As data storage costs have decreased and companies have increasingly instrumented their products and services for data collection, the subsequent influx of data offers forward thinking companies new opportunities to strengthen service offerings and improve efficiencies. The challenge however, is being able to quickly extract actionable insight from the data so it can be used to make better-informed decisions.
According to a recent Gartner report, big data has motivated information and business leaders to look for new opportunities to leverage their data, with 30% of the enterprises surveyed reporting that they had launched a big data project in 2013. At the same time, even with all these big data project launches, companies still don’t fully understand how to extract the most value from their data as shown in the graph below.
And quite honestly, who could blame them? The volume, variety, and velocity of data makes it difficult to know where to start. How do you quickly identify important variables when each record contains thousands or more? How do those attributes relate to and affect one another? How do you extract understanding and value from all of the noise? How do you do it in a way that’s scalable?
Tools like Eureqa, which are capable of discovering and understanding patterns and relationships in raw data, could pose beneficial to those who want to extract more value from the data they’ve collected. Traditional machine learning methods and analytic platforms can help us visualize data or make predictions from it, but as the velocity, volume and variety of data continue to increase, tools that allow us to quickly identify and understand key variables and relationships will prove important to taking advantage of the opportunities posed by big data.