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Episode 5: Escaping the Data Shantytown: Data as a Business-Owned Asset

How do you escape a data shantytown? In this episode, Anand Pandya, Chief Data Officer at Curinos, explains why treating data as a business-owned asset will help you break free of the dreaded data shantytown.

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Nick Cucuru

VP of Advisory Services at Privitar

Anand Pandya

Chief Data Officer at Curinos



Adnand: It’s critical in my point of view that as data has evolved as technology has evolved, data cannot be seen as a technology tool, data has to be seen as a business owned asset.

Intro: Welcome to InConfidence, the podcast for data ops leaders. In each episode, we ask thought leaders to break down the topics and trends concerning data professionals today, and to give us their take on what the data landscape will look like tomorrow. Let’s join the data conversation.


Nick: I’m Nick Curcuru, and this is the InConfidence podcast sponsored by Privitar. InConfidence is a community of data practitioners and encourages conversations that enlighten, educate and inform data leaders of today and tomorrow. Thank you for taking the time to let InConfidence be part of your day. Today, joining our community is Ananda Pandya, the chief data officer for Curinos, a FinTech provider for advanced decision support systems and data to the banking industry, whose team helps equip financial institutions with expanded datasets, intelligence technologies, market expertise, which translates to more clients, and more profitable, data driven decisions.  All right. So, you know, I think the biggest thing I think about when we talk to you is you’ve always had a consultative approach to helping people use data and that’s a unique position. When you think about people who are growing up in this data world, they are usually very technically oriented. Can you describe to the audience, you know, why you take this approach and how it’s benefited you? And the people that you’re helping? 

Anand: Yeah, so you know, at its core, I started as a consultant. So, you know, I’ve always been trained and ultimately brought in to look at the problems needing to be fixed, what’s the situation, and ultimately, what are the problems’ impact to craft their solution. So at its core, I’ve always taken a consultative approach as I’ve always felt that there are problems to be solved. And there’s never been value in a department working in a silo or technology being done for technology sake. So when you’re looking at the problems, you know, they really become more a question of the same. And that is it just to create more reports? Are you actually driving towards insights? And so, if you roll it back 20 years, you know, it was very much let’s create more reports, let’s create more dashboards or scorecards, and it was never about why are we creating these it was just we always had, so we need to create, or we have to track this now. So add another report to the stack. And what started as maybe a single page turned into a 30 page deck that today, a lot of organizations still produce that same deck every day, every week, that has to be the basis for review. And nobody ever takes a step back and says, what are we trying to answer? What are we trying to do? And that kind of comes back to the basics of analytics. And that is, you never stop asking questions, because every time you answer a question, it begs more questions. So when you’re taking that consultative approach, when you’re taking that solutioning approach, it begs for those next questions, it begs for if there’s a situation that has a problem and an impact, and you define the need. It’s not a closed loop, there’s always more to be done. So that’s always been my approach. That’s always been the approach I’ve taken to get to what the business is trying to accomplish, whether that business is internal, ie, you’re looking at your internal organization, trying to look at finance, trying to look at HR, trying to look at sales, or whether it’s external, whether you are a data company, and you are providing products, whether you are providing a software solution, it doesn’t matter. It’s ultimately about answering the questions in a more fastidious way and being able to enable the follow up questions to be answered, and just always looking at how we can look at a problem and solve it? How can we take something and improve upon it? And how do we prepare ourselves for the next steps? Well, that, you know, you bring up a good point, you think about that balance between the details and insights. And there’s never one question, you know, and I think the other part in our last conversation is you talked about that the questions aren’t just from one person, either. It’s from multiple people. So how do you know, you had a great example, or how you were able to use one set of data, say a data set and was able to solve multiple problems across a global enterprise. 


Nick: You know, I think that’s really something most people are even struggling with today is how do we do. How do you accommodate those quick questions, and yet the same data is used to answer them? 

