(upbeat music) >> Hey everyone. Welcome to theCUBE's special program series "Women of the Cloud", brought to you by AWS. I'm your host for the program, Lisa Martin. Very pleased to welcome back one of our alumni to this special series, Dr. Tendu Yogurtcu joins us, the CTO of Precisely. >> Lisa: Tendu, it's great to see you, it's been a while, but I'm glad that you're doing so well. >> Geez, it's so great seeing you too, and thank you for having me. >> My pleasure. I want the audience to understand a little bit about you. Talk to me a little bit about you, about your role and what are some of the great things that you're doing at Precisely. >> Of course. As CTO, my current role is driving technology vision and innovation, and also coming up with expansion strategies for Precisely's future growth. Precisely is the leader in data integrity. We deliver data with trust, with maximum accuracy, consistency, and also with context. And as a CTO, keeping an eye on what's coming in the business space, what's coming up with the emerging challenges is really key for me. Prior to becoming CTO, I was General Manager for the Syncsort big data business. And previously I had several engineering and R&D leadership roles. I also have a bit of academia experience. I served as a part-time faculty in computer science department in a university. And I am a person who is very tuned to giving back to my community. So I'm currently serving as a advisory board member in the same university. And I'm also serving as a advisory board member for a venture capital firm. And I take pride in being a dedicated advocate for STEM education and STEM education for women in particular, and girls in the underserved areas. >> You have such a great background. The breadth of your background, the experience that you have in the industry as well in academia is so impressive. I've known you a long time. I'd love the audience to get some recommendations from you. For those of the audience looking to grow and expand their careers in technology, what are some of the things that you that you've experienced that you would recommend people do? >> First, stay current. What is emerging today is going to be current very quickly. Especially now we are seeing more change and change at the increased speed than ever. So keeping an eye on on what's happening in the market if you want to be marketable. Now, some of the things that I will say, we have shortage of skills with data science, data engineering with security cyber security with cloud, right? We are here talking about cloud in particular. So there is a shortage of skills in the emerging technologies, AI, ML, there's a shortage of skills also in the retiring technologies. So we are in this like spectrum of skills shortage. So stay tuned to what's coming up. That's one. And on the second piece is that the quicker you tie what you are doing to the goals of the business, whether that's revenue growth whether that's customer retention or cost optimization you are more likely to grow in your career. You have to be able to articulate what you are doing and how that brings value to business to your boss, to your customers. So that becomes an important one. And then third one is giving back. Do something for the women in technology while being a woman in technology. Give back to your community whether that's community is gender based or whether it's your alumni, whether it's your community social community in your neighborhood or in your country or ethnicity. Give back to your community. I think that's becoming really important. >> I think so too. I think that paying it forward is so critical. I'm sure that you have a a long list of mentors and sponsors that have guided you along the way. Giving back to the community paying it forward I think is so important. For others who might be a few years behind us or even maybe have been in tech for the same amount of time that are looking to grow and expand their career having those mentors and sponsors of women who've been through the trenches is inspiring. It's so helpful. And it really is something that we need to do from a diversity perspective alone, right? >> Correct. Correct. And we have seen that, we have seen, for example Covid impact in women in particular. Diverse studies done by girls who quote on Accenture that showed that actually 50% of the women above age 35 were actually dropping out of the technology. And those numbers are scary. However, on the other side we have also seen incredible amount of technology innovation during that time with cloud adoption increasing with the ability to actually work remotely if you are even living in not so secure areas, for example that created more opportunities for women to come back to workforce as well. So we can turn the challenges to opportunities and watch out for those. I would say tipping points. >> I love that you bring up such a great point. There are so, so the, the data doesn't lie, right? The data shows that there's a significant amount of churn for women in technology. But to your point, there are so many opportunities. You mentioned a minute ago the skills gap. One of the things we talk about often on theCUBE and we're talking about cybersecurity which is obviously it's a global risk for companies in every industry, is that there's massive opportunity for people of, of any type to be able to grow their skills. So knowing that there's trend, but there's also so much opportunity for women in technology to climb the ladder is kind of exciting. I think. >> It is. It is exciting. >> Talk to me a little bit about, I would love for the audience to understand some of your hands-on examples where you've really been successful helping organizations navigate digital transformation and their entry and success with cloud computing. What are some of those success stories that you're really proud of? >> Let me think about, first of all what we are seeing is with the digital transformation in general, every single business every single vertical is becoming a technology company. Telecom companies are becoming a technology company. Financial services are becoming a technology company and manufacturing is becoming a technology company. So every business is becoming technology driven. And data is the key. Data is the enabler for every single business. So when we think about the challenges, one of the examples that I give a big challenge for our customers is I can't find the critical data, I can't access it. What are my critical data elements? Because I have so high volumes growing exponentially. What are the critical data elements that I should care and how do I access that? And we work at Precisely with 99 of Fortune 100. So we have two 12,000 customers in over a hundred countries which means we have customers whose businesses are purely built on cloud, clean slate. We also have businesses who have very complex set of data platforms. They have financial services, insurance, for example. They have critical transactional workloads still running on mainframes, IBM i servers, SAP systems. So one of the challenges that we have, and I work with key customers, is on how do we make data accessible for advanced analytics in the cloud? Cloud opens up a ton of open source tools, AI, ML stack lots of tools that actually the companies can leverage for that analytics in addition to elasticity in addition to easy to set up infrastructure. So how do we make sure the data can be actually available from these transactional systems, from mainframes at the speed that the business requires. So it's not just accessing data at the speed the business requires. One of our insurance customers they actually created this data marketplace on Amazon Cloud. And the, their challenge was to make sure they can bring the fresh data on a nightly basis initially and which became actually half an hour, every half an hour. So the speed of the business requirements have changed over time. We work with them very closely and also with the Amazon teams on enabling bringing data and workloads from the mainframes and executing in the cloud. So that's one example. Another big challenge that we see is, can I trust my data? And data integrity is more critical than ever. The quality of data, actually, according to HBR Harvard Business Review survey, 47% of every new record of data has at least one critical data error, 47%. So imagine, I was talking with the manufacturing organization couple of weeks ago and they were giving me an example. They have these three letter quotes for parts and different chemicals they use in the manufacturing. And the single letter error calls a shutdown of the whole manufacturing line. >> Wow. >> So that kind of challenge, how do I ensure that I can actually have completeness of data cleanness of data and consistency in that data? Moreover, govern that on a continuous basis becomes one of the use cases that we help customers. And in that particular case actually we help them put a data governance framework and data quality in their manufacturing line. It's becoming also a critical for, for example ESG, environment, social and governance, supply chain, monitoring the supply chain, and assessing ESG metrics. We see that again. And then the third one, last one. I will give an example because I think it's important. Hybrid cloud becoming critical. Because there's a purest view for new companies. However, facilitating flexible deployment models and facilitating cloud and hybrid cloud is also where we really we can help our customers. >> You brought up some amazingly critical points where it comes to data. You talked about, you know, a minute ago, every company in every industry has to become a technology company. You could also say every company across every industry has to become a data company. They have to become a software company. But to your point, and what it sounds like precisely is really helping organizations to do is access the data access data that has high integrity data that is free of errors. Obviously that's business critical. You talked about the high percentage of errors that caused manufacturing shutdown. Businesses can't, can't have that. That could potentially be life-ending for an organization. So it sounds like what you're talking about data accessibility, data integrity data governance and having that all in real time is table stakes for businesses. Whether it's your grocery store, your local coffee shop a manufacturing company, and e-commerce company. It's table stakes globally these days. >> It is, and you made a very good point actually, Lisa when you talked about the local coffee shop or the retail. One other interesting statistic is that almost 80% of every data has a location attribute. So when we talk about data integrity we no longer talk about just, and consistency of data. We also talk about context, right? When you are