The Exchanges

Q either they offer like a 30 day money back guarantee, these are like, you know, uh, certain kinds of businesses. They'll say, uh, if you don't get these results in 90 days, then we'll work with you for free until we get you there. Obviously, I know in your business it's not easy to do something like that because it gets very expensive for you, but have you considered that?

A So Sean, I really don't believe in that. If we are not able to show like a six week engagement, our intention is always to deliver it in three to four weeks. In two weeks, if we are not able to figure out if we are getting in the right direction or not, then there's a problem with us. And most of the places with the first couple of weeks are very, very decisive. They will Tell whether we are going to get to the results or not. And the problem could be ours that we had, we don't have, uh, we misunderstood the problem. We didn't, don't have the skills to do it. Or the other side also, it's possible where a very, very likelihood we didn't understand the complexity of the customer's landscape. And that could be slowing us down. So for me, first two weeks, very critical. That should be the decision making point.

AI assessment note: “So Sean, I really don't believe in that.”

Q How do you see this industry evolving? You said you feel like it's going to become more complex, but do you have any specific thesis or something that you're experiencing right now that's telling, that's informing you of how it's evolving?

A I can give you multiple, couple of reasons where I think, uh, there's going to be a huge change in the industry. So I want to start with the modern data stack. If you go to modern data stack, there are over 3000 tools that exist for data ingestion, for data quality, orchestration, and like, there's no limit to it, BI, AI, 3000 plus tools, and you go to any large enterprise, mid-sized enterprise, they would have had about 10 to 15 of those to put together their data plan. Now, not only they have done this, they have invested in people's skills, The compute that goes with it. And a small change that comes in, they have to go and redo multiple places, changes and adjustments. The cost that this comes with also is like someone, like they have also many of these enterprises or several of them have partnership with SIS whom they are dependent on running their operations. And if you look into A little bit more detail. What you will realize is these 10 to 15 tools that every enterprise is paying for, actually they are not using more than 15 to 20% of their tools capability. But these tools are very, very, highly functional. They have deep, vertical capabilities, and not every enterprise uses them. So I think the first round of disruption and transformation that is going to happen is enterprises are going to look for Platforms and tools and products which can give end-to-end capability.…

AI assessment note: “enterprises are going to look for Platforms and tools and products which can give end-to-end capability”

Q Why should people care more about their data?

A Why should not people care about their data? It's a most, most, most important asset. Today, literally, if you don't protect your data, someone is going to abuse it. Your data is going to be like your personal data is very important. Your company data is equally important. How you can leverage your data to get a better competitive edge, increase the top line, reduce, make your whole operations more efficient. There's a lot can be done with data. And what is also interesting that's happening is like, uh, We talk a lot about our old internal data, but there's a lot of third-party data also that comes in and starts influencing. There are all the time, but I can give you an example from my past where an organization where I worked with for many years, they could save 14% of electricity bill just because they put sensors in them. So suddenly if you're like still and not and working and thinking, suddenly the lights will go up. So you have to move around a little to make sure the lights get on. So they were able to figure out like which offices the ACs go at a certain rate, which offices the lights go off at a certain time, where they could even switch off the corridor lights if 90% of the people are not. So I think that a lot can be done, not just from top line, but also from bottom line point of view. And, uh, access to data and managing this data gives us multiple ways of sort of …

AI assessment note: “It's a most, most, most important asset. Today, literally, if you don't protect”

Q And you think the models themselves are going to become specialized for that purpose or?

A So models specialize for interpretation. So the way Sean, I see it as these models are, uh, They are sending a lot of knowledge and these knowledge, these domain knowledges in the industry knowledge used to be typically with analysts. Those are getting abstracted. What the model is going to is going to help is interpret the data from a lens of knowledge they have on the domain understanding. Whether it is right or not, it's still going to be on a human or who's consuming it, but that The amount of knowledge that each model can, has accumulated can eliminate billions, tons and tons of analysts. And I, I feel we should look at models as interpreters rather than someone who's analyzing. Analysis is still going to be on the consumer side.

AI assessment note: “So models specialize for interpretation.”