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.
Sramana Mitra: You talked about data visualization. Double-click down on that and talk to me about how you deal with visualization. What tools are you using? What are the capabilities of those tools? What are the limitations of those tools? What’s happening in that space?
Ganes Kesari: Data visualization is the last mile of connection when we present insights from data. That’s one area where we’ve seen a lot of organizations falter.
Back in 2011 when we started Gramener, that was one major pain area in the industry. Since then, there have been several platforms and tools which have grown in popularity. When you look at the consumption of insights, that’s an area where people still rely on hundreds of Excel reports.
If we can convert those hundred pages into one simple summary of the key insight that a decision maker needs to know, that is the pursuit of data visualization.
The approach we take to get to that is we have a methodology where we start with the business questions, finding out what the users want, and understanding what kind of decision we need to take. Then we map it back to the data to find out how the data should be analyzed and presented.
What is the right representation? Should it be a bar chart or something else? What are the representations which are relevant for the users and how should the narrative be stitched together. That is the power of data visualization. We use our platform which comes with built-in data visualization libraries.
Earlier this year, we open-sourced it. We’ve been building it for the last eight years. We decided to put it out in the open and give it back to the community. We use this tool to build data science applications which are interactive visualizations which a user can come in and consume. There are several approaches in the industry.
One is data discovery through Tableau. People drag and drop and create a visualization. The approach we take is slightly different. We have people who come in with a background and design data. The average business user may not have the right mix of skillset. We identify what is relevant for the users and bring in a team of data scientists, designers, and developers to build that layer.