>> 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, Rebecca Knight, along with my co-host, James Kobielus. We're joined by Stephanie McReynolds. She is the Vice President of Marketing at Alation. Thanks so much for, for returning to theCUBE, Stephanie. >> Thank you for having me again. >> So, before the cameras were rolling, we were talking about Kevin Slavin's talk on the main stage this morning, and talking about, well really, a background to sort of this concern about AI and automation coming to take people's jobs, but really, his overarching point was that we really, we shouldn't, we shouldn't let the algorithms take over, and that humans actually are an integral piece of this loop. So, riff on that a little bit. >> Yeah, what I found fascinating about what he presented were actual examples where having a human in the loop of AI decision-making had a more positive impact than just letting the algorithms decide for you, and turning it into kind of a black, a black box. And the issue is not so much that, you know, there's very few cases where the algorithms make the wrong decision. What happens the majority of the time is that the algorithms actually can't be understood by human. So if you have to roll back >> They're opaque, yeah. >> in your decision-making, or uncover it, >> I mean, who can crack what a convolutional neural network does, at a layer by layer, nobody can. >> Right, right. And so, his point was, if we want to avoid not just poor outcomes, but also make sure that the robots don't take over the world, right, which is where every like, media person goes first, right? (Rebecca and James laugh) That you really need a human in the loop of this process. And a really interesting example he gave was what happened with the 2015 storm, and he talked about 16 different algorithms that do weather predictions, and only one algorithm predicted, mis-predicted that there would be a huge weather storm on the east coast. So if there had been a human in the loop, we wouldn't have, you know, caused all this crisis, right? The human could've >> And this is the storm >> Easily seen. >> That shut down the subway system, >> That's right. That's right. >> And really canceled New York City for a few days there, yeah. >> That's right. So I find this pretty meaningful, because Alation is in the data cataloging space, and we have a lot of opportunity to take technical metadata and automate the collection of technical and business metadata and do all this stuff behind the scenes. >> And you make the discovery of it, and the analysis of it. >> We do the discovery of this, and leading to actual recommendations to users of data, that you could turn into automated analyses or automated recommendations. >> Algorithmic, algorithmically augmented human judgment is what it's all about, the way I see it. What do you think? >> Yeah, but I think there's a deeper insight that he was sharing, is it's not just human judgment that is required, but for humans to actually be in the loop of the analysis as it moves from stage to stage, that we can try to influence or at least understand what's happening with that algorithm. And I think that's a really interesting point. You know, there's a number of data cataloging vendors, you know, some analysts will say there's anywhere from 10 to 30 different vendors in the data cataloging space, and as vendors, we kind of have this debate. Some vendors have more advanced AI and machine learning capabilities, and other vendors haven't automated at all. And I think that the answer, if you really want humans to adopt analytics, and to be comfortable with the decision-making of those algorithms, you need to have a human in the loop, in the middle of that process, of not only making the decision, but actually managing the data that flows through these systems. >> Well, algorithmic transparency and accountability is an increasing requirement. It's a requirement for GDPR compliance, for example. >> That's right. >> That I don't see yet with Wiki, but we don't see a lot of solution providers offering solutions to enable more of an automated roll-up of a narrative of an algorithmic decision path. But that clearly is a capability as it comes along, and it will. That will absolutely depend on a big data catalog managing the data, the metadata, but also helping to manage the tracking of what models were used to drive what decision, >> That's right. >> And what scenario. So that, that plays into what Alation >> So we talk, >> And others in your space do. >> We call that data catalog, almost as if the data's the only thing that we're tracking, but in addition to that, that metadata or the data itself, you also need to track the business semantics, how the business is using or applying that data and that algorithmic logic, so that might be logic that's just being used to transform that data, or it might be logic to actually make and automate decision, like what they're talking about GDPR. >> It's a data artifact catalog. These are all artifacts that, they are derived in many ways, or supplement and complement the data. >> That's right. >> They're all, it's all the logic, like you said. >> And what we talk about is, how do you create transparency into all those artifacts, right? So, a catalog starts with this inventory that creates a foundation for transparency, but if you don't make those artifacts accessible to a business person, who might not understand what is metadata, what is a transformation script. If you can't make that, those artifacts accessible to a, what I consider a real, or normal human being, right, (James laughs) I love to geek out, but, (all