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Data-Driven Decisions for Those Who Don't Like Math
Organizations are constantly accumulating data to make better decisions about everything from marketing to hiring to strategy. To this end, one must be comfortable with the basics of quantitative analysis in order to understand and improve the use of data.

For example, among the most important steps in starting to make decisions with data is to select the right metrics. Good metrics are cheap, consistent and relatively easy to collect. Most importantly, they must capture what your organization cares about most.

It’s also important to know the difference between analytics and experiments. The former provides data on what is occurring within a given group or organization. The latter actively tests various approaches with different member or employee segments, and then measures the differences in responses. When it comes to statistical analysis, it is up to leadership to frame the questions and review the results. The questions they ask should range from "What was the source of your data?" to "Might other analyses establish causality more clearly?" to "What assumptions are behind your analysis? and “Might certain conditions render your assumptions and your model invalid?"

Spotting correlations in the data is key. Decision makers must not only be comfortable reviewing data and metrics, but also be able to identity correlations and judge their relative importance. Only by doing this can they optimally impact both the short- and long-term health of their organizations.
Harvard Business Review (05/14) Frick, Walter
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