

Eastbanc Technologies is a services company that has spun off a number of product companies based on their services business. More recently, their focus has been on AI and Big Data.
Sramana Mitra: If you would please set some context about Eastbanc Technologies to start off this interview, that would be ideal.
>>>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.
>>>Sramana Mitra: You don’t use off-the-shelf visualization tools like Tableau or FusionCharts? There’s a whole bunch of visualization capabilities available. You don’t use any of that. You do everything from scratch.
Ganes Kesari: That’s right. We don’t use the official product. The approach we follow is more programmatic. Our platform is built on Python and use Javascript. It integrates with D3 charts.
>>>Ganes Kesari: The final aspect is the storytelling. There can be great insights from analytics, but unless it’s used by the organization, it is a waste. That’s where we bring in a data visualization and storytelling layer to convert all of these insights into interactive stories.
That’s consumed by all the teams – product, marketing, and sales. They understand integrated customer experience and what actions their specific teams need to take. That’s one area where we’re seeing a lot of benefits for the customer. They’re able to target their investment and are able to see some ROI.
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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.
>>>Sramana Mitra: It’s not usually easy to sell pure horizontal. In the world of complex queries, what is missing? If you look at the universe, what are the specific things or capabilities that are missing from the ecosystem that would help you and help your customers, and that could be opportunities for new companies to be born?
Marc Alacqua: It seems that the competitors have dumbed down the ability to do complex searches. In our experience, we’ve always been tightly tied to the users in the end. We try to create a user interface that serves a variety of users. From what we’ve seen, the ability to do both separates us from a lot of the other products out there. >>>
Steve Davis: One of our largest clients is one of the largest auto manufacturers in North America. They have a problem analyzing various data sources that might have valuable information that could contribute to analysis of emerging safety issues. All of their data sources are all in separate systems.
They had a notion to incorporate all of that data into a data lake. What they realized is the data lake doesn’t really solve the problem. It just consolidates the access. They brought us in to fuse that data so that the users don’t have to worry about where the data came from. They can just access it all at the same time. >>>
Sramana Mitra: Let’s start with a little bit of an ecosystem map of your space. What is your worldview in terms of who are the players, what are the issues? What does the world around you look like? Where do you position yourself in that universe?
Marc Alacqua: In the Big Data space, there are hundreds and hundreds of companies. We try to focus where our strength is based on our history. It’s about that data fusion side of it. A lot of people talk about data integration. A lot of that focus has been on the data lake. That’s the big trend. Companies feel that they’ve accomplished a lot by creating this data lake. >>>