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.