S6 | 519: The innovation gap, with Tanvir Khan, Chief Digital Officer, NTT Data

Digital StrategyTechnologyConversational AI
Written By Basil Ali

About the Episode

There is a gap between organization’s aspirations to innovate and what they are actually achieving. Recent research from global consulting firm NTT Data uncovered that while 96% of execs believe innovation is a primary source of achieving growth over the next two years, only 21% are able to definitively meet their innovation goals. 

Today we’re going to talk about narrowing this innovation gap and what organizations can do to meet more of their innovation goals. To help me discuss this topic, I’d like to welcome Tanvir Khan, Chief Digital Officer and President at NTT Data. 

About Tanvir Khan

Tanvir is chief digital and strategy officer focusing on technology direction, go-to-market and offering management. With more than 25 years of experience in the IT industry, he is a thought leader in digital transformation, associated core technologies and value realization. He is also a hands-on IT practitioner with five patents and four pending patents in AI and automation. As a spokesperson for NTT DATA Services, Tanvir shares his insights to clients, media and analysts on topics ranging from Generative AI to emerging global service delivery locations. Prior to joining NTT DATA Services, he held global leadership positions at Dell and Wipro Technologies.

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Transcript

Please note that this was AI-generate and there may be errors/misspellings/etc.

Greg Kihlström: There's a gap between organizations' aspirations to innovate and what they're actually achieving. Recent research from global consulting firm NTT Data uncovered that while 96% of execs believe innovation is a primary source of achieving growth over the next two years, only 21% are able to definitively meet their innovation goals. Today, we're going to talk about narrowing this innovation gap and what organizations can do to meet more of their innovation goals. To help me discuss this topic, I'd like to welcome Tanvir Khan, Chief Digital Officer and Chief Digital and Strategy Officer at NTT Data. Tanvir, welcome to the show. Thank you. Thanks for having me. Yeah, looking forward to talking about this with you. Why don't we get started with you giving a little background on yourself as well as what you're currently doing at NTT Data.

Tanvir Khan: Sure. My name is Tanvir Khan. I'm the Chief Digital and Strategy Officer, as Greg explained. Been with the company for the last 11 years in multiple roles, but doesn't matter what my role was, I really look at my role as making innovation work for business, which essentially is taking new technologies, mainstreaming them and driving business outcomes. And that's what I do at Entity Data and for our clients.

Greg Kihlström: Great, great. So let's dive in here. We're going to talk about three key sets of data from the recent report that I mentioned at the top of the show. Let's start with this one, though. 86% of organizations say their tech is outdated, and 32% say this hinders them from delivering high quality customer experiences. With nearly 90% of organizations saying they're relying on outdated technology, what's holding organizations back from doing something about it? Or are they working on it, but simply not making progress on it, or is it both?

Tanvir Khan: It's a combination of both things. As we have built technology in our industry, we have built pretty monolithic technology stacks, which are not very agile and cannot change. the needs of business change at a pace which is much faster than the pace at which technology changes. Now, people who are building technology today are taking into account the needs of the business and keeping it a lot more agile. But back in the day, when we made decisions on how to put a technology stack together, that was really very, very deterministic. We really hardcoded what we wanted in our technology and did not really have probabilistic scenarios in our technology stack design. What that did was it was really optimized to solve the problems that you could see on the day that you design the technology stack. but we're not very flexible in the future. And that is now coming back to bite us. So as we build technology for the future, we need to make sure it's not monolithic technology that you have to rip and replace, but it is plug and play technology that can adapt to the changing needs of the business. And that's really how we need to think of technology today.

Greg Kihlström: Yeah, and building on that, the adaptability, it also means that those multi-year, five-year transformation projects and things like that can deliver some earlier results than just kind of waiting and everything like that. So with that, for that nearly one-third that says their technology is hindering them from delivering great CX, what should they do to prioritize some quick wins there?