going, for example, to a new town you are probably getting some reminders about where your favorite coffee shop is or what telecom company has an office in that particular town. Or if you're an insurance company and a hurricane is hitting southern Florida. Then you want to know how the path of that hurricane is going to impact your customers and predict the claims before they happen. Also understand the propensity of the potential customers that you don't yet have. So location and context, those additional attributes of demographics, visitations are creating actually more confident business insights. >> Absolutely. And and as the consumer we're becoming more and more demanding. We want to be able to transact things so easily whether it's in our personal life at the grocery store, at that cafe, or in our business life. So those demands from the customer are also really influencing the direction that companies need to go. And it's actually, I think it's quite exciting that the amount of personalization the location data that you talk about that comes in there and really helps companies in every industry deliver these the cloud can, these amazing, unique personalized experiences that really drive business forward. We could talk about that all day long. I have no problem. But I want to get in our final minutes here, Tendu. What do you see as in your crystal ball as next for the cloud? How do you see your role as CTO evolving? >> Sure. For what we are seeing in the cloud I think we will start seeing more and more focus on sustainability. Sustainable technologies and governance. Obviously cloud migrations cloud modernizations are helping with that. And we, we are seeing many of our customers they started actually assessing the ESG supply chain and reporting on metrics whether it's the percentage of face or energy consumption. Also on the social metrics on diversity age distribution and as well as compliance piece. So sustainability governance I think that will become one area. Second, security, we talked about IT security and data privacy. I think we will see more and more investments around those. Cybersecurity in particular. And ethical data access and ethics is becoming center to everything we are doing as we have those personalized experiences and have more opportunities in the cloud. And the third one is continued automation with AI, ML and more focus on automation because cloud enables that at scale. And the work that we need to do is too time-intensive and too manual with the amount of data. Data is powering every business. So automation is going to be an increased focus how my role evolves with that. So I have this unique combination. I have been open to non-linear career paths throughout my growth. So I have an understanding of how to innovate and build products that solve real business problems. I also have an understanding of how to sell them build partnerships that combined with the the scale of growth, the hyper growth that we have absorbed in precisely 10 times growth within the last 10 years through a combination of organic innovation and acquisitions really requires the speed of change. So change, implementing change at scale as well as at speed. So taking those and bringing them to the next challenge is the evolution of my role. How do I bring those and tackle keep an eye on what's coming as a challenge in the industry and how they apply those skills that I have developed throughout my career to that next challenge and evolve with it, bring the innovation to data to cloud and the next challenge that we are going to see. >> There's so much on the horizon. It's, there are certainly challenges, you know within technology, but there's so much opportunity. You've done such a great job highlighting your career path the, the big impact that you're helping organizations make leveraging cloud and the opportunity that's there for the rest of us to really get in there get our hands dirty and solve problems. Tendu, I always love our conversations. It's been such a pleasure having you back, back on theCUBE. Thank you for joining us on this special program series today. >> Thank you Lisa. And also thanks to AWS for the opportunity. >> Absolutely. This is brought, brought to us by AWS. For Dr.Tendu, you are good to go. I'm Lisa Martin. You're watching theCUBE special program series Women of the Cloud. We thank you so much for watching and we'll see you soon. (upbeat music)

>> Announcer: Live from San Jose, it's theCUBE. Presenting data, Silicon Valley brought to you by Silicon Angle Media and it's ecosystem partners. >> Welcome back to theCUBE. We are live in San Jose at our event, Big Data SV. I'm Lisa Martin, my co-host is George Gilbert and we are down the street from the Strata Data Conference. We are at a really cool venue: Forager Eatery Tasting Room. Come down and join us, hang out with us, we've got a cocktail par-tay tonight. We also have an interesting briefing from our analysts on big data trends tomorrow morning. I want to welcome back to theCUBE now one of our CUBE VIP's and alumna Tendu Yogurtcu, the CTO at Syncsort, welcome back. >> Thank you. Hello Lisa, hi George, pleasure to be here. >> Yeah, it's our pleasure to have you back. So, what's going on at Syncsort, what are some of the big trends as CTO that you're seeing? >> In terms of the big trends that we are seeing, and Syncsort has grown a lot in the last 12 months, we actually doubled our revenue, it has been really an successful and organic growth path, and we have more than 7,000 customers now, so it's a great pool of customers that we are able to talk and see the trends and how they are trying to adapt to the digital disruption and make data as part of their core strategy. So data is no longer an enabler, and in all of the enterprise we are seeing data becoming the core strategy. This reflects in the four mega trends, they are all connected to enable business as well as operational analytics. Cloud is one, definitely. We are seeing more and more cloud adoption, even our financial services healthcare and banking customers are now, they have a couple of clusters running in the cloud, in public cloud, multiple workloads, hybrid seems to be the new standard, and it comes with also challenges. IT governance as well as date governance is a major challenge, and also scoping and planning for the workloads in the cloud continues to be a challenge, as well. Our general strategy for all of the product portfolio is to have our products following design wants and deploy any of our strategy. So whether it's a standalone environment on Linux or running on Hadoop or Spark, or running on Premise or in the Cloud, regardless of the Cloud provider, we are enabling the same education with no changes to run all of these environments, including hybrid. Then we are seeing the streaming trend, with the connected devices with the digital disruption and so much data being generated, being able to stream and process data on the age, with the Internet of things, and in order to address the use cases that Syncsort is focused on, we are really providing more on the Change Data Capture and near real-time and real-time data replication to the next generation analytics environments and big data environments. We launched last year our Change Data Capture, CDC, product offering with data integration, and we continue to strengthen that vision merger we had data replication, real-time data replication capabilities, and we are now seeing even Kafka database becoming a consumer of this data. Not just keeping the data lane fresh, but really publishing the changes from multiple, diverse set of sources and publishing into a Kafka database and making it available for applications and analytics in the data pipeline. So the third trend we are seeing is around data science, and if you noticed this morning's keynote was all about machine learning, artificial intelligence, deep learning, how to we make use of data science. And it was very interesting for me because we see everyone talking about the challenge of how do you prepare the data and how do you deliver the the trusted data for machine learning and artificial intelligence use and deep learning. Because if you are using bad data, and creating your models based on bad data, then the insights you get are also impacted. We definitely offer our products, both on the data integration and data quality side, to prepare the data, cleanse, match, and deliver the trusted data set for data scientists and make their life easier. Another area of focus for 2018 is can we also add supervised learning to this, because with the premium quality domain experts that we have now in Syncsort, we have a lot of domain experts in the field, we can infuse the machine learning algorithms and connect data profiling capabilities we have with the data quality capabilities recommending business rules for data scientists and helping them automate the mandate tasks with recommendations. And the last but not least trend is data governance, and data governance is almost a umbrella focus for everything we are doing at Syncsort because everything about the Cloud trend, the streaming, and the data science, and developing that next generation analytics environment for our customers depends on the data governance. It is, in fact, a business imperative, and the regulatory compliance use cases drives more importance today than governance. For example, General Data Protection Regulation in Europe, GDPR. >> Lisa: Just a few months away. >> Just a few months, May 2018, it is in the mind of every C-level executive. It's not just for European companies, but every enterprise has European data sourced in their environments. So compliance is a big driver of governance, and we look at governance in multiple aspects. Security and issuing data is available in a secure way is one aspect, and delivering the high quality data, cleansing, matching, the example Hilary Mason this morning gave in the keynote about half of what the context matters in terms of searches of her name was very interesting because you really want to deliver that high quality data in the enterprise, trust of data set, preparing that. Our Trillium Quality for big data, we launched Q4, that product is generally available now, and actually we are in production with very large deployment. So that's one area of focus. And the third area is how do you create visibility, the farm-to-table view of your data? >> Lisa: Yeah, that's the name of your talk! I love that. >> Yes, yes, thank you. So tomorrow I have a talk at 2:40, March 8th also, I'm so happy it's on the Women's Day that I'm talking-- >> Lisa: That's right, that's right! Get a farm-to-table view of your data is the name of your talk, track data lineage from source to analytics. Tell us a little bit more about that. >> It's all about creating more visibility, because