Demystifying Data Science with Dr. Hisham Talukder

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Speaker 1: Welcome to the Quick Take podcast, the show where you

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get targeted advice and coaching for executives by

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executives.

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I'm Suzy Tomlinchuck.

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Speaker 2: I'm James Capps.

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Give us 15 minutes and we'll give you three secrets to

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address the complex topic of issues that are challenging

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executives like you today.

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Speaker 1: Hey, welcome to Quick Take.

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I'm your host, suzy Tomlinchuck , along with James Capps.

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How are you, james?

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Speaker 2: I'm proud to be here.

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Speaker 1: I am super excited too, because you brought a

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friend along.

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You brought a friend.

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Speaker 2: I brought a friend along.

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It's like he's my chaperone but , yeah, very excited, we've got

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a good friend of mine, dr Hitchin Tolerctor, here today

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who has got a great background in data science, super sharp

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individual I've learned a lot from in this space.

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We thought today would be a great opportunity for us to talk

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a little bit about data science , talk about the history and the

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evolution of that space.

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Gosh, so much is going on there , so many of our guests and our

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listeners are asking questions about that.

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We thought we'd bring Hitchinman to talk a little bit

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about that.

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Speaker 1: Dr, this is a person that knows what he's talking

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about.

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I'm in awe.

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Thank you for bringing him.

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Yes, yes, I have a new friend too.

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Speaker 2: There you go, Hitchin .

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Why don't you just set the stage for us on this ecosystem?

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Speaker 3: Yeah sure, James, Suzy.

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Great to be here, James.

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We're officially friends.

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I have it on video now so you can deny it from my friend.

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I think my experience in data science is very unique because

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I've seen the evolution of data science.

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I've been in this data science field for about 10 years now.

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I come from a PhD in applied statistics.

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I remember my first job out of grad school it was like I was

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only working with PhDs, so it was a team of four to five data

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scientists, all PhDs, Two.

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If you look at the data science field now, hey, if you know how

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to code, if you know statistics, if you know math and you know

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how to code really, really well, you can be a data scientist.

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And that's not to say that the skill set has gotten down.

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It's really around the evolution of technology, the

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evolution of computers, that have seen that change.

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And I've worked on data science teams where you're in the back

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room all you're doing is coding, and I've worked with data

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science teams where you're working with the most senior

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leader in an org or a company to try to understand problems that

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you could potentially solve with data science and any

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spectrum in between, and obviously you need different

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skill sets to tackle all of these different problems.

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Speaker 1: Yeah, and I was thinking when you were talking

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about that is such a different place than I am.

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I am not a technologist and so I would never be in those rooms.

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And I actually was just meeting with a friend of mine who's a C

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level person and she said I feel a lot of pressure to know

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more than I do and it makes me really nervous, and so I really

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would like us to really unpack that.

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What do you see at those high levels about what their

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assumptions are?

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Speaker 3: Yeah, I think one of the biggest things that I've

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seen consistently is this data science being treated as a

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silver bullet, a black box.

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You give me a problem, data science will solve it.

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And there's always this discussion, whenever I join a

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company, whenever I join a new team or even an existing team

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that's been there for a couple of years, of talking to

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leadership and trying to explain to them what data science is.

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Data science, like any other role, like any other field, it's

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sort of building a house, right ?

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Like you need to first build a foundation, you need to build on

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top of it, you need to start with the easy problems.

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And then one other thing is data science always needs to

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match the business that it's trying to help.

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Not all businesses need Gen AI, not all businesses need this

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complicated solution, right.

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So it's like really working with leaders, not as a way of

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educating them, but almost partnering with them, to say,

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like, here's what data science is, here's what data science is

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not.

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Now, how can I help your business in the most effective

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way?

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Speaker 1: I think also, what you're saying is don't be afraid

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to ask the questions like how does this fit together?

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Because to me, from a person that is, I was in the business

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piece of it, when I'm working with somebody that really has an

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expertise around data and technology, to really just be

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like how do we make this work?

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Speaker 3: Yeah.

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Speaker 1: Like to your point about the baseline.

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How do we work best together?

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Speaker 3: I think that's a really good point.

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It's not just at a human level how to work with partners but

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also at a technology level.

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Where I have seen in my experience data science be the

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most successful is when it becomes a part of the overall

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system.

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For example, I worked in an ad tech company where we wanted to

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make sure we're showing the ads that is most relevant to our end

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user.

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If you think about that system of showing that ad people come

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into a website.

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It fires over requests, ads get sent, people click it, then

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they go and may or may not purchase it.

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That's the system.

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What you don't want to do is come in and say, hey, let me do

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an analysis and let me tell you build a PowerPoint slide of

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what's going on.

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You want to make sure you build a solution that you could

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almost insert into a system.

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Now the system becomes people come into a website, they

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request an ad.

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The ad goes to my data science system, gets the scores out, it

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tells it what ad to show, then it goes back and continues it.

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It's not only just building a partnership of togetherness at a

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human level, of making sure you have the right partners and

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you're working together, but also at a technology level, and

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making sure everything is working together and talking to

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each other.

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Wow.