Anand: Absolutely. I think this is kind of the depth and breadth vector to my point where an answer begs more questions. Sometimes it’s not just a single train of thought. Sometimes it is about okay, you answered finances questions, and now operations have to tag on and you have to go a different direction. So how to use that same core set of data to answer multitudes of questions and that feeds off itself. So you know, we’re talking about it before I gave you an example from my consulting pass of a global restaurant. But this is a long time ago. And each one of their physical locations had a physical database and a physical implementation, the way that the historic leadership of the company was very much decentralized. It wasn’t about corporate decision making, it was very much empowering the distributional you know, the distribution of the restaurants on site. That leadership changed over on new leadership came on came in, pardon me, it became a question of how can we start to look at things from a core and centralized function it became about how do we start to look cross functionally drive economies of scale start to look at, you know, implementation factors without losing that local flavor? So the idea again, was you have hundreds of different physical locations, hundreds of different databases that have been custom tailored to each physical location? How do you start to aggregate that from a technical exercise, you’re just looking at a very classic data warehousing scenario, and you have multiple sources that are all reflecting a similar, you know, conformed dimensionality, similar metrics set, you’re getting all that data in in a timely fashion, again, time being the kind of ultimate denominator from anything you’re looking at. You’re bringing that on a daily basis, you’re able to aggregate that information and provide that ability for somebody at corporate, to look at the overall aggregation of the 100 stores and say, How are we doing? How are we performing? If I’m a finance person, I’m saying how am I performing from an FTA perspective? How do I use that to tweak my forecast next week, next month, whatever it might be? That same set of sales information is going to also trigger operational studies? How do we need to handle our staffing, both at the location itself? How do we provide a means for managers to be able to lay out staff plans and hours for the next week? How do we look at that from an internal context of supply chain management? Not the situation we’re in today. But a couple decades ago, it was really about how we make sure that we’re delivering the right inventory to the right location. So it’s about understanding that West Coast stores consumed more fish and Midwestern stores consumed more beef so it was really about how do you start to be more dynamic and supply chain management? It got into the in store operations, same set of data were just looking at sales day in day out being able to go all the way from an aggregate How do we do for the day to receipt recreation, but you’ve already talked about finance, operations, sales and marketing very clearly, you know what selling where how you can create local marketing, when you start to look specific to the to the restaurant industry, kitchen display systems, if you understand the ins and outs flows of different times of day, different terms, different meals, meal segments, different hour segments, how can you plan your kitchen lines and your kitchen staff accordingly to ensure that you are you know able to produce plates in a in the most mysterious manner, you’re able to get those table turns, which is really what the restaurants looking at flip side of the coin, same exact sales information. When you’re looking at a liquor beer one set, how can you ensure that pores are significant? How can you track the supply chain around your liquor orders if you know that you’re selling this much each pours this many ounces, I know how many of these bottles are, these are all things that restaurants have done forever. But when you have that data and you have it centralized, and you’re able to apply more context to it, you start to answer different questions for different people. And this kind of feeds into Doug Laney. His latest kind of article about turning data into a balance sheet asset. It is the ultimate asset, there’s a low cost of storage, there’s no cost of transmission, and it’s able to be reproduced multiple times. So that single data asset cost you almost nothing to hold on to. But when you recontextualize it, it’s its output turns that valuation of that asset over multiple times and the more you can reuse it, the more valuable that becomes. And it becomes almost a pure play. So, you know, that’s just one example of how once you’ve answered one question, it’s gonna drive how we do more with it? Again, if I’m an operations person I may be looking at how I look at the sales and then start to bring in Labor Day. I’m going to be looking at, you know, my GL information, I’m gonna bring in those pieces. If I am looking at inventory and supply chain, I’m going to be looking at those type of information points and understand that sales and marketing especially now in a digital landscape, if I can readily tweak back to what my digital footprint looks like, I can start serving up custom micro segment engagements based on that same core sales data. Well, now I’m just turning and burning more. Well, you know, the interesting part is what you just described, that you say were decades ago are some of the same things that we’re facing today. 

Nick: Absolutely. Right. I mean, especially in your industry, and you take a look at the financial industry, where they’ve had some really good consolidation over the last 10 years and all the disparate systems that exist. And as you have these different divisions, it’s actually getting all those data points together to be able to do what you just described, to answer those many questions that different people have. Right, you satisfy that and with the ultimate goal, as you said, in the restaurant, is the service, how do I actually know the service person on the other end? I think one of the things you brought up is, you know, just take a look at that. You also said, you know, data used to be back then seen as technology. I think that’s a profound statement, because you don’t see data as technology, do you, right? 

