Nenshad Bardoliwalla: Then I went to work for SAP where I met Prakash. I was running the enterprise performance management product line – planning, budgeting, forecasting, legal consolidation, and activity-based costing. I was deeply involved in acquiring companies like Pilot Software, OutlookSoft, and then Business Objects. After an extensive process of trying to rationalize the Business Objects in SAP portfolio, I went to work in the office of the CTO at SAP. I then co-authored a book on business analytics called “Driven to Perform” where I try to take a lot of what I had learned from Sibyl, Hyperion, and SAP to put together a blueprint of what the next generation analytics landscape would look like.
With that knowledge, I went on to found my first company and spent three years there building a product. When I met with Prakash, Dave, and Chris, they had originally decided that the data integration and data quality and enrichment market was right for disruption. They also had a very interesting algorithm that could identify relationships in multiple data sets. When the four of us got together, we hit upon the idea that we would build a next generation data preparation platform—one that would be built from the ground up as a single solution. You wouldn’t have separate data integration, data quality, and master data management products, but an all-in-one solution that we would build on the new technology stack with capabilities like Hadoop and other distributed technologies like Spark, which has become quite prominent lately.
In the core BI segment, there’s been a huge push towards democracy. I know you probably hear that term a lot. There’s this inexorable push for any technology to become more broadly distributed. We felt that the disruption in the market opportunity was to put data preparation in the hands of a community that had been completely underserved for the last 30 years. That is the analyst community. We would take this technology and build an interactive self-service data preparation solution that analysts would use so that we could ultimately flip the ratio of the amount of time they spend doing data preparation, and the amount of time they spend doing analysis. That’s the vision we set off to build Paxata in early 2012.
Sramana Mitra: Let me get a couple of things clarified and see if I understood it. The key positioning point that I heard was that you wanted to give the power in the hands of the analyst.
Nenshad Bardoliwalla: That’s right.
Sramana Mitra: And you wanted to combine various aspects – whether it’s data quality, master data management – and put in one single application.
Nenshad Bardoliwalla: That’s right.
Sramana Mitra: Is there any major positioning point that I missed?
Nenshad Bardoliwalla: No, as we go through the discussion forward, I will be able to highlight some of the key things. You’re absolutely right. It’s a single solution. It’s built to be interactive, so it’s not the traditional batch mode, run time, design time separation.
Sramana Mitra: It’s real-time.
Nenshad Bardoliwalla: That’s right. The other key point that’s worth pointing out is we are using the algorithmic techniques to pioneer, what we call, an information-centric approach to data preparation. The example I gave you is in today’s environment, people are doing very interesting analytics use cases with graph databases, hierarchical databases, and document stores. The world is not just relational anymore.
This segment is part 2 in the series : Thought Leaders in Big Data: Nenshad Bardoliwalla, VP of Products, Paxata
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