Mikko Jarva: There is a prediction that in 2016, the world won’t have enough data storage space to store all the data that is currently being generated. Some of the challenges that we need to think about also is how we manage that data and what do we do with it.
Third challenge that we see is data preparation. Getting from data to insights requires a lot of data preparation. The data scientists would be better off actually helping generate the revenue generating function rather than preparing the data. Those are the challenges that we’ve identified.
Maybe, the fourth challenge we have is while Big Data has insights, it’s mainly utilized in batch mode. We have an approach in Big Data which we call intelligent fast data. The idea is to automate the data analysis in real-time so that we refine different kinds of data in real-time towards recommended actions. Data only stops after that. It doesn’t stop during the analysis.
Sramana Mitra: What you presented as a general framework is true but there’s also been a lot of business logic that has come in. A lot of vertical Big Data solutions have come into existence. It’s not as much of a virgin territory anymore as it used to be. In the last couple of years, there are a lot of business applications and use cases that have been developed.
Mikko Jarva: Yes, that is true. Generally, how we see the adoption of new technologies and trends is first, the industry goes about adopting the technologies, and thinking less about actual use cases on how to apply those. We are aiming for a proposition that makes the data actionable in real-time, independent of the context or vertical we’re talking about. We are also approaching Internet of Things, which is very much related to Big Data. To enable this fast intelligent actionability from the data is what we aim for rather than having the data stored and analyzed later.
Sramana Mitra: Any other thoughts that you want to add?
Mikko Jarva: In order for Big Data technologies and in order for collection of data to make sense, the data should be made actionable. That actionability should be done in real-time so that we can capture the value of a context. Context means identification of opportunities and threats of current events. Those opportunities and threats can be captured, monetized, and managed by having intelligent fast data processing. That is what we want to do with Big Data – make it fast and actionable.
Sramana Mitra: Thank you for your time.