Navigating AI with Junaid Farooq

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Hi, Sandy Estrada here. Welcome back to another episode of How I Met Your Data.

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Anjali and I are thrilled to be joined this week by Junaid Farooq.

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He is a seasoned expert in data governance and strategy.

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He's currently leading enterprise data initiatives at First Citizens Bank.

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I first met Junaid in April at FEMA Boston.

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It was clear to me then that we had to have him on the podcast.

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Junaid and I had so many conversations on data democratization,

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data governance, data strategy. He left a lasting impression on me, to say the least.

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So as we go into our seventh episode, I just want to take a moment to thank you, our listeners.

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We've gotten a lot of feedback, some via email, some via LinkedIn,

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some via one-on-one meetings that I've had with folks.

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And we're just very thankful for you continuing to listen and continuing to

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just kind of be there with us in these conversations.

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And as we continue to have these conversations and record these episodes,

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I am personally growing more and more excited about our decision to keep them

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topic forward, but completely unscripted.

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And the conversation that you're going to hear today with Junaid perfectly exemplifies

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what we were looking for when we took this approach.

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We cover so many topics, and I'm not going to go through them right now.

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I'm going to let you discover those as the conversation unfolds.

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So without further delay, here's...

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Music.

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So, Janae, thank you for doing this again. Ever since I met you at FEMA in Boston.

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I was immediately, I think I came up to you after our first day hanging out

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together there and said, I would like to have you on my podcast.

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Really enjoyed just kind of

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your perspective on the topics that we were talking about at hand, right?

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Data democratization, data governance, data strategy. I mean,

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we covered a lot of different topics during the FEMA Boston event.

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So thank you again for taking the time to be here with us today.

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Yeah, thanks for having me. I similarly felt the same.

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I just loved your energy, your passion for the topic.

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And it's always fun meeting others who have the same energy and passion on the

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topic and similar views.

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Yeah, absolutely. I agree. I think your passion came through as well.

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And I appreciate that. I always find that if I'm energized, it's because of

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everybody else, not necessarily my energy.

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I'm just feeding off other people's energy. That's a plus.

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I always think we said once at our experience level, we didn't study data.

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We didn't choose this as a career. Many of us just wound up here.

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If you wound up here, it's generally because you really enjoy the topic.

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I have this joke. I say it like no one gets into the data game for the accolades and for the applause.

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It is a tough, tough business and you have to really love it to stay in it.

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If I have my data governance hat on, no one's really happy to see me, right?

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If I have a leather hat on in terms of data quality, our data is never good

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enough. No one's ever come up to me and said, you're done, Junaid.

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We've achieved our utopian state of data quality. It's a tough field and you

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have to love it to be in it. Yeah, absolutely.

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Maybe where we can start is a little introduction to you, if you could share

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with our audience a little bit about yourself.

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Sure. I'm currently at First Citizens Bank. I lead our enterprise data governance

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function, and I also lead our enterprise customer data function.

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My job's been very interesting. First Business Bank has had two major acquisitions in the last two years.

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They acquired CIT Bank in January, 2022, and they doubled in size when they.

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That. And they went from roughly $40 or $50 billion to $100 billion.

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And that put them into a category that required some regulatory scrutiny.

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So data governance and data management became a focus at that time.

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And then last year, out of nowhere, First Citizens Bank also acquired Silicon Valley Bank.

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And with that acquisition, they doubled again. So roughly $100 billion to over $200 billion.

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And quite suddenly, they're a category four bank. And so work has been exciting.

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You can imagine bringing three institutions together.

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Even in a single institution, there are so many different views and opinions on data.

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And even within the same institution, you sometimes struggle with lexicon and vocabulary.

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And then when you start assimilating two other large institutions,

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you can imagine the cultural dynamics, the varying views that everyone has on

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data and approaches to data.

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So it's been an interesting journey. It was probably very modern,

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very forward thinking, a little different than a more traditional bank.

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Yeah. One of the benefits that I think we had in acquiring Silicon Valley Bank

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is they had a very good data program in place and they were headed in the right

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direction on a lot of things.

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And so they were pointed in the right direction in terms of their data maturity journey.

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And there was a lot of synergies that we found that there were some things that

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Silicon Valley Bank was further along than we were. And then there were other

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things where we were moving further along.

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Bringing those efforts together, there's definitely a lot of synergies to be

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found. But culture is very different.

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And you know, you and I said when we were talking about data strategy,

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strategy is great because it's a type of culture, eat strategy for breakfast.

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Aligning on culture, aligning on literacy, essential to anything data related.

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So you have mentioned that you're leading data governance, politician in that

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respect, but also you're in charge of the customer data strategy as well. Well,

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How pervasive is AI? How much pressure is there within the bank in terms of

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the things you're trying to do with AI at the moment?

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Is there a ton of pressure around it or is it very measured because of the high

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regulation being part of a large financial institution?

