Signals versus noise
Small sets of data that can have an impact on the business are called signals, while irrelevant data are considered noise. However, a piece of data can be both; it simply depends on what you are trying to accomplish. For example, a customer looking to buy a house can be identified proactively by her traffic to home buying sites, increased liquidity in her checking account, her location graph showing increased visits in the neighborhood and her searches for mortgage. But for a bank that does not sell mortgage products, this information can be irrelevant. The key is being able to capture the signals amidst all the noise.
Capturing signals starts with data
Spotting signals is becoming more difficult as the sheer volume of data increases. Today’s digital business environment as well as the growth of Internet of Things (IoT) have contributed to the dizzying pace of data generation. IDC predicts that the collective sum of the world’s data will grow from 33 zettabytes this year to a 175 zettabytes by 2025, for a compound annual growth rate of 61 percent.1 As the amount of data companies own—and have access to—continues to grow, being able to isolate signals becomes even more challenging.