From the Outskirts to the Center
with Jitendra Putcha, EVP & Global Head of Data Analytics and AI, LTIMindtree
EVP & Global Head of Data Analytics and AI, LTIMindtree
Jitendra Putcha is the EVP and global head of data analytics and AI at LTIMindtree, a technology consulting company that is based in Mumbai and employs more than 84,000 people around the world. He mentors the next generation of tech leaders while acting as a digital transformation partner to help clients leverage the power of AI.
Co-founder & CEO of Alation
As the Co-founder and CEO of Alation, Satyen lives his passion of empowering a curious and rational world by fundamentally improving the way data consumers, creators, and stewards find, understand, and trust data. Industry insiders call him a visionary entrepreneur. Those who meet him call him warm and down-to-earth. His kids call him “Dad.”
Hello and welcome to Data Radicals. In today's episode, Satyen sits down with Jitendra Putcha — or Jit for short. Jit is an IT industry senior executive with over two decades of experience, helping clients craft next-generation solutions that transform data into value. Today he's the EVP and global head of data analytics and AI at LTIMindtree, a technology consulting company that is based in Mumbai and employs more than 84,000 people around the world. In this role, Jit acts as a digital transformation partner helping global clients leverage the power of AI to drive business results.
This podcast is brought to you by Alation. Meet us at Snowflake Summit this June. We'll uncover how Alation cuts through the complexity to help you find valuable insights in the data cloud, learn how leading enterprises in every industry are using cloud migration to drive innovation and efficiencies. Snowflake Summit runs from June 26th to the 29th. Attend virtually or in person in Las Vegas. We can't wait to connect. Learn more at snowflake.com/summit.
Satyen Sangani: (01:09)
Today on Data Radicals, we have Jitendra Putcha. Jitendra is the Enterprise VP and global head of data analytics and AI at LTIMindtree, a technology consulting company. As an IT executive with over two decades of experience, Jit builds solutions to help global businesses across Europe, North America and Asia–Pacific turn data into actionable insights. He's a regular speaker on AI, data science, big data and analytics and he's passionate about building sustainable businesses and mentoring the next generation of leaders. Jit, welcome to Data Radicals.
Jitendra Putcha: (01:40)
Thanks for the opportunity, Satyen. Happy to be here.
Satyen Sangani: (01:44)
So, in November of 2022 — so not very long ago — your company Larsen & Toubro Infotech merged with Mindtree to form one of India's largest IT services providers. Can you tell us a little bit about LTIMindtree because many may not have heard of the firm in its current incarnation?
Jitendra Putcha: (02:00)
Yeah, that's a good question to start with. It's a fascinating and interesting opportunity as an organization we're going through. Last year it announced a merger and integration. Both our organizations are midterm organizations who've been constructed as there are challenges in many of the client opportunities and both of them, the organizations are growing rapidly over the years and each of them are actually solving the clients’ problems in a slightly different way. What I mean by that is LTI historically focused on solving data analytics, AI. That's been the pillar of strength of the organization along with enterprise applications and what we call as engineering DNA. That's the puzzle LTI was solving. And when it comes to Mindtree, Mindtree is like a born-digital company. They're solving the experience side of the problem for clients as well as how the next-generation IoT technologies are solving.
Jitendra Putcha: (02:51)
So when these two organizations came together, what we are trying to solve — the puzzle — is about a client's experience needing transformation, data transformation, and overall business transformation. So now we are a big force in the marketplace in competing with the [other] players and providing a unique value proposition to clients. It's not just size. The way we look at it is, while there are different angles where these companies are solving problems, we have something common we realized when we came together. One, customer centricity. I think whatever it takes to make customers successful, that's been the first. And the second thing is always about being inquisitive and challenging the status quo. I think that's been the culture of these two companies. That's where we are super excited about bringing this together. We have an opportunity with 700 customers, 30 countries, and 100 nationalities. So we're super excited. We just completed day 1, we are in day 2, and have a lot of opportunities to help our clients.
Satyen Sangani: (03:45)
You mentioned some of the things, 700 clients, 30 countries but give us a sense for the variety of practices and the scale and how you might compare to the other large Indian SI firms that folks know — like Wipro or Infosys and the like — and just for people can orient around when you would engage and how.
Jitendra Putcha: (04:02)
Yep. This is in the classic and the usual questions when I interact with the customers. The first question most often asked is this. The way we look at it is probably we’re one of the few companies in the world who looked at an opportunity in terms of the practices, data analytics and AI. I'll talk specifically about having the right talent base but more than the talent base, we believe in software-led data management, software-led solving the problems for the clients. What it means by that is, let me elaborate. One is, we have platforms and products which actually combined with the talent, which will solve much faster, better, cheaper for the client. So case in point: in the last few years, an amount of organizations are moving into data and cloud, which is a modernization. We have built an accelerator which helps clients to assess the amount of complexity, help them to prioritize, and most importantly help them to migrate safely and land them in the right place. So the short answer is, we are differentiated by the accelerators and platforms we've built and combine that with a right capable team, helps organizations to modernize it as well as drive value from the business. That's where we differentiate and actually the number speaks for itself. Last 2, 3 years, we've been growing rapidly and making a space for ourselves, particularly in the modern data stack.
Going long on technology
Satyen Sangani: (05:19)
Yeah and it makes sense because people talk a lot about this idea of sort of technology-enabled services and you would think that a lot of services work can be systematized, automated, and yet obviously the incentive models of a services in organization is literally to do the opposite. I mean, automating things makes it less billable, even if maybe you might be able to make some money off the technology. Talk about how you've fought that compulsion and how you've gone along on technology and how you've been able to carve a business model where perhaps you're competing on things that are different than many of your peers.
Jitendra Putcha: (05:52)
Yeah, so thanks for that, Satyen. I think this is what we internally call us in a paradoxical world, where everyone wants to acquire a lot of capability within the organization but unfortunately the amount of money organizations can invest parallel running their business as well as modernizing has been a biggest challenge. I separate your question into two parts. One question is about running your business, how much more automation you can do to your business and do some cost takeout from the business and in this back into your transformation programs. So the way we look at it is, we don't look at this as probably a loss of revenue, loss of opportunity. We look at this as an opportunity to be part of transformation programs because when we save this money back to our clients and 100%, whenever we get involved in these kinds of programs, we realize we will be involved and we'll be engaged in this transformation journey. So we look at automation as the way to help clients to make cost savings and translate that back to transformation.
