Althea: Good day and good greetings from Abu Dhabi, the United Arab Emirates. Today we are going to be conducting an interview with Lalitha Krishnamoorthy, and we will be going through a series of questions to get some insights and some valuable tips from her on her experience, working at IBM and what she believes the future is going to bring in the data space and the information economy.

Althea Davis: Being the Chief Data Officer with my Chief Data Officer hat, I’m going to dig a little bit deeper into your planning process. To what extent is your planning process for data capabilities? And I’m not talking just a digital asset, I’m talking data assets that are managed holistically as a discipline with multiple data capabilities, and the infrastructure embedded in or tailored to the various organizations you work with versus the central enterprise plan. What did you do when it came to foundational data management practices? And how did you embed it in terms of your organization? 

Lalitha Krishnamoorthy: That is really the heart of digital transformation. It’s the how — how do you do it? It’s not a one-month game. It’s a continuously developing story. As I look back at where we started and where we are now, what I walk away with are three strong points.

One, you have to have the mindset that you have a solid data strategy backbone. When you talk to clients, they tell you, “I have a very solid strategy roadmap here. I’ll show you where I want to be by the end of the year.” They show you a one-month roadmap. They’ll tell you, “Here’s what features we need. This is the market gap, the competition gap.” I ask them, “Where is your data strategy for the year?” And there is silence in the room because they don’t have one. 

I ask, “How are you making sure that you’re getting value from the data that you’re capturing?”They say, “We captured a lot of data. You want behavioral data? You have it. You want audience profile? We have it.” But do you know how to stitch the two? The mindset needs to change. It’s a critical piece for becoming digitally ready for the future.

The digital tsunami is here. It’s like somebody was saying, AI used to be optional before, now it’s mandatory.

Davis: I echo you even more. In my journey as a Chief Data Officer, this was the missing link for many of the organizations that I worked with, and I had to bring that in and make them realize that this is a real, tangible thing. It’s not something you create and put in a desk drawer. You’re living the strategy, and if you don’t have the marching plans — I’m talking about the vision and the whole coordination — how can you actually win the war? You wouldn’t expect the military to go to battle without a strategy. I don’t think companies today can go to battle on this highly competitive global landscape. These companies’ life spans are getting shorter and shorter. So, there might be a need to have some aligned strategy and everybody’s noses in the right direction.

If we look at the issue of information economy and how everything’s so hyper-connected, data interoperability and data integration remain a huge challenge for every organization. It’s not just most organizations, it’s everybody. When companies have to integrate new data sources that are both high-volume and high-complexity veracity and all the other Vs, what are the IT ops challenges involved in that?

Krishnamoorthy: The number one IT ops— and this is why I brought up the third piece of my portfolio, which is built around running DevOps — includes DevSecOps, a security focus, and there is the DevDataOps. Recently, somebody told me that they have data ops and then there’s the IT dev ops. Now, the major issue that I see from an integration standpoint is the quality. Your data is only as good as its quality and how it’s being produced.

There are several times teams present information and reports, and I look at the data and I think, “This doesn’t add up.” We’re going to act on that data. The whole point is to take an action and drive to it. I think the biggest challenge for everyone — including us — is how do you standardize and govern the data?

How do you have the right pipelines built, the right plumbing in place, so that you have the right data flowing through the systems? And how can you check that and keep those checks and balances? This is where automation is super important. This modernization of your infrastructure is super important. So, for companies that have multiple systems that have legacy data, data on the cloud, and they have streaming data that’s coming in directly from various social media platforms, they’ve created the right habitat for a lot of data quality issues. 

You’ve created that perfect storm.

So, you need to know the standards and governance that you need to put in. Who are the data stewards who can actually take a position of ‘this data is good or not good’ before it even makes it into a data platform?

If the data’s already on my platform, I feel there is data corruption already. I need things to be filtered before it reaches the data platform. Because it’s an engine, there’s an API layer, there are micro services that are going to pick up the data. And then, the top of the funnel, the mid funnel, the end of the funnel — everything is corrupted.

And then the data scientists are going to take that data and build models out of it. And you can see how this is going to go. So, I think those are some of the biggest challenges we have. My mindset is that we need to leverage the best of the breed technology out in the industry.

Gone are the days when the “winner takes all” style works. Today, it’s all about ecosystem and partnership with even our competition, the best of the breed technology, so that we can all divide up the profits and make progress in the direction that we want.

The mindset needs to be around “How do I collaborate so that I bring the best technology that’s out there and help us progress faster on this journey because we’re not going to be able to do it all manually.”

Rashed Alsuwaidi: What advice would you give a mid-career data leader? Here in the UAE, they have a big push on youth to go into technology and coding. What alternative career routes would you suggest or advise people in the data specialty specifically?

Krishnamoorthy: When I reflect on things that I’ve done or what I would have done, I think a couple of things. One is, I would tell people, “Take more risks in your career.” We tend to be very calculated, asking “Is this the right job for me? What if I fail? What if this doesn’t go well?” I think that’s even more so as a woman in STEM. I feel I have the responsibility to share that message with the people, the girls, who I mentor; it’s OK to take risks. As women, that’s a lot more taxing on us. We worry a lot more about these things. It’s something that I wish I had done more often. I jumped over to biz dev, but that was after I had spent 10 to 12 years solid in product development.

The other piece of advice I have is —and again, this could be more of a woman thing — if there’s an opportunity, you have every right to raise your hand and ask for it.

Davis: I also just want to add that, as a fellow woman in the conversation, I think it’s wonderful … a woman in STEM. I’m not in STEM, but I’m right beside STEM; I’m in data. And I think that it’s really important to get the message out there to all the girls and women in the world that a great way to get financial equity is to get into these professions that are in the new economy. So, for all the women out there who have a good brain for figuring out things or connecting things, there’s a whole bunch of work in data for you. 

And there’s a need for women data leaders. There’s not enough of us. And I think what Lalitha said is absolutely true. I think men, especially in their careers, take it for granted that they don’t need to know everything to fill that role while women are the worst, the hardest, judges on themselves … “But I’ve got to have every skill, or I can’t apply for the job,” or “I have to have everything, or I don’t deserve the bonus.” It’s important for women to understand that part of doing a good job is being willing to learn and move beyond comfortable.

Sometimes I make the best data designs and strategies when I’m uncomfortable. So, I think it’s extremely important for us to encourage other women to take that on wholeheartedly and not be afraid to go in a different direction than the herd.

Coding and STEM — it’s all great, and there’s a lot of push for that, especially here in the UAE. But to go in the direction of data leadership and data capability, there’s a big, bright future.

Is there anything else you would like to share with us before we sign off? 

Krishnamoorthy: I just want to say thank you for the opportunity to spend this time with you. Thanks for giving me the opportunity to share what I know. There’s still so much more to learn. It was fantastic!

Davis: I think it’s extremely important for women in leadership positions, in STEM, in data, to keep that dialogue open and to share, share, share. So, thank you so much. We’ve had a wonderful talk. Rashed, any last words?

Rashed: We learned a lot about you, from when you started your career until now. It’s really fascinating! It’s really fascinating! Thank you!