The Foreclosure Crisis—A Lesson on the Power of Data and the Issues Surrounding It
As a marketer, I could spout all kinds of business benefits derived from lots of sources of data with a serving of analytics on the side. I could tell you about companies I’ve worked with and the operational efficiencies and increased revenue they derived from their data analytic projects. Of course, I would have to honor our confidentiality agreements which allow me to only speak in the broadest of terms.
But hey, there are more and more publicized success stories to look at as well. I could tell you how HP added $20 million to its bottom line when it used predictive analytics to identify fraud activity. Or how Kelly Blue Book transformed its business model when it became a data-driven company.
That’s all well and good. But at this point in the online discussion, I think most business people (okay, most people) believe.
Now, I am a cynic by nature (one could argue that being in marketing almost necessitates this as much of my job revolves around influencing people to make purchasing decisions) and it’s very rare for me to get caught up in a story (you know, the narrative) but… if you haven’t read this story, you should read it now: Lack of foreclosure data leaves big questions.
This article, published by the Seattle Times and ProPublica, describes how we could have discovered we were headed into the worst foreclosure crisis in history much earlier (more than three years) if the government had a better tracking system for mortgage data. Of course, it’s more than a tracking system that’s required—the government would also need access to more mortgage data, such as loan terms and the age and credit score of borrowers (personal identifiers, like names, addresses, etc., would be scrubbed).
This is a story about the power of data and analytics that goes far beyond a single company or organization. And it highlights the issues that can sometimes confound and confuse those of us in the analytics business:
- Should we have access to all data—down to the most granular level?
- If so, how do we shield personal identifiers?
- What does privacy really mean in the digital age of Big Data?
- What is the difference between data transparency and information?
Big questions with no easy answers. And I will be talking about all of them in 2011.
Happy New Year!