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Yeah, I'd say the latter. First Citizens has a history of doing everything in a measured way.

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And we are generally not the first to market in these spaces.

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I think it's good to sort of observe what's happening and where things are heading.

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Do you remember a few years ago, Sandy, like people were all about blockchain,

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right? The way that we talk about Gen AI today, it was blockchain.

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And everything was going to be, there's gonna be some component of blockchain in everything.

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And like, where are we today with that, right? So definitely a very measured approach.

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I do think any institution would be foolish not to at least do some exploratory

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work in this space. My personal opinion is that this is definitely more real than blockchain.

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And I do think this is going to be a game changer. We're still very much in

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our infancy, but I do believe that this is not going to go away the way that other things have.

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Yeah. I believe that Gen AI has put a highlight on AI just in general,

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right? So even the things have been around for a while, right?

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Everybody was hot on machine learning over the last five years.

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And now it's even accelerated because a lot of organizational executives are

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coming to individuals saying, what can I do that's different and novel?

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You know, starting with Gen AI as the point of the conversation,

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but then that evolves into other topics and areas that AI can help with,

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which then brings along a lot of machine learning opportunities.

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I, for one, find that there's still this challenge of,

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yep, these are all great ideas and the technology can move quickly,

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but most places still struggle getting their house in order in terms of ensuring

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they have the right data and data quality.

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Yeah, listen, I think that there are a few fundamental things like the term

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Gen AI and the work around it sounds so exciting.

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It's hard not to get sort of get wrapped up in it.

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But this work, these initiatives, these platforms and these technologies are

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still dependent on the things that we've been working on for the last decade.

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Good data governance, good data quality, excellent metadata strategy.

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Like these are still foundational.

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And I think that these are essential. Poor data quality will skew results, will create bias.

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You know, it's interesting how we're so ready to try this and look at ways that

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we can leverage it without really understanding the criteria like needed for the foundation.

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Like, you know, what does a good model look like? How do we know when we're done training a model?

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That's a very basic question that I don't think I've heard a good answer for.

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You know, I would say we know that the content that's produced out of Gen EI isn't 100% accurate.

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And so the question is, how accurate is it? Is another question that we can't

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really quantify very easily.

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We're still in that phase of asking questions that we still don't know the answer to.

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I'd almost be astonished if people felt strong that they knew the answers to

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every question in this space.

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What I'm finding to be the case is that even if you think you're getting it

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part of the answer, the answer changes just down the line because there's a new innovation.

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There's a new way of handling it. I went to Snowflake's event last week and

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they were showing their front end to JetAI capabilities for many different models.

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And a lot of the use cases they showcased, it had citations.

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You can click on the citation and it will show you the document it found the

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answer from and the section of the document and highlight it for you.

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So So in the past, people said, well, I can't cite what my model is coming from.

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And they showed that you can.

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You can do that. There's opportunity for that kind of engagement with Gen AI,

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which is, again, moving the needle forward and answering a question that people

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couldn't answer before, right? Yeah.

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It's definitely moving in that direction. And I was super excited just to see

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that one capability that I hadn't seen anywhere else.

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That's an interesting point is like, how do you create a roadmap, right?

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Even if it's like a couple quarters, right? And say, okay, I'm going to go do something.

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And you'd be foolish to think that you'd ideate on something,

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create a pilot, define an outcome, understand the dependencies,

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and then go do it even within a year.

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Like that's a thing kind of aggressive. But if the pace of change is faster

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than that, then you're almost always playing from behind.

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You know, I'd say it's sort of tangentially related to this.

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I was talking to somebody about data, data programs, and how I've been in and

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out of so many institutions, both as an FTE and a consultant.

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I've never implemented anything the same way twice, whether it was like rolling

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out a data catalog or putting in data quality framework or even setting up governing bodies.

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And I always caution others, if they feel like they have a data governance playbook,

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there are thematic things that you will follow.

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And there are themes and principles that will guide you. But you will never

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do something the same way twice, period.

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You shouldn't, right? Every institution will have its own sort of nuanced way

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of working. And Gen AI just is an exponential.

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And now you layer in technology and the pace of change in technology.

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And I told this person that I was looking at work that I did three and a half

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years ago. I thought I can go back and leverage some of that work to what we're doing.

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In many cases, that work is obsolete today.

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The way of thinking about data, even three and a half years ago,

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is so different today and has become obsolete.

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And so that's another question that's come up, which is if we endeavor to do

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something and we create a plan to do it and it takes 12 months,

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how do we adjust for the change?

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How do we be good about seeing the next corner in the road?

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Yeah. Well, one, it makes me laugh that we're talking about playbooks and really

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not having a playbook because I really think of it more as a checklist.

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What are the handful of things we need to get right?

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And, you know, we need to adjust those criteria to meet the culture of the organization,

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where they are in their maturity.

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But I'm curious, Junaid, because you talked about multiple acquisitions over the last few years.

