Speaker 1 - Kevin Benedict: Welcome to the podcast The Next Big Think. I'm your host, Kevin Benedict, a partner and futurist here at TCS, and want to thank each of you for listening today. My very first guest in this series, I'm so excited to introduce her—Susan Cook. She's the CEO of Zaloni. Susan, thanks for joining me this morning,
Speaker 2 - Susan Cook: Kevin, thank you. I'm so honored. I'm so appreciative of being the first guest. Thanks for having me.
Kevin: I'm so excited about this series here. We're going to be talking to the founders and CEOs of some of the coolest technology startups anywhere. Reading your profile, Susan, you and I have both been in the technology space for a long time. What did you see in Zaloni that attracted you to them? What trends were they addressing that you said—that's the space I want to be in.
Susan: So, my whole career, I am embarrassed to say I'm aging myself, the last multiple decades has been at companies like Hyperion and Oracle and MicroStrategy. It's always been in the data and analytics space. So, I truly love this space. And when I started exploring Zaloni, this notion that data should be treated like the golden asset that it is, almost like a product. Therefore, data has its own supply chain, like any other product would have. And you have to manage that supply chain to produce the best product that you can—so that's what Zaloni does. In layman's terms, we manage the supply chain of your enterprise data. And I thought, wow, today, lots of companies do that with lots and lots of different tools and technologies. And it's really fragmented, really complex, and really messy. And if we can simplify that and unify that, then we've got something here. So that's what intrigued me about Zaloni.
Kevin: Yeah. And if we could extend that example of data supply chain, then I guess you'd have some of the same challenges a physical supply chain would have, there could be friction in the system. So, things get bottlenecked and all that as well. Does that metaphor, if you would, extend into that space?
Susan: Absolutely. Yes, there are security issues in your supply chain, there’s supplier issues, there's unwarranted expense, unnecessary complexity. Obviously, you want a reliable, safe, secure, efficient supply chain. And that's exactly what we are striving to do.
Kevin: That's fascinating. So let me just delve into your career a little bit. You've worked for some of the largest companies out there, and you've worked for smaller startups. What's the good about large companies and then what's the good about small companies from your experience?
Susan: It's funny, when I worked for Oracle, I used to say you know, because I come to Oracle from a much smaller company; and in the Battle of David and Goliath, after being the David for a really long time, but it was nice to be the Goliath (laughs). You know, when you work for an SAP or Oracle or IBM, there's no such thing as a cold call. Everybody knows who you are, and what's your brand, and you just have this wealth of resources at your disposal. That's kind of nice. But conversely, here I am back at another startup. There's nothing more fun than building, starting, exploring, discovering, figuring it all out of small companies. I mean, one day you're taking out the trash, next day you're building a product, next day you're selling a big fortune 500 company, you know, startups are a ball, they're just fun.
Kevin: You saying the word Zaloni just makes me want to ask—what’s the origin of that name?
Susan: Our founder, Ben Sharma, a dear, dear friend of mine, grew up in the northeast corner of India, it's a state called Assam. And in Assamese, there is a term of organizing nodes or objects into a pattern. And so the word Zaloni is that. So, if you think about data and making it valuable and organizing it, that is where that word comes from in the language of Assam.
Kevin: Okay, let me next step here, let me ask you to finish this sentence, if you would, Susan. Data is the new _____. What would you put in that blank?
Susan: Gold, and that kind of analogy has a couple of applications. The first is—it is extremely valuable. It needs to be protected and kept safe. But if you manage it well, it could be, it could grow in value and increase in value. And that whole paradigm extends to—if you manage your data, right, you should be able to monetize it in some way. And that doesn't mean you have to sell data to monetize, it means you could use it to create new revenue opportunities, you could use it to create new partnerships. You know, data should be treated like gold, and it should pay you back over time.
Kevin: Absolutely. But, I imagine that data, some data also has a shelf life is more valuable immediately than it would be three years from now.
Susan: So, the analogy would fall down there, because certainly some data needs to be thrown away over time. So, but I do believe that some people are saying data is like oil or data like oxygen, or, you know, I've heard analogy after analogy, I believe it is this truly valuable asset that should be treated as such.