for audit reasons, for understanding how many copies of my data is created, valued my data had been, and who accessed it, creating that visibility is very important. And the last couple of years, we saw everyone was focused on how do I create a data lake and make my data accessible, break the data silos, and liberate my data from multiple platforms, legacy platforms that the enterprise might have. Once that happened, everybody started worrying about how do I create consumable data set and how do I manage this data because data has been on the legacy platforms like Mainframe, IMBI series has been on relational data stores, it is in the Cloud, gravity of data originating in the Cloud is increasing, it's originating from mobile. Hadoop vendors like Hortonworks and Cloudera, they are creating visibility to what happens within the Hadoop framework. So we are deepening our integration with the Cloud Navigator, that was our announcement last week. We already have integration both with Hortonworks and Cloudera Navigator, this is one step further where we actually publish what happened to every single granular level of data at the field level with all of the transformations that data have been through outside of the cluster. So that visibility is now published to Navigator itself, we also publish it through the RESTful API, so governance is a very strong and critical initiative for all of the businesses. And we are playing into security aspect as well as data lineage and tracking aspect and the quality aspect. >> So this sounds like an extremely capable infrastructure service, so that it's trusted data. But can you sell that to an economic buyer alone, or do you go in in conjunction with anther solution like anti-money laundering for banks or, you know, what are the key things that they place enough value on that they would spend, you know, budget on it? >> Yes, absolutely. Usually the use cases might originate like anti-money laundering, which is very common, fraud detection, and it ties to getting a single view of an entity. Because in anti-money laundering, you want to understand the single view of your customer ultimately. So there is usually another solution that might be in the picture. We are providing the visibility of the data, as well as that single view of the entity, whether it's the customer view in this case or the product view in some of the use cases by delivering the matching capabilities and the cleansing capabilities, the duplication capabilities in addition to the accessing and integrating the data. >> When you go into a customer and, you know, recognizing that we still have tons of silos and we're realizing it's a lot harder to put everything in one repository, how do customers tell you they want to prioritize what they're bringing into the repository or even what do they want to work on that's continuously flowing in? >> So it depends on the business use case. And usually at the time that we are working with the customer, they selected that top priority use case. The risk here, and the anti-money laundering, or for insurance companies, we are seeing a trend, for example, building the data marketplace, as that tantalize data marketplace concept. So depending on the business case, many of our insurance customers in US, for example, they are creating the data marketplace and they are working with near real-time and microbatches. In Europe, Europe seems to be a bit ahead of the game in some cases, like Hadoop production was slow but certainly they went right into the streaming use cases. We are seeing more directly streaming and keeping it fresh and more utilization of the Kafka and messaging frameworks and database. >> And in that case, where they're sort of skipping the batch-oriented approach, how do they keep track of history? >> It's still, in most of the cases, microbatches, and the metadata is still associated with the data. So there is an analysis of the historical what happened to that data. The tools, like ours and the vendors coming to picture, to keep track, of that basically. >> So, in other words, by knowing what happened operationally to the data, that paints a picture of a history. >> Exactly, exactly. >> Interesting. >> And for the governance we usually also partner, for example, we partner with Collibra data platform, we partnered with ASG for creating that business rules and technical metadata and providing to the business users, not just to the IT data infrastructure, and on the Hadoop side we partner with Cloudera and Hortonworks very closely to complete that picture for the customer, because nobody is just interested in what happened to the data in Hadoop or in Mainframe or in my relational data warehouse, they are really trying to see what's happening on Premise, in the Cloud, multiple clusters, traditional environments, legacy systems, and trying to get that big picture view. >> So on that, enabling a business to have that, we'll say in marketing, 360 degree view of data, knowing that there's so much potential for data to be analyzed to drive business decisions that might open up new business models, new revenue streams, increase profit, what are you seeing as a CTO of Syncsort when you go in to meet with a customer, data silos, when you're talking to a Chief Data Officer, what's the cultural, I guess, not shift but really journey that they have to go on to start opening up other organizations of the business, to have access to data so they really have that broader, 360 degree view? What's that cultural challenge that they have to, journey that they have to go on? >> Yes, Chief Data Officers are actually very good partners for us, because usually Chief Data Officers are trying to break the silos of data and make sure that the data is liberated for the business use cases. Still most of the time the infrastructure and the cluster, whether it's the deployment in the Cloud versus on Premise, it's owned by the IT infrastructure. And the lines of business are really the consumers and the clients of that. CDO, in that sense, almost mitigates and connects to those line of businesses with the IT infrastructure with the same goals for the business, right? They have to worry about the compliance, they have to worry about creating multiple copies of data, they have to worry about the security of the data and availability of the data, so CDOs actually help. So we are actually very good partners with the CDOs in that sense, and we also usually have IT infrastructure owner in the room when we are talking with our customers because they have a big stake. They are like the gatekeepers of the data to make sure that it is accessed by the right... By the right folks in the business. >> Sounds like maybe they're in the role of like, good cop bad cop or maybe mediator. Well Tendu, I wish we had more time. Thanks so much for coming back to theCUBE and, like you said, you're speaking tomorrow at Strata Conference on International Women's Day: Get a farm-to-table view of your data. Love the title. >> Thank you. >> Good luck tomorrow, and we look forward to seeing you back on theCUBE. >> Thank you, I look forward to coming back and letting you know about more exciting both organic innovations and acquisitions. >> Alright, we look forward to that. We want to thank you for watching theCUBE, I'm Lisa Martin with my co-host George Gilbert. We are live at our event Big Data SV in San Jose. Come down and visit us, stick around, and we will be right back with our next guest after a short break. >> Tendu: Thank you. (upbeat music)

from San Jose in the heart of Silicon Valley it's the kue covering big data sv 2016 now your host John furrier and George Gilbert okay welcome back on we are here live in Silicon Valley for the cubes looking angles flagship program we go out to the events and extract the signal from the noise i'm john furrier mykos george gilbert big data analyst at Wikibon calm our next guest is 10 do yoga coo to yogurt coo coo I you see your last name yo Joe okay I gots clothes GM with big David sinks or welcome back to the cube sink starts been a long time guess one of those companies we love to cover because your value publishes is right in the center of all the action around mainframes and you know Dave and I always love to talk about mainframe not mean frame guys we know that we remember those days and still powering a lot of the big enterprises so I got to ask you you know what's your take on the show here one of the themes that came up last night on crowd chatters why is enterprise data warehousing failing so you know got some conversation but you're seeing a transformation what do you guys see thank you for having me it's great to be here yes we are seeing the transformation of the next generation data warehouse and evolution of the data warehouse architecture and as part of that mainframes are a big part of this data warehouse architecture because still seventy percent of data is on the mainframes world's data seventy percent of world's data this is a large amount of data so when we talk about big data architecture and making big data and enterprise data useful for the business and having advanced analytics not just gaining operational efficiencies with the new architecture and also having new products new services available to the customers of those organizations this data is intact and making that part of this next-generation data warehouse architecture is a big part of the initiatives and we play a very strong core role in this bridging the gap between mainframes and the big data platforms because we have product offerings spanning across platforms and we are very focused on accessing and integrating data accessing and integrating in a secure way from mainframes to the big data plan one is one of the things that's the mainframe highlights kind of a dynamic in the marketplace and wrong hall customers whether they have many firms are not your customers who have mainframes they already have a ton of data their data full as we say in the cube they have a ton of data do it but they spend a lot of times you mentioned cleaning the data how do you guys specifically solve that because that's a big hurdle that they want to just put behind they want to clean fast and get on to other things yes we see a few different trends and challenges first of all from the Big Data initiatives everybody is really trying to either gain operational efficiency business agility and make use of some of the data they weren't able to make use of before and enrich this data with some of the new data sources they might be actually adding to the data pipeline or they are trying to provide new products and services to their customers so when we talk about the mainframe data it's a it's really a how you access this mainframe data in a secure way and how you make that data preparation very easy for the data scientists the