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.

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!

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 used for decision making, right? So I think that When you're using data internally, right, it is, you have a lot of latitude in terms of, right, um, how you're collecting the data, how you're using it, right, what metrics you want to focus on, and, and it's really vital, right, as long as it's tied to your business value, and the way I think of it is that, does the data value chain and the business value chain having the right handshake?

Right. So if you have various components in the business value chain, and if you look at the data, like, for example, you have supply chain and [00:11:00] finance and you have HR and all these different departments within a company, and you have this data value chain that's supplying to all of these different businesses, are we having the right Water and pipes that we have the right architecture and the data flows are happening, you know, so the latitude that you have is that you're really able to invest and you're able to really appropriate funds to that business value chain based on the organizational needs.

It's purely internal. Right? So now your ability to prioritize or if it's a year that you're cutting costs, or it's a year where you really want to focus on that really, um, driving revenue across a particular market, right? You can hyper focus in that, right? You have a lot more latitude when it's used, uh, internally.

Um, and, and again, right, you are able to transform very specifically in, in niche areas as in when it's needed now. When data is your business and then there are a lot of other stakeholders and a lot of other partners in this market, in this [00:12:00] ecosystem and a lot of competition. For me, the biggest concern in my mind is about two things about data velocity and data integrity.

Velocity in not just speed with which we are going. It's not just 30 miles per hour speed. It's 30 miles per hour north or south, right? What is directionally? How fast do we want to go? Right? Who do we want it in? What kind of data? For example, some of the data that we're working in the past from the automotive industry is all think about vehicle usage and all of around the behavior of the driver and whatnot, right?

That impacts insurance and whatnot. And sometimes when we are trying to monetize data on the. Uh, on the on the financial services side, right? There are all these specific needs. And so velocity is really important, right? Ability to go from the source to destination is extremely, um, vital, but then layer it with data.

Integrity is right. Our governance and compliance and quality and making sure that the data that is reaching is enriched and it's secure and it's it's this data that [00:13:00] is refined and usable, right? So there is that. You know, a huge credibility factor, and you can't just return data once somebody uses the data, right?

And then you want to make sure that it meets your customers need and the market's needs, etc. Right? And then when you have, you're in the business of, um, not just, you know, Having a data marketplace, or you have analytics, self serve analytics, or self serve AI models and anything that you're talking about these advanced technologies.

Now, all the more when that's part of the business is that the velocity and integrity become extremely vital. And that's where I feel like, You know, the investments and the focus on the people process and tech, uh, becomes so critical. And if organizations miss either one, right, one of the levers are not operating in sync with the others, um, is where, um, you know, you starting to see latencies [00:14:00] happen or performance issues, uh, happen.

And, um, and again, in both scenarios, I think that it's really important that we. Have the right operating model. I think it's extremely important, especially if data is a business, right? You know, the, the, the, the folks that are internal core of the heart of the data business, like, or even really enabling these data pipelines.

Have to have this view of the markets have to understand what businesses, what business, uh, objectives are or what market needs are, right? It's not just sitting in a silo and creating, um, models or creating reports, et cetera. So it's that the operating model becomes extremely vital.

Solomon Kahn: Yeah, a hundred percent.

And I think to dive in on two of those things, I think that until you've had customer, like internal customers are way kinder to your data issues than external customers. Like, and, and many

Maya Harita: customers might not, and customers are sometimes [00:15:00] harder.

Solomon Kahn: And, and there are, and there are far more external customers.

So it's like if you, if, if. If, if there is an issue with something that goes out to someone on the marketing team and they look at it, it's very different than if there's an issue that goes out to a thousand different marketing teams. And then each one of those people is pinging your customer success.

It's like a whole different. It's a whole different ball game. And what I found is that many people underestimate the amount of QC that has to go into a lot of these data products that are actually reaching any type of commercial scale, because without that, you just, you know, churn all your customers super quickly.

And then the other point that you made around the data, people understanding more of the business context, I think is also super crucial because when data is your business, [00:16:00] there's no, there's no harder bar to reach than someone paying for your analysis out in the market, when there's a lot of competitors that are also trying their best to take that customer from you.

