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 a series of experience in technology with half of that 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 terms of scaling the company and solving business problems. Today, I help the 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 consumer-built data story using data visualization.
The twin focus are data science and data visualization. We built an open source platform which we use to create applications. We follow a consultative methodology at Gramener. We strongly 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 where data can help, going through the execution process, and maturing them in the process in terms of how they can do more. All the while, keeping in mind the business ROI. It’s adopted organization-wide. That’s what we do.
We work with enterprises. We also work with NGO’s and other public entities. We work with all kinds of organizations, large and small. My interest is in simplifying data science for everyone. That’s something that keeps me active.
I write about it in my blog on how we can talk data science in simple terms without technical jargon and focus more on applications so that people can understand and 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 the customer experience. Every enterprise is trying to make their customers 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 and be able to serve that, retain, and grow the customer base. We work with a large technology manufacturer.
The problem they’re trying to address is understanding what drives their customer satisfaction rating. They’re trying to understand how they can drive up the satisfaction store and more importantly, understand what customers want. We used multiple channels to identify the data. The second most popular one is social listening through Twitter and Facebook.
There are several other indirect ways where you can identify and find out what customers are talking about. Collecting all of those inputs and using analytics to find out what drives the emotions of customers using advanced analytics like deep learning to find out what are the categories and topics that customers are talking about.
What is the relationship of all of this to the NPS? This is one engagement. It’s an ongoing work we are doing. It’s a fairly exciting area where we continue to detect newer sources of area, apply newer modes of analytics to get into a deeper understanding of what customers are talking about.