Kevin: Talk to me about the growth in quantities of data that we're all seeing, where's it all coming from? And how does that lead to what we all describe as data sprawl?
Susan: I can't even begin to quantify the growth of data, I mean, we are multiplying orders of magnitude every single day, and where it's coming from, is everywhere; as I look down at my wrist at my Apple Watch, or look at my refrigerator, or my car, or, you know, a traffic light, or, I mean, it's just, it's coming from everywhere. And so, when sprawl occurs, it means that the complexity of trying to collect it, manage it, organize it, govern it—the challenges around managing data are just going to increase more and more and more, and then you pile on to that the regulatory and privacy challenges around managing data. It's, uh, let's put it this way Kevin, we've got job security for a long time in the data space.
Kevin: I was reading recently about the intelligence in defense sector, they have something called intelligent…activity-based intelligence, which just they watch, for example, a street corner, and they can look at all the patterns that happen regarding the street corner, how many cars, buses, trucks go by a street corner, what time of day, what kind of food is sold there, where do people come from that go through that intersection; and I would just, to me that just emphasizes this unlimited, infinite amount of data that somebody is going to find useful out there.
Susan: Absolutely. And think about these devices we all carry now and depend on for every facet of our lives. These cell phones are streaming telemetry data, activity data all of the time and certainly, all of that data could be used for nefarious purposes. There is no question, but let's say, you are out exercising, and you fall, or you have a heart event or something like that; the fact that your device could call 911 for you and actually send, you know, your heart rate or your pulse, or the fact that you have drug allergies or whatever. Data can also be life-changing, and lifesaving. So, I choose to look at probably the more Pollyanna view of the use of all of this vast amount of data. But there are great, great applications of data out there.
Kevin: So, data has been around, though forever, for thousands of years, in some form or another, the written language or even hieroglyphics or whatever somebody does, that can be considered data. But why is everybody talking about the value now? Why is it so much more important today?
Susan: Because technology is finally catching up. When you thought about somebody having vast amounts of data on their mainframe or big client-server computers, it was mostly their internal data, you know, out of their normal operational systems like a general ledger, or CRM system. And then you had to put all of that data, you couldn't exactly report against the operational system, because it would like to bring it to its knees; it would slow it down to operate the business and the technology has come so far that we can truly digest petabytes of data, and in a timely fashion, so that it's actually still usable. And then with augmented artificial intelligence, machine learning (ML) type algorithms, we can automate the analysis of that data and even automate some actions to take from that data now. So, the technology has come so far, now we can truly utilize all of this data, we're in the past, sure we were generating it, but nobody can get to it, nobody could use it. And now I think the technology has caught up. And we're, I mean, we're moving at lightning speed now in innovation in this area. So, I think the sky's the limit, in how much data we can consume and utilize.
Kevin: CNN had an article about the first crew-less, zero-carbon cargo ship that could leave port, sail to the next port with all its supplies, and unload, and all of that; there was no crew on the ship, right? The first stage, they were still gonna have manned loading and unloading. But eventually, even the loading and unloading would all be in the automated robotic system. And I was thinking through that, as I was reading that story—how much data is that going to take to do all that; that's going to be, massive amounts!
Susan: Massive, but on the vehicle or the ship itself, on robotics, on the arm. I mean, think about, like, how many split-second decisions that that automation, or that operating system is going to have to make? I mean, what if a storm kicks up, you know, all the adjustments that would have to be made on timing, on navigation, on direction, you know, on making sure that when the ship arrives, that all the automation is ready to receive it, if it's delayed for two hours. It's just, it's mind-boggling. But it's also realistic now that we can do that. And like I said, data is the coolest spot to be. I know, I'm biased, but I just think we have so much opportunity here.
Kevin: They've also started doing long-haul carriers that are in autonomously driven vehicles. I think today, they still have a tech in the seat with the trucks, but they're running them already in convoys, and testing it out so that long haul carriers will very quickly—it looks like within the next five to 10 years—be driving on the freeways. And so that's just a very similar example that's already in proof of concept for the last few years. So, and again, the amount of data and as you pointed out with that example of the storm, you're going to have to find data from ships that have been through storms. Make sure the data is labeled and then train machine learning to know how to navigate during those storms based on the prior data, prior collected data so that you can train new systems. And so, there's, I mean, just, not only do you have to find the data, you have to store the data, label the data, you have to protect the data. And that's right down your area, isn't it?