Anand: No, I don’t, I think you know, just the situation you described where everybody needs to use it. And how do they use it, it created a pendulum swing, where, again, we’ll 20 is a nice round number 20 years ago, people knew there was data, and they knew there were reports and they knew they could use the reports to ultimately quantify their decision making. And what you saw was nine times out of 10, even though it’s specifically called beyond the act of the enterprise data warehouse, the ETL aspects of it was all couched as technology, we the business have to wait for technology to get all this stuff ready for us. And then we can start analyzing. And that was great. And everybody had this very kumbaya around the fire moment where technology and business were partners. And then the business got anxious, and they got impatient. They said, Well, wait a minute, I don’t want to wait for technology to put this thing together. I don’t care what department is doing, because that doesn’t impact me. And he started to break apart and you had this idea where, okay, you had new entrants in the market, where let’s just focus on building this smaller data mark, let’s start to introduce, you know, more economically friendly technologies that allowed this department to do their own thing. And this department can do their own thing and technology, still trying to fuse all these things together. But when you started to deviate from the business’s need to understand data, you kind of reverted back to this. Well, everybody’s got data somewhere, they’ve got their reports somewhere, and you start answering the question.  So it was a very long winded way of essentially saying that when you treat data, and you treat analytics as technology, you shortchange the business and business value, the business no longer feels as though they own it, and then they’re waiting. And then invariably, you see the business start doing their own thing, or whatever that means they start doing their own thing. And it’s almost as though this snake is eating its own tail, where the reason you asked technology to do data and analytics is because you don’t want everybody to have their own departmental shard of information. But because the business doesn’t feel they own that data, because it’s been charted as technology, the business goes and does their own thing. And you create invariably the shantytown of data across your organization. So it’s critical in my point of view, that as data has evolved as technology has evolved, that data cannot be seen as a technology, data has to be seen as a business owned asset. Technology is absolutely a steward, you know, critical with regards to governing it and making it compliant and securing it especially, you know, the evolution of technology has brought significant information security risk factors. So the idea of shaping engineering securing and and surfacing the access points democratizing data, absolutely technology, but the data itself, the asset, the context, that is where the business has to own it and it becomes this joint ownership, it’s in a very rough metaphor, it is it is a parent as two parents and a child, it is very much a familiar relationship where the impact of both that maternal paternal instinct for the child gives them that kind of two sides of a coin view, I think data when treated with business and technology hands, really shapes that holistic view and makes that asset, you know, truly as valuable as it can be.


Nick: Well, that’s it, you know, the one thing that you brought up is, is context, you know, what you’ve talked about is I like the term that you use context is king. And that context is like you just said, it’s the opportunity to say, I need to take data in a technology context, right? Or the business context, or the consumer contexts now, which is that third piece that comes in, especially as chief data officer, you look at it. So can you expand a little bit for our listeners about when you say context is king, you know, go into a little bit more detail about you know, how you look at that when you’re talking to different personas, you’re looking at different ways, or usages or intent or context. I like context, the best term. I’ve heard other people use intent, etc. But I like context. Can you just expand upon that a little bit? 