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So as you're onboarding your acquired bank, I guess, are you changing or really

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reflecting differences in the banks and the cultures and their expectations

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during those integration points?

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Listen, I think there's, again, like lots to be said about culture and the cultural challenges.

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You know, when we think about integrating data, our biggest challenges in simulating

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the data is that there are just different approaches that the different institutions have taken.

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Hi, Sandy here. Pardon the interruption. Junaid is about to introduce the concept

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of a CDE, and I wanted to make sure everybody was on the same page in terms of what that is.

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So a CDE is a critical data element.

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So for example, in a banking context like First Citizens Bank,

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a CDE might include something like customer account numbers,

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transactional amounts, regulatory reporting fields.

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Now, these elements are critical because errors or inconsistencies in these

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types of elements can lead to significant operation issues, compliant risks,

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or inaccurate insights.

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You can imagine what that could lead to in the context of a bank.

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So with that in mind, let's get back to our conversation with Junaid.

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We have this notion of CDEs. That's a thematic thing that most people have.

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What is the most critical data that we want to govern?

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We have an additional layer of nuance, which is let's tier our CDE.

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So you might have a CDE that is derived from other CDEs.

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And now what is the difference between a tier one and a tier two?

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Getting alignment on these things is sometimes very difficult because you don't know when to stop.

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We had one particular case where we had a CDE where if you followed it to the

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end, it had something like 53 or 63.

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And we're not going to increase our governance from one CDE to 63 times that amount, right?

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It's just not. And so the question and the debate becomes, what's the right

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number? Where do we stop? And how do we tier?

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And things like that. So it's the nuance where we find our challenges.

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Yeah. Just in talking to organizations about their critical data,

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I think one of the first challenges that we run into is really aligning on a

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definition of what critical truly means.

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Yeah. And then from there, starting to align data that fits that criteria for criticality.

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Yeah, it's like, again, it's like we know that thematically we should identify

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critical data and then the definition of criticality will vary from organization to organization.

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And what's critical to someone in reg reporting will be very different to someone

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who sits in marketing. today.

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Yeah, I love the comment you made earlier where you're not going to take a playbook

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and repeat it in the next org or even division of an organization, right?

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And apply it because it is unique for the reasons Anjali stated and then some.

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Every organization even is structured differently and you need to restructure

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how you do governance because the org structure is inherently different.

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They may have business and IT, they may have something in the middle.

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They may have a product team, an engineering team and a business team,

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right? There's no one size.

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Yeah, there's absolutely no one size fit, but you have to find that middle ground

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of having that checklist, having maybe some frameworks that you can reuse.

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You know, parts of to ensure that you're covering all the bases, right?

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At least that's the way I think about it. So, and I also agree with you that

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things are outdated as soon as you put them on paper.

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One thing that I believe is completely outdated is how we think about data quality

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within organizations and how we address it.

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So I wanted to maybe jump on that topic with you a little bit.

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Given that you're in a highly regulated environment, you're probably doing things

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a little bit of an old school way, is my guess. There's probably some legacy systems involved.

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If the organization were to move on to AI, what do you think would be critical

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to get right before you start that?

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I think that there's, without a doubt, out, there are probably three disciplines

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in data that I would say are essential.

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And again, this is debatable. You could add four or five, right?

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And I would say your metadata strategy, like your underlying metadata is crucial

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in having a really well thought out, robust.

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Data catalog. Without a doubt, good data quality. You need data that is fit for purpose.

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And you need to be really clear on what the purpose is and what the criteria of good looks like.

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So data quality is another discipline that I would call essential.

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And then your governance and oversight function, the policies and procedures

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that define how you do your metadata strategy and your data quality,

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the usage of things like AI. These are three essential.

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Without a doubt, if you're not thinking about these three, you would run into

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trouble with any initiative, especially a Gen AI initiative.

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And then when I think about other fundamentals to a Gen AI initiative,

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there are probably three things that I would tell people to be comfortable with.

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Machine learning and data science.

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You should be grounded in the basics and fundamentals in both of these.

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I would advise people to learn programming.

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Python, R, and these languages Languages are very easy to learn.

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I grew up, you know, I'm going to date myself a little bit. Like we studied C and C++ in college.

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And the amount of debugging for the older list, all the, you know, commiserating with me.

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These new languages you can honestly learn in a weekend. It's not very, very hard.

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And then the other sort of concept or skill that I tell people to develop is

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be comfortable with math.

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That's a little bit harder for some. But these algorithms rely on some foundational

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understanding of probability, statistics, some linear algebra.

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I think doing those data disciplines is foundational.

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And then really thinking about these other things and grounding yourself in

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some basic knowledge is essential.

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We talk about Gen AI without talking about all those other things.

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Grounding yourself and experience in those is essential. And depending on your

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level, a certain amount of effort or granularity you'd want to get into with those.