data scientists are still spending close to eighty percent of their time in data preparation and if you can't think of it when we talk about the compute frameworks like spark MapReduce flink versus the technology stack technologies these should not be relevant to the data scientist they should be just worried about how do i create my data pipeline what are the new insights that I'm trying to get from this data the simplification we bring in that data cleansing and data preparation is one well we are bringing simple way to access and integrate all of the enterprise data not just the legacy mainframe and the relational data sources and also the emerging data sources with streaming data sources the messaging frameworks new data sources we also make this in a cross-platform secure way and some of the new features for example we announced where we were simply the best in terms of accessing all of the mainframe data and having this available on Hadoop and spark we now also makes park and Hadoop understand this data in its original format you do not have to change the original record format which is very important for highly regulated industries like financial services banking and insurance and health care because you want to be able to do the data sanitization and data cleansing and yet bring that mainframe data in its original format for audit and compliance reasons okay so this is this is the product i think where you were telling us earlier that you can move the processing you can move the data from the mainframe do processing at scale and at cost that's not possible or even ii is is easy on the mainframe do it on a distributed platform like a dupe it preserves its original sort of way of being encoded send it back but then there's also this new way of creating a data fabric that we were talking about earlier where it used to be sort of point-to-point from the transactional systems to the data warehouse and now we've basically got this richer fabric and your tools sitting on some technologies perhaps like spark and Kafka tell us what that world looks like and how it was different from we see a greater interest in terms of the concept of a database because some organizations call it data as a service some organizations call it a Hadoop is a service but ultimately an easy way of publishing data and making data available for both the internal clients of the organization's and external clients of the organization's so Kafka is in the center of this and we see a lot of other partners of us including hot dog vendors like Cloudera map r & Horton works as well as data bricks and confluent are really focused on creating that data bus and servicing so we play a very strong there because phase one project for these organizations how do I create this enterprise data lake or enterprise data hub that is usually the phase one project because for advanced analytics or predictive analytics or when you make an engine your mortgage application you want to be able to see that change on your mobile phone under five minutes likewise when you make a change in your healthcare coverage or telecom services you want to be able to see that under five minutes on your phone these things really require easy access to that enterprise data hub what we have we have a tool called data funnel this basically simplifies in a one click and reduces the time for creating the enterprise data hub significantly and our customers are using this to migrate and make I would not say my great access data from the database tables like db2 for example thousands of tables populating an automatically mapping metadata whether that metadata is higher tables or parquet files or whatever the format is going to be in the distributed platform so this really simplifies the time to create the enterprise data hub it sounds actually really interesting when I'm hearing what you're saying the first sort of step was create this this data lake lets you know put data in there and start getting our feet wet and learning new analysis patterns but what if I'm hearing you correctly you're saying now radiating out of that is a new sort of data backbone that's much lower latency that gets data out of the analytic systems perhaps back into the operational systems or into new systems at a speed that we didn't do before so that we can now make decisions or or do an analysis and make decisions very quickly yes that's true basically operational intelligence and mathematics are converging okay and in that convergence what we are basically seeing is that I'm analyzing security data I'm analyzing telemetry data that's a streamed and I want to be able to react as fast as possible and some of the interest in the emerging computer platforms is really driven by this they eat the use case right many of our customers are basic saying that today operating under five minutes is enough for me however I want to be prepared I want to future-proof my applications because in a year it might be that I have to respond under a minute even in sub seconds when they talk about being future proofed and you mentioned to time you know time sort of brackets on either end our customers saying they're looking at a speed that current technologies don't support in other words are they evaluating some things that are you know essentially research projects right now you know very experimental or do they see a set of technologies that they can pick and choose from to serve those different latency needs we published a Hadoop survey earlier this year in january according to the results from that Hadoop survey seventy percent of the respondents were actually evaluating spark and this is very confused consistent with our customer base as well and the promise of spark is driven by multiple use cases and multiple workload including predictive analytics and streaming analytics and bat analytics all of these use cases being able to run on the same platform and all of the Hadoop vendors are also supporting this so we see as our customer base are heavy enterprise customers they are in production already in Hadoop so running spark on top of their Hadoop cluster is one way they are looking for future proofing their applications and this is where we also bring value because we really abstract that insulate the user while we are liberating all of the data from the enterprise whether it's on the relational legis data warehouse or it's on the mainframe side or it's coming from new web clients we are also helping them insulate their applications because they don't really need to worry about what's the next compute framework that's going to be the fastest most reliable and low latency they need to focus on the application layer they need to focus on creating that data pipeline today I want to ask you about the state of syncsort you guys have been great success with the mainframe this concept of data funneling or you can bring stuff in very fast new management new ownership what's the update on the market dynamics because now ingestion zev rethink data sources how do you guys view what's the plan for syncsort going forward share with the folks out there sure our new investors clearlake capital is very supportive of both organic and inorganic growth so acquisitions are one of the areas for us we plan to actually make one or two acquisitions this year and companies with the products in the near adjacent markets are real value add for us so that's one area in addition to organic growth in terms of the organic growth our investments are really we have been very successful with a lot of organizations insurance financial services banking and healthcare many many of the verticals very successful with helping our customers create the enterprise data hub integrate access all of the data integrated and now carrying them to the next generating generation frameworks those are the areas that we have been partnering with them the next is for us is really having streaming data sources as well as batch data sources through the single data pipeline and this includes bringing telemetry data and security data to the advanced analytics as well okay so it sounds like you're providing a platform that can handle the today's needs which were mostly batch but the emerging ones which are streaming and so you've got that sort of future proofing that customers are looking for once they've got that those types of data coming together including stuff from the mainframe that they want might want to enrich from public sources what new things do you see them doing predictive analytics and machine learning is a big part of this because ultimately once there are different phases right operational efficiency phase was the low-hanging fruit for many organizations I want to understand what I can do faster and serve my clients faster and create that operational efficiency in a cost-effective scalable way second was what our new for go to market opportunities with transformative applications what can I do by recognizing how my telco customers are interacting with the SAS services to help and how like under a couple of minutes I react to their responses or cell service is the second one and then the next phase is that how do I use this historical data in addition to the streaming of data rapidly I'm collecting to actually predict and prevent some of the things and this is already happening with a guy with banking for example it's really with the fraud detection a lot of predictive analysis happens so advanced analytics using AI advanced analytics using machine learning will be a very critical component of this moving forward this is really interesting because now you're honing in on a specific industry use case and something that you know every vendor is trying to sort of solve the fraud detection fraud prevention how repeatable is it across your customers is this something they have to build from scratch because there's no templates that get them fifty percent of the way there seventy percent of the way there actually there's an opportunity here because if you look at the health care or telco or financial services or insurance verticals there are repeating patterns and that one is fraud for fraud or some of the new use cases in terms of customer churn analytics or cosmetics estate so these patterns and the compliance requirements in these verticals creates an opportunity actually to come up with application applications for new companies start for new startups okay then do final question share with the folks out there to view the show right now this is ten years of Hadoop seven years of this event Big Data NYC we had a great event there New York City Silicon Valley what's the vibe here in Silicon Valley here this is one of the best events I really enjoy strata San Jose and I'm looking forward two days of keynotes and hearing from colleagues and networking with colleagues this is really the heartbeat happens because with the hadoop world and strata combined actually we started seeing more business use cases and more discussions around how to enable the business users which means the technology stack is maturing and the focus is really on the business and creating more insights and value for the businesses ten do you go to welcome to the cube thanks for coming by really appreciate it go check out our Dublin event on fourteenth of April hadoop summit will be in europe for that event of course go to SiliconANGLE TV check out our women in check every week we feature women in tech on wednesday thanks for joining us thanks for sharing the inside would sink so i really appreciate it thanks for coming by this turkey will be right back with more coverage live and Silicon Valley into the short break you