Anand: Yeah, I mean, sure. I think I’m certainly not going to take ownership of the phrase, I think it belongs to Eric Fish if anybody. But the idea that context is king is really about the lens you’re viewing it through and ultimately your value. Okay, that doesn’t mean anything, right? When I contextualize what five means, well, now all of a sudden the data becomes information. Information is usable information is an asset. So five means nothing. But if I told you that your child got a five on their math test, that means something and that’s information to you. And you know, that you can take that and do something with that in my mind at its core. What does context mean? If data is ones and zeros, data is assets stored nowadays, it’s a parquet file on an s3 bucket. Data doesn’t. It’s nothing, it’s just an asset. It’s something that’s out there. But the minute you contextualize, if I’m extending this AWS metaphor, the minute I build a data catalog on top of that parquet file, I can start looking at it through an Athena. Well, now I’ve given context. Now it means something. The business context is the next step. And that is I need to use this information to measure something to tell me a story. And I think that is where you’ve seen this explosion of the CTO and the CTO office in the last five to seven years. Where is the glue that really holds the technology and business sides together? So it’s, how do you take this parquet file and an s3 bucket? Somebody threw it out there? How do you work with the technology side to apply the context, i.e. build a data catalog and make a query table? And then how do you use the business context? How do you work with the business to give them the access to work with that data? And it’s not just a, you do this, and you do this, but it’s now where you start to introduce governance and compliance. So yeah, I want to say one of the things is for you, that business context is the last five or seven years. But I’ll challenge that a lot of the chief data officers were brought in 5,6,7 years ago to actually do what you described, which is we just got to catalog our data, which is scats, a profiler of our data, we just understand where it is right. And therefore, you know, everyone has been in this space. That’s what they did. Yeah, I think it’s finally it’s finally we’re at that point where people are like, yeah, you know, what, we have catalogs. We now have a profile of data, we now can do this, even in real time, you know, as data is coming in through chatbots, and stuff, we’re getting better at it. But now I think the point that you bring up as the most important is that evolution of the Chief Data Officers, you’ve got to be a business strategist, you’d better have some business acumen. Because it’s not just knowing I’ve got customer data, right? It’s knowing how that data can be used, so that the business can achieve its goals. The CDOs ultimate remit is to answer the question. Now what? I like that, it’s now that we have all this data, you found all this data, you went through the exercise of drawing up a nice Visio diagram that says here’s where all our data is. If you ask your DBAs, you report to the CTO or CIO to catalog databases, or you’ve asked them to put all their data dictionaries in SharePoint somewhere. The difference in the CTO and CTOs office is now what? Now what? I think you’re right in that the CDO creation has really been about, okay, what is it? Go catalog our data? And I think governance has been a big part of that, where it’s not just cataloging it, but what is it? What is it supposed to be? Who’s allowed to see this? Why should they see it? And what does this mean? And does everybody agree on what this means? So there’s a little bit of handshaking and baby kissing involved with everybody agreeing that this is what this means. It is, you know, privacy has become a huge component over the last decade, it’s ensuring that compliance is well defined and understood and adhered to. So there’s ties back to the CIOs and CTOs. There’s ties back to your CISOs. It’s really about now what if we haven’t done what we do with it and how can we maximize it? And I think that that’s what CDOs are today. I think you bring up a good point here is anybody who analogy of guardians is what I’m finding, were seen as the evolution is that Chief Data Officers now sitting between, let’s say, data owners, you know, own the data itself, per se, it could be technology, it could be your data curators, those consumers who want to use that data to your point, right now what what now that we have it and those data guardians, that Chief Data Officer is like squarely in between if you created a three circle Venn diagram between the three areas should guys now live right in the middle? Yeah, right. It is like being a parent, trying to keep two children at bay. Everybody wants to play the Xbox, whose turn? Yeah, I think that’s a good analogy to think about, because that has really helped bring people like, well, the chief data officers, it’s just dead. It’s like, no, it’s that now what they really have inserted themselves into not just into the strategy into the picture, and really answered the conversation, when it comes to working with the business. I think what’s fueling a lot of that as well is the explosion of consumer tech. And that a lot of people just can’t get their hands on. Whether it’s phones, tablets, laptops, cloud computing applications, everybody has access to the bleeding edge. This can be you know, apps that have aI built in apps that are driving the