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But even executives like senior leaders, I would say the same thing to ground

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yourself in what it takes to write a Python script, how to leverage those libraries,

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have an understanding of probability and statistics.

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Those, I think, are the foundational sort of concepts.

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I'm sure others would have so many other things, but those are the things that

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come to my mind. Yeah, I would agree with that list.

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I also learned C++ in college.

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And after that class, I never looked at it again. But I'm jealous of the younger

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generation because the amount of hours I probably spent looking for that road semicolon.

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And right now you can go into Hugging Face and just copy or put it in there.

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And it'll be like, oh, you're missing this here. And you're not.

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Yeah, I still have PTSD whenever I have to use a semicolon in a regular session.

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It always was worse, right? You spend like literally 15, 20 minutes looking for it. Oh, yeah.

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And then your colleague will find it in two seconds, right? It's just like, can you look at this?

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And like two seconds later, they're pointing right at the line.

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The new generation definitely has it so much better.

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The amount of tools and technology and platforms that they have at their disposal

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and free, the best part of it.

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The amount of technology at our disposal today is just mind-blowing.

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I also have another element to add to your list.

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You mentioned executives being familiar with coding languages to some extent,

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just to understand the efforts that it takes for a team to facilitate the solution of a problem.

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I think that's really important. I would flip that on its head as well with

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the teams who are developing, because oftentimes those teams are making decisions

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in terms of what questions to ask of the data to find answers.

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And they're lacking those economic concepts that they need to be aware of to

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ensure they're asking the right questions in their analysis in order to find

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the right statistical model to go after.

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Just because you have an answer to a question, it doesn't mean you're asking

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the right question. That's a very, very good point.

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And you're right. I think that there is still so much learning to be done at every level.

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The people who are actually working hands-on keys probably have the biggest challenge.

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And I would say for any data person, one of the challenges that I have isn't

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those guys, isn't the team that has hands-on keys.

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I feel like they're invested and on the journey. right?

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It's the executive sponsorship, to be honest. And I feel like getting executives

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and leaders who don't have a history in data on board is to me is an age old problem.

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And I heard somebody say like the way that executives today have to have some

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foundational understanding of finance, like they have to know earnings per share.

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They have to know EBITDA. There are many of these CEOs, they're all grounded on certain mechanics.

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I heard somebody say that they're They're going to have to equally,

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to be successful CEOs, they're going to have to be equally grounded in the information

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age when it comes to data literacy.

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Yeah, I would agree with that. I would agree with that. I was in board readiness

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events and there were a number of current board of directors,

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advisory boards, individuals that they're currently on multiple wards and some public boards.

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And one of the things that they highlighted, which I could not believe,

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was the fact that most board members are actually illiterate in terms of digital

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and data and technology concepts.

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They're all over cyber because they have to be, right? That's part of risk.

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But they're unable to think about, is the organization doing the right things

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in order to ensure they're ahead of the curve, getting access to data or ensuring

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they're getting value from their data? They're just not there yet.

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And further, the CEO, they may delegate out to a CIO.

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But how do you find the right C-level

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executives for your team if you're unaware of what good looks like?

321
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Yeah, you know, I'm hopeful that that change is coming and it's coming quickly.

322
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The CDO office didn't exist, right?

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Not in the way it does even 10 years ago, 15 years ago. It was virtually non-existent 15 years ago.

324
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And it really started catching on probably 10 years ago.

325
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In the last five years is where you've seen probably a spike.

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I haven't seen any statistics on this, but I'd imagine it'd be like a hockey

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stick curve where in the last 10 years, you'd see this sort of like low number of CDOs.

328
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And so that's probably the first

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phase of this, which is the advent of the office and the role of CDO.

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And then I think what I've seen is that there's been often a misalignment of where the CDO sits.

331
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And I think that's still a debate. We probably have another podcast on.

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And so that's a whole other thing. So at least they exist.

333
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And then I think the other challenge has been, what does the CDO do?

334
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The CDO role is on average 24 months, some 22, 26 months, right?

335
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And so just as you're getting settled, there's a shift.

336
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There's a lot of that that comes into place. I have the personal view that a

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CDO should be a direct to a CEO.

338
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That's often not the case. If you can bring that CDO closer to the CEO and peers

339
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with everybody else, that's how you would accelerate the data literacy objective

340
00:22:48,915 --> 00:22:51,055
that exists at the C-suite. Right.

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I agree with you wholeheartedly on that. I like to think of it as you're either

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a capital C DO or you're not.

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And the capital C is when you're reporting to the CEO.

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And you have a seat at the table. You know, non-capital C is when you're in

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a division or you're part of a business unit or you're, because they have those as well.

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I've seen a lot of organizations where I have the CDO of this part of our region or our division.

347
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They really are in those positions. They don't own the technology stack.

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They don't own the engineering team. They don't sometimes don't even own the

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product side of it. And you're literally just brokering conversation and ideas

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for strategy without any of the ability to actually execute on it.