(energetic music) >> Hi, and welcome to another Cube conversation, where we go in-depth with the thought leaders in the industry that are making significant changes to how we conduct digital business and the likelihood of success with digital business transformations. I'm Peter Burris. Every organization today has some experience with the power of analytics. But, they're also warning that the value of their analytics systems are, in part, constrained and determined by their access to core information. Some of the most important information that any business can start to utilize within their new advanced analytic systems, quite frankly, is that operational business information, that the business has been using to run the business on for years. Now, we've looked at that as silos and maybe it is. Although, partly, that's in response to the need to have good policy, good governance, and good certainty and practicably in how the system behaves and how secure it's going to be. So, the question is, how do we marry the new world of advanced analytics with the older, but, nonetheless, extremely valuable world of operational processing to create new types of value within digital business today? It's a great topic and we've got a great conversation. Tendu Yogurtcu is the CTO of Syncsort. Tendu, welcome back to The Cube! >> Hi Peter. It's great to be back here in The Cube. >> Excellent! So, look, let's start with the, let's start with a quick update on Syncsort. How are you doing, what's going on? >> Oh, it's been really exciting time at Syncsort. We have seen a tremendous growth in the last three years. We quadrupled our revenue, and also number of employees, through both organic innovation and growth, as well as through acquisitions. So, we now have 7,000 plus customers in over 100 countries, and, we still have the eight 40 Fortune 100, serving large enterprises. It's been a really great journey. >> Well, so, let's get into the specific distinction that you guys have. At Wikibon theCube, we've observed, we predicted that 1919, 2019 rather, 2019 was going to be the year that the enterprise assert itself in the cloud. We had seen a lot of developers drive cloud forward. We've seen a lot of analytics drive cloud forward. But, now as enterprises are entering into cloud in a big way, they're generating, or bringing with them, new types of challenges and issues that have to be addressed. So, when you think about where we are in the journey to more advanced analytics, better operational certainty, greater use of information, what do you think the chief challenges that customers face today are? >> Of course, as you mentioned, that everybody, every organization is trying to take advantage of the data. Data is the core. And, take advantage of the digital transformation to enable them for taking, getting more value out of their data. And, in doing so, they are moving into cloud, into hybrid cloud architectures. We have seen early implementations, starting with the data lake. Everybody started creating the centralized data hub, enabling advanced analytics and creating a data marketplace for their internal, or external clients. And, the early data lakes were for utilizing Hadoop on premise architectures. Now, we are also seeing data lakes, sometimes, expanding over hybrid or cloud architectures. The challenges that these organizations also started realizing is around, once I create this data marketplace, the access to the data, critical customer data, critical product data, >> Order data. >> Order data, is a bigger challenge than I thought that it would be in the pilot project. Because, these critical data sets, and core data sets, often in financial services, banking and insurance, and health care, are in environments, data platforms that these companies have invested over multiple decades. And, I'm not referring to that as legacy because definition of legacy changes. These environment's platforms have been holding this current critical data assets for decades successfully. So-- >> We call them high-value traditional applications. >> High-valude traditional sounds great. >> Because, they're traditional. We know what they do, and there's a certain operational certainty, and we've built up the organization around them to take care of those assets. >> But, they still are very very high-value. >> Exactly. And, making those applications and data available for next generation, next wave platforms, is becoming a challenge, for couple of different reasons. One, accessing this data. And, accessing this data, making sure the policies and the security, and the privacy around these data stores are preserved when the data is available for advanced analytics. Whether it's in the cloud or on premise deployments. >> So, before we go to the second one, I want to make sure I'm understanding that, because it seems very very important. >> Yes. >> That, what you're saying is, if I may, the data is not just the ones and the zeroes in the file. The data really start, needs to start being thought of as the policies, the governance, the security, and all the other attributes and elements, the metadata, if you will, has to be preserved as the data's getting used. >> Absolutely. And, there are challenges around that, because now you have to have skill sets to understand the data in those different types of stores. Relational data warehouses. Mainframe, IBMI, SQL, Oracle. Many different data owners, and different teams in the organization. And, then, you have to make sense of it and preserve the policies around each of these data assets, while bringing it to the new analytics environments. And, make sure that everybody's aligned with the access to privacy, and the policies, and the governance around that data. And also, mapping to metadata, to the target systems, right? That's a big challenge, because somebody who understands these data sets in a mainframe environment is not necessarily understanding the cloud data stores or the new data formats. So, how do you, kind of, bridge that gap, and map into the target-- >> And, vice-versa, right? >> Yes. >> So. >> Likewise, yes. >> So, this is where Syncsort starts getting really interesting. Because, as you noted, a lot of the folks in the mainframe world may not have the familiarity of how the cloud works, and a lot of the folks, at least from a data standpoint. >> Yes. >> And, a lot of the folks in the cloud that have been doing things with object stores and whatnot, may not, and Hadoop, may not have the knowledge of how the mainframe works. And, so, those two sides are seeing silos, but, the reality is, both sides have set up policies and governance models, and security regimes, and everything else, because it works for the workloads that are in place on each side. So, Syncsort's an interesting company, because, you guys have experience of crossing that divide. >> Absolutely. And, we see both the next phase, and the existing data platforms, as a moving, evolving target. Because, these challenges have existed 20 years ago, 10 years ago. It's just the platforms were different. The volume, the variety, complexity was different. However, Hadoop, five, ten years ago, was the next wave. Now, it's the cloud. Blockchain will be the next platform that we have to, still, kind of, adopt and make sure that we are advancing our data and creating value out of data. So, that's, accessing and preserving those policies is one challenge. And, then, the second challenge is that as you are making these data sets available for analytics, or machine learning, data science applications, deduplicating, standardizing, cleansing, making sure that you can deliver trusted data becomes a big challenge. Because, if you train the models with the bad data, if you create the models with the bad data, you have bad model, and then bad data inside. So, machine learning and artificial intelligence depends on the data, and the quality of the data. So, it's not just bringing all enterprise data for analytics. It's also making sure that the data is delivered in a trusted way. That's the big challenge. >> Yeah. Let me build on that, if I may, Tendu. Because, a lot of these tools involve black box belief in what the tool's performing. >> Correct. >> So, you really don't have a lot of visibility in the inner workings of how the algorithm is doing things. It's, you know, that's the way it is. So, in many respects, your only real visibility into the quality of the outcome of these tools is visibility into the quality of the data that's going into the building of these models. >> Correct. >> Have I got that right? >> Correct. And, in machine learning, the effect of bad data is, really, it multiplies. Because of the training of the model, as well as insights. And, with Blockchain, in the future, it will also become very critical because, once you load the data into Blockchain platform, it's immutable. So, data quality comes at a higher price, in some sense. That's another big challenge. >> Which is to say, that if you load bad data into a Blockchain, it's bad forever. >> Yes. That's very true. So, that's, obviously, another area that Syncsort, as we are accessing all of the enterprise data, delivering high-quality data, discovering and understanding the data, and delivering the duplicated standardized enriched data to the machine learning and AI pipeline, and analytics pipeline, is an area that we are focused with our products. And, a third challenge is that, as you are doing it, the speed starts mattering. Because, okay, I created the data lake or the data hub. The next big use case we started seeing is that, "Oh yeah, but I have 20 terabyte data, "only 10% is changing on a nightly basis. "So, how do I keep my data lake in sync? "Not only that, I want to keep my data lake in sync, "I also would like to feed that change data "and keep my downstream applications in sync. "I want to feed the change data to the microservices "in the cloud." That speed of delivery started really becoming a very critical requirement for the business. >> Speed, and