I see a recurring three step pattern with data waves:
First data collected.
Then it’s presented.
Last it is made actionable.
In every data wave, the companies that transform data into actionable insights are the most valuable because their products not only identify but solve problems. Knowing about an issue without understanding the mechanics required to fix it causes paralysis.
Quantified self tools like FitBit and JawboneUp and Nike Fuel capture and display sleep data and while these devices confirm that I wake at 130am every night, they can’t help me sleep through the night. This user experience induces neurosis. The same is true for the pedometer function - I can measure the steps I walk but not how much healthier I become (how much longer I’ll live).
Similarly, social media monitoring tools apprise marketers of the thousands of conversations across the web that are relevant to their brands, but do little to recommend who to engage with, what to write about and how to prevent large scale negative viral loops from spiraling out of hand.
On the other hand, true data based intelligence provides huge value to customers. For decades, software logging faced a similar challenge. Engineers would log data into increasingly larger databases which rapidly became a cost-prohibitive practice. When Splunk developed tools to mine this data, they unlocked the insights within the data which enabled engineers to act, improving product performance.
The measure of data is how much intelligence a user can derive from it and how quickly the user can respond to reinforce or counteract a new trend.