Susan: It is. I'm glad that you said training machine learning models take vast amount of data. Because if you were to only use a little bit of data to train a model, guess what? While though bias enters into the equation, that's how invalid results enter into the equation. So, to train a model, it takes vast amounts of data. And absolutely, we have a number of customers use case applications where we are automating and operationalizing just feeding data to train a model again and again and again and again. And the more data model has this, the smarter it can be.
Kevin: So, the quality improvement guru, W. Edwards Deming, has a quote that says, “without data, you're just another person with an opinion.”
Susan: I love that quote, I use that quote in slides all the time.
Kevin: All right. So, I have been in more situations than I care to mention where I'm arguing the point and I quote data, because I read it somewhere. But I can't remember where it came from. Have you ever been there, Susan?
Susan: Yes, might be more indicative of my age (laughs). But no, I think all of us want to make fact-based decisions in our lives and in business. In fact, you know, as a…in my role as CEO, when somebody says, “I want to hire more engineers, I want to hire more salespeople, I want to do this, I want to spend some money to buy this marketing list,” you know, my response is—prove it. You know, what, you know, if you want to hire more support engineers, are we not supporting our customers? Are we missing our SLAs, you know—prove it. And I think all of our business culture, decision-making culture should be fact-based, data-driven. And I think driving that in my role as CEO, driving that through the organization and into our culture. First of all, we're in the data business, so we should practice what we preach, but I think it's just a better way to conduct business. Absolutely.
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Kevin: So, we're talking about just a very small personal example, you and I trying to figure out where we read some data. But with large enterprises that are multinational, that have all kinds of different instances, have different earpiece and systems throughout their organization—how do they find data when they need data?
Susan: So, what Zaloni’s platform provides, first of all, is a catalog so that you know what data you have. But a dumb catalog in and of itself is not very useful unless you enrich and augment that with lineage. Because if you're going to use, for example, a piece of data to make a financial decision, you want to know the provenance. Where did that data come from? Did it come from a valid source? Did somebody muck it up? Did somebody touch it that wasn't supposed to? Did they change it in some way that was not auditable or sanctioned? So, catalog, lineage, and then understanding who else has touched it, who else validates this data, there's a…there's a few rules that we serve, certainly the data engineer that has to do all of the technology, engineering, plumbing to get the data from where it grew, where it was born, to where it ultimately needs to end up. But, there's also this role called a data steward. And that is a person who's responsible for vouching for that data and governing that data. And, and we are seeing increasingly, that role is growing in importance in the enterprise. Because you want to have confidence in the data that you use to make decisions. And you need to know from where it came, so that you can trust it.
Kevin: So, it sounds like that data steward would then need to have metadata available to describe data about the data, in order for them to manage it. Is that the right context for the word metadata?
Susan: It is. And metadata can be like, like many other terms, it can be its most basic, simple form. But we look at metadata a little bit more broadly. So, the classic definition of metadata in technology terms is data about data, but it was usually data is in some sort of data store and it's this kind of field, you know, it's text, or it's numeric, or it's video, and it's this long, and it sits in this technology, you know, that is like the most simplistic definition of data, or metadata. We kind of take that a whole lot further and include operational metadata, technical metadata, the lineage, but also kind of what we call a collaborative metadata. And that is, hey, this guy, Kevin, in marketing, he's really smart. And when he was running this loyalty, cross-sell upsell campaign, he used this data. And this is what Kevin says about this data—we capture that. And so, when you are trying to shop for or investigate or think about the data that you want, it's thinking about any kind of shopping or research experience, if you're going to go on Amazon, and you're going to buy, I don't know, a book, you want to know, the author, what it's about, how old is it? When was it written, when was it edited, what kind of media it can be delivered in? What are the reviews of it; and then some commentaries from people who have liked similar books to you, all of that, that entire body of metadata, if you will, kind of forms this rich, kind of holistic perspective of that book that you might want to buy. And that is how we define metadata. It's not a numeric field that's 10 characters long, right? But there's a lot more that will help you understand the data that you want to use for what purpose.
