1. Data science → Decision science → Institutionalize data-driven decisions Deepinder Dhingra at @MuSigmaInc explains why data science misses half the equation, and that companies instead need decision science to achieve a balanced creation, translation, and consumption of insights. Requisite decision science skills include "quantitative and intellectual horsepower; the right curiosity quotient; ability to think from first principles; and business synthesis."

2. Statistical model → Machine learning → Good prediction Microsoft is quite good at predicting American Idol winners – and football scores. Tim Stenovec writes about the Bing Predicts project's impressive record of correctly forecasting World Cup, NFL, reality TV, and election outcomes. The @Bing team begins with a traditional statistical model and supplements it with query data, text analytics, and machine learning.

3. Environmental concern → Good feelings → Bad recycling ROI From a data-driven perspective, it's difficult to justify the high costs of US recycling programs. John Tierney explains in the New York Times that people's good motives and concerns about environmental damage have driven us to the point of recovering every slip of paper, half-eaten pizza, water bottle, and aluminum can – when the majority of value is derived from those cans and other metals.

4. Prescriptive analytics → Prescribe actions → Grow the business Business intelligence provides tools for describing and visualizing what's happening in the company right now, but BI's value for identifying opportunities is often questioned. More sophisticated predictive analytics can forecast the future. But Nick Swanson of River Logic says the path forward will be through prescriptive analytics: Using methods such as stochastic optimization, analysts can prescribe specific actions for decision makers.

5. Graph data → Data lineage → Confidence & trust Understanding the provenance of a data set is essential, but often tricky: Who collected it, and whose hands has it passed through? Jean Villedieu of @Linkurious explains how a graph database – rather than a traditional data store – can facilitate the tracking of data lineage.

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