Tanvir Khan: So it needs to be a two-pronged approach. One is, what is the best outcome that you can deliver within the constraints of the current environment that you have? And a lot of people underestimate the ability of technology to do what it can. So with current technologies, you still have the ability to layer on things like mobile apps, things like chatbots, things like natural language self-service, things like avatars. So there is a lot you can do to layer on new technologies on your legacy infrastructure by building some smart interfaces. But really, in the long term, you really need to deliver a digital experience at every touch point in the customer journey. And it's the customer-centric infrastructure design that has to eventually replace what you have. But can you do stuff with what you have today? There's enough that you can do.

Greg Kihlström: Yeah. Yeah. And so, it's well documented that the failure rate of digital transformations is pretty high. And that's not necessarily a 100% failure, but failing to live up to expectations would be considered a failure in some of those cases as well. So, partial success, in other words, is also not complete success. So, it's something like 70% of transformations are failing to live up to their expectations. Is there a better way for some of these exercises to be embarked on rather than these large scale change initiatives?

Tanvir Khan: Initiatives that are based on strategic intent and a very clear roadmap usually succeed. Initiatives that are based on the fear of missing out that everybody is doing digital transformation. We are not we need to do something or everybody is using AI or everybody is using generative AI. we need to catch up, we need to show some progress. So the fear of missing out projects, even when they're successful, fail to deliver tangible business results. It's only well thought out plans which have a strategic plan, a roadmap, investment and management support behind it succeed. And I would say that is where the 70-30 split occurs. So it is Making sure you're doing something because it has tangible business value, there is a strategic roadmap and execution plan behind it, is the only way to succeed. Trying to catch up by playing with shiny objects is usually a path to failure.

Greg Kihlström: Yeah. I think that's a really important distinction to make. And also, it's I think you're saying the same, but you know, it's, it's hard to measure that you've achieved success when success is a bit nebulous, right? It's, it's just, it's catching up, which is, you know, it's, it's hard to put exact metrics to that. So I think that's a, I think that's a great point. Another aspect from the report that I wanted to talk with you about is, you know, 43% of organizations, which is a threefold increase from 16% in 2021. say that a lack of employees with necessary data analysis skills is their biggest challenge for using their data. So, you know, it's always been a challenge to find enough highly skilled employees in, you know, whether it's technology or data-related practices. But is the increase in reliance on AI and other data-heavy tools and practices making this even worse?

Tanvir Khan: I think the reliance on data and AI tools is actually going to make it better. And I really believe that, and here's why. Making sense of data is a problem that we've struggled with for generations. It is not that companies didn't have data. They didn't know what to do with it. About 10, 12 years ago, we had this big data democratization with the invention of dashboards and visualization. With dashboards, You had the rise of the citizen data scientists. You had the average business manager that can look at a dashboard and say, why is my forecast for next month down by a million dollars compared to the same month last year? So the ability to use data to make data-driven decisions became very, very democratized for people who were smart managers who could use dashboards, who could use visualization and the modern tools. I believe AI is going to create the next level of data democratization. by creating conversational interfaces to data analytics. So think about being able to talk to your data and ask questions. So instead of pulling up a dashboard and being a power user of something like, say, Power BI or Tableau, you can actually ask your data, why is my forecast down by a million dollars next month? And the data gives you that answer. So the rise of citizen data scientists, which essentially were power users, really drove data driven insights. AI is going to drive the next level of power users. The difference is these guys don't have to be expert in data and analytics. They need to understand that business and AI is going to help.

Greg Kihlström: Yeah, yeah, definitely. Totally agree there. And yeah, I've used both, you know, enterprise tools that allow that. I've also, I've even used chat GPT to, you know, to analyze some, some smaller sets of data and stuff. And really, really interesting because, you know, I don't consider myself a a data scientist, but definitely some real possibilities, not just interesting ones, but real possibilities there. How do you then look at what is the relationship? Obviously, data scientists, more traditional data scientists are still needed in that scenario. But how do you look at that relationship then between the need for data scientists and the need for these citizen data scientists? How should leaders look at that? Either how to hire, when to hire, or just some things as the months progress?