And if you're not on, on point, when it comes to understanding actually what they need from a business perspective, there's no way they're going to buy it. So

Maya Harita: if perspective to that, what I've seen, if data is your best businesses, right? In a certain angle, your customer is also your competition. Right.

Because what happens is when they're consuming this data, right, they are going to have their own in house development teams. They have their own in house analysts. They are now looking to see how to further enrich the data that they have consumed. For example, if you are like, we used to have over a thousand plus data sources, right?

When I was at in the financial services space, and then we would have all this data that comes and ingested and process it. [00:17:00] And then, and, and our consumers. would further enrich it. And if they saw a lot more issues right now, they're thinking, do we do a lot of this in house ourselves, right? So how do we continue to incrementally add value to our customers and make sure that they are not having to think about, okay, what is the geopoint, like some of the QC around this and governance and compliance around it is like that refined data that they are receiving is.

Is valuable. So it's that, you know, that, that chain of integrity has to be tight throughout.

Solomon Kahn: Yeah, definitely. Um, how do you see the connection between data and engineering for these types of businesses and these types of products? Like where does data start? Where does engineering end? Does it matter? Like, is it all just one mashup?

Like how, how do you approach that?

Maya Harita: I think, um. [00:18:00] That's a great question. Actually, we often put data engineering together. Right. And if you really look at, you know, um, I think that again, going back to the water and the pipes analogy. Right. And I know it's commonly referred to. If we really look at, you know, to me, building those pipes is that engineering, right?

Having the right architecture that enables the data flows, right? How we are ingesting the data and how we are managing and processing, what is that generating that velocity throughout and having the upstream and the downstream data quality, setting up these platforms and setting up the right architecture is so important and having the right engineering teams, but these engineering teams need to understand what is the Purpose off our business.

What is the purpose of the certain data flow? So, for example, if you're building a platform that's that's facing customers, right? Where if it's, let's say it's a subscription model. So customers are paying for this data and this all of these insights and analytics, right? We want to make sure that we have the [00:19:00] right.

Um, you know, cloud infrastructure. We want to make sure that we have the right platforms in place. It's really important that the engineering teams have that perspective of the purpose behind what they are building and why they're building on the privacy side. When we build the engineering, right? It's a completely, uh, I wouldn't say completely.

It's a significantly different model because a lot of it is, you know, focused on security of a lot of it is focused on every op of the data. We want to make sure that that is the right level of data quality, uh, measures in place. Right. So what is really important is faster. Data flow is important. Plus quality artists, a lot of it's around compliance.

Having that perspective for the engineering teams is extremely important and having the right level of prioritization and being able to scale faster is also critical from an engineering standpoint at a high level. For me, from a data standpoint, right, where I see the difference is that oftentimes it gets, we used to call them content specialists in my prior role.

Um, [00:20:00] and if you look at it, even when I was a GM, right, there were all these folks that were data stewards or data specialists, they knew the data, they knew the business metadata, they knew the technical metadata. They were able to understand data at an in depth level, and they were kind of, they would be partners to the business, um, SMEs as well, right?

So they would understand data at an intricate level, and they will help really curate the data through that process. So for me, um, from a, from a data standpoint, I think that we're now starting to merge these. Um, I, I, one thing that I'm particular about that we don't lose focus on these data specialist people who are not just analyzing the data.

People who really understand are able to curate that data end to end and they know the mutations. They know that after a certain point in time, after a certain level of processing, after you enrich the data, then what happens to the data and how it mutates, right? Having that end to end view, I feel like is super critical.

Solomon Kahn: Yeah, everybody's a [00:21:00] data

Maya Harita: engineer now, but I feel like that to me, it's close to my heart that I used to work in data and I used to understand these data sets. So in and out and remember the good old days when people wrote data dictionaries, right? It's just that that expertise is super important.

Solomon Kahn: Yeah, I can think to a number of people over my previous jobs who just had a sixth sense they could look.