Jitendra Putcha: (06:46)
And if they're able to narrate this entire thing, I have not seen a single customer saying, "Oh, we don't have investments." Because everyone is worried about the dollars and if you're able to show how you save dollars and how the dollar can be invested. So that's been the philosophy with which we've been working on that, Satyen, and that that's working for us. And there are many examples to talk about a manufacturing company who's investing X amount of millions of dollars and year after year the maintenance costs are going up and they're struggling to modernize their platforms. And if we can look at the macro view and then start helping them and that's been our success.
Satyen Sangani: (07:20)
And how does this work in the world of data and analytics? So you have a data analytics practice — that can mean a lot of things. Where are people investing today and what practice areas do you describe to your clients when you say “We can help you in data and analytics”?
Jitendra Putcha: (07:32)
I will go with the industry notation. All of us are familiar with data and cloud. Data engineering has been a classic area, which is primarily from an IT perspective. And then, naturally, your data lifecycle management, the way I talk about it's a cusp of business and IT and then finally I look at data science and AI as well as now the latest big buzz, generative AI. So this is totally helping the solving a business problem whether it's a top line, bottom line. So I see the opportunities coming across all these areas for us. But the way I look at an approach perspective is about, when we go and start talking to clients about — you don't say, “I'm going to solve your data engineering problem.” You'll never start with that. You always talk about how you're investing X amount of money and your workloads are increasing, your batch window is taking a long time, your business users are complaining about how they just completed a merger and acquisition, they're not able to get the new data sets. You talk about all these challenges; they're going through in their motion. So the best way we start in this journey is: What are you thinking about?
Jitendra Putcha: (08:41)
What is the business value you are looking at it? What is the object with your company? And then look at it, the ROI for that and modernize their state. That's one service offering we do that we do really well. That's one. The second is, most of the data sense AI engagement, historically even industry talks about a 70% engagement still not bringing value to what they're supposed to be starting with. The classic problem is still data collection, data quality, data governance are most importantly, you don't know what data you have; the data cataloging part of it. So I think some of these things we approach with the live sessions and out of possible. And most importantly, what does it mean? Because I've been in the industry for 25-plus years and we've been talking about data cataloging, metadata, and all these things and sometimes people wonder, "Oh I heard enough about it. Just talk to us. What is a real-life example?" So the approach for us in helping clients is to make them visualize the real problem and make them see what does it mean. I can take one quick example. One of the organizations, a bank, they didn't collect the right data in their customer database. Now they want to do cross-sell upsell and they were struggling with the even simple email RAs.
Jitendra Putcha: (09:32)
Now they're investing almost a million dollars to collect email RAs by giving some kind of coupons to people to give their RAs. So imagine the whole exercise is probably not going in the right direction if there is an opportunity to do collection, opportunity to do cataloging, opportunity to do upsell. So these are the scenarios we talk to clients and help them to visualize what's the value of data.
The evolution of the services provider
Satyen Sangani: (09:57)
So you've come to this job with over 20 years of experience. You mentioned that you'd been seeing cataloging and metadata and having these words be used for such a long time. Tell us a little bit about the history that you've seen over the last 20 years and what you see to be the biggest evolutions. Because certainly as a services provider, you are at the intersection of the technologies that you've heard about, the ones that didn't quite take off, the things that customers are doing. How has the conversation changed and what's been different over the last 2 decades?
Jitendra Putcha: (10:26)
Something I think, let me first acknowledge, I think probably when started more than 2 decades back, probably this is not a place most people often want to be. Everyone wants to be something more. But today it's been a different world. Everyone wants to be part of the data and their business. So, very happy to be here.
Satyen Sangani: (10:40)
But that wasn't the case 20 years ago, right?
Jitendra Putcha: (10:42)
No way. It's probably, it's considered as a second-rated citizen, maybe stepmother treatment. You may wonder why I ended up there. I think what fascinated me is, at an early stage of my life I started working with the business. When you work with the head of sales, head of marketing, and how whatever you are generating is an output people are using to make decisions. For example, one of the first things very fascinating for me is about an organization driving a half-million-dollar campaign. They wanted to be able to achieve $10 million and the output we produce and how end-to-end of that campaign will generate $10 million. When you see that, I think that's the first time you really start realizing the power of data — value of data. So in a way I was lucky to see that and I continued the journey. But coming back a little bit about the question you asked about.
Jitendra Putcha: (11:25)
These days it's probably most often that data has been looked at more for operational reporting or for regulatory reporting. So that's the most important use cases, but not necessarily a significant amount of decision-making or even sometimes look at it in old ways, whether it resonates with the hypothesis we have or is it aligning with the intuition we have. So those are the ways with which people are doing. And second, those days probably when you built out data assets, you never built data assets keeping in mind metadata or data governance and all. All of them are afterthoughts. I'll just back it up. The same example: If one customer called and he said he got a $15 million campaign, he's going to sue us, he'll get one more, something like that. And then we realized the quality of data is bad.
Jitendra Putcha: (12:09)
We updated part of the duplication: 50 customers with the same address and one customer with a 50 different address. So all kinds of things were there, then you realize the value. So it was always [an] afterthought: data quality, data cataloging, and metadata connections. Today I'm very happy. It is at the heart and center of “How do you really connect the dots?” Because those days people realized bringing everything in one common database was the nirvana. When large enterprise data warehouses get everything together. But these days we realized we can't do that. So which means what is most important is going back to the same analogy of catalog in the library. You need to have everything in place but you need to have a catalog which is going to help you to bring pieces together. And the beauty today is we have a technology which can enable that and thanks to evolution in AI, thanks to evolution in parsing, whether we call about all that and they're helping not to do all this manually because a few years back we had to do rules-based, we had to do, every time you bring something new, again, you have to go back and do that. So, the technology is helping. People realized this is most important, heart and center, and most importantly the boardroom conversations are happening on data and decisions. So naturally all these became the most important steps for organizations to spend on in, Satyen. That's what I see.
