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The availability of big data drives organizations to invest in analytical techniques to improve their decisions and make smarter decisions.

These investments range from improved business intelligence and reporting infrastructure to interactive dashboards, advanced visualization tools and, increasingly, data mining and predictive analytics.

While these technologies are powerful, many projects fail to get as much value from them as they might.

The key problem for these projects is that they generate insight without impact.

That means the tools can be used to develop beautiful visualizations, highly interactive environments and powerful predictions.

Yet ad-hoc, intuitive decision making remains the dominant approach for most employees.

To reap the strategic benefits of big data analytics, businesses need to connect the output to the operational decisions that drive processes.

To make the connections businesses must have a deep understanding of these decisions and the logic behind them, the data that drives them and the processes they direct.

To address these need organizations need to frame their analytic requirements in a new way, using decision modeling to explicitly and clearly define the decision-making to be improved.