The other side of data-driven decision making

Data-driven decision making has changed industries in ways that’s not possible to recount here. Perhaps its biggest achievement is in removing expensive trial & error and making intelligent decision-making accessible to masses (instead of just depending on those few individuals who have developed intuition). This is a phenomenal achievement in advancing society. There perhaps is not an industry that has not benefited by Big Data.

That being said, there is a down-side to blindly relying on data. Data will drive correct decisions only if right data is being looked at. Identifying the right metrics or the right data points is key to this process. Managers who are big believers in data-driven decision making should labor over picking the right metrics. If they delegate the job of picking the right metrics, it is highly critical for them to verify that the right metrics are picked.

How does a manager know that the right metrics have been picked? Industry research, triangulation of many points of view and/or intuition from years of experience all play a part in determining that the chosen metrics are correct. Without that due diligence, it can be easy for indecisive managers to hide behind data and make wrong decisions. It can also be easy for the staff to present a rosier picture. But this is a relatively low-impact scenario. In the most extreme and dysfunctional scenarios, unethical practices or behavior can occur and can go completely unnoticed until it’s too late. Look at what happened with Wells Fargo. The numbers looked great because the metrics being used to determine success were wrong. Metrics have to be a combination of quantitative and qualitative data and needs to be counter-balanced with gut instincts or at least common sense. And of course holding oneself accountable to higher standards and ethics is also imperative.

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