351
00:23:36,949 --> 00:23:40,569
That's a good point. And like, it begs the question, where should AI initiatives sit?

352
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Should they sit with a CDO or should they sit with a CIO or a CTO, right?

353
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Because it's actually a technology, but it's producing, Gen AI is a content

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producer, so it's producing data and it requires data.

355
00:23:54,149 --> 00:23:57,769
I've started seeing cheap AI officer roles too, right? Right.

356
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And maybe that's the answer. I'm not quite sure.

357
00:24:00,149 --> 00:24:02,429
It's another one of those questions where I don't know that there's a clear

358
00:24:02,429 --> 00:24:04,569
answer. Yeah, I could debate that all day.

359
00:24:04,789 --> 00:24:10,909
I think it just depends, right? It depends on how the organization defines those two roles.

360
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And if both roles are in charge of leveraging data to push business strategy

361
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in the right direction, it's the same role.

362
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I think that's a valid argument. That's the only way I look at it. It's the same role to me.

363
00:24:25,789 --> 00:24:29,769
You basically undress your chief data officer, the moment you hire a chief AI

364
00:24:29,769 --> 00:24:34,629
officer, because the AI officer is going to create that value within the workstream,

365
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within the operations, within the business.

366
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And a chief data officer then becomes a governance body immediately.

367
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To me, it just depends on what you're looking to achieve with that role.

368
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And maybe that's the evolution, the CDAO title, where the A is then replaced,

369
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where the analytics is then replaced with AI, perhaps, is where we're headed.

370
00:24:55,169 --> 00:24:58,369
I think it's going to be interesting to look back at this conversation in 24

371
00:24:58,369 --> 00:25:02,089
months and see where the answers are, right?

372
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And say, where is the industry headed?

373
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That's one of the reasons I like talking about this topic, about how just new

374
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it is and how we don't know the answers. I think it's one of the most exciting aspects of it.

375
00:25:12,889 --> 00:25:17,209
Yeah, well, it might be actually interesting to reflect back on this conversation.

376
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In about two months, I have an upcoming talk with a friend and a thought leader

377
00:25:22,009 --> 00:25:27,589
in data and analytics in July, where we talk about embedding data and analytics

378
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within an organization's data culture.

379
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And really it comes down to the responsibility of the chief data officer.

380
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Yet we haven't really seen our CDOs being as effective as we hope.

381
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And now where does AI actually sit?

382
00:25:43,349 --> 00:25:49,369
And our intention is to actually start solutioning some of the challenges that we've seen,

383
00:25:49,815 --> 00:25:55,595
with a group of CDOs as part of this discussion. I'll share back what we learn later in the summer.

384
00:25:55,775 --> 00:25:57,995
That would be interesting. You know, you got me thinking about something,

385
00:25:58,195 --> 00:26:02,595
Anjali. So, you know, Sandy and I talked about data democratization a few months ago now, I think.

386
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You just made me think of the concept of AI democratization.

387
00:26:06,395 --> 00:26:09,835
You know, I'm a big proponent to data democratization. I think,

388
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you know, there are noted challenges around governance and security and access.

389
00:26:14,315 --> 00:26:17,375
And somebody asked us the question on our panel, other than data governance,

390
00:26:17,375 --> 00:26:21,375
what's the best way to ensure appropriate use of data and data democratization?

391
00:26:21,495 --> 00:26:23,875
And I said, I know you don't want to hear that the answer is data governance,

392
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but the answer is data governance is what I said.

393
00:26:26,695 --> 00:26:30,975
And I think that you want to put data out there. I'm of the belief where we

394
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should view any institution where everybody's a data person.

395
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If you're an accountant and you're producing data, consuming data,

396
00:26:36,635 --> 00:26:39,315
and you're producing an earnings per share report, you're a data person.

397
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And so I have the belief that you should democratize data. Now the question

398
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is, do we democratize AI?

399
00:26:44,775 --> 00:26:49,275
And do we put AI in the hands of everybody? I'm convinced it's on the doorstep.

400
00:26:49,675 --> 00:26:52,995
At the end of the day, if you look at enterprise applications.

401
00:26:53,975 --> 00:26:57,775
They're every single enterprise application company in the world.

402
00:26:57,895 --> 00:27:02,015
There is no enterprise application company that is in the market today.

403
00:27:02,175 --> 00:27:05,155
And if unfortunately they're not doing this, they won't be in the market for long.

404
00:27:05,275 --> 00:27:08,935
But they are all infusing Gen AI capabilities into their platforms.

405
00:27:08,935 --> 00:27:14,955
I haven't seen a demo in the last six months that does not include some aspect of that.

406
00:27:15,155 --> 00:27:19,435
And if you work for a company and they're not doing this, find another job.

407
00:27:19,515 --> 00:27:22,375
I'm telling you this right now, because they're going to be left behind.