At the results that came out of a system and they would just like hone in, they'd be like, this number is wrong. Something went wrong here. This is a thing. It just like, it was, it's not always fun to deal with because, because they can very quickly highlight all of the things that you made mistakes on, but they're like so crucial for the success of.

Your business, because they, they have this deep, deep sort of intuitive understanding of how things should look. And there [00:22:00] it's important to have those people internal, because if you don't have someone like that, you're just gonna have a lot of mess on your hands.

Maya Harita: Right. And that's where the ID support gets bombarded with these questions.

And sometimes they don't have that context. Right. Data with context is content and they don't have that context. They just have the data or they face all these issues. And then that's when we go into these rabbit holes about really what happened here, right? Because you don't have people. Sometimes that's why when we talk about operating model, do we have the right roles and and folks that have this institutional knowledge and this understanding of data.

Data hops and data mutations. And they like, as you said, right, they look at a number and say, Oh, that shouldn't be this particular data point. Something happened, you know, and they're able to quickly, um, help resolve.

Solomon Kahn: Um, so obviously you've, you've had a lot of sort of transformation type roles and data by its very nature is often playing some sort of transformative role [00:23:00] inside companies.

What are some of the lessons that you've learned over the years on how to execute these transformations effectively using data inside really big companies?

Maya Harita: Um, so, so transformations, right, are what we, what we, when we think of transformation, right, so we often think of it in terms of moving from A to B, right?

I feel like So much of the effort is in that tube from A to B, oftentimes getting people to one align on what does B really mean, right, to them? What does B mean to the organization? What is the value that B is going to generate? And getting them to all buy in and to move at a time is the mammoth of an effort.

And you may have heard, I was part of GM's digital transformation. And oftentimes when we could, it's almost like, you know, Herding cats when you get one group in the other groups moved back and then you're like, okay. All right, let's come back um, and and it's a it's People have gone through [00:24:00] these transformations I can kind of really attest to the scars that come from the the one key issue about is the buy in right and I feel like If we can get across the board a buy in on one, what is that one trans, what is that end state like?

Do we all align on that end state? Do we all buy into that end state? And are we willing to put up with some pain today so that we have a longer term gain, right? Getting that rationale, I think, comes through data. And if we can articulate our story that A to B, What that looks like, the starting point, ending point, that end state, and that future benefit, um, and why this pain is worth it, that articulating that story through data, uh, have seen that very powerful, because People often tend to like stories that are very nebulous, and it's very nice to hear, but what really gets that buy in is the [00:25:00] data.

If we are able to showcase, highlight one word, how is the ecosystem trending, right? So that's one of the things that we did is we How are the markets tending? So I used to lead these initiatives around leads and loyalty, right? When you look at our competition, how are they managing the leads, right? What does that lead lead processing end to end, right?

How does that translate into business value? Because these leads convert into. customers, right? Having the right set of processes, having the right transformation will enable us to sell X number of cars, right? And this is what the market's doing and being able to articulate that story in terms of data, in terms of observations of the market, the ecosystem, the trends, I think is the most pivotal way in, in aligning teams and stakeholders into, into moving to the next level or the next stage.

Solomon Kahn: Yeah, yeah, it's a great, it's a great perspective, you know, in my, in my experience, the challenge, the [00:26:00] challenge is always that, like, getting buy in and alignment isn't binary. And then, you know, you get, like, 80 percent alignment, and then you have to drag the extra 20 percent over and then the projects end up being a lot messier to implement than you, you plan, obviously, you plan for things to go well, and you say that there's going to be challenge as we do this, but challenge feels different in the moment when you're facing it, then, um.

You expect that it's going to feel before you face it. Um, so yeah, it's, it's, uh, there's a lot of wisdom behind what you said. And I think you're,

Maya Harita: Now we have enough data that shows that organizations that transformed. Versus companies that were left behind, right?

We can clearly look at now, right? At least, you know, 15 years into this transformation by enlarge, right? And I think the, the [00:27:00] financial crisis really. Um, triggered a lot of the transformation for the financial and services industry. Right. But if you really look at it 15 years after and and companies are still transforming.

