Thought Leaders in Big Data: Ganes Kesari, Cofounder & Chief Decision Scientist of Gramener (Part 1)

This is an excellent discussion on visualization products in the Big Data space and the gaps that could be filled by new entrepreneurs.

Sramana Mitra: Let’s start by introducing our audience to yourself as well as to Gramener.

Ganes Kesari: I have 16 years of experience in technology and half of that has been as an entrepreneur in the data science industry. In the early part of my career, I focused on driving strategic technology initiatives for clients like General Electric and AT&T.

In 2011, I founded Gramener, a data science company. I played pivotal roles in scaling the company and solving business problems. Today, I lead analytics and innovation for Gramener. The role I play is that of a data science advisor. We use data science and AI to identify insights and convert those insights into custom-built data stories using data visualization.

The twin focus areas are data analytics and data visualization. We built an open source platform which we use to create applications. We follow a consultative methodology at Gramener.

We believe that there are a lot of great platforms and tools out there.

The struggle is in applying data science to solve specific business problems. That’s where we come in. We handhold our clients in terms of identifying the right problems to be solved.

We find where data can help, and go through the execution process in delivering value. We help clients move up in data science maturity. All the while, we focus on the business ROI and ensure adoption organization-wide. That’s what we do.

We work with enterprises. We also work with NGO’s and other public entities, large and small.

My interest is in simplifying data science for everyone. That’s something that keeps me active.

I talk and write about it. I explain data science in simple terms without technical jargon and help people understand the application aspects so that they can start using it.

Sramana Mitra: Let’s talk about specific customers and specific ways in which your platform is enabling them to extract ROI. Take maybe two or three use cases and talk me through what’s happening in each of those.

Ganes Kesari: One example would be in customer experience. Every enterprise is trying to make their customers happy and successful. Managing the customer experience is a top priority.

Given the data available today and the power of analytics, it’s possible to exactly find out what a customer wants. If organizations can do that they can retain and grow their customer base.

We worked with a global technology manufacturer.

They wanted to understand what drives their customer satisfaction. They wanted to drive up the satisfaction scores and more importantly, understand what customers really want. We adopted a CX Intelligence framework to solve this problem. We used multiple channels to source the customer data such as Voice of Customer surveys, social listening from places like Twitter and Facebook.

There are several other indirect ways where you can identify and find out what customers are talking about. We collected all of those inputs and used analytics to find what causes a customer to be a Promoter. We used deep learning language models to find the categories, topics and emotions of customers.

Finally we established the relationship between all of this and the Net Promoter Score. Using this, every effort from the organization was channelled into areas that can drive up the NPS score. This was one engagement, where we do ongoing work by detecting newer signals of data, and apply newer modes of analytics to gain a deeper understanding of the customer.

This segment is part 1 in the series : Thought Leaders in Big Data: Ganes Kesari, Cofounder & Chief Decision Scientist of Gramener
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