408
00:27:22,555 --> 00:27:26,755
Everyone has it. And if I think about AI governance within an enterprise,

409
00:27:27,155 --> 00:27:32,455
one of the tasks that they have is understanding how is that Gen AI used within

410
00:27:32,455 --> 00:27:36,955
an enterprise application that we now license or looking to license and ensuring

411
00:27:36,955 --> 00:27:39,695
that it's not going to lead us down a bad path, right?

412
00:27:39,855 --> 00:27:43,675
I mean, if you look at just some of the things that came up with HR functions,

413
00:27:43,915 --> 00:27:45,135
for example, that was an issue.

414
00:27:45,255 --> 00:27:49,035
Group inside, it was an issue on performance management, helping people figure

415
00:27:49,035 --> 00:27:50,435
out who to promote, those kinds of things.

416
00:27:50,515 --> 00:27:54,755
They were all issue areas that I think have evolved since those issues came

417
00:27:54,755 --> 00:27:56,915
up, I don't know, it was a year ago or so.

418
00:27:57,055 --> 00:28:00,815
But it just kind of shows every enterprise application is moving in that direction

419
00:28:00,815 --> 00:28:04,675
because they want to make the use of the application as frictionless as possible

420
00:28:04,675 --> 00:28:06,015
and user-friendly as possible.

421
00:28:06,135 --> 00:28:09,835
And there's nothing more user-friendly than a chatbot. So that's where they're headed.

422
00:28:10,035 --> 00:28:15,135
I mean, even Copilot on Microsoft, they're throwing it onto your desktop to

423
00:28:15,135 --> 00:28:17,775
the point where I don't have to go navigate to a file.

424
00:28:18,235 --> 00:28:22,475
I could just say, give me X, Y, Z, and a file opens up on my Microsoft computer,

425
00:28:22,675 --> 00:28:23,815
right? So I have an Apple.

426
00:28:23,935 --> 00:28:26,415
But anyway, you get my point? I think that's where we're headed.

427
00:28:26,655 --> 00:28:30,775
I also have seen, we're going to have another episode where we talk about the

428
00:28:30,775 --> 00:28:34,935
Snowflake conference, but I'll just add this to quick little thing I saw at

429
00:28:34,935 --> 00:28:37,695
the conference, which was they were releasing all these capabilities.

430
00:28:38,095 --> 00:28:41,375
And at one point they said, everyone should be able to build a chatbot.

431
00:28:41,535 --> 00:28:44,935
And then he kept repeating it. And then he said, we're going to grab an audience member.

432
00:28:45,035 --> 00:28:48,035
We're going to have them come up and build a chatbot. And in five minutes,

433
00:28:48,175 --> 00:28:52,655
this person who isn't a coder, and I believed her, she was in the back of the

434
00:28:52,655 --> 00:28:56,055
in the middle of the room, they called her name, she came up,

435
00:28:56,115 --> 00:28:59,375
she did what they told her, she hit the wrong button a few times,

436
00:28:59,535 --> 00:29:04,255
they messed up the demo for a second, but they built a chatbot bot off of PDFs.

437
00:29:04,255 --> 00:29:06,895
And it took her five minutes, and it worked.

438
00:29:07,095 --> 00:29:10,495
Pretty enticing demo. And I'm sitting there going, well, if anybody can do that,

439
00:29:10,615 --> 00:29:15,655
imagine the possibilities of the ability to just really ensure that all the

440
00:29:15,655 --> 00:29:19,315
knowledge within an organization is readily available to everybody within the organization.

441
00:29:19,535 --> 00:29:25,915
That's amazing, right? And it adds to the excitement of what's out there, what the potential is.

442
00:29:26,075 --> 00:29:28,055
It sounds amazing. And I think you're right.

443
00:29:28,295 --> 00:29:31,915
Institutions that don't embrace this will likely get left behind.

444
00:29:32,135 --> 00:29:38,035
Even if it's a measured approach, I think it'd be advisable to get grounded in the topic.

445
00:29:38,355 --> 00:29:42,275
Absolutely. I avoided it when it first happened as well. Because like you,

446
00:29:42,495 --> 00:29:45,095
we've all been burned by these ideas, right?

447
00:29:45,575 --> 00:29:50,815
When I graduated from college, the dot-com era busted out of my first job.

448
00:29:50,815 --> 00:29:53,835
I was laid off because those jobs were gone overnight.

449
00:29:53,995 --> 00:29:58,055
The market failed and all these startups turned into nothings, right?

450
00:29:58,155 --> 00:30:00,215
I was part of a unicorn company and

451
00:30:00,215 --> 00:30:04,235
that turned into, I think my stock was worth $2 by the time I was gone.

452
00:30:04,415 --> 00:30:08,275
So it felt very much like that.

453
00:30:08,435 --> 00:30:12,215
And I think we will see that wave of companies getting created,

454
00:30:12,335 --> 00:30:17,375
entering semi-unicorn status, and then being thrown out the door because maybe

455
00:30:17,375 --> 00:30:21,635
they were trying to build something that should be part of a different application, et cetera.