Tanvir Khan: So you can create citizen data scientists by creating better tools and interface with data. But to create that, you need the real data scientists. So right now, there is a scarcity of talent. It's not that data scientists are not available. But in the new world of AI, there is not enough people with a relevant experience in data science. So there is, on one hand, a huge number of data scientists which are in a poor supply and we are seeing that across the industry. On the other hand, when you look at the new world of generative AI, people who've actually built large language models, people who've actually fine-tuned the models, Those people are not easily available and are very expensive to hire, very difficult to hire. So we have created a situation where to enable citizen data scientists, we need to create the underlying tools. To create the underlying tools, we have a very different genre of data scientists that we need, which are in short supply. So there is a lot of work to be done by the industry to take our yesterday's data scientists and give them the right upskilling so they can create those tools and platforms that we really need to create more citizen data scientists. The direction clearly is create more citizen data scientists, but that's not a matter of training. That's a matter of tooling and empowerment. But to do that, you need the underlying tools for which there is a competition for talent today.

Greg Kihlström: The last aspect I wanted to talk about from the report from NTT Data that I mentioned at the top of the show is nearly half or about 46% of organizations don't use data to gain insights or make decisions because a whopping 72% don't consider their data a strategic asset. This was rather surprising to me, just knowing what I know and working with the companies that I do. Maybe it's, again, because my work as a consultant, as well as all the conversations I have on this show, data is definitely a topic that's front and center. But what types of issues or thinking is preventing nearly three quarters of organizations from considering their data a strategic asset?

Tanvir Khan: So, I would actually You know, I'm a data geek. I deal with data all day. And I tend to agree with them. That data is not a strategic asset for them. Now, why do I say that? My belief is data is an asset. Organized data is a valuable asset. Usable data is a strategic asset and easily usable data is a transformational asset. So if your data is not usable, it is actually not a strategic asset just because you have reams and reams of data. You've got 100 years of consumer behavior data sitting in various places, but you can't use it. It is an asset, but it is not a strategic asset. To be able to really make it a strategic asset, you need to do what oil companies do, which is once they find oil, they have drilling, they have refineries till they actually get a product that is usable. So data is an asset for every organization. If they've got it organized, it becomes a valuable asset. But it is only when you have the tools, the technology, the data science behind it, and the analytics to be able to drive insights and use it to drive business results. It's only then it becomes a strategic asset. So my take would be all the companies that believe data is not a strategic asset have underinvested in making their data usable. So therefore, it is still an asset. kind of sort of valuable asset, but it hasn't become a strategic asset because they haven't invested enough in usability.

Greg Kihlström: Yeah, yeah, that's I really I like the way you frame that because I think that that's really interesting. And do you think also that it has to do somewhat with There's been such a focus on collecting data. I mean, big data was the buzzword over 10 years ago at this point, but there was so much emphasis on data lakes and warehouses and lake houses and whatever term you want to use, that now these companies are just flooded with data. But to your point, it's not necessarily strategic data because it's just kind of sitting there without a real use case. Is the volume of data that we're dealing with without purpose? Is that is that kind of is that part of the problem here?

Tanvir Khan: It is definitely hurting. Because with big data, people started gleaning and collecting all sorts of data, which has value. But there is certain data that is more valuable than others. And the more the volume of data the bigger problem you have in sorting the signal from the noise. So with very, very large amounts of big data that companies have collated, they've just changed the odds from the signal to the noise ratio.

Greg Kihlström: Yeah. Yeah, makes sense. Do you think that there still are, even in some of those organizations where data is more usable and thus, to your earlier point, more strategic, are there still skeptics out there in, let's just say, in the enterprise, our main audience for the show are really those in larger orgs. Do you think there's still data skeptics out there or is it really, you know, really going back to what you're saying is there's just some organizations that just don't have data in the right manner to be able to use it strategically?