laugh) at some point, not everyone is going to understand. >> She's the normal human being in this team. >> I'm normal. I'm normal. >> I'm the abnormal human being among the questioners here. >> So, yeah, most people in the business are just getting our arms around how do we trust the output of analytics, how do we understand enough statistics and know what to apply to solve a business problem or not, and then we give them this like, hairball of technical artifacts and say, oh, go at it. You know, here's your transparency. >> Well, I want to ask about that, that human that we're talking about, that needs to be in the loop at every stage. What, that, surely, we can make the data more accessible, and, but it also requires a specialized skill set, and I want to ask you about the talent, because I noticed on your LinkedIn, you said, hey, we're hiring, so let me know. >> That's right, we're always hiring. We're a startup, growing well. >> So I want to know from you, I mean, are you having difficulty with filling roles? I mean, what is at the pipeline here? Are people getting the skills that they need? >> Yeah, I mean, there's a wide, what I think is a misnomer is there's actually a wide variety of skills, and I think we're adding new positions to this pool of skills. So I think what we're starting to see is an expectation that true business people, if you are in a finance organization, or you're in a marketing organization, or you're in a sales organization, you're going to see a higher level of data literacy be expected of that, that business person, and that's, that doesn't mean that they have to go take a Python course and learn how to be a data scientist. It means that they have to understand statistics enough to realize what the output of an algorithm is, and how they should be able to apply that. So, we have some great customers, who have formally kicked off internal training programs that are data literacy programs. Munich Re Insurance is a good example. They spoke with James a couple of months ago in Berlin. >> Yeah, this conference in Berlin, yeah. >> That's right, that's right, and their chief data officer has kicked off a formal data literacy training program for their employees, so that they can get business people comfortable enough and trusting the data, and-- >> It's a business culture transformation initiative that's very impressive. >> Yeah. >> How serious they are, and how comprehensive they are. >> But I think we're going to see that become much more common. Pfizer has taken, who's another customer of ours, has taken on a similar initiative, and how do they make all of their employees be able to have access to data, but then also know when to apply it to particular decision-making use cases. And so, we're seeing this need for business people to get a little bit of training, and then for new roles, like information stewards, or data stewards, to come online, folks who can curate the data and the data assets, and help be kind of translators in the organization. >> Stephanie, will there be a need for a algorithm curator, or a model curator, to, you know, like a model whisperer, to explain how these AI, convolutional, recurrent, >> Yeah. >> Whatever, all these neural, how, what they actually do, you know. Would there be a need for that going forward? Another as a normal human being, who can somehow be bilingual in neural net and in standard language? >> I think, I think so. I mean, I think we've put this pressure on data scientists to be that person. >> Oh my gosh, they're so busy doing their job. How can we expect them to explain, and I mean, >> Right. >> And to spend 100% of their time explaining it to the rest of us? >> And this is the challenge with some of the regulations like GDPR. We aren't set up yet, as organizations, to accommodate this complexity of understanding, and I think that this part of the market is going to move very quickly, so as vendors, one of the things that we can do is continue to help by building out applications that make it easy for information stewardship. How do you lower the barrier for these specialist roles and make it easy for them to do their job by using AI and machine learning, where appropriate, to help scale the manual work, but keeping a human in the loop to certify that data asset, or to add additional explanation and then taking their work and using AI, machine learning, and automation to propagate that work out throughout the organization, so that everyone then has access to those explanations. So you're no longer requiring the data scientists to hold like, I know other organizations that hold office hours, and the data scientist like sits at a desk, like you did in college, and people can come in and ask them questions about neural nets. That's just not going to scale at today's pace of business. >> Right, right. >> You know, the term that I used just now, the algorithm or model whisperer, you know, the recommend-er function that is built into your environment, in similar data catalog, is a key piece of infrastructure to rank the relevance rank, you know, the outputs of the catalog or responses to queries that human beings might make. You know, the recommendation ranking is critically important to help human beings assess the, you know, what's going on in the system, and give them some advice about how to, what avenues to explore, I think, so. >> Yeah, yeah. And that's part of our definition of data catalog. It's not just this inventory of technical metadata. >> That would be boring, and dry, and useless. >> But that's where, >> For most human beings. >> That's where a lot of vendor solutions