456
00:30:21,855 --> 00:30:24,255
So that's definitely going to happen, I think, in this wave.

457
00:30:24,415 --> 00:30:29,595
But what happened to me initially was, oh, here we go, another interesting flash in the pan thing.

458
00:30:29,795 --> 00:30:32,555
Even Gartner was like, this is a shiny object a year ago, right?

459
00:30:32,655 --> 00:30:37,335
And now we're all over it all I would say I have a slightly different view of

460
00:30:37,335 --> 00:30:40,975
what will happen to all these tech companies.

461
00:30:41,650 --> 00:30:47,970
My view is that I'm with you, that they sprout up and they create a bunch of very niche solutions.

462
00:30:48,390 --> 00:30:51,690
But what I've observed is that they won't necessarily go out of business the

463
00:30:51,690 --> 00:30:53,030
way that dot-coms did overnight.

464
00:30:53,210 --> 00:30:58,270
I see them just getting assimilated, like being acquired by other larger companies.

465
00:30:58,530 --> 00:31:00,590
And then I think that you'll see consolidation.

466
00:31:00,910 --> 00:31:04,790
Sometimes I think that's why they start these companies is to plan to get acquired.

467
00:31:05,090 --> 00:31:09,610
Right. And some players in the marketplace, some cloud providers out there,

468
00:31:09,690 --> 00:31:15,090
keep it nameless this for now, but they are playing smart in terms of enabling

469
00:31:15,090 --> 00:31:19,290
a platform that people can build on. I've seen it in a number of instances.

470
00:31:19,590 --> 00:31:23,430
Salesforce has always been one of those players where, as an example,

471
00:31:23,570 --> 00:31:27,150
and they've always kind of been in this marketplace of build for Salesforce,

472
00:31:27,410 --> 00:31:28,750
build an app on top of Salesforce.

473
00:31:29,310 --> 00:31:33,950
And anybody who has a startup, and I'm not saying Salesforce specifically,

474
00:31:34,270 --> 00:31:38,710
but they should look at the marketplace and say, should I make a bet on a provider

475
00:31:38,710 --> 00:31:43,590
on whether it's AWS, Microsoft, Salesforce, Snowflake, Databricks, whatever it may be.

476
00:31:43,690 --> 00:31:47,810
Should I make a bet on a provider and build my application to work specifically

477
00:31:47,810 --> 00:31:52,570
for that platform and already is integrated inherently into that platform?

478
00:31:52,810 --> 00:31:56,130
Because that provider may gobble you up. That's your exit strategy,

479
00:31:56,270 --> 00:31:59,250
right? I'm going to make it so compelling for them. And I'm going to sell to

480
00:31:59,250 --> 00:32:00,530
every single one of their clients.

481
00:32:00,690 --> 00:32:06,090
And I'm going to ensure that I make a splash every single year with them from

482
00:32:06,090 --> 00:32:08,410
a revenue standpoint so that they can't avoid me.

483
00:32:08,570 --> 00:32:12,130
And at some point, they're going to acquire you, especially if it's on their

484
00:32:12,130 --> 00:32:15,650
platform, it's fully integrated, it's kind of native to their ecosystem.

485
00:32:16,130 --> 00:32:19,210
I think that's an easy play for them to say, hey, I'm going to go ahead and

486
00:32:19,210 --> 00:32:21,270
acquire you and relieve you of this.

487
00:32:22,410 --> 00:32:28,030
I'm with you. What would be your advice to someone who's either mid-career,

488
00:32:28,130 --> 00:32:31,290
kind of later in their career, just trying to keep up with the madness?

489
00:32:31,830 --> 00:32:35,670
You know, one advice that I've given recently is, you know, I think that in

490
00:32:35,670 --> 00:32:39,590
the last 10 years, there's been sort of these nuanced roles that people fit into.

491
00:32:39,790 --> 00:32:45,010
You know, I'm of the belief that versatility is a key to success.

492
00:32:45,350 --> 00:32:51,330
Having this skill to work across data disciplines is the advice that I would

493
00:32:51,330 --> 00:32:54,290
give people coming up. I'll give like a basketball analogy.

494
00:32:55,001 --> 00:32:59,241
So in basketball, you have these positions, you have a point guard and you have a center, right?

495
00:32:59,341 --> 00:33:03,161
And historically very different, right? In terms of size and style of play.

496
00:33:03,341 --> 00:33:07,941
But the most exciting basketball and the ones that people, teams I see most

497
00:33:07,941 --> 00:33:11,901
successful is where there is this concept of positionless players,

498
00:33:12,141 --> 00:33:15,281
people who can play at any position at any given time.

499
00:33:15,281 --> 00:33:20,941
I think the future of like data professionals is someone who is so versatile

500
00:33:20,941 --> 00:33:26,021
and can be sort of positionless and have enough skill working across all these

501
00:33:26,021 --> 00:33:29,521
disciplines is the advice that I would give. And be innocent.