The transformation of the Indian talent base
Satyen Sangani: (13:22)
Yeah, it is very interesting how the positioning has changed. As you answered that question, what struck me is that you and I met a couple of weeks ago at our new offices in Chennai and what was really interesting to me was how India itself had also changed. So you guys are obviously primarily an India based SI. Can you tell us a little bit about your staffing model? What percentage of your people are based in India versus the United States or other geographies? And I guess also tell us a little bit about how the systems integration work has changed over the last 20 years, because I think it's been also quite a transformation, particularly given the Indian talent base.
Jitendra Putcha: (13:56)
Absolutely. Backing up a little bit, we operate in an onsite/offshore model helping clients with the most important tasks and other things that need to be done with the clients in terms of interactions, in terms of helping them in doing their acceptance testings and bringing business value, walking the corridors. But in the last 3 years, lots of myths have been broken. I couldn't visualize myself 3, 4 years back. Some of the things that we are doing remotely is practically possible. The system integration business in the last 3 years has dramatically changed in terms of this onsite/offshore mixes and in terms of what is the best way to do that, the rest of the world has emerged.
Jitendra Putcha: (14:30)
So the second thing is that India probably has reaped these benefits, primarily about the engineering talent. Every year you get thousands of people coming out to the colleges and all requires last 3 years of a huge transformation going on. The system integration business driving remotely from places like India or even other places like Poland and other things has dramatically increased. Now what does is it mean? You have to start getting back to the colleges, getting back to the schools. Get the talent ready before they come out of it. So in the past, when the technology evaluation is happening so fast, the toughest challenge is, how do you make sure your curriculum, your students coming out of schools and colleges get ready for it. I think that's the second part. Countries like India have done reasonably well, which helps organizations like us to reap the benefits.
Jitendra Putcha: (15:14)
The third thing is, there is always the value work and there is always automated and heavy lifting that needs to happen. So when you think about the monetization business, when you have a lot of engineering work, the engineering work can happen remotely, even with less interaction. And these days with all the channels available, we are able to drive that. But the core of the value work, which you have to be in the field, working with the business, working with the things that continue to happen in places which are closer to the business. And that probably has not changed much. But the percentage, what used to happen to that time to now, has changed. And given opportunity for even organizations to invest more together, Satyen. How long we can keep this benefit in my opinion, I think today it's just there is an article talking about, I don't know right/wrong reasons. India just became the largest population in the world — with the population between 20 and 30 [years old] the highest in the world who could be useful for employment. So hopefully that can help to transfer more transformation, more digital transformation for across the globe, probably operating out of India.
Satyen Sangani: (16:11)
What struck me in my visit was that there was also a mindset change. The young talent doesn't behave in the exactly the same way as the old talent it used to. Is that something you've seen or is that just my imagination?
Jitendra Putcha: (16:23)
No, it is not your imagination. I think this has been a constant topic of interest, which I'm trying to do research about. I guess, every time, these generational shifts will bring different perspectives and the current generation is probably distracted or pampered and opportunity with a lot of things. Now when they come to the work, I think one of the challenges about the mentorship and the way they go through and the work/life balance perspectives they have is totally different to probably a few years back generation who came to the workforce. And we are seeing a little bit of challenges.
Jitendra Putcha: (16:52)
And then when we talk about hybrid models, now at least we have started slowly coming back to hybrid models from completely work-from-home models, at least in India and of course across the globe that's been happening but we are doing. So part of that narrative is simple. We are telling people that the way you learn by coming to work, it’s about situational learning. Situational learning is about, you can't sit at home and do your own program and code. You will never be able to learn situational learning. The second one, we talk about coming to work; it’s observational learning. Observational learning is all about you keep observing around and you get ingrained into that and you start doing that. So that's something probably missing. And the third one we talk about is osmosis learning.
Jitendra Putcha: (17:32)
The reason I'm talking about these things is broad. Today's generation sometimes says, “Give me the spec I'll code when I get up in the morning, when I get up in the evening.” I can talk about my nephew who says, “I prefer to work till 3 a.m. I don't like to get back to work, and just gimme the spec.” The challenge with that is the teamwork and ways of working and most importantly, getting an outcome, which is a collective effort; that has been a challenge. But all I can say is the good news is, we now found ways to work on it and we now found ways to tell people about how this generation has to adopt and how we also have to adopt as managers to make that easy for them than making it as, “This is the only way to work.” So I think there is a big shift that's happening in this world, Satyen.
Satyen Sangani: (18:10)
I completely agree that it's on us to adopt. One fun example, we had our kickoff event in India with almost 200 employees located all around India. Many of them had not seen each other before or met each other before. And as a part of the event, we did what we typically do, which is we had engineers and product managers demo the things that they had built. And so there were 3 demo pods and we had roughly 20, 30 engineers coming to every single pod. And I was a part of the crowd that was just trying to get in to see these video monitors. And what was really interesting to me was that 10 years ago, as the CEO, the oceans would've parted and people would've looked at me and been like, "Oh, wow — Big Guy’s here. We gotta move out of the way."
Satyen Sangani: (18:52)
And today they didn't care. They were just like, "Oh, we're asking our questions and we're getting engaged with the demo and we wanna know why you built it this way and which customers care about this and what are they trying to do?" And that I thought was awesome, which was that they were more concerned about the problem than they were concerned about positional authority, which to me, I think as an entrepreneur, I think the opportunities are incredible because people then think for themselves and need to get to independent conclusions. And if I'm consuming, now, talent from India but I don't think about it as talent that maybe just will just defer and not sort of question what I do and it's the lowest race to the bottom, I mean these are people who can really materially impact my business because they might think outta the box. And I thought that was pretty awesome.
Jitendra Putcha: (19:32)
Absolutely. If I just might add a couple of points to that. I think probably gone are the days of looking at India as the back office and factory model, to looking at this is an opportunity. There were 2, 3 reasons for it. One reason is the startup ecosystem and the unicorns we started building, created an aspiration for people and created curiosity for individuals even during schools itself, which wasn't the case a couple of decades back. That's one. The second is the promotion during the schools itself to encourage people to be driving incubation start ups and through their ideas has dramatically increased. There are many forums today. So that's the second one. The third one, even the large-sized like us today, we have what we call intrapreneurs, maybe not entrepreneurs. Intrapreneurs are within our own ecosystem. People can come up with an idea, put an entire canvas, business plan, go to the board, get the funding, create that as an incubator and go and test the market, if it is working, create that as an entire product line. I think when young generation is able to get exposure, already been educated and also they see in their workplace this kind of opportunity, I think that's the biggest benefit the younger generation is able to do. Which probably wasn't the case before.