Right. And it's an art and a science. And I think that Oftentimes we tend to think of data, like to your point, we tend to think of data as this, um, you know, it's, it's two dimensional. It's not, it's actually three dimensional because through data, we can really look at it in various ways. And we now see hindsight, right?

There's so much, um, evidence, uh, that, uh, shows, you know, the difference between companies that really jumped on, took on that short term pain and have been able to get that long term gain versus the others.

Solomon Kahn: Got it. Yeah, that's, uh, that's very true. Um, so you've spoken a lot about women and data leadership and sort of different leadership styles.

Can you share some of the key lessons that you like to talk about just on that topic with the, [00:28:00] with the audience?

Maya Harita: Absolutely. So, When I first started, right, I didn't have a lot of women leaders. There were a few and, um, and over the years as I grew, uh, in my career, um, it, it took me a long time to first realize that you need champions and you need sponsors and mentors in the workplace, which I didn't have, uh, at least in the first decade, uh, because I didn't, I didn't go after Or seeking for mentors or champions.

And so my career was, you know, um, pretty, I would say we grow a little and then plateau and then grow a little and plateau. Um, and what I've come to realize is that. Organizations need to take them in leadership really seriously and not just it's not about, you know, having quotas or saying, you know, which is a great start, right?

Saying that we really want to push for men, women, leadership and organizations. We want to make sure that we really [00:29:00] finding these. There's, you know, leaders who are hidden in caves and corners and we're doing exceptionally well in their areas. But how do we help them grow? That's absolutely important. I will never diminish the value of it.

But more than that, I think what is really important is this larger literacy and education around. The fact that women leadership, um, we don't have a, you know, we're now starting to see right in the last 20 25 years, we've seen this huge influx of leaders that are emerging and that are doing well and that are showing that, um, you know, leadership styles have always been predominantly designed after male leadership, right?

I am not a table banging, not, not that other bank tables, I'm not one of those very loud, but my style's pretty assertive. I I care about collaborative leadership, empathetic leadership. I care about really fostering an environment that's nurturing and yet high performance oriented, right? So there's so many different styles.

[00:30:00] And I think by and large, we need to have this understanding around what, Women's leadership looks like we have so much examples in so much legacy and lineage of male leadership, right? Sometimes we want to superimpose that on women leadership. And I think it's time that by and large, like my son, he was in college.

Now, he said something the other day. He said, Mom, I can see stark differences between male leadership and women leadership, right? The end point, the destination is the same. It's about outcomes and productivity. But I see differences, but I'm so happy that he's able to see that and able to bring that into the fold.

But, uh, what I'm seeing is that that's not the case everywhere, right? And women are still struggling to, you know, Prove their case, make their rational for growth and for promotion. So, uh, simply because they are not evaluated on their own merit on their own capabilities. There is a lot of this, uh, expectations around what we think is [00:31:00] leadership.

And I think that literacy and that education is super important.

Solomon Kahn: Got it. That's great. And maybe dig, dig in just to, just to follow up on, on that, like how you start, you said at the beginning about the importance of. Finding mentors and finding sponsors to sort of help you get to the next level like How, what are, what are some examples of either how people should tactically go about doing that, or some examples of where that sort of make it made a big difference for you?

Maya Harita: Absolutely. Um, so I've, I've had, um, mentors throughout like last 15 years or so. I've had some really good mentors in my career. Some of my managers, when I was at General Motors, um, they really gave me projects that I thought were. That I wasn't sure if I could handle. They're like, you know, give this a go.

We feel like you can do it Give us stretch projects and you know it almost uh, you're able to get past your own self doubt and being able to um [00:32:00] See yourself succeed, right? That's where it starts. Everything starts from in the mind and in One self image of success, right? Once you see yourself do these hard things, then there's just you keep, you know, then the momentum starts picking up tactically in terms of finding mentors.