the targeting of the delivery. >> Speed of the targeting, exactly. Because, I think the bottom line is, you really want to create an architecture that you can be agnostic. And, also be able to deliver at the speed the business is going to require at different times. Sometimes, it's near real-time, and at batch, sometimes it's real-time, and you have to feed the changes as quickly as possible to the consumer applications and the microservices in the cloud. >> Well, we've got a lot of CIO's who are starting to ask us questions about, especially, since they start thinking about Kubernetes, and Istio, and other types of platforms that are intended to facilitate the orchestration, and ultimately, the management of how these container-based applications work. And, we're starting to talk more about the idea of data assurance. Make sure the data's good. Make sure it's been high-quality. Make sure it's being taken care of. But, also make sure that it's targeted where it needs to be. Because, you don't want a situation where you spin up a new cluster, which you could do very quickly with Kubernetes. But, you haven't made the data available to that Kubernetes-based application, so that it can, actually, run. And, a lot of CIO's, and a lot of application development leaders, and a lot of business people, are now starting to think about that. "How do I make sure the data is where it needs to be, "so that the applications run when they need to run?" >> That's a great point. And, going back to your, kind of, comment around cloud, and taking advantage of cloud architectures. One of the things we have observed is organizations, for sure, looking at cloud, in terms of scalability, elasticity, and reducing costs. They did lift and shift of applications. And, not all applications can be taking advantage of cloud elasticity, then you do that. Most of these applications are created for the existing on-premise fixed architectures. So, they are not designed to take advantage of that. And, we are seeing a shift now. And, the shift is around, instead of, trying to, kind of, lift and shift existing applications. One, for new applications, let me try and adopt the technology assets, like you mentioned Kubernetes, that I can stay vendor-agnostic, for cloud vendors. But, more importantly, let me try to have some best practices in the organization. The new applications can be created to take advantage of the elasticity. Even though, they may not be running in the cloud yet. So, some organizations refer to this as cloud native, cloud first, some different terms. And, make the data. Because, the core asset here, is always the data. Make the data available, instead of going after the applications. Make the data from these existing on-premise and different platforms available for cloud. We are definitely seeing that the shift. >> Yeah, and make sure that it, and assure, that that data is high-quality, carries the policies, carries the governance, doesn't break in security models, all those other things. >> That is a big difference between how, actually, organizations ran into their Hadoop data lake implementations, versus the cloud architectures now. Because, when initial Hadoop data lake implementations happened, it was dump all the data. And, then, "Oh, I have to deal with the data quality now." >> It was also, "Oh, those mainframe people just would, "they're so difficult to work with." Meanwhile, you're still closing the books on a monthly basis, on a quarterly basis. You're not losing orders. Your customers aren't calling you on the phone angry. And, that, at the end of the day, is what a business has to do. You have to be able to extend what you can currently do, with a digital business approach. And, if you can replace certain elements of it, okay. But, you can't end up with less functionality as you move forward in the cloud. >> Absolutely. And, it's not just mainframe. It's IBMI, it's the Oracle, it's the teledata, it's the TDZa. It's growing rapidly, in terms of the complex stuff, that data infrastructure. And, for cloud, we are seeing now, a lot of pilots are happening with the cloud data warehouses. And, trying to see if the cloud data warehouses can accommodate some of these hybrid deployments. And, also, we are seeing, there's more focus, not after the fact, but, more focus on data quality from day one. "How am I going to ensure that "I'm delivering trusted data, and populating "the cloud data stores, or delivering trusted data "to microservices in the cloud?" There's greater focus for both governance and quality. >> So, high-quality data movement, that leads to high-quality data delivery, in ways that the business can be certain that whatever derivative work is done remains high-quality. >> Absolutely. >> Tendu Yogurtcu, thank you very much for being, once again, on The Cube. It's always great to have you here. >> Thank you Peter. It's wonderful to be here! >> Tandu Yogurtcu's the CTO of Syncsort, and once again, I want to thank you very much, for participating in this cloud, or this Cube conversation. Cloud on the mind, this Cube conversation. Until next time. (upbeat electronic music)

>> Live from San Jose, in the heart of Silicon Valley, It's theCUBE, covering DataWorks Summit 2018. Brought to you by Hortonworks. >> Welcome back to theCUBE's live coverage of DataWorks here in San Jose, California, I'm your host, along with my cohost, James Kobielus. We're joined by Tendu Yogurtcu, she is the CTO of Syncsort. Thanks so much for coming on theCUBE, for returning to theCUBE I should say. >> Thank you Rebecca and James. It's always a pleasure to be here. >> So you've been on theCUBE before and the last time you were talking about Syncsort's growth. So can you give our viewers a company update? Where you are now? >> Absolutely, Syncsort has seen extraordinary growth within the last the last three year. We tripled our revenue, doubled our employees and expanded the product portfolio significantly. Because of this phenomenal growth that we have seen, we also embarked on a new initiative with refreshing our brand. We rebranded and this was necessitated by the fact that we have such a broad portfolio of products and we are actually showing our new brand here, articulating the value our products bring with optimizing existing infrastructure, assuring data security and availability and advancing the data by integrating into next generation analytics platforms. So it's very exciting times in terms of Syncsort's growth. >> So the last time you were on the show it was pre-GT prop PR but we were talking before the cameras were rolling and you were explaining the kinds of adoption you're seeing and what, in this new era, you're seeing from customers and hearing from customers. Can you tell our viewers a little bit about it? >> When we were discussing last time, I talked about four mega trends we are seeing and those mega trends were primarily driven by the advanced business and operation analytics. Data governance, cloud, streaming and data science, artificial intelligence. And we talked, we really made a lot of announcement and focus on the use cases around data governance. Primarily helping our customers for the GDPR Global Data Protection Regulation initiatives and how we can create that visibility in the enterprise through the data by security and lineage and delivering trust data sets. Now we are talking about cloud primarily and the keynotes, this event and our focus is around cloud, primarily driven by again the use cases, right? How the businesses are adopting to the new era. One of the challenges that we see with our enterprise customers, over 7000 customers by the way, is the ability to future-proof their applications. Because this is a very rapidly changing stack. We have seen the keynotes talking about the importance of how do you connect your existing infrastructure with the future modern, next generation platforms. How do you future-proof the platform, make a diagnostic about whether it's Amazon, Microsoft of Google Cloud. Whether it's on-premise in legacy platforms today that the data has to be available in the next generation platforms. So the challenge we are seeing is how do we keep the data fresh? How do we create that abstraction that applications are future-proofed? Because organizations, even financial services customers, banking, insurance, they now have at least one cluster running in the public cloud. And there's private implementations, hybrid becomes the new standard. So our focus and most recent announcements have been around really helping our customers with real-time resilient changes that capture, keeping the data fresh, feeding into the downstream applications with the streaming and messaging data frames, for example Kafka, Amazon Kinesis, as well as keeping the persistent stores and how to Data Lake on-premise in the cloud fresh. >> Puts you into great alignment with your partner Hortonworks so, Tendu I wonder if we are here at DataWorks, it's Hortonworks' show, if you can break out for our viewers, what is the nature, the levels of your relationship, your partnership with Hortonworks and how the Syncsort portfolio plays with HDP 3.0 with Hortonworks DataFlow and the data plan services at a high level. >> Absolutely, so we have been a longtime partner with Hortonworks and a couple of years back, we strengthened our partnership. Hortonworks is reselling Syncsort and we have actually a prescriptive solution for Hadoop and ETL onboarding in Hadoop jointly. And it's very complementary, our strategy is very complementary because what Hortonworks is trying and achieving, is creating that abstraction and future-proofing and interaction consistency around referred as this morning. Across the platform, whether it's on-premise or in the cloud or across multiple clouds. We are providing the data application layer consistency and future-proofing on top of the platform. Leveraging the tools in the platform for orchestration, integrating with HTP, certifying with Trange or HTP, all of the tools DataFlow and at last of course for lineage. >> The theme of this conference is ideas, insights and innovation and as a partner of Hortonworks, can you describe what it means for you to be at this conference? What kinds of community and deepening existing relationships, forming new ones. Can you talk about what happens here? >> This is one of the major events around data and it's DataWorks as opposed to being more specific to the Hadoop itself, right? Because stack is evolving and data challenges are evolving. For us, it means really the interactions with the customers, the organizations and the partners here. Because the dynamics of the use cases is also evolving. For example Data Lake implementations started in U.S. And we started MER European organizations moving to streaming, data streaming applications faster than U.S. >> Why