Kon Leong is the chief executive officer of ZL Technologies. He studied at the IIT in Bombay, holds an MBA from the Wharton School and a degree in computer science from Concordia University. He spent more than eight years in various IT management and engineering positions and has created several successful startups, such as GigaLabs, a vendor of high-speed networking switches. In this interview Kon goes into detail about the unstructured data space and talks about the trend of merging structured with unstructured data. Furthermore, he shares with us his interesting views of the future of this space.
Sramana Mitra: Kon, let’s start with some of your background as well as an introduction to ZL Technologies. What do you do and what trends are you leading?
Kon Leong: In terms of my personal background, it is a little bit unusual, but no so unusual for Silicon Valley. My parents come from China, and we moved to India during the Communist revolution. I was born and raised in India and began my studies at IIT in Bombay. Then I had a chance to go to Canada, and I stayed there for about eight years and became a Canadian citizen. Then I moved to the U.S. and became a U.S. citizen. I started off in IT, although it was called data processing in those days. Then I spent about 10 years on Wall Street. After that, I felt I would be better off back in where I started off, making high-tech products. So I moved to California and started making startups. This is my third one and it is taking a lot longer. We do very big software to manage all of the data in an enterprise. That kind of data is quite large in volume, typically more than most people imagine. A large enterprise would usually have data that is of Google-type volume.
SM: What part of that data management do you do?
KL: We started off in unstructured data management, which is all of the textual data – Word documents, PowerPoint, social media, wireless data, etc. All of this has one commonality: It is unstructured data that is written by humans, generated by humans for consumption by humans. It is quite different from the typical data we are used to seeing under SAP, for example, which is mostly structured data.
SM: How do you differ from Autonomy, for example?
KL: Autonomy was a very good positioning vendor that was in our space at about the same time. They articulated part of a vision, what we can do with unstructured data. However, we executed on the strategy technically. Now our vision is much larger than what Autonomy says, because we are going into the Global 500 and we deploy solutions that are much broader in footprint and that have much more impact in terms of the enterprise needs. Autonomy was a good start in articulating it, but the vision has extended far beyond what they are saying.
SM: Tell us more about the big tree of your customers and specifically about what you do.