Modernizing digital modernization
Satyen Sangani: (20:37)
I couldn't agree more. So maybe shifting gears a little bit, you obviously talked a lot about the joint company's ability to drive modernization, digital modernization initiatives. And I specifically wanted to talk to you, obviously, given your role about data modernization initiatives. People just are arbitrarily moving to the cloud and I think in many cases over the last 10 years that was just something you would do. And ROI wouldn't necessarily be something that people would consider but certainly, I think, now, in a more constrained environment, that's something that people are looking at. Are these data modernization and cloud modernization initiatives — are they straightforward, hard, easy? Do people underestimate them? Tell us a little bit about your experience in doing this work.
Jitendra Putcha: (21:17)
No, absolutely. There are all kinds of categories in the customer journey. There are customers who have done enough groundwork before embarking on, there are customers who are probably learning because they didn't have the capabilities that were required to make these kinds of things. But people have this FOMO, fear of missing out, let's also embark onto the journey without enough preparation. The way I take leadership when I talk to my teams before we embark on any data modernization program, this is like renovating a house as you still live in the house in some of the other rooms. An amount of things you have to deal with. A particular room within your house is getting repaired and fixed and all the noise and the dust and the paint smells and all kinds of things that happen. So sometimes data modernization, when you have an existing estate which is required to maintain your business. A parallel here, embarking on something new while you're modernizing from here to there, the amount of efforts required from your SMEs and business and time commitments most often are underestimated. That's number 1.
Jitendra Putcha: (22:13)
Number 2, sometimes you have an organization whose systems are very well adopted over a period of time, because the systems were built 10 years, 15 years back. Sometimes people consider that as a bible. Whatever output comes from the system is the bible. That's the right thing. So when you have such an adoption, you go and talk to them about, “That's not the way you're going to do it.” You have to be prepared enough to educate and get them ready for why they should switch. Third, there is a phase when you're switching from one to the other. There is a phase, people need to have a big picture why they're going there and why their time commitment is important and why their knowledge is important to make these initiatives. So most often this is not done. People embarking on the cloud, assuming probably “I just need to take from point A to point B and I just need to repoint, the world is going to be brighter.” The reality is the world is not going to be as simple. The good news is about a size like us, and there are a few more, but a size like us takes time in terms of making this life easier. The way we talk about it is, we want to make all our customers go to the future smoothly and modernize themselves smoothly. And then how do you do that? The way you can do this is faster, by doing automating. These days, there is an opportunity for us to scan your source systems and analyze that and market to that. That's a relatively easy problem to deal with.
Jitendra Putcha: (23:27)
As a technologist, it's a very easy problem to solve from this to that. But what is a difficult problem to solve is about, as I said, the time commitments, underestimating and managing both the systems at the same time. And most importantly the culture with which when your interfaces, your experiences that’s going to change. Because when you modernize your system, experience can be dramatically different during what you have seen in your demos and your areas as well as when you start embracing. I also say in the light note to people, as a technologist and as a data person, I try to promote this to people but now during the integration [chuckle], then I'm going to consume, as a user, some of this data. I have my own strong views about what is the best way that information can be consumed, what is the right way to [make a] decision. I think as long as we can give that picture to people, it'll happen, but most often these are all underestimated. And the final one, some of the organizations that still approach “train the trainer” are probably training people to use the new technologies. In my personal opinion, gone are those days, you can't be doing that.
Jitendra Putcha: (24:23)
Rather, you have to have a little bit of innovative approaches. Things like, you may have to run some crowdsourcing, you may have to run some hackathons to get people together to learn on the fly. And you may also have to figure out influencers to really rate, I mean think about like a Netflix and Amazons of the world. People consuming, using this report, rating “This is a good report, the highest quality report.” I think these are the ways with which consumption patterns have to be thought through in embracing the organization. Otherwise your data moderation efforts, you can have the best technology, you can do everything but people may not see enough value out of it, that's the thing. So we focus on, before we begin, looking at these areas whether the organization is ready for that. If they're not ready, we help them to gap between that they're ready for it or not and how to make them ready and then modernize.
When things go wrong
Satyen Sangani: (25:08)
I love the Bible example and analogy because I think people produce systems ultimately over time and there's so much implicit knowledge that you forget to declare when you set a variable, build a rule, build a workflow — and then people just over time, to your point, forget who wrote this stuff and how it actually got generated. And then all of a sudden, you've got this sacrosanct output that you're now trying to replicate but you don't even know why anything got built in the first place. And that complexity I think is really the essence of why these efforts are so hard because you start unpeeling the onion but there's just layers and layers and layers. I think that's just a really challenging thing because people think about it as technology and really it's all of the human process surrounding that technology. Have you seen these efforts go wrong? I mean, can you give us some examples of efforts where customers have done it incorrectly and what the costs have been when they have?
Jitendra Putcha: (25:58)
One of the things is about people underestimating the complexity. And most often people talk about, “We have all the required documentation, we have all the required cataloging, we are good to go. We have a thousand programs which can be migrated. We have all the data required in one place, all that.” But when you start getting at that, you uncover your program, get delayed. The reasons for that is there are some of the legacy systems, some Excels are still used. I'll probably pick up a technical term, like I'm in a lookup tables or some Excels are looked at as reference information. And a lot of these things are not captured but in your daily batch or a daily flow; they're all used and it's like a black box. You get some output, you're happy with it. But once you start this modernization program, you'll start uncovering some of these things about some reference data is somewhere, some additional sources are there, and some downstream systems are expecting your output in such a fashion. A classic example is in one of the places a downstream system is doing an FTP server.
