Now that efficiency means data-driven leadership, its time to apply it to the most consequential leaders.

Over 50% of mid and larger-sized companies are now utilizing data-based methods to discover aspects of their processes and decision-making that can be made more efficient and reliable. People remain the executive leaders, but are now turning to their data-science teams to tease-out these undetected areas of their own leadership that may be improved.

The biggest, costliest, and most wasteful organizations are governments. They are also the most inefficient since their survival is not beholden to market competitiveness; the people fund them involuntarily because the law requires them to. I think many would agree that the ideas that some government officials advocate are absurd, impractical, and in some cases pure pandering or demagoguery. The likes of them would never be taken seriously in a business context.

So although may politicians will bristle at having the quality of their leadership rated by holding it to account against factual precendents (historical data), there is no area in which data science can reveal more significant and important efficiency and policy improvements than federal and local governance.

It is unlikely that politicians would pay much heed to such studies, but if the rationalists among the general public were armed with their statistical results, some of them might vote differently. In any case, well-constructed statistical predictive models are apolitical. If the data says conclusively that Keynesian stimuli do not work in our modern globally-interconnected economy, governments and the people may go along with it anyway, but they will proven wrong most-often.

As long as forecasted results of certain policies are proven reliably accurate through time, it should become less easy for these results to be ignored. It all depends on how seriously people regard real results. They are quite highly-regarded in business now.

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