Thought Leaders in Big Data: Ganes Kesari, Cofounder & Chief Decision Scientist of Gramener (Part 4)

Sramana Mitra: You have not thought about productizing some of the visualization capabilities that you have developed?

Ganes Kesari: That’s something that we are currently doing. There are several solutions like this that are domain-specific where we have highly specialized implementations of analytics or visualization solutions. We are creating horizontal solutions. We already have a few solutions.

For example, we have some solutions for media and marketing campaigns. These are point solutions. Similarly, we are creating point solutions.

Earlier you were mentioning about areas where entrepreneurs can look at gaps in the industry, this is one thing I strongly believe in. This is one area that people can look at. 

Sramana Mitra: Talk about it. Let’s actually switch to that discussion on open problems and where you see interesting product opportunities. Clearly, the visualization area seems to be a gap.

Ganes Kesari: There are several platforms today. If you talk about analytics, Google, Microsoft, and AWS have everything built it into their platform. There are other products that are also popular. When you look at libraries of models, they are a dime a dozen.

There is no space to create analytic models. I don’t see much of an opportunity there. On the other extreme, businesses are facing very steep challenges in implementing data science solutions. We talked about the 80% failure rate.

In spite of all these platforms and the huge supply of people in this market, they are not able to build solutions and scale the solutions internally. People can take up the rapid advance of horizontal AI trends like computer vision and NLP. All of these are huge advances in horizontal narrow AI.

An example is the crowd-counting we talked about. That becomes a solution that is relevant to almost every pharma company: creating several solutions by piecing all of these together.

Every client we talk to has a license for Tableau and multiple other licenses but when it comes to adoption of visualization within the enterprise, it’s poor. It’s not as bad as machine learning adoption, but it’s not much better. How do you marry the business workflow and visualization?

That’s where using data to solve a business problem by bringing in the narrow AI and visualization, and bringing in the domain layer on top of it is useful. That is a great opportunity.

In the B2C space, Grammarly is making waves today. Grammarly is a classic case where they brought in narrow AI and applied it to the narrow space of writing. They’ve gone beyond that in terms of detecting phrases. It’s fabulous.

Sramana Mitra: Very cool. I really enjoyed listening to you. How far along is the company? What is the revenue? How many people?

Ganes Kesari: We are 180 plus people. We are based in three locations. We are in New Jersey. We also have a presence in Singapore and India. We have our engineering team in India.

Sramana Mitra: Where in India?

Ganes Kesari: Hyderabad and Bangalore.

Sramana Mitra: Have you bootstrapped the company?

Ganes Kesari: Yes, there are six co-founders. We bootstrapped it. Now we are looking at specific sources of growth funding. We are reaching out to some investors.

Sramana Mitra: I’m very happy to hear that things are going well. Thank you for your time.

This segment is part 4 in the series : Thought Leaders in Big Data: Ganes Kesari, Cofounder & Chief Decision Scientist of Gramener
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