Today we’re talking about the power of data and AI in the enterprise with Krishnan Venkata, Chief Client Officer at LatentView Analytics. We’ll explore how data can drive meaningful business change, demystify the applications of generative AI, and discuss strategies for successful adoption of data initiatives.
With over 20 years of experience in the Technology and Knowledge Services, I am a seasoned analytics executive who leads global client services at LatentView Analytics, a leading data and analytics firm. As the Chief Client Officer, my mission is to build long-term and mutually beneficial partnerships with our clients, and to bring them innovative and impactful solutions across each stage of our relationship.
About Krishnan Venkata
I have a deep technical knowledge and a strong track record of managing C-level relationships and leading sales and business development. I have also successfully abstracted solutions delivered into products and platforms, and evangelized them across organizations with similar pain points across industries.
In addition to my role at LatentView, I also serve as a board advisor to early-stage technology companies, where I leverage my expertise and network to help them grow and scale.
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Transcript
Note: This was AI-generated and only lightly edited
Greg Kihlstrom:
Welcome to today’s episode, where we’re going to dive into the power of data and AI in the enterprise with Krishnan Venkata, Chief Client Officer at LatentView Analytics. We’re going to explore how data can drive meaningful business change, demystify the applications of generative AI, and discuss strategies for successful adoption of data initiatives. Krishnan, welcome to the show. Thank you for having me. Yeah, looking forward to talking about this with you. Why don’t we, before we dive in here, why don’t we get started with you telling us a little bit about your background and your role at LatentView Analytics?
Krishnan Venkata: Absolutely, Greg. I’ve been in the analytics services industry for over 16 years now, and overall have an experience of over two decades in the consulting services business. And in the last 16 years, we’ve been working with fairly large brands in solving their business questions with a lot of analytical thinking and using big data, advanced analytics techniques, and making sure that I’m able to give them actionable insights to make better decisions. I look forward to having this discussion, Greg. Thank you for inviting me.
Greg Kihlstrom: Yeah, absolutely. Yeah, let’s let’s get started here. So first thing I want to talk about is a problem first approach to data strategy. Certainly, data and data strategy is on the tops of many people’s minds right now. want to look at how businesses can leverage this, what you term a problem first approach. So first, why don’t why don’t we start with definitions here? You know, what, what do you mean by a problem first approach? You know, what’s what does that look like?
Krishnan Venkata: Thanks for this question and it’s actually a very important question if you think about what generally happens with a lot of firms when they look to adopt new technology. Our approach as a firm which consults with the leading brands in adopting technologies is we don’t go with taking a technology and finding out a problem to solve with it. Rather than going with that, we have conversations and try to understand what are the problems that the industry is facing or the company is facing, and then look at what are the solutions that we could go with, it could be database, it could be something simple, it doesn’t need to be something very complex, or does it need a complex solution? Does it need the cutting edge solutions like, say, AI or Gen AI, as the case may be, and then advocate that to the client? So rather than going with saying that, hey, I want to look at implementing AI in my processes, if the question becomes, what are the problems that I’m looking to solve, and let’s try to find a solution to that, or let’s look at a better mousetrap for that, that’s the problem first approach, where you start with the problem and then go to the solution rather than go from a technology and then look for a problem for the technology to solve.
Greg Kihlstrom: Yeah, I think that’s such a smart approach. I started a newsletter of my own called AI is not a goal or a strategy, kind of based on similar enough ideas of just trying to throw AI into the mix without clearly defining, to use your words, what the problem we’re trying to solve is, I think is just the wrong approach. It’s kind of jumping on the bandwagon, so to speak. To kind of give a better picture here, could you maybe share an example of where this kind of problem first approach has driven some success, rather than just, hey, we want AI?