Jitendra Putcha: (26:56)
I don't know from when was that FTP server and from the FTP server they're picking up. Now we have modernized that to data and cloud. Somebody's saying, “We can't ask downstream systems to connect to the cloud, we have to move from here to FTP server,” then it actually dilutes the purpose of having everything in the cloud. The world of cloud is all about you can access anywhere, anytime, ease of access — of course with all the right security. But then you're going to again dilute that by talking about “This is a process I'm going to do.” So sometimes people as an afterthought identify, uncover process and try to value and then that's where we start realizing that programs are slipping, programs are not giving the value that is required. So that's one.
Jitendra Putcha: (27:35)
Second is, I think this is a few years back, not recent one. People get excited about a cloud journey, and I'm sure you would've heard about a lot of horror stories about cloud consumption getting used more and more because people have not done a good job with this thing. So there are some stories around “the consumption is increasing,” people have not liberally used without optimizing things. So that's a second problem. I've seen some of the implementations when we embark. Clients talk about, "Oh, we are not getting value from the new technology. The new greater modern in a modern data stack is not giving value." It's not a problem with the stack. It's your approach. It's your inability to understand the magnitude of the challenge. So that's where I think I'll go back to a few minutes back, what I said about assessing whether you're ready is the first step. But most often people underestimate these challenges and jump into that. I've been in this company for the last 20 years. I have already seen moving from file system to RDBMS, RDBMS to MPP, MPP to Hadoop, now going to cloud is one more step, I can deal with it, I wish, but I don't think it's as simple as that.
Satyen Sangani: (28:28)
No, not at all. You mentioned a lot of technology that you bring to bear. Can you tell us a little bit about, so the sorts of technologies that help do this archeology work and do this migration work and as a technology-enabled services firm, I think it's helpful to understand the sorts of problems that you're solving for people to understand what can be accelerated and what the class of problems are that they're gonna deal with.
Jitendra Putcha: (28:47)
Absolutely. So over a period of time, one of the unique value propositions or differentiators I talked about when you do particular, I'll just start with the modernization migration. How do you understand all the sources that you have and how do you read them and how do you write them to a format that is readable by the target? That's been our focus, and over a period of time we automated, we found end soft sources with the all types of different RDBMSs, different file systems log, migrating them to data cloud providers, for example, like Snowflake and other hyperscalers. We built a strong platform and we learn over a period of time with the more scenarios, which get fed into the system and automation maybe a few years back, 50%, now automation can go up to 80%, 90% kind of a thing. So that's what we were able to build. Now the way it happens, and I think in the last two months even actually it started increasing further thanks to generated AI efforts where even we are able to increase the outputs with more maintainable, better documentation, better readability of that converted code, converted data.
Jitendra Putcha: (29:46)
So that's been a philosophy of building the platforms, what we've been doing and this is really helpful, not just “this helps,” it also can connect to the other technologies and ecosystem. For example, if there is a cataloging tool like yours. This entire parsing and entire scanning of this information can be parsed back to that. And you'll have a rich data — not just the modernized migrated world but you also have data just available to get into the system. And I'm also super excited these days thanks to the microservices and APA-based approach in the data world, we can just exchange this information much faster. So that's been the stepping stone to make this more suitable for the consumption of it. I'll just say this, I'll start conversing with people: It's all data commerce. Like how 20 years back e-commerce started, data commerce is the way to operate with all these microservice and APAs capability that we are able to do and each of the organizations can also freely exchange this information and it's no longer that one monolithic company can solve all the problems. There are best-of-the-breed capabilities come from product companies and best-of-the-breed capabilities come from companies, like with automation, can solve the client's problems in the difficult world.
The data marketplace and “data as a service”
Satyen Sangani: (30:52)
What I find interesting is that these technologies that you're talking about, these file format readers, the parse chain and understanding the workloads, I mean all of these things are things that people want but often don't feed into the process of building large, sustainable software companies because people don't use these on an ongoing basis. What's awesome is that you're — as a services firm — doing the exact work and providing it with the technology that would accelerate the work. And I think, actually, the business model is a much more natural fit to many of these things that are otherwise software utilities and therefore can't scale or get into the R&D level that would actually make a difference for clients. And so it's actually a really interesting business and economic model in the sense that you're doing work that I think neither a software company can do, nor a services company could easily do. And that place that you found seems like it can be quite transformational. Speaking of transformation, I want to switch gears a little bit and talk a little bit about this idea that seems to be coming even more into vogue right now. Data modernization, data marketplaces. You've talked about this term and written about this term “data as a service.” Can you tell us what that means? 'Cause a lot of us have heard about data products but maybe not so much data as a service. Tell us what that's all about.
Jitendra Putcha: (32:02)
So the way I look at it is the entire ecosystem. Just a few minutes back, I just talked about data commerce. If I have to think about how to extract value from data, if that is the big picture, I can look at it. All the pieces of the puzzle like your data marketplace, data products, data as a service, I'll differentiate each of them but that's a big picture of data commerce. The data marketplace and data as a service and other things, the way to look at it is about today, I'm in sales and marketing for a consumer goods company. I would like to run a campaign and I just need to get some syndicated data or third-party data. I don't need to build the capabilities internally, all I have to do is go and find out.
Jitendra Putcha: (32:38)
Work with a partner who is capable of giving me that information. To me, data as a service is all about, you get that service like a software as a service model, where your entire stack is ready. Like the data as a service is a capability that they are providing an output of data that is required to solve the problem for that time or for a long time. It doesn't matter once or multiple times but you don't need to go through the difficulties of starting from infrastructure, to modeling, to building, to et cetera, et cetera. There are people, right? So things like data as a service or analytics as a service or insights as a service is a way of organizations adopting faster, better and in a seamless fashion of, today it is possible simply, as I said, like an APIs and microservices based, as well as these are all secured and they're giving enough coverage from a 24x7 perspective, so that's what it is.
Jitendra Putcha: (33:25)
Now, you can just switch the gears a little bit into, how does this come into the overall marketplace concept? I can't find a better time than this, the collaboration that's happening with the partners, right? I think we spoke a little bit about the product vendors like you, SIs like us, and customers who are trying to solve a business problem of generating more millions or somebody is trying to bring $10 billion inventory to $8 billion inventory, so this is the thing. But the biggest piece is about how a company, for example, we're working with a trucking company, how they can share the information with a battery manufacturer and battery manufacturer share that information with somebody else in the value chain. The data marketplace today is providing an opportunity for clients to become both publish and subscribe and start making money.