is that? >> Yeah. >> Why are Europeans moving faster to streaming than we are in North America? >> I think a couple of different things might participate. The open sources really enabling organizations to move fast. When the Data Lake initiative started, we have seen a little bit slow start in Europe but more experimentation with the Open Source Stack. And by that the more transformative use cases started really evolving. Like how do I manage interactions of the users with the remote controls as they are watching live TV, type of transformative use cases became important. And as we move to the transformative use cases, streaming is also very critical because lots of data is available and being able to keep the cloud data stores as well as on-premise data stores and downstream applications with fresh data becomes important. We in fact in early June announced that Syncsort's now's a part of Microsoft One Commercial Partner Program. With that our integrate solutions with data integration and data quality are Azure gold certified and Azure ready. We are in co-sale agreement and we are helping jointly a lot of customers, moving data and workloads to Azure and keeping those data stores close to platforms in sync. >> Right. >> So lots of exciting things, I mean there's a lot happening with the application space. There's also lots still happening connected to the governance cases that we have seen. Feeding security and IT operations data into again modern day, next generation analytics platforms is key. Whether it's Splunk, whether it's Elastic, as part of the Hadoop Stack. So we are still focused on governance as part of this multi-cloud and on-premise the cloud implementations as well. We in fact launched our Ironstream for IBMI product to help customers, not just making this state available for mainframes but also from IBMI into Splunk, Elastic and other security information and event management platforms. And today we announced work flow optimization across on-premise and multi-cloud and cloud platforms. So lots of focus across to optimize, assure and integrate portfolio of products helping customers with the business use cases. That's really our focus as we innovate organically and also acquire technologies and solutions. What are the problems we are solving and how we can help our customers with the business and operation analytics, targeting those mega trends around data governance, cloud streaming and also data science. >> What is the biggest trend do you think that is sort of driving all of these changes? As you said, the data is evolving. The use cases are evolving. What is it that is keeping your customers up at night? >> Right now it's still governance, keeping them up at night, because this evolving architecture is also making governance more complex, right? If we are looking at financial services, banking, insurance, healthcare, there are lots of existing infrastructures, mission critical data stores on mainframe IBMI in addition to this gravity of data changing and lots of data with the online businesses generated in the cloud. So how to govern that also while optimizing and making those data stores available for next generation analytics, makes the governance quite complex. So that really keeps and creates a lot of opportunity for the community, right? All of us here to address those challenges. >> Because it sounds to me, I'm hearing Splunk, Advanced Machine did it, I think of the internet of things and sensor grids. I'm hearing IBM mainframes, that's transactional data, that's your customer data and so forth. It seems like much of this data that you're describing that customers are trying to cleanse and consolidate and provide strict governance on, is absolutely essential for them to drive more artificial intelligence into end applications and mobile devices that are being used to drive the customer experience. Do you see more of your customers using your tools to massage the data sets as it were than data scientists then use to build and train their models for deployment into edge applications. Is that an emerging area where your customers are deploying Syncsort? >> Thank you for asking that question. >> It's a complex question. (laughing) But thanks for impacting it... >> It is a complex question but it's very important question. Yes and in the previous discussions, we have seen, and this morning also, Rob Thomas from IBM mentioned it as well, that machine learning and artificial intelligence data science really relies on high-quality data, right? It's 1950s anonymous computer scientist says garbage in, garbage out. >> Yeah. >> When we are using artificial intelligence and machine learning, the implications, the impact of bad data multiplies. Multiplies with the training of historical data. Multiplies with the insights that we are getting out of that. So data scientists today are still spending significant time on preparing the data for the iPipeline, and the data science pipeline, that's where we shine. Because our integrate portfolio accesses the data from all enterprise data stores and cleanses and matches and prepares that in a trusted manner for use for advanced analytics with machine learning, artificial intelligence. >> Yeah 'cause the magic of machine learning for predictive analytics is that you build a statistical model based on the most valid data set for the domain of interest. If the data is junk, then you're going to be building a junk model that will not be able to do its job. So, for want of a nail, the kingdom was lost. For want of a Syncsort, (laughing) Data cleansing and you know governance tool, the whole AI superstructure will fall down. >> Yes, yes absolutely. >> Yeah, good. >> Well thank you so much Tendu for coming on theCUBE and for giving us a lot of background and information. >> Thank you for having me, thank you. >> Good to have you. >> Always a pleasure. >> I'm Rebecca Knight for James Kobielus. We will have more from theCUBE's live coverage of DataWorks 2018 just after this. (upbeat music)

>> Announcer: Live from midtown Manhattan, it's theCUBE, covering BigData New York City 2017, brought to you by SiliconANGLE Media and its ecosystem sponsors. >> Hello everyone, welcome back to theCUBE's special BigData NYC coverage of theCUBE here in Manhattan in New York City, we're in Hell's Kitchen. I'm John Furrier, with my cohost Jim Kobielus, whose Wikibon analyst for BigData. In conjunction with Strata Data going on right around the corner, this is our annual event where we break down the big data, the AI, the cloud, all the goodness of what's going on in big data. Our next guest is Tendu Yogurtcu who's the Chief Technology Officer at Syncsort. Great to see you again, CUBE alumni, been on multiple times. Always great to have you on, get the perspective, a CTO perspective and the Syncsort update, so good to see you. >> Good seeing you John and Jim. It's a pleasure being here too. Again the pulse of big data is in New York, and it's a great week with a lot of happening. >> I always borrow the quote from Pat Gelsinger, who's the CEO of VMware, he said on theCUBE in I think 2011, before he joined VMware as CEO he was at EMC. He said if you're not out in front of that next wave, you're driftwood. And the key to being successful is to ride the waves, and the big waves are coming in now with AI, certainly big data has been rising tide for its own bubble but now the aperture of the scale of data's larger, Syncsort has been riding the wave with us, we've been having you guys on multiple times. And it was important to the mainframe in the early days, but now Syncsort just keeps on adding more and more capabilities, and you're riding the wave, the big wave, the big data wave. What's the update now with you guys, where are you guys now in context of today's emerging data landscape? >> Absolutely. As organizations progress with their modern data architectures and building the next generation analytics platforms, leveraging machine learning, leveraging cloud elasticity, we have observed that data quality and data governance have become more critical than ever. Couple of years we have been seeing this trend, I would like to create a data lake, data as a service, and enable bigger insights from the data, and this year, really every enterprise is trying to have that trusted data set created, because data lakes are turning into data swamps, as Dave Vellante refers often (John laughs) and collection of this diverse data sets, whether it's mainframe, whether it's messaging queues, whether it's relational data warehouse environments is challenging the customers, and we can take one simple use case like Customer 360, which we have been talking for decades now, right? Yet still it's a complex problem. Everybody is trying to get that trusted single view of their customers so that they can serve the customer needs in a better way, offer better solutions and products to customers, get better insights about the customer behavior, whether leveraging deep learning, machine learning, et cetera. However, in order to do that, the data has to be in a clean, trusted, valid format, and every business is going global. You have data sets coming from Asia, from Europe, from Latin America, and many different places, in different formats and it's becoming challenge. We acquired Trillium Software in December 2016, and our vision was really to bring that world leader enterprise grade data quality into the big data environments. So last week we announced our Trillium Quality for Big Data product. This product brings unmatched capabilities of data validation, cleansing, enrichment, and matching, fuzzy matching to the data lake. We are also leveraging our Intelligent eXecution engine that we developed for data integration product, the MX8. So we are enabling the organizations to take this data quality offering, whether it's in Hadoop, MapReduce or Apache Spark, whichever computer framework it's going to be in the future. So we are very excited about that now. >> Congratulations, you mentioned the data lake being a swamp, that Dave Vellante referred to. It's interesting, because how does it become a swamp if it's a silo, right? We've seen data silos being antithesis to governance, it challenges, certainly IoT. Then you've got the complication of geopolitical borders, you mentioned that earlier. So you still got to integrate the data, you need data quality, which has been around for a while but now it's more complex. What specifically about the cleansing and the quality of the data that's more important now in the landscape now? Is it those factors, are that the drivers of the challenges today and what's the opportunity for customers, how do they figure this out? >> Complexity is because of many different factors. Some of it from being global. Every business is trying to have global presence, and the data is originating