Krishnan Venkata: I can actually talk about something we did with a home services client of ours, where we started with finding that they were having customer satisfaction issue, right? And we found that a lot of these customers were not happy with this one, that their NPS scores were dropping. We were seeing customer share declining as well. And then we went to understand saying that, or rather the client came up with an approach saying that we want to improve this metric, which is what they call 95% of the issues to be solved within five days or less. And they had about, I think, 75-5, which means 75% of the issues were actually being solved within five days. And their average turnaround time for issues were about 25 days. When we started with the problem, we actually found that Rather than going to saying that, okay, let’s look at an analytical solution to approach it, we first found out the entire history of the problem. And a lot of places we understood that different departments were having data that could be relevant for other departments which they were not using. For example, the marketing team was running campaigns in certain regions. Say, for example, you’re running, during summers, a campaign in Texas, right? You would expect a lot of sales to happen around that, and as a result, a lot of customer service requests to keep coming through that. But the customer service team never had access to this data, and so they did not know that these campaigns were running, and as a result, where the sales would spike, and as a result, where would the customer service issues happen? And we found that just by integrating these two data sources, which is the marketing data, along with the customer service, predictive forecasting, volume metrics, we were able to very quickly forecast better the call volumes and as a result plan for them better. So something as simple as this was implemented, and this is just scratching the surface, but we implemented many such things across this, where we went to identifying the problem, what is the gap that is there and addressing that problem rather than going with what a solution or what an analytical solution or what’s the best analytical thing that we could take and find the problem to solve for it.
Greg Kihlstrom: Yeah, and that’s that’s a great example. And I think the other interesting thing that that brings up in addition to Identifying a problem to solve is also just the ability of AI to play a more, I mean, I call it a horizontal role of, you know, it’s not just, when you just try to use it around marketing or customer service or something in a silo, so to speak, it can certainly have benefits, but there’s a lot greater benefit when you’re able to tie those things across whether they’re siloed within an organization or just separate departments or things. It sounds like kind of what you’re seeing as well. Is that so?
Krishnan Venkata: Yep, absolutely. No, and I think a lot of times it is about integrating data across the different departments and democratizing data is the word that is used a lot. And that’s very powerful because we found out that solutions can significantly be improved when you have access to better data and better information, so to say.
Greg Kihlstrom: So to kind of follow this thread a little bit, I mean certainly there’s been a lot of hype around AI and generative AI in particular. I mean AI you know, has been around for decades at this point in various forms, but generative AI may be a little bit newer. Can you help maybe explain from your experience and the customers that you work with, you know, how do you separate the hype from the transformational potential? Because there is truly transformational potential, but, you know, what should businesses realistically expect from generative AI?
Krishnan Venkata: This is a great question and I’m sure a lot of people have heard about generative AI and a lot of people are looking to see how they can incorporate it in their daily life, right? One thing that we have found with a lot of our clients is we have now gone past this hype cycle where we would say that everybody was talking and there was a lot of buzz around it but now we are in that place where we are There is a bit of disillusionment that has happened with investments. People have done a lot of things with Gen AI. They’ve tried a few things, trying to find out problems that can be solved with Gen AI, and have been disappointed a bit. Where we are going to right now is where businesses are now saying that they just don’t want to do, they want to find real value. This is where what we have gone with is trying to understand how we can go with simple pilots, very quick turnaround pilots, take it and make it a bite-sized implementation and see whether that is able to generate the value, right? Are we able to generate an insight? Are we able to create a tangible improvement in the process before we scale that out? So rapid prototyping or rapid learn, test and learn, failing and retooling actually helps a lot in this case. And there are a lot of businesses that have been actually doing pretty well with generative AI. I’ve heard about L’Oreal using a generative AI for trend analysis. They have been looking at generative AI to help with trend spotting. Walmart uses it for product recommendations. ebay for example uses generative ai to help the sellers write descriptions in fact if you go to linkedin now you can see that almost every post has questions and prompts which are generative ai based right so there are companies that are looking at very interesting ways by which they can give intelligence back or give better intelligence back to customers, or use that better intelligence back into their decision processes. But the real point is to make sure that you run very quick experiments, test it, and learn from where the tests are not working out, and then improve the process before rolling it out in a large fashion. I think that has helped us in quite a few instances where we are able to show these rapid prototypes, show the value, and then scale them. Clients really see the benefits of it.