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Speaker 2: I really see the need to decompose that mystery.

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Really.

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Take from hey, we've got this new thing I saw on the InFlight

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Magazine and I want my company to implement it to hey, let's

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take a look at what we're trying to solve, for what are the

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problems we have and how do we articulate them?

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I love that jump from magic bullet to individual problem

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solving.

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I think that's something that so many leaders lose track of

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and expect.

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Maybe my data sciences will just solve my problem.

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Speaker 3: I think where that is the most successful is, once

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again, when it's a partnership and you're working together.

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I always tell my business partners you know the business a

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lot better than I do.

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What you don't want to do is come to take lessons from me of

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here's a business problem you should be solving.

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You should be bringing up a business problem.

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We should have a discussion, and I know the technology, you

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know the business.

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That's where we make the best partnership.

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It's not a one way street.

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It's not them telling us or us telling them.

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It's really working together.

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And I do think that evolves as you gain more and more trust

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with your partners.

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As you gain more and more trust , sort of the opportunity start

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to open up.

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Right Now we can take some big bets that they might not be

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thinking about because they have that trust that, hey, we've

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been able to build certain things.

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That's become ROI positive.

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Speaker 2: So what I'm hearing you say, if the first real you

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know the nugget we want to talk about, is really realize that

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it's not a silver bullet the second thing I'm hearing you say

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, though, is you've got to create that trust and you've got

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to create that dialogue, and to do that, you really need to

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start small, and is that really the best way you've seen people?

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Speaker 3: work together.

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That's right.

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I think you need to start small and you also need to, going

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back to that house analogy, build the foundation and sort of

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a vision of how that will evolve, right?

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So it's like, hey, for example not surprising to anyone data

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science needs data.

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Data needs to be part of that foundation, right?

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Many times I've been in sort of the final presentation of a data

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science model where they will bring up issues, but it's not

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really issues around the model, it's issues on the data, right.

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And it's like hey, like yes, but we've assumed that we have

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issues with the data, right, you need to assume certain things

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about the data to move further, because what you can do, also on

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the other side, is wait for perfect data, right?

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So, like, you need a foundation of data, you need a culture of

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data.

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The culture of data means, hey, data science not all the time

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will work.

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You need that ability to fail.

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You need that ability to learn from your failures.

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You need the ability to experiment.

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So I think, like, If you pair up data, if you pair up the

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right culture and the right buy-in from the senior

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leadership, that's where you start to evolve this culture of

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data science.

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You can put a vision forward that is not just project to

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project but more around business challenges to business

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challenges.

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Speaker 2: Yeah, I think that it's so fascinating how so many

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of our implementations, our journeys, our changes are really

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foundational on culture.

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You can do a lot of things.

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You can bring in consultants, you can bring in new tools,

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technology.

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At the end of the day, you've got to have a conversation

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around culture because, as I always say, the technology is

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the easy part, the people is the hard part.

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That culture has got to be part of the conversation.

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One thing around.

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Speaker 3: That is, that culture doesn't just stop.

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You don't need investing in that.

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Culture doesn't just stop.

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I've worked with the most technology savvy company to

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startups that are just getting into technology Both of those

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sides.

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You need to continuously invest in the culture.

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You need to make sure the culture is there, but also you

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need to make sure culture is evolving.

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The culture that might have worked when your company was 10

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people with one data scientist is probably not going to work

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when it's 100 people with 2 data scientists.

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Speaker 1: Especially at the top in leadership, because you can

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assume that everybody has the same mindset as you and, as

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things are growing, that everybody is on board and

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energetic.

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It is really important.

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I love what you said about the relationship piece and that

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trust, re-evaluating that and re-syncing around making sure

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everybody understands the parameters, if you will, around

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each of the business challenges.

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Speaker 3: Absolutely.

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I always told the story to friends.

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I always realized I made it in.

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A job is when I have a senior person that's not from the

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technology side, either the business or product side just

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calling me out of the blue and saying hey, I just want to

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brainstorm something with you.

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That's when you know, as a technologist, you've made it in

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that room.

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It's not just a technology thing, it's like, hey, I just

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want to brainstorm something with you that might start at the

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high level.

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There's definitely a data science element to it and we're

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going to get to that.

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That happens to me at every job .

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That's when I'm in my head I'm like all right, I've made it,

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I've arrived.

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I think I've made it in this job.

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It's a great feeling because it goes both ways.

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You've shown the product and business leader that there's

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value you can bring, and also they see the value in a

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technologist's leader.

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They see the value in a data science leader.

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Speaker 1: It makes me think.

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As a salesperson, I was resentful of the engineers, the

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technology people, even the finance people.

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Speaker 2: Wait a minute.

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Why ask me all?

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Speaker 1: these questions.

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Speaker 2: Wait, do you not resentful me?

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You love me, I know I was like just get out of here.

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Speaker 1: I'm going to tell you when I need you.

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You're right that influence is so important.

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One characteristic of influence is when you get that call.

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People depend on you.

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I didn't expect this conversation to go to

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relationships and culture, but it really is great to know that

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both are so important to the success of this kind of project.