from web, from mobile, from many different data sets, and if we just take a simple address, these address formats are different in every single country. Trillium Quality for Big Data, we support over 150 postal data from different countries, and data enrichment with this data. So it becomes really complex, because you have to deal with different types of data from different countries, and the matching also becomes very difficult, whether it's John Furrier, J Furrier, John Currier, you have to be >> All my handles on Twitter, knowing that's about. (Tendu laughs) >> All of the handles you have. Every business is trying to have a better targeting in terms of offering product and understanding the single and one and only John Furrier as a customer. That creates a complexity, and any data management and data processing challenge, the variety of data and the speed that data is really being populated is higher than ever we have observed. >> Hold on Jim, I want to get Jim involved in this one conversation, 'cause I want to just make sure those guys can get settled in on, and adjust your microphone there. Jim, she's bringing up a good point, I want you to weigh in just to kind of add to the conversation and take it in the direction of where the automation's happening. If you look at what Tendu's saying as to complexity is going to have an opportunity in software. Machine learning, root-level cleanliness can be automated, because Facebook and others have shown that you can apply machine learning and techniques to the volume of data. No human can get at all the nuances. How is that impacting the data platforms and some of the tooling out there, in your opinion? >> Yeah well, much of the issue, one of the core issues is where do you place the data matching and data cleansing logic or execution in this distributed infrastructure. At the source, in the cloud, at the consumer level in terms of rolling up the disparate versions of data into a common view. So by acquiring a very strong, well-established reputable brand in data cleansing, Trillium, as Syncsort has done, a great service to your portfolio, to your customers. You know, Trillium is well known for offering lots of options in terms of where to configure the logic, where to deploy it within distributed hybrid architectures. Give us a sense for going forward the range of options you're going to be providing with for customers on where to place the cleansing and matching logic. How you're going to support, Syncsort, a flexible workflows in terms of curation of the data and so forth, because the curation cycle for data is critically important, the stewardship. So how do you plan to address all of that going forward in your product portfolio, Tendu? >> Thank you for asking the question, Jim, because that's exactly the challenge that we hear from our customers, especially from larger enterprise and financial services, banking and insurance. So our plan is our actually next upcoming release end of the year, is targeting very flexible deployment. Flexible deployment in the sense that you might be creating, when you understand the data and create the business rules and said what kind of matching and enrichment that you'll be performing on the data sets, you can actually have those business rules executed in the source of the data or in the data lake or switch between the source and the enterprise data lake that you are creating. That flexibility is what we are targeting, that's one area. On the data creation side, we see these percentages, 80% of data stewards' time is spent on data prep, data creation and data cleansing, and it is actually really a very high percentage. From our customers we see this still being a challenge. One area that we started investing is using the machine learning to understand the data, and using that discovery of the data capabilities we currently have to make recommendations what those business rules can be, or what kind of data validation and cleansing and matching might be required. So that's an area that we will be investing. >> Are you contemplating in terms of incorporating in your product portfolio, using machine learning to drive a sort of, the term I like to use is recommendation engine, that presents recommendations to the data stewards, human beings, about different data schemas or different ways of matching the data, different ways of, the optimal way of reconciling different versions of customer data. So is there going to be like a recommendation engine of that sort >> It's going to be >> In line with your >> That's what our plan currently recommendations so the users can opt to apply or not, or to modify them, because sometimes when you go too far with automation you still need some human intervention in making these decisions because you might be operating on a sample of data versus the full data set, and you may actually have to infuse some human understanding and insight as well. So our plan is to make as a recommendation in the first phase at least, that's what we are planning. And when we look at the portfolio of the products and our CEO Josh is actually today was also in theCUBE, part of Splunk .conf. We have acquisitions happening, we have organic innovation that's happening, and we really try to stay focused in terms of how do we create more value from your data, and how do we increase the business serviceability, whether it's with our Ironstream product, we made an announcement this week, Ironstream transaction tracing to create more visibility to application performance and more visibility to IT operations, for example when you make a payment with your mobile, you might be having problem and you want to be able to trace back to the back end, which is usually a legacy mainframe environment, or whether you are populating the data lake and you want to keep the data in sync and fresh with the data source, and apply the change as a CDC, or whether you are making that data from raw data set to more consumable data by creating the trusted, high quality data set. We are very much focused on creating more value and bigger insights out of the data sets. >> And Josh'll be on tomorrow, so folks watching, we're going to get the business perspective. I have some pointed questions I'm going to ask him, but I'll take one of the questions I was going to ask him but I want to get your response from a technical perspective as CTO. As Syncsort continues your journey, you keep on adding more and more things, it's been quite impressive, you guys done a great job, >> Tendu: Thank you. >> We enjoy covering the success there, watching you guys really evolve. What is the value proposition for Syncsort today, technically? If you go in, talk to a customer, and prospective new customer, why Syncsort, what's the enabling value that you're providing under the hood, technically for customers? >> We are enabling our customers to access and integrate data sets in a trusted manner. So we are ultimately liberating the data from all of the enterprise data stores, and making that data consumable in a trusted manner. And everything we provide in that data management stack, is about making data available, making data accessible and integrated the modern data architecture, bridging the gap between those legacy environments and the modern data architecture. And it becomes really a big challenge because this is a cross-platform play. It is not a single environment that enterprises are working with. Hadoop is real now, right? Hadoop is in the center of data warehouse architecture, and whether it's on-premise or in the cloud, there is also a big trend about the cloud. >> And certainly batch, they own the batch thing. >> Yeah, and as part of that, it becomes very important to be able to leverage the existing data assets in the enterprise, and that requires an understanding of the legacy data stores, and existing infrastructure, and existing data warehouse attributes. >> John: And you guys say you provide that. >> We provide that and that's our baby and provide that in enterprise grade manner. >> Hold on Jim, one second, just let her finish the thought. Okay, so given that, okay, cool you got that out there. What's the problem that you're solving for customers today? What's the big problem in the enterprise and in the data world today that you address? >> I want to have a single view of my data, and whether that data is originating on the mobile or that data is originating on the mainframe, or in the legacy data warehouse, and we provide that single view in a trusted manner. >> When you mentioned Ironstream, that reminded me that one of the core things that we're seeing in Wikibon in terms of, IT operations is increasingly being automated through AI, some call it AI ops and whatnot, we're going deeper on the research there. Ironstream, by bringing mainframe and transactional data, like the use case you brought in was IT operations data, into a data lake alongside machine data that you might source from the internet of things and so forth. Seem to me that that's a great enabler potentially for Syncsort if it wished to play your solutions or position them into IT operations as an enabler, leveraging your machine learning investments to build more automated anomaly detection and remediation into your capabilities. What are your thoughts? Is that where you're going or do you see it as an opportunity, AI for IT ops, for Syncsort going forward? >> Absolutely. We target use cases around IT operations and application performance. We integrate with Splunk ITSI, and we also provide this data available in the big data analytics platforms. So those are really application performance and IT operations are the main uses cases we target, and as part of the advanced analytics platform, for example, we can correlate that data set with other machine data that's originating in other platforms in the enterprise. Nobody's looking at what's happening on mainframe or what's happening in my Hadoop cluster or what's happening on my VMware environment, right. They want to correlate the data that's closed platform, and that's one of the biggest values we bring, whether it's on the machine data, or on the application data. >> Yeah, that's quite a differentiator for you. >> Tendu, thanks for coming on theCUBE, great to see you. Congratulations on your success. Thanks for sharing. >> Thank you. >> Okay, CUBE coverage here in BigData NYC, exclusive coverage of our event, BigData NYC, in conjunction with Strata Hadoop right around the corner. This is our annual event for SiliconANGLE, and theCUBE and Wikibon. I'm John Furrier, with Jim Kobielus, who's our analyst at Wikibon on big data. Peter Burris has been on theCUBE, he's here as well. Big three days of wall-to-wall coverage on what's happening in the data world. This is theCUBE, thanks for watching, be right back with more after this short break.