bleeding edge, think crypto mining, however far out there you want to go, those are all consumer tech now. So people can see it, touch it, feel it, and they want to bring it into their business. So I don’t know how many companies have just decided, well, we’re going to start using AI, to what end? To do what? So it comes back to that same Now what kind of question, okay, if you want to do that, that’s great. But to what end, and it comes back to what we started with, and that’s about that solution and Solutioning approach to things like consultative approach to things. First and foremost, why do we want to use AI and ML? What are we going to get from it? What is the solution that we’re applying this to? What is the problem we’re trying to solve? And then it starts to, to your point of that kind of the middle of the Venn diagram, once you understand the business’s angle, why they want to go with something does it fit, then it’s about working on the technology side? Are you ready, and it’s establishing a roadmap to be ready, connecting that roadmap with the solutioning side of that coin. And I think that is where, you know, elements like governance and cataloging and cleansing and standardization and democracy, all the buzzword ease, you know, the buzzwords that are out there right now, that’s where they apply? How do you connect, getting data ready, and making data usable? How do you connect those two together? And that is, that is the CDO. I think the other part is, in our last conversation, we talked about privacy. It’s that the intent, which goes to context is understanding now now what and the question then comes, now what? And should we do that? Right? Or should it be used for that purpose? I think that’s the other component that comes into the Chief Data Officer, because you are essentially you are the guardian. So I love that analogy of being the guardian. But now I want to go a little bit off script here a little bit and say, in the financial services world, as you start to see this evolution of how people are using data, you know, how you know, are you being able to help your the organizations you work with, and your organization use their data better, and within that governance, compliance and within that realm of solving business problems. I mean, especially since you’re in the financial services industry, you’ve got to go through a tremendous transformation. And in the last 18 to 24 months with things going online. Yeah, I think it’s been a very interesting 20 months for financial services across the board, whether it’s institutional finance in the markets, whether it’s, you know, retail banking and insurance, I think it’s, it’s been, it’s a very rapid evolution over the last 20 months or so. And, you know, you know, at our company, our tagline is really about navigating today and anticipating tomorrow. And that’s really where we try to focus our energies. And it’s about, you know, we’re not a consultancy, we are not actively going in and saying, Well, let’s do this project bank x. That’s, that’s not what we’re doing, we are saying is, our team has banking expertise, we have, you know, former banking executives who are working to strategize with the data assets we have with the solutions we have to bring to bear a means to enable the retail banks and insurance companies across the globe, to be able to spend less time churning and more time acting. So whether it’s the last 20 months around digital transformation and digital footprints, whereas it’s been a huge factor, it is our team looking at both the engagement cycles, but also the customer journeys that are being provided. And it’s giving banks the insights into what their competition is putting out there, but also providing guidance on what fits the customer, the bank’s strategy as a whole. If your strategy is to retain versus to capture, you’re gonna want a different journey, you’re gonna want a different engagement approach. So it’s providing that expertise on top of, you know, ready to go solutions, it’s really about understanding the market around you, I think one of the metaphors that I’ve always used is, our job is to provide a window where you might have had a mirror before. So it’s not about how you’re doing, it’s about how the world outside is doing and making sure that your strategy aligns to what the markets are doing. So don’t celebrate a 10% growth figure if the market just did 15%. So that’s the kind of context that we’re providing, again, context, that’s kind of our magic word here. It’s about understanding where you are today, so that you have the agility to set up where you want to be tomorrow. And I think that’s just been, you know, hyper accelerated in the last 20 months, which, you know, on one hand, great for us, because our expertise is being leaned into. But it’s also, you know, it’s provided us that same opportunity to really look at how we can be better? How can we do more? How can we really understand ourselves where the markets are going, and that’s really, you know, my, my love by role didn’t exist back, you know, six months ago. So it’s really tasking this CDO ribbon saying, Okay, what do we have? What can we do more of? How can we really provide our customers more so that we are preparing them not just for tomorrow, but the week after, and the month after, and the year after? So when the next wave comes through, whether that’s crypto and tokenization, and permissioning, at an individual level, we’re not behind that curve, we’re helping our customers get there and aligned to what they need to be. 