502
00:33:29,781 --> 00:33:32,241
That's the other piece of advice. Expect continuous learning.

503
00:33:32,381 --> 00:33:35,501
And if you aren't going to embrace it, this isn't for you.

504
00:33:35,621 --> 00:33:40,441
I wholeheartedly agree with you primarily because I see myself as someone who

505
00:33:40,441 --> 00:33:43,841
started in left field and ended up going through all these iterations.

506
00:33:43,841 --> 00:33:45,861
In my career was not a straight line.

507
00:33:46,021 --> 00:33:53,121
It was a bunch of loop-de-loops to the point where the way I look at it is you

508
00:33:53,121 --> 00:33:58,761
cannot influence change without understanding what is required of the other person's role.

509
00:33:59,081 --> 00:34:03,521
So how are you going to influence them to primarily do the right thing if you

510
00:34:03,521 --> 00:34:05,921
don't have enough understanding to push them in that direction,

511
00:34:06,081 --> 00:34:07,301
to get what you need done?

512
00:34:07,461 --> 00:34:10,961
I think that's going to be critical. And the only way to get there is to have

513
00:34:10,961 --> 00:34:15,541
the knowledge of of how those individuals have to work and what they're trying

514
00:34:15,541 --> 00:34:18,921
to achieve and what they're doing. Is that the right thing?

515
00:34:19,061 --> 00:34:23,261
Or is there something else and you should be pushing them in the right direction?

516
00:34:23,621 --> 00:34:27,201
Yeah, fair enough. I will say that I think I personally prefer a career that

517
00:34:27,201 --> 00:34:29,661
was like loop to loop versus a straight line as well.

518
00:34:29,741 --> 00:34:34,341
So I think you're fortunate. And I think it probably adds to your skill set.

519
00:34:34,761 --> 00:34:36,241
I'm very fortunate, yes.

520
00:34:36,741 --> 00:34:41,781
I didn't appreciate it until later in life. So we completely went off script on this.

521
00:34:42,521 --> 00:34:46,101
For those listeners out there, we were actually going to talk about how Gen

522
00:34:46,101 --> 00:34:47,641
AI can solve data quality issues.

523
00:34:47,901 --> 00:34:53,081
I still think it's an incredible topic. Yeah, I do think Gen AI is prime for

524
00:34:53,081 --> 00:34:54,941
solving data quality issues.

525
00:34:55,101 --> 00:34:57,641
You know, I'll say this, you know, I think I shared with you,

526
00:34:57,661 --> 00:35:00,761
I started my career as an accountant. My first job was...

527
00:35:01,078 --> 00:35:05,718
There's an intern reconciling accounting entries for inter-entry transactions.

528
00:35:06,298 --> 00:35:10,118
What it was, was an accounting problem, a data problem. And the data problems

529
00:35:10,118 --> 00:35:14,198
were missing data, format of data, just anomalies around data.

530
00:35:14,358 --> 00:35:19,258
And if you think about it, Gen AI is perfect for these kinds of problems to

531
00:35:19,258 --> 00:35:21,038
solve when you think about data cleansing.

532
00:35:21,238 --> 00:35:27,418
But yeah, maybe another podcast hyper-focused on data quality and Gen AI,

533
00:35:27,518 --> 00:35:28,838
data governance and Gen AI.

534
00:35:29,098 --> 00:35:33,138
Yeah, absolutely. I would love it. I went in last week with that in mind,

535
00:35:33,158 --> 00:35:37,878
and I actually ran around the conference floor looking at companies who are

536
00:35:37,878 --> 00:35:42,738
solving data quality issues with ML, AI, Gen AI capabilities.

537
00:35:43,558 --> 00:35:45,898
And they are many, many out there.

538
00:35:46,078 --> 00:35:50,238
It was eye-opening to see that because it's addressing an old problem in new

539
00:35:50,238 --> 00:35:53,898
and novel ways that removes some of the headaches of how to solve the problem.

540
00:35:54,038 --> 00:35:56,238
Definitely interesting conversation to be had.

541
00:35:56,578 --> 00:36:00,558
Thank you so much for having me. It was so wonderful just chatting without boundaries.

542
00:36:00,558 --> 00:36:04,418
Yeah, as advertised, I told you that when we first chatted about the episode,

543
00:36:04,558 --> 00:36:06,258
I said, hey, we're going to have a construct.

544
00:36:06,498 --> 00:36:10,938
But if we go in another direction, we'll just take it there if the conversation is good.

545
00:36:11,038 --> 00:36:15,318
And it was. So thank you again for your time, Junaid. And I hope you have a

546
00:36:15,318 --> 00:36:18,518
fabulous weekend. Thank you, Sandy. Thank you, Angela. Appreciate it.

547
00:36:18,640 --> 00:36:32,155
Music.