Being able to view massive amounts of Big Data is one thing; actually being able to manipulate it is quite another. Historically, combining various data sets in a way that allows them to be visualized and explored required a lot of intervention from someone with a lot of skills that these days are in short supply.
But an Adaptive Data Preparation application developed by Paxata now allows end users to directly combine data sets without the aid of data scientists or, for that matter, anyone else from IT.
According to Paxata CEO Prakash Nanduri, advances in algorithms and the increase in processing power that is now readily available to users now make it possible to allow end users to self-service their own data preparation needs. For example, rather than having to rely on someone else to join data sets and tables, the idea is to make end users completely independent by letting them use data exploration tools such as Tableau against data sets they combined themselves.
Nanduri says Adaptive Data Preparation represents a significant advance in the trend toward self-service of Big Data at a time when organizations are investing heavily in data visualization tools. The problem is that for those tools to have value, a data scientist or someone of that ilk has to manually prepare the data that end users want to visualize. That generally means a high-priced analyst or data scientist winds up spending a lot of time doing low-level data preparation and manipulation work versus writing algorithms that discover actual new business insights.
In contrast, Nanduri says the Adaptive Data Preparation offering automatically discovers duplicate data across multiple data sets and identifies all the white space that can be compressed. The end result is an ability to truly do ad-hoc analysis of data in a way that frees up business users to maximize investments in Big Data platforms and a variety of data visualization tools.
When it comes to adding value to the business, Big Data clearly has a lot of potential. But the difference between creating yet another IT science project that might deliver that value now versus three years from now is going to come down to how well each organization manages to put the power of all that Big Data directly in the hands of the business people who understand its relevance to the business.