What about the data that should never, ever get in your attention? According to Wharton's Peter Fader, the least valuable data is the noisiest in the Big Data space: social and mobile.

Why is it we can predict a consumer's propensity to read Hunger Games, upgrade their iPad or download music featured on the Voice, yet we fail miserably at predicting life-threatening events, such as developing breast cancer? 

"At this very moment, there’s an odds-on chance that someone in your organization is making a poor decision on the basis of information that was enormously expensive to collect." -- Corporate Executive Board

Andreas Weigend's 8 Rules for Big Data are spot-on. If your organization's Big Data conversations center on the processing of big data, you are missing the point.

Are we in IT so busy managing everyone else's data, that we forget to use data for own decisions?

Using self-tracking, an eclectic mix of early adopters, fitness freaks, technology evangelists, personal-development junkies, hackers and patients are converting everyday activities into data points in order to improve their lives. Could it also improve their bank accounts?

If we are to classify contextless data dirt, this first case illustrates superfluous, distracting data. I posit that an equally insidious type of data dirt forms when data is analyzed without regard to origin, business-system and business goal contexts.

For the vast majority, Lin's breakthrough is a complete surprise. However, for numbers hobbyist Ed Weiland, Lin's breakthrough was merely a matter of time.

In a way, you could say data science leans towards innovation, while business intelligence leans towards optimization. Each are critical for business, government and societal progress.

When you think about the human resistance in adopting data-driven decision-making, or really any change, at the root is the me question. What is the impact on my job, my span of control, my future opportunities?