Greg Kihlstrom: Yeah, I mean, I think that’s, I think that’s really important, because at the end of the day, I’m seeing what you’re seeing as well, which is there is a little bit of disillusionment for those organizations that maybe went in big, but didn’t have the clear objectives to start, they just, you know, wanted to be early adopters, but figured they’d figure it out as they went or so. So I definitely I’m a fan of the, the iterative or agile approach there. To kind of go back to one of the first things we were talking about and how AI and data, just good usage of data works well when it works across an organization. Part of that certainly is integration of data and making sure that the right teams have the right access to things. But another big component of that is getting people to adopt and utilize the data and to do things with it. So I wanted to talk a little bit about that part as well is, you know, supposing that there is access and that the, you know, the infrastructure is the technical and the data infrastructure is in place there. How do how have you seen organizations get better user adoption of data within within the enterprise?
Krishnan Venkata: It’s a great question Greg and in fact a lot of solutions actually fail because of the lack of adoption and a lack of adoption is because of a lack of clear understanding of purpose. So what we do in our solutions is first start with the purpose, define the purpose, right? Talk to your audience, try to understand and educate them in terms of what is it that they’re trying to accomplish. Clearly identify the business problem, try to identify what are we looking to solve and what are we seeking their help with. involve the people right one of the big things that we have found out is do not build data solutions away from people involve the users get them to give you feedback iterate with them they feel involved in the process they feel actually committed to this and they they kind of feel part of the solution right the third part of it is Prioritize user feedback and also user experience. So when the solution is being presented, you’ll always get a lot of feedback. Prioritize them. And we found that very, in fact, when we even build dashboards with clients, I think that whole aspect of taking the user feedback about what they would like, what is actually ailing them in terms of the user experience is very powerful into improving this. And when you do these three things, what you are able to do is you have taken the people along, you’ve clearly defined what you’re looking to accomplish, you’ve involved them early into the whole process, and then you’re also building a solution that prioritizes their experience. you come up with solutions that cater to their needs and they’re also well embedded into this whole decision-making and they feel part of the entire thing. That we call is building a culture of data-driven decision-making and bringing the users along with you. I think that’s, I would say, a very simple way of putting it, but it’s been very powerful in our experience.
Greg Kihlstrom: Yeah, yeah. And, you know, I think there’s, there’s a pretty good understanding in, in organizations of the, let’s call it the end user experience. So, you know, the customer, the consumer level, but what you’re, what you’re describing here is the internal user and internal customer experience. Do you see and I totally agree with what you’re saying. Do you see that more organizations are aware of this and and eager to adopt this? Or does it? Is it still something that a lot of organizations need to be kind of taught and and encouraged to do? In other words, is it an instinct already? Or is there still work to do in kind of encouraging this?
Krishnan Venkata: I think It’s not something that organizations do not want to do, but I think it’s something that we need to emphasize and we need to not get caught up into the whole thinking about only the solution and keeping the solution away from the user. And basically, we call about it as not trying to do analytics and get caught in the weeds, right? You want to be able to zoom out, look at the bigger picture and say, are we solving what we set out to do, right? I would say it’s always in almost every case, a question of being able to continue to stay true to that course. And organizations are maturing to be able to understand that there is an agile methodology. they want to iteratively develop, they want to continuously improve, they want to get business users involved. And I feel that analytics organizations are embedding with business users a lot more than what they did about a decade back. So I would say that definitely we are in the right path of doing it, but we should do a lot more. I think we as analytics professionals also need to understand that at the end of the day, The business users needs to adopt our solutions and we need to be able to build solutions that they’re willing to adopt and they see value out of. And it has to be a two-way street.
