Transforming With Data (Maya Harita)

In this episode, I chat with my friend Maya Harita, a very senior data leader - currently at HP and formerly at S&P.

Solomon’s Substack is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

We chat about navigating your career as a data leader, customer facing products vs. internal analytics, data vs. engineering, key career lessons, the work she does in mentorship and career development for women and much more!

Check it out!

Solomon’s Substack is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Transcript

Solomon Kahn: Hello everybody. And welcome back to the delivery layer podcast. I've got Maya Harita with me. Maya is a, uh, very well established data executive and friend. And we sort of know each other from the data world. And I'm really excited to have this conversation. Maya, thank you so much for joining.

Maya Harita: Awesome.

Thanks. Thanks, Solomon. It's wonderful to be here. It's such a, you know, we've had so many conversations in the past and it's great to reconnect.

Solomon Kahn: Cool. And I'm happy to have one now in front of. Whoever is going to watch this. Um, do you want to share a little bit just around sort of like your background and data and some of the really cool jobs that you've had along your career journey?

Maya Harita: Yeah, absolutely. So I started my career, um, way back in the, in the nineties, uh, Really around coding and I started as a developer and, uh, just grew my way through various parts of the business. Uh, a point in time [00:02:00] I was actually in the HR managing a lot of the recruitment processes and then moved on to recruitment systems, uh, HRIS.

systems and then, um, kind of worked my way into being a client partner, facing stakeholders internally and externally for a professional service in a professional services environment when I was at Siemens Business Services. And, um, and then kind of, as decades rolled out, moved really, uh, into the large scale program management.

Uh, around, uh, establishing data pipelines. And, uh, from there, um, really looking at connecting the dots across, uh, people, process and technology across companies like General Motors and S& P Global Ratings, uh, as setting the data organization and, uh, currently I'm at HP managing all of the data governance and customer privacy engineering platforms.

Solomon Kahn: Yeah, super, super interesting. And I know that we got connected. Obviously I've got. [00:03:00] Uh, a strong place in my heart for data, data businesses, and you were leading data at S and P ratings, which is essentially, you know, the largest data business in, in the world, kind of, so super, super interesting. And, you know, Thinking to some of your experiences there, because at S& P and other companies that you've, you've, you know, led data at, um, a lot of these businesses were data businesses before data and technology were tied together.

Uh, and so how have you. Navigated that, what was it like to have, you know, on the one hand, you've got this established data business, a lot of best practices on the other hand, you've got an industry in data that's moving so quickly outside of. Those big businesses, like how do you navigate that as a data leader?

Maya Harita: Yeah, absolutely. See many of these institutions, right. That had, uh, you know, lots of information, right. Before, uh, we [00:04:00] really started calling it data and then intelligence and now, you know, business intelligence, performance management, and then now we are in the AI machine learning, right. This in this trajectory.

The organizations that were, they've had information, right, in different formats, even before this technological revolution happened. And if you really see at that time, they were considered innovative, right? I think there was a, you know, they talk about, we talk about data growing exponentially, right? And now it's so ubiquitous.

It's growing at what, like, the more slides, what, 80, every 18 months or so, it's doubling or tripling. And, If you really look at it, that, at that, at whatever point in time, right, the companies that have been around 7, 500 years, they've managed information, right, they've been moving along with the pace of technology and innovation as best as they can, but what's happened is that the, the, the skills, the talent, the [00:05:00] processes that were established that fit the needs of the business then.

Some of them never got modernized, right? We got stuck with these old processes and, you know, generations after generations handed off these processes. We tried to make small tweaks here and there. Um, even with the, uh, really, um, rapid growth of all of our Six Sigma, um, foundational attributes, we still, like, held on to a lot of these processes in these institutions.

So there's so much institutional knowledge. I think that A lot of the folks that have been in the company, I called each of them a mini data warehouse because they have so much knowledge and information in their, uh, intelligence in their, uh, brains. And however, the actual data flows and, and the, what I, what we call as the water and the pipes, right?

The pipes being all of the data architecture and the water being the data that flows through it, that never got as modernized simply because [00:06:00] right. You, as you know, run each transform. Breakfast, lunch and dinner. And so what I have seen is that as much as there's an appetite and desire for organizations to transform quickly, it's not that easy because you start kind of started the people layer and people are used to operating a certain way and they want to make sure that they are meeting business goals and their outcomes and their KPIs.

And oftentimes business KPIs are not the same as the data organization's KPIs. So again, from a modernization standpoint, that's a critical aspect to look at. And the next is the process layer, right? And okay, you can bring in a lot of fresh talent and you train the people that are there. But from a process standpoint, just revamping end to end, right, is a challenge in itself.

And then add on to that now you bring in the new technology. So what I've seen is that there is this great desire and appetite and organizational readiness to transform. But where we see a lot of [00:07:00] challenge is that, um, the time, right? That, you know, when you, business consumes all of the time, how do you find time in a given, you know, Quarter or a given year to really push transformation.

I think that across the globe, organizations have been successful. They've really tried to prioritize and get further, but I see that prioritization as the key challenge, right? All of these other things can happen. Um, and, um, to, can you repeat the second part of the question? I didn't quite capture that. I

Solomon Kahn: think, I think that, that was a great answer to, to, to the question, right?

It was sort of like, you know, it's, it's so interesting because. I don't, I don't, I don't think most people appreciate how brutal some of these transformational efforts are in businesses that have substantial amounts of revenue running through them. It's, it's easy to, it's easy to play around with data infrastructure when it's just like your five person analytics team that is [00:08:00] using it.

But when you've got thousands of customers and hundreds of millions of dollars of revenue that are going through these systems, It's not nearly as easy to switch them out and make big changes. Um, so that's

Maya Harita: one example is reporting. I've seen that across many of these institutional organizations, even with the emergence of Tableau and click and all of these power, you know, BI and all of these tools that have come in.

If you look at the legacy reporting, there's thousands and thousands of report that still, you know, go out right now. Maybe it's not in thousands today. It's still in the hundreds. That go out across the organization and oftentimes you wonder, uh, what's the ROI or the value of this, uh, reporting. It's just that it's, uh, it's something that co companies have always done so right.

And that, that transformation to break away from the mold of the old into the new is, uh, where all of us data [00:09:00] people actually come in and we wear these multiple hats.

Solomon Kahn: Yeah. Uh, and, and I see this now. I, I saw it from inside previously and now I see it from the vendor side because when I go into some of these businesses with delivery layer to rebuild an existing product that's out there is big, big effort.

Um, most of the customers that I speak with, either they are starting something new or their old system has gotten so old that it's starting to, like, lose them enough clients that they recognize that it's existential if they don't change it. But you've got a long middle there where you've built a system, clients are using it, they're generally happy.

And replacing that is extremely challenging and generally not worth it. So I've definitely lived that as well. Um, what are some of the big differences that you see because you're, you're, you're someone who has experience of [00:10:00] data, not just as an asset that's used to make decisions, but also as like a business.

What do you see as some of the biggest differences when data is your business versus when data is just something that you use internally for better decision making?

Maya Harita: Yeah, I want to start off by data just internally us