Kevin: So, we're having this huge increase in the value of data. We're having massive amounts of new data thrown at us all the time. That must mean that the role of the individuals that are responsible for it is becoming more important. So, let me just ask you this, considering all IT positions on a scale, Susan, how sexy is data operations in data optimization today?
Susan: I think data rolls are right up there on the sex appeal scale with Brad Pitt. They are way, way up there on the sex appeal scale. Um, so I think these are incredibly valuable and important jobs. So yeah, on the sex appeal scale I'm saying Brad Pitt.
Kevin: Oh, wow. I started my career a long time ago in relational databases, and then left it. If only I knew it was a Brad Pitt level job.
Susan: Well, now you know, this is the cool place to be.
Kevin: So why is it so important right now for the whole data operations, data optimization component?
Susan: Because everything that we've talked about data is like gold. And it has to be managed well, in order to be utilized effectively, efficiently, cost effectively, safely, securely. I mean, just imagine the companies that God forbid have had some sort of data breach or their data has been held hostage for ransom. I can't even imagine if you're a healthcare company, or you're a bank, and you have any kind of a data breach, you come to a grinding halt, I mean, you just have to address that, you have to fix that. Now that's kind of on the defense, right? Protected.
But also, one of our customers is a stock exchange. So, thinking about how to make trading data available, so that others can do kind of predictive analytics on where the market is moving. Or another one of our customers uses vast amounts of data to score ESG stocks. So ESG, environmental, social, government stocks, so you know, should I put my money into Company A, who has no diversity in their executive ranks? And has had, you know, multiple claims against them? Or should I put my money in Company B, that's highly diverse, gives a lot of its proceeds back to making the world a better place, yada, yada, yada, right? Like, there's just so many core, just bread and butter type things about data that companies; it’s becoming core to everything they do. And if it goes wrong, it's really, really bad. And if it…if it's used right, it could be really, really game-changing and good.
Kevin: Oh, yes, I can see. And that other component is data optimization. And especially as more of our customer and partner and supply chain interactions all move to digital, that means everything has to be moving and optimized, so that business continuity can happen, I imagine.
Susan: That is such a good way to put business continuity, digital transformation, I would say that data is the key enabler to all of those things. One of our customers is an airline. So, I mean, just imagine these last two years they've been through hell, I mean, they're flying at 5% of the capacity that they were pre-COVID. And so, the transformation that they have had to do in their operations, how they have responded, how things are so dynamic in various geographies are changing. Remember, India thought that they were getting through COVID okay, and then they had a terribly horrifying surge. And then United States, we thought we were okay. And then delta variant hits us like a ton of bricks. So, I just think, understanding all the data across every geography, across every facet of the business, I mean, that's the key enabler to digital transformation right now.
Kevin: So, let's talk about that concept, the digital transformation. Where does data management and even cloud migration fit into a company's digital transformation journey?
Susan: We're really smack dab right in the middle. Critical path, essential. You think about how companies; let's say, you're a bank, and you're switching to try to get everybody to do online banking, so they don't have to, you know, put themselves at risk by going into a branch. Online banking is great. Sure, everybody has a really good mobile app, everybody is doing this, but, the smarter you can make your mobile app because you know Susan Cook so well, you know? You know that she gets paid on this date and the moment she clicks open her mobile app if you say, ‘Hey, I know it's payday. Are you, you know, are you here to deposit a cheque?’ or you know, a lot of that stuff happens directly.
But, I just feel like the notion of digital transformation is not just moving stuff to the cloud or moving stuff to mobile, moving everything technologically. It's also making it more personalized, customized, and more predictive—'I have a pretty good guess. Susan, based upon your history, and your buying behavior, and you're all of the information we have on you, I have a pretty good guess on what you want to do. So, let me go ahead and say, hey, is this what you want to do? And let me make it easy for you’—that to me, is…is digital transformation. But it's driven by a whole heck of a lot of data behind the scenes.
Kevin: Oh, absolutely. And it's…and a lot of it is, is finding patterns of data usage, right? So, it's not just net new data, because you already have it, it’s now how's that being used? Is that the accurate way? Does that fit Susan's daily habits and patterns? So, then you have data on the data? And it just keeps growing, doesn't it? Now, tell me, what's the problem that unified data ops is trying to address?
