The Importance Of Making Big Data Accessible To Non-Data Scientists

Close behind “Big Data” as one of the most utilized enterprise technology terms today is “Data Scientist.” Many postulate that the explosion in Big Data will usher in an insatiable demand for data scientists able to slice and dice data to guide more informed decision making within the organization. Others go a step further, bemoaning that a chronic data scientist shortage will hold back the full potential of Big Data.

Concern is unsurprising. For years, the BI and data analytics conversation was framed around how to aggregate massive volumes of data and then unleash the data scientists to find the value. Today, despite the information deluge, enterprise decision makers are often unable to access the data in a useful way. The tools are designed for those who speak the language of algorithms and statistical analysis. It’s simply too hard for the everyday user to “ask” the data any questions – from the routine to the insightful. The end result? The speed of big data moves at a slower pace … and the power is locked in the hands of the few.

Data scientists represent important cogs in Big Data’s future. But a strategy built around throwing more people at data challenges is a near-term fix that overlooks the need to consumerize the Big Data experience. What if any user in an organization could ask a question in natural language and the have their analytics tool look across data sets, answer the question in the most relevant and meaningful visualization, and deliver a social learning layer where the users continually train the tool to their needs? [...]