Greg Kihlstrom: Yeah, yeah, absolutely. So looking ahead, what are you seeing as far as emerging trends or technologies? Certainly, as you said, AI, even Gen AI is not new anymore. But what are you seeing on the horizon? in the application of some of these things that businesses should be aware of and keeping in mind as they’re making investments?
Krishnan Venkata: I think what we’re seeing is evolution in the analytics space. What we found a lot with cloud and data becoming ubiquitous is basically that we’re moving away from number crunching to storytelling, right? It’s not about just doing the analytics, but it’s also about telling a story for businesses to make decisions. And this has actually been improved manifold by Gen AI, right? The whole idea of prescriptive analytics is becoming much more prominent now with Gen AI with all the narrative insights that it’s able to provide. The second part of it that we are noticing a lot is that people want to start looking at analytic solutions, not just as standalone things to refer to, but they want to productionize this. So we are seeing a lot more solutions moving into production, and we are seeing the industrialization of data science, which is Basically, the data science is no more this gimmicky tool that is there outside. This is now part of businesses. And Amazon did this a bit with their whole recommendation engine and made analytics as part of the business process. But we’re seeing this across the businesses right now. And that’s a fairly big trend that we are seeing. And there are other things that are happening, but I would say that these are the big things, which is analytics is moving towards storytelling, which is kind of interesting because it’s moving away from the numbers to the narrative. And we are also seeing a lot of productionization of analytic solutions.
Greg Kihlstrom: Yeah, so with that trend, I mean, I think that goes back to the democratization that you mentioned earlier. What’s the role of the data scientists in this world where, you know, everybody or most people are going to have access to data or be able to ask questions of the data and get some answers through gen AI tools or things like that. So, you know, where do you see the distinction between the more, you know, the pure data scientist and the citizen data scientist, let’s say?
Krishnan Venkata: So I think it’s about what we are going to see evolving a lot more. And I tell this with a lot of my clients is data science is going to become easier, simpler, and there are tools that are going to enable the data scientists. We are going to get the augmented data scientist who is able to see Gen AI, able to help him develop solutions. What we are going to see to the citizen data scientists or what we’re going to see a lot more is the data scientists will need to be able to tell the story of how the solution is able to help the business. They will have to educate about the nuances of the solution and how it can improve the things for the business. They will have to demystify the analytics to make it more business friendly. This is going to be fairly important. And I’m seeing that a lot of times we tell clients that the data scientist should also be an evangelist. He should be also a storyteller. And we tell them that do not worry about the data. Do not worry about the analytic solution. We will help you build all of it. You spend focusing on getting the business on the board, being able to tell the story to them. So I think the point is that it is going to move from a pure tech But it’s going to become a techno-functional role. So we are going to see the data scientist needs to understand more about the domain, more about the problem, more about the business than just about the statistics and the analytical rigor or the SQL and the Python, et cetera. That’s going to be important, but the other things are also going to be equally important.
Greg Kihlstrom: Yeah, yeah, that’s great. Well, Krishnan, thanks so much for joining today. One last question before we wrap up. Going back to the first topic that we were discussing, for those organizations that want to become and adopt more that problem first mentality, what would your advice be to them? What could they do today or what kind of changes could they make to move in that direction?
Krishnan Venkata: Right. I think we tell the organizations to always take a step back, right? Focus on why we are doing it rather than how can we do this, right? Don’t just start jumping into implementing the newest technology, but take a step back in terms of saying, why are we considering this? What are the challenges that we have? What is our strategic goals? And what do we need to accomplish? If you start with the why, then you will figure a way of how to get to the right solution to address to that. So I think that is the most important part. The second part of it is, it is very easy to get caught in the hype. And it’s just but natural. And AI has a lot of potential. It’s quite exciting. Everybody’s talking about it, but it’s it is clearly important to focus on things that are going to deliver tangible benefits. So if you look at your problems, you look at solutions, and then be able to run quick pilots, then measure the outcomes from there, quickly iterate, you’ll be able to get better value for some of the solutions that you’re looking to build. So I would say start with the why, then look at the how, and measure and improve.