I think that there are many organizations. Now, for starters, I've always appreciated the work that women in technology does. Right? And there are so many other forums and organization. I think one must actively seek out. a champion and a mentor, uh, in their own line of works. For example, if somebody is in technology, somebody is in data, or somebody is in finance or HR, finding a mentor, finding a sponsor in the same space is extremely important.

Sponsors are typically people who are in leadership level roles, who can talk about you, uh, on your behalf, when they are on a table talking about high potential candidates. Right? You've got to seek them out, find ways to connect with them, have a, even, you know, request a [00:33:00] coffee chat, and then start building these relationships over a period of time, right?

Taking time to network and taking time to do that is extremely important. Um, the second thing to that is, you know, finding sponsors and, uh, champions and mentors outside Your line of work is also important because if you want to have opportunities to talk or speak, be part of a panel, you need people to get you open doors and get you into other areas as well.

So LinkedIn is a great resource looking for women leaders that there's so many that raise their hand and say, I'm here to support. I'm one of them, but there's so many out there, right? And seek them out and start connecting with them and, uh, you know, start, start getting a, you know, getting a feel for what it means to have a champion.

Solomon Kahn: Yeah, that's, that's, uh, it's great advice and one of the interesting thing, interesting thing sort of bringing up LinkedIn is how it's made everybody far more accessible. And I'm still shocked at [00:34:00] how few people take advantage of that. Um, because it's sort of the way that you framed it, I think is good.

Many senior leaders want highly motivated, smart, younger people to help them develop in their careers. And it, you know, I, I also had a number of mentors that helped me over the years. It's one of those things where they're far, they're more accessible now than they've ever been. And I don't think that many junior people understand a lot of the implications of that.

So I think that's great advice for sharing. Thinking about that sort of like, Career development, professional development for data people. Um, one of the things I recently launched, it's professional development program for, for data people. So I'm like talking a lot about what makes the top data people.

Maya Harita: I'm glad you did it. It's really, it's [00:35:00] timely.

Solomon Kahn: It's been super interesting because we invest so much in. Trying to succeed with our data programs, millions of dollars in infrastructure and technology and team. And we really, there's like a whole category of data work that we really don't have any resources to help people learn about, like on the business side of data work, which is, it's just, it's just one of those things that sort of like, as I'm going and doing this work, it just is just so clear to me of how big of a gap it is.

It's sort of monumental. But I'm having so many conversations about what top data work looks like for people. So, like, when you think about a top data person versus a typical data person, what are some of the key differences that you see?

Maya Harita: I think, uh, the, the biggest difference that I see is, um, data people who are techno functional, right?

[00:36:00] Not just technical. When you're purely thinking of data as a technical exercise, right? I'm collecting data, I'm cleaning data, I'm analyzing data. I am, you know, um, building these models, right? And it's all super fun, right? You know, making sure that you're building all these regression models and whatnot is, is fantastic and being able to build analytics dashboards, right?

People are fascinated by it, right? It's such a creative process and it is, um, and it's, it's like, you know, and those who are very statistical and mathematical get to enjoy it. Fantastic. But the functional component of it is so important, right? When I say techno functional is not just thinking of data as pure play data and cleaning up data and putting all these parameters is to really think about what is this deep data mean for the organization, right?

Why are we. working on this particular data sets? What are we looking for in these correlations? What is the business [00:37:00] objective, right? And I think that while there is technology and there is business, I think data is one that ties both, right? Because it is that from, from data all the way to this insight and wisdom.

If data folks don't understand, That metaphorical layer, right? Not just the technical attributes more about what does this really mean that I think that then you become very myopic, right? And I think that's I think sees a big difference. So folks data folks like as we were talking earlier about folks that understand content right from a contextual standpoint and being able to bring that perspective and is important.

Now we're talking about a I right? A lot of the AI governance is about, they're talking about having diverse teams that are part of building these models so that that can really help minimize and eliminate bias. Think about it. That's a techno functional, right? So you're not just, I mean, this is not just, you know, Solomon coming [00:38:00] in and building these AI models.