Jitendra Putcha: (34:08)
Data as a service is a way probably you can procure that services but data marketplace is a way with which you can actually have a larger marketplace to deal with it but sometimes it's actually a little probably used more often these days with, everyone has a marketplace, so where is the value that's going to come, right? I think the data marketplace is also very specific in terms of data asset providers, product vendors, as well as SIs who are going to bring these accelerators and capabilities and most importantly, we're actually providing capabilities like analytics, outcomes, our algorithms are available and as well as how this can fit back into that. So this entire data marketplace is going to generate the big data commerce we are envisioning. Already, I think billions are spent on it and billions are being generated, but there will still be some skepticism in regulated industries, Satyen but I see this as the way forward and it's actually good going in the right direction.
Satyen Sangani: (35:00)
So you see data marketplaces, for example, actually, in places like AWS and Snowflake. They both have data marketplaces that exist where many of their clients are posting data sets that can otherwise be purchased or transferred through their platforms, and in fact, one of the features that we recently announced was this capability to search public data sets. What we found is that there's a lot of interest around some of this work but these ideas of this internal marketplace, you get into certain problems, like how do you actually value the data and how do you figure out what to monetize and how does somebody know what to pay for it? How do you see those sort of issues working themselves through? You mentioned that it'd be shared between sort of vendors and suppliers, but what do you think are the big problems that need to be solved in order to make these even bigger than they currently are?
Jitendra Putcha: (35:45)
Again, I pick up an analogy. This is like today in the Play Store and the App Store, we have billions of apps but very few are used. Of course, the expectation of that. The reason I'm just talking about even in the marketplace, I won't say we're just starting but we are at the initial phases where everyone is trying to figure out the best model to make out of it. So there are lots and lots of assets, lots and lots of models, lots of them getting published. If I'm a customer, one of the challenges for me is about, there are similar capabilities. There are similar things, how do I identify in the middle of these complicated assets what can bring value for me? As the type of data asset I'm looking at as a retail bank versus consumer goods versus a media entertainment OTT platform is totally different. So there is a space for these micro segments of data assets. There is a space for micro segments of the capabilities that are available. So the verdict is not out but I think we are going in the right direction. That's my first response to the challenge we're going through.
Jitendra Putcha: (36:39)
The second thing is about, I think a few years back, when cloud started, we were all very paranoid and panicked about the security. And thanks to the pandemic, last three years, we're even worried about the physical security of sitting in one place to drive with it. They've all embraced both physical and cyber security. I think the best way is to promote and make people realize the same thing with the data marketplace, the fear of competitive, fear of the value of data will get a little bit of gamified. We'll get better off more rating based and other things with which we'll start seeing the value of it. I think we have seen that in the marketplaces, right? When Amazon is selling their marketplace, the capabilities and others, we have seen over a period of time, how things evolve. And I'm a big believer that the exact same thing is going to happen in these large marketplaces. But today we may have quite a few marketplaces. We are solving a different problem.
Jitendra Putcha: (37:27)
And I take again the same analogy, while Amazon is solving the marketplace problem globally but in every segment, in every industry, in the UK there is an electronic commerce company who's taking care of a need. The same way in Indonesia, somebody else is taking care of it, not just a marketplace of Amazon. The same way there is a space for all micro segments of marketplaces solving a problem for an industry. And now coming back to the price points, price points are going to evolve. Today, probably, everyone is experimenting. I think it goes back to the software licensing model. These models also will evolve. But the best approach when I spoke to some of the customers who are trying to put data assets, the way they're thinking about it is, “Today we don't get any money out of it. Today we get nothing out of it because we internally keep the data. We don't share, we don't anonymize and use that for anything else.” Even if it starts generating some revenue, any revenue is a bonus.
Jitendra Putcha: (38:13)
So I think people are at early stages of making money out of it. I'm not talking about the hardcore people who make data, selling by data. I'm talking about a traditional customer. So they're looking at this, any incremental revenue is a good revenue to start with. Once we do the true potential, probably they'll change the models, how to make more money out of it.
When to make an investment in data quality
Satyen Sangani: (38:32)
It's a really interesting field. And I agree that it's super, super early. You do see some customers investing but it is, I think, the exception and not the norm. And I think the operational risk right now is, feels like people are erring on the side of conservative but everybody's sort of interested. And I think there's a moment where there's gonna be a tipping point and it'll be interesting to see how that plays out. You have also spoken and you earlier referenced this idea of data quality, which obviously is now a hot topic. There's new companies, you mentioned the modern data stack. And with that has come this concept of data observability and in that, you sort of also said, look, when you think about data quality investments, they need to make a significant difference in order to have yield, which kind of implies, at least in the way you wrote it, that you might be sometimes bearish about data quality investments. Is that a fair interpretation, first of all? And second of all, tell us about how you think about when a company should make a data quality investment and why?
Jitendra Putcha: (39:27)
The way I look at it is, data quality is also one of the most grossly underestimated and not well understood. Nobody wants to say their data is not of good quality. Let me probably start with that. And most often then we go and try to do a consulting exercise and data quality for organizations, every single time I can say the results are surprising to the people who are maintaining the data. I'm not going to talk about the usual data quality measures, like completely accurate, etcetera, but I think the most important thing is they're not well understood. The way I look at it is within always is about, go back with examples to talk to them about: What does that mean? If a particular information is wrong, it's not complete, it is not available, what does it mean? I think most often as a technologist, people tend to just talk about, “You don't have the right quality data.”
Jitendra Putcha: (40:13)
I think if you're able to start giving examples about: Because of that, what are we missing? Are we missing an opportunity to freely take a revenue opportunity, a market and other things? That's the thing. And second is about, because not sharing the right quality of data, we are sharing to the markets, we are sharing to the regulators, which we are going to have a significant penalization. I think examples of that nature will actually open up in people's minds — what is the data quality about? And second is about not just data created. I think we still talk about a significant amount data created, but a significant amount of data is part of our integration. We massage it in such a way that sometimes we induce more data quality problems. So we need to also talk about how induced problems are creating more difficulty with examples if we are able to talk to them.
