Playing Poker with Big Data

Is finding insights a skill or just luck?

In Forbes' "Big Data News of the Week" several points were raised about the insights side of the equation.  

• Machine learning requires a human touch.  We can't just implement a silver bullet because the problems require either business domain or technical knowledge in how to address the problem in the first place.
• More doesn't mean more. There are marginal returns to more data.  In fact, the proportion of value derived probably decreases rapidly the closer we move to the realm of self-expression. Drawing the appropriate line in the sand requires judgment.
• Entrepreneurs love scarcity.   The lack of 'data scientists' is leading to the funding of companies that will do the job analyzing the data for you.  Even the reporting platforms are embedding analytics into their offerings.  Clearly a disruptive innovation in the making.

All this suggests knowing when to hold 'em and when to fold 'em is the critical skill.   In an article in the Economist, skilled poker players had an ROI of 30% while the unskilled were down 15%.  Victory was based on the ebb-and-flow of in game decisions, just like in the world of big data.