**This post originally appeared on Stefan’s blog**
Some people look at data and see integers, booleans and strings. When I look at data, I always wonder what story is behind it. In the past few years, we have worked hard to implement our vision of shrinking the gap between the technical experts who are able to work with the data and the subject-matter experts who actually need to use it.
Our ultimate vision is to allow each and every person to look at data and see the stories and insights behind it. To see why customers churn, to see how people are engaging, to understand why a device or process breaks, or to see whether someone tried to cheat the system—almost like the image translator in The Matrix that decoded a data stream into an image that humans could understand.
Today we introduce Datameer 4.0 to the market and we are a step closer to this vision. With Datameer 4.0, we are unveiling our Flip Side technology that allows the user to effortlessly flip between the raw data in a spreadsheet and a visual profile of the data.
Our talented engineers worked on our Flip Side technology for more than a year. From the initial concept to smooth integration with Hadoop and finally to a polished user interface, the engineering effort took countless hours and many cycles. I’m very excited about this launch, as I believe it is another major stepping stone in making big data analytics accessible to a broad audience. We observed that these enhancements, once again, significantly shorten the time required for users to gain valuable insights from their data.
The reason a traditional analytics process needs sophisticated data specialists is that analyzing data is like flying blind. It is a long journey to get from complex, raw data, to compressed precise insights that are actionable for a business, with many obstacles along the way. Only experienced analysts can navigate this process since data is always dirty—and who knows what an outer-left join really is? I have also observed that while left-brained engineers are great at working with the raw data and navigating the data analytics process, it is usually the right-brainers that come up with innovative ideas that help to find game-changing new insights. Now a user can easily switch between a raw data spreadsheet and a condensed visual profile of the data.
There are a lot of incremental improvements happening in the big data space, but true innovation must remove complexity. To achieve breakthroughs in ease of use is much more challenging than just making an execution engine faster or more scalable. In the end, the software needs to give the power to the user.