our members are the chief privacy officers of companies around the world, so about 130 companies, ranging from many of the big tech companies. And as new sectors start becoming tech and data, they join us. So, the auto industries dealing with self-driving cars, connected cars, all those issues. Wearables, student data, so about 130 of those companies. But then the other half of our group are advocates and academics who are a little bit skeptical or worried. They want to engage, but they are worried about an Orwellian future. So we bring those folks together and we say, 'Listen, how can we have data that will make cars safer? How can we have wearables that'll help improve fitness? But also have reasonable, responsible rules in place so that, we don't end up with discrimination, or data breaches, and all the problems that can come along?' [Jeff] - Right, cause it's really two sides of the same coin and it's always two sides of the same coin. And typically on new technology, we kind of race ahead on the positive, cause everybody's really excited. And lag on kind of what the negative impacts are and/or the creation of rules and regulations about because this new technology, very hard to keep up. [Jules] - You know the stakes are high. Think about AdTech, right? We've got tons of adtech. It's fueling free content, but we've got problems of adware, and spyware, and fake news, and people being nervous about cookies and tracking. And every year, it seems to get more stressful and more complicated. We can't have that when it comes to microphones in my home. I don't want to be nervous that if I go into the bedroom, suddenly that's shared across the adtech ecosystem. Right? I don't know that we want how much we sweat or when it's somebody's time of the month, or other data like that being out there and available to data brokers. But, we did a study recently of some of the wearables, the more sensitive ones. Sleep trackers, apps that people use to track their periods, many of them, didn't even have a privacy policy, to say 'I don't do this, or I don't do that with your data.' So, stakes are high. This isn't just about, you know, are ads tracking me? And do I find that intrusive? This is about if I'm driving my car, and it's helping me navigate better and it's giving me directions, and it's making sure I don't shift out of my lane, or it's self-parking, that that data doesn't automatically go to all sorts of places where it might be used to deny me benefits, or discriminate, or raise my insurance rates. [Jeff]: Right, right. Well, there's so many angles on this. One is, you know, since I got an Alexa Dot for Christmas, for the family, to try it out and you know, it's interesting to think that she's listening all the time. [Jules] - So she's not >> And you push the little >> Let's talk about this >> button, you know. >> Or is she not? >> This is a great topic to [Jules] -talk about because a sheriff recently, wanted to investigate a crime and realized that they had an Amazon Echo in the home. And said, 'Well maybe, Amazon will have data about what happened >> Right >> Maybe they'll be clues, people shouting,' you know. And Amazon's fighting because they don't want to hand it over. But what Amazon did, and what Google Home did, and the X-Box did, they don't want to have that data. And so they've designed these things, I think, with actually a lot of care. So... the Echo, is listening for it's name. It's listening for Alexa... >> Right. And it keeps deleting. It listens, right it hears background noise, and if it didn't hear Alexa, drops it, drops it, drops it. Nothing is said out of your home. When you say 'Alexa, what's the weather?' Blue light glows, opens up the connection to Amazon, and now it's just like you're typing in a search or going directly >> Right, right. [Jules] - And so that's done quiet carefully. Google Home works like that, Siri works like that, so I think the big tech companies, despite a lot of pain and suffering over the years of being criticized, and with the realization that government goes to them for data. They don't want that. They don't want to be fighting the government and people being nervous that the IRS is going to try find out information about what you're doing, which bedroom you're in, and what time you came home. >> Although the Fit Bit has all that information. >> Exactly >> Even though Alexa doesn't. [Jules] - So the wearables are another exciting, interesting challenge. We had a project that was funded by both Robert Johnson Foundation, which wants Wearables to be used for health and so forth. But also from a lot of major tech companies. Because everybody was aware that we needed some sort of rules in place. So if Fit Bit, or Jaw Bone, or one of the other Wearables can detect that maybe I'm coming down with Parkinson's or I'm about to fall, or other data, what's their responsibility to do something with that? On one hand, that would be a bit frightening. Right, you got a phone call or an email saying 'Hey, this is your friendly friends at your Wearable and we think >> showing up at your front door >> You should seek medical, you know, help. You would be like, whoa, wait a second, right? On the other hand, what do you do with the fact that maybe we can help you? Take student data, alright. Adtech is very exciting, there's such opportunities for personalized learning, colleges are getting in on the act. They're trying to do big data analytics to understand how to make sure you graduate. Well, what happens when a guidance counselor sits down and says, 'Look, based on the data we have, your grades, your family situation, whether you've been to the gym, your cafeteria usage, data we took off your social media profile, you're really never going to make it in physics. I mean, the data says, people with your particular attributes... Never, never... Rarely succeed in four years at graduating with a degree. You need to change your scholarship. You need to change your career path. Or, you can do what you want, but we're not going give you that scholarship. Or simply, we advise you.' Now, what did we just tell Einstein? Maybe not to take Physics, right. But on the other hand, don't I have some responsibility, if I'm a guidance counselor, who would be looking at your records today, and sort of shuffling some papers and saying, 'Well, maybe you want to consider something else?' So, either we talk about this as privacy, but increasingly, many of my members, again who are chief privacy officers if these companies, are facing what are really ethical issues. And