Jitendra Putcha: (40:58)
I've seen every single business owner, which you have seen, consistently always come back and say, “I get it. Now let's talk about what we need to invest in. And I see what you're talking about.” What's simply going and talking about “Your data quality is 3 out of 5. You have to quickly fix it,” I don't think anyone is going to accept it. So I think it's all about doing that. I'm personally not bearish but in the past I was, probably. The reason for that is making people realize how it works is more important than simply saying, “Everyone is doing data quality. You also do data quality.” I don't think any of us, as a personal decision, do that, right? Somebody bought a car versus somebody who doesn't. We want to see what's the value for us and I think that's where I was bearish in the past. Today I'm bullish because we are able to explain the examples. We're able to show real-life scenarios.
Satyen Sangani: (41:45)
Yeah but your point I think is right, which is the semantics around quality are quite poor and data quality as a problem doesn't necessarily mean data quality as a tool or a software or a solution because, to your point, why is the data bad quality? Well, it could be because the source system inputs are wrong. You're not gonna fix that with a tool. When somebody says the words “data quality,” very similar I think in ways to data governance. People say the words “data governance” and often there's just a lot of like, “Well what do you mean by data governance, and what problems do you try to solve, and why is it bad and what is it that you're trying to govern?” I mean those are all things that I think often get lost in the translation of doing this work.
Defining data literacy
Satyen Sangani: (42:24)
We are hearing, as a consequence of all of this, a lot of talk on the topic of data literacy. We recently had the CDO of Salesforce, Wendy Batchelder, who basically said, "I hate the term data literacy because it implies that people who are not data literate or needing to get these data literacy courses are in fact illiterate and nobody wants to be called illiterate." And so there's an interesting question around what to do with literacy but is this a hot topic for you. How are you seeing clients absorb and respond to this idea of data literacy and do you see these topics trending up or is it fixed relative to what's historically been going on?
Jitendra Putcha: (43:00)
I don't think it's fixed. The reason for that is we are talking about data having to be embraced by every single person in an organization. For example, we are now a more than 90,000-people organization and I'm sure large organizations… We work with Fortune 500 companies. We work with 1 out of 5 companies. They will have hundreds and thousands of people. Now the nirvana of entire investments of data is about every single person’s day-to-day job being able to use the data the right way and make the right decisions. That ensures you are building products and what I'm working for. Now, data literacy is not just building products, building capabilities. Data literacy is also making people consume the way it's supposed to be consumed. I agree, we have to find, maybe, a different name for literacy because nobody wants to be called “data illiterate.”
Jitendra Putcha: (43:45)
I don't think anyone is going to agree to that. But I think the question is more about “What is the way you're going to consume and how to increase the value of data in people's mind from their consumption part?” And all of us look at data differently, right? I think when you look at data, each of us looks at it from a different angle and makes our own inferences. But to me, one of the things — as data practitioners for all these years — it's our job to make it easy for them to prescribe so that the literacy rates can increase. Instead of making people learn, learn, learn, can we create some prescriptions with which it becomes easy for them to make decisions instead of forcing them to learn. So there is a gap between what we are expecting they should start learning how to use this versus how to make it easy for them to consume. That’s the way I continue to see that as the bridge.
Jitendra Putcha: (44:31)
Now we can bring them up or probably we can make systems down or whatever is the easiest way but there is a need. I don't think we solve the problem as an SI. Sometimes I'm super excited with technology evolution and solving problems but at the same time I also feel sometimes sad because the gap with which what we need to bring people to use versus where it is happening, it's a continuous journey. So that's probably why — I say this for data literacy in 2 simple steps. One is about, we need to make a complete outcome. What is the outcome of this initiative? Second is, we need to make everyone’s learnability increase for a period of time at the end. I think gone are the days — I'm sure some of our parents wouldn't have used the sophisticated phones we are using but today it is necessary — the same way people in the organization do their work, they need to figure out how to use the data. We can come out with whatever term for it but this is the necessity for their business. With the amount of availability of information structured, unstructured. If they don't know how to use it and they don't know how to make use of the systems available, they're not going to be effective in their job.
Satyen Sangani: (45:32)
It's funny you mentioned the word outcomes in the context of literacy. Literally earlier this week I had a conversation with a woman named Julia Bardmesser who is the CDO at Voya — or was — and one of the things that she's really passionate about is this idea, I think, what she called data literacy but I actually think it's data infrastructure literacy. Because what she finds is that everybody wants better data quality, better data outcomes, more models, more AI, and wants to solve problems in the business but then has a hard time investing in things like master data management and data cataloging and data governance, which are fundamental to actually getting those outcomes. So she's like, “Look, I want to go broadly” — and I'm not taking her pitch completely well — but she basically said, “Look, I'd like to go explain to people or make a systemized practice of explaining to people this idea of having some sort of infrastructural literacy. What that makes me think about particularly in this day and age is this moment where we're in globally, maybe not in India but certainly in the U.S. and western nations in the midst of a recession.
Defining investment in — and ROI of — data tools
Satyen Sangani: (46:35)
One of the things that I think is happening particularly regularly is that there is an ROI conversation around a lot of data tools. How do you see that playing out? Are people investing today in these tools as much as they were 12 months ago? Is there more inspection in the investments around data tools? Like how are the business cases being built and have you seen any change in behavior between now and you know, say, 2021 when it was really sort of the go-go days of data?
Jitendra Putcha: (47:02)
Yeah. So I think, again, I break this into two parts. One is about whenever there are tough business conditions, I always see more investment in data — we'll talk about investments and products versus whatever — but investment in data I’ve always seen in the difficult times actually equal or more. Now the question is, will organizations continue to live with their existing difficult infrastructure and try to extract more value out of it or are they willing to invest in difficult times for the future? That’s the big question mark. I would look at that more as a culture and environment that organizations are in but still, as an SI, as a consultant, I always try to go and propose to them and give an opportunity to think through: these are all passing clouds, whether the passing cloud is going to happen for a month or a year.