Susan: Complexity, fragmentation. So, as I talked about earlier in our discussion, the way that enterprises and companies have solved this in the end riddle of managing data, the entirety of the supply chain of data, is they've had a lot of tools and technologies along the way. So maybe, you know, to extract and transform and load the data, there was one set of tools and technologies. And by the way, that set of tools and technologies grew up in the on-prem environment. Hmm, well, now we've moved a bunch to the cloud. So maybe we need a different set of tools for the cloud. And then we got to inventory or catalog all the data. Well, maybe in this environment, I'm going to use this tool and in this one, I'm going to use another tool. And then my data scientists want to, kind of, do their own manipulation of the data before they feed it into their algorithms, or their user tools. So, they're going to pick their own way of doing that. So, they're going to buy some technologies that I got to integrate with, you know, my security standards. So, that's another set of technology.
I mean, I could go on and on and on. And what has happened is now there's anywhere from five to 20 different technologies and tool sets, or in touching your data on its journey to be used for something. And I think, this notion of unifying that kind of having one pane of glass, to manage that your data's journey from start to finish, simplifies, gives you more control, gives you more confidence, and probably takes out a lot of unnecessary cost and complexity.
Kevin: I can imagine that, you know, you don't want to just let people copy a database and take it away, do something with it, and then throw the data back into your same database. That would, of course, screw up everything. So, then versioning and everything else, and what was done to this version of it, and oh boy, I'm glad you guys are handling that and not leaving it up to me.
Susan: (Laughs) We are happy to take that responsibility.
Kevin: So, let's talk about the future here. What trends are you, kind of, carefully watching out there that you think is going to have a huge impact on the world over the next five years?
Susan: We've already talked a lot about security, data privacy, you know, California has CCPA. Europe has GDPR. I do think the regulatory pressures of protecting data security and privacy are only going to increase. So, that's a that's a big trend we're keeping an eye on. The other big trend is this notion of data marketplaces and monetizing data. I think everyone is becoming increasingly aware that this has huge value. Now, without breaking any of those social covenants or laws or privacy, how do we make it available in public or semi public forums where our data as a company—if I'm a credit card company or stock trading platform or healthcare insurance provider, if I'm a hospital in New York City at ground zero for COVID—how do I share data so that other people can get the value of the insights that we are gleaning, and perhaps even enrich it and add to it? So, this notion of marketplaces and data sharing and is growing. So, I think, you know, we always have to be thinking toward, what is the ultimate use? Where's this data going to end up? And where can, not just the single enterprise get value out of it; how can a much larger population get value out of this data?
So, I would say just the defense, protecting it, securing it regulatory. And on offense, just more and more opportunities for the greater good and revenue opportunities, by sharing data more widely.
Kevin: Bonus Question for you, Susan. Oh, as you were speaking, it just it occurred to me that, and in the mix with GDPR, and all these other initiatives and regulatory efforts, is the concept of right to be forgotten? How do you guys address that as well, in the countries that require that today?
Susan: Yeah, so um, those people have everything as opt in opt out, which, guess what, is another piece of data. So, when we have that piece of data, then we can build rules around that, that says—hey, this, this person has opted out; therefore, we will build a rule that this person gets excluded as we're building this database for whatever marketing campaign is about to happen.
So, even opting out is a piece of data that can be used to create algorithms and rules to ensure that that person gets left out and that their choices are honored.
Kevin: And that choice, that opt in or opt out, that choice must follow that data wherever it's used, I imagine. So, that thing, yeah, their choices can be honored across 1000 databases.
Susan: And a 1000 databases, and through every step of that, that personal record’s journey. So, all the way to when a marketing analyst is running some sort of a clustering campaign of, you know, these types of customers, we should treat this way. As long as that piece of data that they've opted out has traveled with it, then that person would be eliminated from that equation.
Kevin: Wow. Things get simpler and more complex, all at the same time, Susan.
Susan: Like I said, I think those of us in the data business, have a pretty good job security for the future.
Kevin: Susan, I want to thank you so much for joining us today and sharing your stories, your background, the progress, and the cool stuff that Zaloni is doing in the use cases out there. So, thank you so much for your time today.
Susan: Kevin, this was a delight. I thoroughly enjoyed speaking with you. Thank you.