there may be risks, there may be benefits, and they need to help decide, or help their companies decide, when does the benefit outweigh the risk? Consider self-driving cars, right? When does the self-driving car say 'I'm going to put this car in the ditch Because I don't want to run somebody over?' But now it knows that your kids are in the backseat, what sort of calculations do we want this machine making? Do we know the answers ourselves? If the microphone in my home hears child abuse, if 'Hello Barbie' hears a child screaming, or, 'Hey, I swallowed poison,' or 'My dad touched me inappropriately,' what should it do? Do we want dolls ratting out parents? And the police showing up saying, 'Barbie says your child's being abused.' I mean, my gosh, I can see times when my kids thought I was a big Grinch and if the doll was reporting 'Hey dad is being mean to me,' you know, who knows. So, these are challenges that we're going to have to figure out, collectively, with, stakeholders, advocates, civil libertarians, and companies. And if we can chart a path forward that let's us use these new technologies in ways that advances society, I think we'll succeed. If we don't think about it, we'll wake up and we'll learn that we've really constrained ourselves and narrowed our lives in ways that we may not be very happy with. [Jeff] - Fascinating topic. And like on the child abuse thing, you know there are very strict rules for people that are involved in occupations that are dealing with children. Whether it's a doctor, or whether it's a teacher, or even a school administrator, that if they have some evidence of say child abuse, they're obligated >> they're obligated. [Jeff] - Not only are they obligated morally, but they're obligated professionally, and legally, right, to report that in. I mean, do you see those laws will just get translated onto the machine? Clearly, God, you could even argue that the machine probably has got better data and evidence, based on time, and frequency, than the teacher has happening to see, maybe a bruise or a kid acting a little bit different on the school yard. [Jules] - You can see a number of areas where law is going to have to rethink how it fits. Today, I get into an accident, we want to know who's fault is it. What happens when my self-driving car gets into an accident? Right? I didn't do it, the car did it. So, do the manufacturers take responsibility? If I have automated systems in my home, robots and so forth, again, am I responsible for what goes wrong? Or, do these things have, or their companies have some sort of responsibility? So, thinking these things through, is where I think we are first. I don't think we're ready for legal changes. I think what we're ready for is an attitude change. And I think that's happened. When I was the chief privacy officer, at AOL, many years ago, we were so proud of our cooperation with the government. If somebody was kidnapped, we were going to help. If somebody was involved in a terrorism thing, we were going to help. And companies, I think, still recognize their responsibility to cooperate with, you know, criminal activity. But they also recognize that it is their responsibility to push back when government says, 'Give me data about that person.' 'Well, do you have a warrant? Do you have a basis? Can we tell them so they can object? Right? Is it encrypted? Well, sorry, we can't risk all of our users by cracking encryption for you because you're following up on one particular crime.' So, there's been a big sea change in understanding that if you're a company, and there's data you don't want to have to hand over, data about immigrants today, lots of companies, in the Valley, and around the country, are thinking, 'Wait a second, could I be forced to hand over some data that could lead to someone being deported? Or tortured? Or who knows what?' Given that these things seem to be back on the table. And, you know again, years ago, you were a good asterisk, you participated in law enforcement and now people participate, but they also recognize that they have a strong obligation to either not have the data, like Amazon, will not have data that this sheriff wants. Now, their Smart Meter and how much water they're using, and all kinds of other information, frankly about their activity at home, since many other things about our homes is now smarter, may indeed be available. How much water did you use at this particular time? Maybe you were washing blood stains away. That sort of information is >> Wild [Jules] - going to be out there. So, the machines will be providing clues that in some cases are going to incriminate us. And companies that don't want to be in the middle, need to think about designing, for privacy, so as to avoid, creating a world where, you know, whole data is available to be used against us. [Jeff] - Right and then there's the whole factor of the devices are in place, not necessarily the company is using it or not, but, you know, bad actors taking advantage of cameras, microphones, all over and hacking into these devices to do things. And, it's one thing take a look at me while I'm on my PC, it's another thing to take control of my car. Right? And this is where, you know, there's some really interesting challenges ahead. As IT continues to grow. Everything becomes connected. The security people always like to say, you know, the certainty attack area, it grows exponentially. [Jules] - Yeah. Well cars are going to be an exciting opportunity. We have released, today, a guide that the National Auto Dealers Association is providing to auto dealers around the country. Because, when you buy a car today, and you sell it or you lend it, there's information about you in that vehicle. Your location history, maybe your contacts, your music history, and we never would give our phone away without clearing it, or you wouldn't give your computer away, but you don't think about your car as a computer, and so, this has all kinds of advice to people. Listen, your car is a computer. There's things you want to do, to take advantage of, >> Right. [Jules]- New services, safety. But there are things you want to also do to manage your privacy, delete. Make sure you're not sharing your information in a way you don't want it. [Jeff] - Jules, we could go on all day, but I think I've got to let you go to get back to the sessions. So, thanks for taking a few minutes out of your busy day. [Jules] - Really good to be with you. [Jeff] - Absolutely. Jeff Frack, you're watching The Cube. See you next time. (closing music)