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Speaker 3: I think you learn it with experience, because I'm

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sure there are people that I work with in the sales

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department early in my career that would agree with you, Suzy.

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Speaker 2: It's like hey, I hate to work with that guy at shop.

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Speaker 3: He thought he knew everything.

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But then you kind of evolve, and that's where I meant of just

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building that partnership I think is honestly one of the key

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factors of data science success .

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You need to make sure you have partners that you could rely on,

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that you know you could work with to ultimately solve the

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problem.

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Speaker 2: Yeah, it's, you know it's always about the people.

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We say that so often on the show, but and I do think this is

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a unique space you know there's so many technologies that are

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that are people adjacent or people use, but this is problem.

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This is looking at problems and addressing issues that are very

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complex and are not simply just an implementation of a piece of

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hardware or a new plant or some code that's, you know, doing

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additional information security.

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Speaker 3: This has the opportunity to really bridge a

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lot of areas that I think you're you're highlighting a really

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unique part of this, of this emerging space that I think we

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need, we need to take into consideration and and the space

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Continuously evolved right, like , if you think about once again

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going back to sort of my journey , when I came in like

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statistical models, like that was data science, right, and

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then it went machine learning.

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That became sort of a big, big, sexy word in data science.

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Then it went artificial intelligence, ai that now you're

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seeing a lot of buzz around gen AI and a lot of times what

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happens is Something gets introduced huge buzz and then

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people start to go, okay, how can I really use this Right?

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What can I leverage this for?

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Like, in this gen AI space?

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I think a lot of companies now are starting to think about okay

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, what do I use this for?

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Right, and it's not.

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It's not something you use for every solution, but there's

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clear use cases of it.

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You know, I was just looking at a demo the other day of like

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basically talking to a computer where it makes videos for you or

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it makes images for you right, that's fantastic.

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If you're in the ad tech space, you can create ads without any

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resources.

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That's huge right.

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So if you're an ad tech company , you should be investing in gen

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AI, you should be Doing that research right.

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But not, you know, not if you're any companies I think

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like no matter what that technology is within data

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science, what I've seen is it applies to different companies

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in different sectors in in very different ways.

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Wow, all right, why.

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Speaker 2: That's just a great insights, hisha, I'm let me let

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me kind of wrap up here and see if I can take, you know, some

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some three takeaways from for our listeners.

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You know, I think the first one that I heard which is so great

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is remember this isn't a magic bullet.

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It doesn't solve all problems.

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And have a realistic perspective on on what this is.

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Second, you know, start with small, start with those little

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initiatives so you can start to grow and understand where it

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works for you.

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Should we be doing this for our company?

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Are we aligned with this?

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That is the technology.

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Really, where do we want to see it applied?

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And then, third, it's really about the culture, and you've

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got to have a data focused culture and your firm has got to

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understand and embrace that there is a culture shift if

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you're really going to take advantage of technology in this

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space.

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You equally have to shift the way that you think about data.

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How'd I do there?

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Is that a good summary?

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Speaker 1: No, I thought that was good.

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I think that I learned a lot.

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That there is the collaboration is really important to your

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last point of being there.

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Wow, I think we've brought it up a level having a doctor here.

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Thank you so much, ishim, for being here today and

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enlightening all of the quicksters out there, so we

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appreciate you, thanks.

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Speaker 3: Absolutely.

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This was really fun.

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Speaker 2: Hey Susie, I was wondering if we were to have a

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mascot for our podcast.

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What animal, fictional or real, character or not?

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Fictional means not real.

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Which would you choose?

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And Regis Philbin is chosen.

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Speaker 1: Okay, so my mind went all through the zoo just

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because it just allowed me.

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I have to like kind of do that, and so what came to mind is a

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mountain lion, a mountain lion, and fantastic and and you that

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obviously I have to explain why well, there's a bit of like

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they're.

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They're a beautiful animal and so they're interesting to kind

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of observe and they're unexpected, like they're what

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they do is unexpected and they're also kind of feared

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Because you don't know where they are.

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And so I like the the a bit of Intrigue and surprise about them

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and their strength, like I just think they're so cool, like I

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love to watch them walk and just see that, that I do that very

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often, but I always feel like when I'm on trail some one of

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them was watching and just making a choice like easy, easy,

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no way.

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We're just gonna leave her alone .

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We'll wait till there's somebody that's challenging,

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that goes by, but I I just think there's something that's really

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captivating About them and interesting and that would make

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a good mascot for us, because they are clearly captivating,

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intelligent and Powerful yeah, and I think it would draw

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attention.

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I don't think that's a.

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I think that that it's a good one.

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It just reminded me of us.

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We hope that we're being intriguing and people want to be

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a part of it, what we're giving them a strength.

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Thanks for listening to this week's episode of quick take,

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where we talk about the questions that are on the mind

00:18:44
of executives everywhere.

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Connect with us and share what's on your mind.

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Speaker 2: You can find us on LinkedIn, youtube or whatever

00:18:51
nerdy place on the internet.

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You find your podcasts.

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Our links to the show are in the show notes.

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Speaker 1: We appreciate you.