Jitendra Putcha: (47:43)
Is it difficult for any of us to predict? But you can't stop thinking about your future so it is important for you to start investing incrementally. The good news is, we are no longer in the world of buying $20 million licenses right upfront to start a new initiative. We're not in that world, right? So while initiatives are always worried about a long-term impact, incremental value we can start doing is one approach. I have seen organizations picking up: “How do we start getting into that?” The second one I have seen is, they can't invest more and more than their budgets because budgets are shrinking. However, there is an opportunity: “Can I reimagine my existing state and look at it; is there still a big picture going on?” Short answer, I have not seen a single client come back and say, “We are going to cut down our data analytics investments.” Everyone is serious about it but the only question everyone is asking is “Can I do the same for less? Can I do anything differently?”
Satyen Sangani: (48:34)
Is there a focus on ROI though? Like are the types of projects that are getting funded different?
Jitendra Putcha: (48:38)
Yes, there is absolutely ROI that exists. No longer people probably last 2, 3 years when the demand patterns or transformation journeys are there. Maybe some businesses may be relatively easy to acquire but today there is enough scrutiny to understand are we just embarking on the sake of new gadgets or are they going to generally fit into fit for purpose? Right? I think it will fit into the overall things that we are trying to do. That scrutiny is there but I have not seen anyone saying, “We are not going to invest on this.” I have not seen it.
Satyen Sangani: (49:06)
Yeah, makes total sense. And so as we're thinking about the area of new investments, I probably would lose my theoretical license to be a technology podcast host if I didn't ask you about generative AI — because that seems to be the topic that everybody asks everybody about these days. But I guess, look, I would actually wanna specifically focus on your area of expertise, which is where clients are actually investing. So we all know everybody's talking about it. How many people are actually now starting to do it? What is the incidence of people now starting to be willing to invest in projects? Have you seen an acceleration there? Because I mean it's been like, probably 6 months, 8 months since ChatGPT has come onto the scene. Are you now seeing people put money behind it?
Jitendra Putcha: (49:44)
No, no, absolutely. People are putting money [into it]. Is it big yet? I think it's still in the pilot stages in PMOs except in some areas. Most of them are experimenting that. That's my first answer. The second thing is about what, as an organization, we actually have started this few months back working with the partners and large scale players and experimenting all the LLM model. The genesis of this — while some of the audience may agree or disagree — to me, yeah a phenomena is there for quite some time. But the best part of generative AI is it helped us with LLM models as well as putting “human” in the loop to customize for our own areas. It's created a different opportunity than altogether a new phenomena as far as I'm concerned. Well, some may disagree. Where I have seen investments are significantly happening right away.
Jitendra Putcha: (50:27)
Take, for example, a financial services insurance kind of customer where there are a lot of wealth management fund researchers and others who are doing significant amounts of manual effort or a little bit of automation. In the past, outputs of machine-generated were not as good as human-generated ones. Now, thanks to the new LLM capability, that's a first use case. I started seeing people adopting to that. So that's from a data and analytics perspective. Now, on the other side of it, on the experience side of it, particularly in the marketing tech kind of areas, the content generation, which is historically done, people actually started embarking on it to drive the content generation. That's not necessarily a data analytics business kind of thing but that's actually another area which we started working and we are seeing people are embarking on it.
Jitendra Putcha: (51:09)
The third one is about some of the consumer goods companies that are experimenting with their brands and areas of their products and other things where again, they're experimenting and they want to take this in a much larger way. So the phenomena I've seen for the first time, generative AI, even as an SI, we are seeing this opportunity across the board. The way I defined it today, I'm also part of the task force in our organization. The way I look at it is, this is 4 towers. One tower is about going and solving for every industry vertical, their business problem. Second is how to embrace that in your own products. In your products, how do you use the prompt engineering? How do you use this to come out with a better outcome of that? The third one is about your own internal organization, like the CA organization of ours. How do you create this for employee experience and employee productivity?
Jitendra Putcha: (51:51)
And then the final is making entire organizations aware of this generative AI opportunities that can create — like how a few years back when cloud came, we made everyone aware of cloud. Now generative AI is the thing. It's no longer going to be the responsibility and no longer just restricted to the data AI group or data analytics group. For the first time, generative AI is actually cutting across everything and there is a huge opportunity, and particularly people who are in this business of data AI, probably they can really reap the benefit, helping the other practices and other groups how to make use of AI primarily with the guardrails, primarily with the pitfalls, which we are familiar with. Whether it is about privacy, security, explainability, all others — with which we are familiar — maybe we should help enter other parts of the organizations.
Satyen Sangani: (52:35)
Yeah. Jit, this has been an amazing conversation. Thank you for taking the time to speak with us and to give us your perspective and given particularly your breadth, I mean there's so many things that you're able to see and cover, given your perch and so, just phenomenal to have you on and really appreciate you taking the time.
Jitendra Putcha: (52:51)
Thank you so much. And this is so close and passionate about it probably couldn't go on forever but it's amazing. Thanks for the conversation and I think collectively we can help our customers go to the future much faster together, with a lot of the capabilities we're bringing, and at the end of the day, to me, if the client is able to see the ROI for this investment, and I'm sure we have done our job. Thank you so much and have a nice one.
Satyen Sangani: (53:10)
Satyen Sangani: (53:17)
In speaking with Jit, I'm struck by how much has changed in our industry in the past couple of decades. India has gone from being the economical back office to a frontline innovator. And data — once overlooked and undervalued — has emerged as the key differentiator for competitive businesses. It's become the new center of the business universe. Let's put it this way: 15 years ago in the high school cafeteria that is Silicon Valley, the data crowd were the pariahs, the geeks, the nerds. Today, they're the cool kids. Crazy, right? Jit has been at the edge of that transformation. In his career, he's been one of the key players pushing data from the periphery to the core. And in many ways the cloud is accelerating that innovation. We've only just scratched the surface of what's possible with data. I'm your host, Satyen Sangani, CEO of Alation And data radicals, you stay the course. Keep learning and sharing. Until next time.
Producer 2: (54:13)
This podcast is brought to you by Alation. The act of finding great data shouldn't be as primitive as hunting and gathering. Alation data catalog enables people to find, understand, trust and use data with confidence. Active data governance puts people first so they have access to the data they need within workflow guidance on how to use it. Learn more about Alation at alation.com.
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