start, right? >> Yeah. >> And that's an important foundation. >> Yeah, for people who don't live 100% of their work day inside the big data catalog. I hear what you're saying, you know. >> Yeah, so people who want a data catalog, how you make that relevant to the business is you connect those technical assets, that technical metadata with how is the business actually using this in practice, and how can we have proactive recommendation or the recommendation engines, and certifications, and this information steward then communicating through this platform to others in the organization about how do you interpret this data and how do you use it to actually make business decisions. And I think that's how we're going to close the gap between technology adoption and actual data-driven decision-making, which we're not quite seeing yet. We're only seeing about 30, when they survey, only about 36% of companies are actually confident they're making data-driven decisions, even though there have been, you know, millions, if not billions of dollars that have gone into the data analytics market and investments, and it's because as a manager, I don't quite have the data literacy yet, and I don't quite have the transparency across the rest of the organization to close that trust gap on analytics. >> Here's my feeling, in terms of cultural transformations across businesses in general. I think the legal staff of every company is going to need to get real savvy on using those kinds of tools, like your catalog, with recommendation engines, to support e-discovery, or discovery of the algorithmic decision paths that were taken by their company's products, 'cause they're going to be called by judges and juries, under a subpoena and so forth, and so on, to explain all this, and they're human beings who've got law degrees, but who don't know data, and they need the data environment to help them frame up a case for what we did, and you know, so, we being the company that's involved. >> Yeah, and our politicians. I mean, anyone who's read Cathy's book, Weapons of Math Destruction, there are some great use cases of where, >> Math, M-A-T-H, yeah. >> Yes, M-A-T-H. But there are some great examples of where algorithms can go wrong, and many of our politicians and our representatives in government aren't quite ready to have that conversation. I think anyone who watched the Zuckerberg hearings you know, in congress saw the gap of knowledge that exists between >> Oh my gosh. >> The legal community, and you know, and the tech community today. So there's a lot of work to be done to get ready for this new future. >> But just getting back to the cultural transformation needed to be, to make data-driven decisions, one of the things you were talking about is getting the managers to trust the data, and we're hearing about what are the best practices to have that happen in the sense, of starting small, be willing to experiment, get out of the lab, try to get to insight right away. What are, what would your best advice be, to gain trust in the data? >> Yeah, I think the biggest gap is this issue of transparency. How do you make sure that everyone understands each step of the process and has access to be able to dig into that. If you have a foundation of transparency, it's a lot easier to trust, rather than, you know, right now, we have kind of like the high priesthood of analytics going on, right? (Rebecca laughs) And some believers will believe, but a lot of folks won't, and, you know, the origin story of Alation is really about taking these concepts of the scientific revolution and scientific process and how can we support, for data analysis, those same steps of scientific evaluation of a finding. That means that you need to publish your data set, you need to allow others to rework that data, and come up with their own findings, and you have to be open and foster conversations around data in your organization. One other customer of ours, Meijer, who's a grocery store in the mid-west, and if you're west coast or east coast-based, you might not have heard of them-- >> Oh, Meijers, thrifty acres. I'm from Michigan, and I know them, yeah. >> Gigantic. >> Yeah, there you go. Gigantic grocery chain in the mid-west, and, Joe Oppenheimer there actually introduced a program that he calls the social contract for analytics, and before anyone gets their license to use Tableau, or MicroStrategy, or SaaS, or any of the tools internally, he asks those individuals to sign a social contract, which basically says that I'll make my work transparent, I will document what I'm doing so that it's shareable, I'll use certain standards on how I format the data, so that if I come up with a, with a really insightful finding, it can be easily put into production throughout the rest of the organization. So this is a really simple example. His inspiration for that social contract was his high school freshman. He was entering high school and had to sign a social contract, that he wouldn't make fun of the teachers, or the students, you know, >> I love it. >> Very simple basics. >> Yeah, right, right, right. >> I wouldn't make fun of the teacher. >> We all need social contract. >> Oh my gosh, you have to make fun of the teacher. >> I think it was a little more formal than that, in the language, but that was the concept. >> That's violating your civil rights as a student. I'm sorry. (Stephanie laughs) >> Stephanie, always so much fun to have you here. Thank you so much for coming on. >> Thank you. It's a pleasure to be here. >> I'm Rebecca Knight, for James Kobielus. We'll have more of theCUBE's live coverage of DataWorks just after this.