Big Data is, well, bigger than ever. More and more companies are accumulating and storing massive amounts of data in hopes of analyzing it to pinpoint that perfect price, offer the most-wanted product, or track down the most valuable customer.
But big data also can mean too much data. While we love data and tell clients there is no piece of data too big to crunch, big data can become expensive to analyze for those of whom don’t possess that capability. Enterprise storage systems that once housed terabytes of data have now moved into warehouses of petabytes.
In fact, data is probably the most overinvested yet underutilized asset in companies today. Yet there are innovative ways to harness big data.
To harness Big Data, the answer can be found with machine learning, first defined in 1959 by computer gaming pioneer Arthur Samuel as a “field of study that gives computers the ability to learn without being explicitly programmed.” Largely, this is done with algorithms.
To apply this concept, to a real life example, here’s how machine learning can help with big data:
- A hotel chain might be storing reams of data on social media and website traffic. Every click is being recorded, but to what end and expense? Machine learning helps by keeping big data from getting too big because the right algorithms can select the appropriate data to be analyzed – thereby slicing a giant pie down to more digestible bites.
- A manufacturer might be using sensors to record every step in the production process, producing so much data that it cannot be stored without great expense. Applying a machine learning model, like exponential smoothing, can reduce the amount of data needed for storage.
Marrying machine learning with social media and website traffic data, or production data, or even ticket transaction data, can be hugely beneficial to companies. For example, a machine learning tree algorithm helped a movie theater chain get to the bottom of negative ticket price data, which was the result of discounted or zero-priced tickets and accounting procedures.
The bottom line is that if big data is getting too big for your company, machine learning can bring it down to a manageable size.