Tanvir Khan: So a lot of people, a lot of executives, people like me, have been burned by very large data projects that have sunk in tens of millions of dollars and not really yielded anything worthwhile. And a lot of them are gun-shy sponsoring the next project because they know that some of these data projects have a huge promise but a huge amount of risk because getting all that data organized and usable is and has been problematic. So is there a healthy number of data skeptics in the industry? Yes, there is. And it's kind of sort of the same ratio of successful to failed data projects. But as we start seeing new use cases and people seeing data becoming more and more usable, that's where we will see value. So we've got certain types of failures. One is a project that never delivered what it was supposed to deliver. There are a few of those. But the largest number is the project delivered what it was supposed to deliver, but it is so difficult to use that nobody's using it. That's been where the bulk of data skepticism is that we spent $10 million. We put all sort of data. We've got the ability to slice it and dice it. And we want every sales manager to design a monthly campaign using data. But those sales managers don't know how to use the data to design a campaign. So it's been that which is I built it and nobody uses it. Once we get to a place where the data is actually put to use and people are using it in driving insights, making data-driven decisions that are either driving revenue or margin, we will see the number of data skeptics decline. And I believe the next wave of data democratization with the whole concept of a conversational interface to data is really going to help. But until people see it, the skeptics want proof.

Greg Kihlström: Yeah, yeah, makes sense. And so just to kind of paint the picture, so to speak, you know, what, what happens in an organization when, you know, it's, it sounds like there's, there's several things that need to happen for, for true success here. But, you know, the number of data skeptics decrease because To your point just now, data is not only organized, but accessible and usable. But what are some of the things that that enables in an organization? What do we see when that comes together in that way?

Tanvir Khan: I think that allows companies to make decisions that matter at the point in time when they matter to drive results. And results for most corporations are driving revenue. up driving down cost, gaining market share, gaining a competitive advantage. And all that happens if data driven decisions can be made at the point in time when they're needed. You know, one of my favorite examples is we sponsor the NTT IndyCar series. And we as a technology partners provide a lot of data. So there are some 200 telemetry points of data coming out of every car during the race, which are getting collected. And we've got a team that is taking that data, converting that data into information, converting that information to insights, converting those insights into actions, and then implementing it all while going 200 miles per hour in the middle of a race. And very often, the implementation is on the next car, brake, brake, brake. Now, that is data at work, which is converting the data into insights, into plans, into actions, and executing it in real time. Simplicity is extremely, extremely important in managing those data use cases, but it is that use of data that excites me and that's where it will change the view of the skeptics.

Greg Kihlström: Yeah, yeah. And I love that analogy, too, for I mean, I know that was a real example, but I love that analogy for business as well, because I think sometimes it feels like that in an organization. Well, Tanvir, thanks so much for joining the show. I've got one last question before we wrap up here. You've given a lot of great insights and advice already, but what's one next best action that you'd recommend for those listening that want to make a a first step towards greater and more consistent innovation with data?

Tanvir Khan: So, you know, I go back to the basics of innovation. I actually teach a class on innovation and I say there are four steps for innovation. One is a great problem definition. Define a problem well and make sure it's a problem that is worth solving. The second part is ideation, which is, you know, thinking of what's the best way to solve the problem. The third part is creating a solution within the means that are available to you. It shouldn't be a fantasy solution that you can never build within budget, time, resources available to you. And the last part is execution. Go and implement it. Because innovation without execution is hallucination. So make sure you've got an idea of what's solving, a solution that is within the constraints of the resources available to you, and then go and execute.

Greg Kihlström: I love that. Well, again, I'd like to thank Tanvir Khan Chief Digital and Strategy Officer at NTT Data for joining the show. You can learn more about Tanvir and NTT Data by following the links in the show notes. Thanks again for listening to the Agile Brand, brought to you by Tech Systems. If you enjoyed the show, please take a minute to subscribe and leave us a rating so that others can find the show more easily. You can access more episodes of the show at www.GregKihlstrom.com. That's G-R-E-G-K-I-H-L-S-T-R-O-M.com. While you're there, check out my series of best-selling Agile Brand guides covering a wide variety of marketing technology topics, or you can search for Greg Kihlstrom on Amazon. The Agile Brand is produced by Missing Link, a Latina-owned, strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging, and informative content. Until next time, stay agile. The Agile Brand.

Tanvir Khan, Chief Digital Officer & President, NTT Data

Basil Ali
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