Nick: And I think that’s another good point that you just made. I like to make sure we, our audience really hears it, because when you think about it, it’s not just using data to answer today’s question, as we talked about the beginning, is trying to answer tomorrow’s questions, or anticipate, I want to know, let me check my notes. You said to anticipate what the questions of tomorrow will bring? Right? It’s the anticipation not that if you’ve predicted it, I think that’s important is and that’s goes to the point of gate is not a technology, you know, data in itself within the right will keep using the word context, but within the right world will allow you to produce the insights to do what you need to do, within within guidelines that you can set up or guidelines that have been set up by you. Obviously, you have different regulatory bodies. But that’s an important aspect to it, is that that is not a technology, and that’s the myth, people, I think still claim to today, data as a technology. So in that vein, are there any other myths other than data’s not technology you’d like to bust? 

Anand: Say it’s not it’s not a myth, Nick, it’s actually validation. Yeah, I think, you know, I think CDOs often are tagged as the data police because of things like governance and compliance. And I don’t think you know, I don’t think that’s fair. Personally, I think that is something that needs to be busted as a myth, just like, you know, the CISOs probably feel the same way when it comes to security compliance that they’re often seen as the police but it’s really about understanding that again, if, if we all agree that data is a valuable asset, we have to make sure it’s governed and enforced. So, you know, there’s got to be a give and take. And it’s not about slowing down growth so much as ensuring that, you know, those aspects are being protected. So I think that would be something I think the huge myth that I hope a lot of people appreciate is that, you know, there was a, there was a term coined, I won’t say, by who I’m going to, I heard it in at Microsoft, I’m sure it’s been used elsewhere. But a coin is an information worker. So it wasn’t about data. At the time, you know, you had data DBAs were really the only people who quote unquote, work with data, but you always had download it to Excel and to do something in Excel, and you know, analyst is kind of are termed as you are, but I think information worker was kind of there. And I think the impression was, anybody who worked with data was just monkeying around with Excel. And I think it’s important to know that working with data is not about being an industrial laundromat for information. It’s about actually analyzing how data works in tandem. It’s creating correlations. And understanding correlation is probably a better term there. And how you create relationships where they may not be as visible, you have to be able to walk in without assumptions around the date, I think that’s the biggest thing is that people assume that CDOs just know what things are supposed to be. And I think that’s false. It requires, again, our favorite word here context, it requires the business to provide the definitions, what does good look like? And let’s make that clear. And crystal clear. I think those are probably the biggest myths about what a CDO does, what is governance, governance is defining, again, what is data? What is it supposed to be? What does good look like? It’s about how do you start to leverage that to create more robust solutions. So again, identifying relationships, correlations, causations, modeling, data science, all those fun terms here today, it’s really about understanding how to maximize the value of the asset that’s there. It’s not a data ops role. It’s really about the strategic application of data and providing the answers to now what that’s really what it’s about. Well, you know, in our last conversation, you set the answers to what I think one of the biggest things that I liked about your myths was to stay away from jargon. put things in business terms. Yeah. And knowledge, just business terms, but layman’s terms. Yeah, I think that the big myth is everyone thinks they have to talk about, you know, different ways, the jargon, the model language, the mathematics, you know, all the different types of analysis and everyone gets lost. And you’re just like, that’s a myth that we can’t continue to perpetuate, to make yourself look important. Let’s get down to it in a clear, concise business in layman’s terms. Yeah, if you’re going to democratize data, you’ve got to actually democratize all of it, not just access to it. That’s that, you know, a lot of vendors out there today will tell you, they can democratize data. And what they’re really doing is making it accessible. That’s only halfway there. You have to actually provide people the means to understand what they are looking at, to make it usable by everybody. 


Nick: You know, as we begin to wind down our conversation a little bit, I want to take a little different tact here. I mean, you’re a chief data officer, and you’ve been in this position for a while. But I also know that you take good pride in being able to help develop people, and making sure that people understand that there’s a career path. Right? And if you can, you know, if we take a look at those chief data officers that are out there, or even the people that are working in the Chief Data Officer, how, you know, do you approach, making sure that people know there’s a career and people know, you know, that, you know, they can progress along this path, I think it’s important for people to understand that, that everybody ultimately is working with data. So there’s always going to be a need for that, I think as many acronyms that are out there of AI and ML and BI, it always requires human intelligence to be built. It always requires human intelligence to train to understand to ensure that it’s being done right. So that that reinforcement can be lost. And that’s probably the biggest way to combat a lot of that fear of replacement. And I think, just like we say data requires context. It requires a vision to go somewhere where there’s a value being created. There’s a strategy in place, there’s a direction in hand, and when you tie the need for human intelligence to that strategy, it gives people a good assuages The fear that they’re going to be displaced. The show shows them where they can go. So I’ll just give an example from my end: when we defined a product roadmap, and we defined how it stacked from very similar to data and reporting all the way through decision support systems, it was important to have people understand how they were involved and each step of that process. And through, you know, tying it to learning and development, tying it to knowledge, you know, knowledge analytics as well, giving them the path to be able to evolve with the data evolve with the product, it allowed them to pay, build their own skills, understand their value to the business, but also define different career trajectories and progressions for them. At some point, they were given, you know, this, this kind of, if I’m really going to nerd out for a minute here, if you’re envisioning something like an RPG, you’re gonna have to decide what skill tree you want to build. Do you want to, you know, do you want to build your technical skill set? And then you start to build out those data engineers? Are you going to build out your business skill set? Do you start to build out the product management focus? Are you going to build out a customer skill set, now you start to build a lot of that sales mentality. And it’s all based on that progression and roadmap, it’s not untethered, it’s not a separate track, it’s all one. So I think it’s really important to take anybody from your summer interns, to your most seasoned DBAs, and tie them into that lifecycle and roadmap of data and progression of data into products and value chain analysis.