This is Solomon who's bringing a certain perspective from life. Right. As either as a parent, you're coming and you're looking at this, Hey, is this model really functioning? Are these requirements right? Are we introducing some kind of a bias or you are coming in from a particular city? Are we doing the right level of parameterization by eliminating somebody else that's outside the city?

We're seeing so much of this, um, Bias is getting introduced, right? So even from a, from a AI governance standpoint, it's really important to, uh, having these techno functional, uh, talents within our, uh, you know, development teams and our build teams. And so it's, uh, I think that is a key, um, differentiator between somebody who's just pure play using and playing with the data to really, you know,

Solomon Kahn: Yeah, that's great.

And, you know, you, you have had this, this career where you've had sort [00:39:00] of both that technical and, you know, business leadership role, maybe starting from the technical side, like how did you develop on the business side? What were some of the things that you did to be able to, you know, operate at a senior business level coming from maybe more of that technical background?

Maya Harita: Um, I think where I, when I was very pure playing the technical side, right? And, uh, every time I had to really focus on storytelling, I felt like I was very successful in my closer loops and closer circles. The moment I had to take that and articulate it to somebody that is a business partner, right? And that's when, you know, I was seeing that we were not able to make much progress.

That's simply because we were talking two different languages, right? And if you really look at it at the end of the day, I read one of your, um, you know, messages a while ago that data is a sidekick, but if data does a good job, it [00:40:00] could be a hero like a while ago. And it stuck with me. It is so true.

Actually, you know, data is an enabler, right? And technology is an enabler, but what does it enable? It enables business. It enables business progress and objectives and growth and all of this, right. And this I need to speak that language. That's how it started for me. I started taking much interest in really, what am I building?

It goes back to the techno function. What am I building? Why am I building this? Who does it serve? What outcomes, right? Moving from outputs to outcomes. Also shifted my mindset around understanding Business value, business ROI. And then I started, once I understood that more, I was very fascinated because I wanted to be part of that camp so that I can bring my data and technology expertise to support business.

Solomon Kahn: Super interesting. And thank you for bringing that, that up. You know, it's, it's that, that particular, um, idea, the data is the sidekick is one of those things where, [00:41:00] like, it, it probably doesn't seem as profound as it feels to me, because this is one of those things when you write content or when you, like, when you're a senior person, there are like some lessons that are like many, many years of pain.

That sort of coalesce into like a little LinkedIn post. And so it's so nice to hear some other senior data person, like pinpoint that one in particular, cause that one is like, there, there's so many things that people get wrong in the way that they approach data. And, and anyways, it's just, it's interesting to hear.

Maya Harita: I really liked it. It was a, it was a fun little, like, you know, the content.

Solomon Kahn: I love it. I love it. Um, well, this has been such a fun conversation. I'm glad, you know, we've been planning to do this for so long. I'm glad we finally were able to get it done. Um, how can people follow along with the work that you're doing and follow along with whatever you're talking about?

Maya Harita: Um, I'm on [00:42:00] LinkedIn. They can reach out to me. They can DM me, especially if somebody's looking for a mentor. Uh, they want to chat or they're looking for a friend who has been in the same space or wanting to grow or, um, have had, uh, maybe similar background like mine. I spoke very little English when I came to the U.S. I had to teach myself the language and a whole lot of other cultural attributes, um, uh, as I moved to the States. So I can relate to a wide Uh, range of, uh, women who may have similar backgrounds. So I'm always, um, available. I coach a good group of, uh, aspiring very, very smart women. Um, and I've been doing so for years.

This is part of just my personal, you know, uh, way of giving back and connecting and helping people grow. Uh, and so I'm available. I'm on LinkedIn, um, and if somebody wants to reach out to me.

Solomon Kahn: Well, that is an extremely kind offer and my [00:43:00] strong recommendation for anyone who's listening to this that hasn't already gone to LinkedIn to look at your profile is that they do it immediately and send you a message and, uh, Yeah.

Don't, uh, don't not take advantage when people offer help is one of my maxims of life. So, all right, Maya, thank you so much. Thank you for joining and, uh, really had a great conversation. Have a great day.