Anand:I like that. I actually like that a lot. At a time in my early career. I wish someone would have done that with me, because I was just solving problems. Right. So, you know, as we, you know, when we think about this, in your mind, what’s that next big thing? In terms of data and innovation? Do CDOs need to be on the radar? Right? You know, I think there’s two ways to answer them. I think if you’re looking at just what if you read the journal, you read the papers, and you read in shows my age, if you’re reading sites and blogs as to what what’s coming next, you may be looking at, again, you may be focusing on AI, and machine learning, and tokenization and automation and digitization you may be looking at those concepts. I think the reality is that there’s just one way to look at it. If you want to look at the next big thing that CDOs or even a CTO CIO, who’s got a data rebuild. I think it’s really about understanding how to break out of the shantytown, you’ll probably end today. So it’s great to look at the next waves of technology, I think it’s about really not going too far to getting where you are today. So the next big thing is probably the understanding of how to get out of a, maybe a current legacy RDBMS footprint into a more modernized structure might be the next big thing on a lot of people’s radar. I think when you look at it from a technology perspective, I do believe it is going to be individual permissioning of data, it’s going to be tokenization, it’s going to be those type of exchange points where you have more, you know, if you’re looking at a healthcare perspective, and maybe the fire standard for interoperability, if you’re looking at financial services, it could be open banking as the standard, whatever that means to you. But it’s going to start to look at those types of exchange points as the standard for interoperability. And how do you tokenize into that? How do you borrow from that data, clean rooms, those types of pieces? I think that will be where the next wave is coming, where people should be focused. But I think, you know, rather than focusing on the next big thing, I think a lot of people just need to figure out how to put the fires out in front of them. So focusing on how you leverage today’s technologies to set you up for tomorrow, I think would be really the next thing a lot of people need to focus on. 

Nick: Let me ask you this question. When you think about the next big thing, what would you describe? What’s your opinion on data marketplaces or data exchanges? And the opportunities you talked about interoperability and you talk to develop those not just, you know, I’ve heard about people like, well, that’s what we gotta do internally. But externally, what’s your thoughts on marketplaces and exchanges? 

Anand:I think I’m a huge advocate. I think that the ability, especially now with data claims, the ability to sanitize and protect, you know, in aggregate, how do you sanitize protected then permission those aggregate data points? I think it removes that stigma of, of, you know, a violation of compliance of security. And I think it really provides that democratized access as a whole. The value of data is not the data. The value of data is how you use. So I think if you can enable more people to get more data, it’s going to it’s the rising tide lifts all ships. So I’m a huge proponent of those exchanges and marketplaces that are out there, whether they are snowflake, Azure IO. If you’re looking at those types of scenarios, I think those are huge positives to the overall kind of analytics footprint. And, and I’m a huge proponent of, you know, providing the steps to get there. And I think getting there will be a huge turning point in accessibility and capability and insight. 


Nick: All right. So as we think about this, let’s just do some quick round up questions that have nothing to do with technology, nothing to do with CDR. But if you had anywhere in the world where you could go, just to unwind, relax, where would it be?

Anand: I went on my honeymoon with my wife to the Maldives, and that is the place that I think I would go back to.

Nick: Alright, so what would you take to the Maldives? Obviously, your wife, right?

Anand: Yes, I would, I would absolutely. You know, take her. I would basically make sure that I had a memory card full of unread books loaded onto my Kindle and ready to go.

Nick: Alright, so while you’re sitting on the beach, enjoying your wife’s company reading your book? Is it a wine beer cocktail, or just a soda?

Anand: It is going to be dependent on the time of day he would like to open, open with, you know, open with cocktails, move to move to beers, go back to cocktails, and then a wine with dinner, assuming I’m still awake.

Nick: There you go. Now, it’s been a pleasure having you on the show. We’d love to have you back. As we look forward to it. Enjoy the rest of your time. As we wrap up 2021 Sounds great. And look forward to having you back. 

Anand: Oh, appreciate it. Thank you, Nick. 

Nick: Thank you for listening to InConfidence. It’s a podcast with the community of data leaders. We hope you find your time with us well spent, we would enjoy your feedback. You can provide your feedback to us at InConfidence via And once again, that’s InConfidence, which is all one word that’s available via Or you can reach me directly on LinkedIn. Again, while you’re giving us your feedback on on the InConfidence website, go and check out our safe analytics resource hub where you’ll find a variety of resources to help you maximize your innovation through safe data and analytics or take the quiz on modern data maturity to find out your level of maturity and data provisioning. Thank you once again for listening and spending time with us in the InConfidence community. I look forward to our next conversation. 

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