We are all biased


While most data scientists and analysts are wary of their cognitive bias impacting their data collection and basis of their algorithms, its a good time to see if analytics in itself can combat the laziness of the human mind that falls prey to such bias!

We tend to favor information and factoids that fall within our beliefs. So, when we want to present a compelling argument, we rely almost solely on the statistics and data that conform to the conclusion we wish the audience to draw. Several of the big analyst research firms have paid special attention to this and have incorporated techniques to include information outside regular conformed boundaries and challenging assumptions.

We know that randomness plays an enormous role in business and the competitive marketplace, thus making it a business imperative to account for randomness. If we allow the bias to creep in, its likely to steer a model in an entirely wrong direction, negatively affecting the final outcome and hence minimizing the impact of data and analytics. The results of applying such flawed outcome may also create indelible damage to the firm.

Many savvy business operations executives expiate themselves in how they create the utility chain for the analytics outcomes, by creating effecting change management process or challenging the status quo. This is still better than where the starting point may have been before the use of such intelligence, however, deploying robust analytical algorithms that allow the heuristic characteristics of analysis to be overcome with problem design, math and statistics will make the bigger impact.

The planning fallacy often plagues many enterprise budgeting and operations process. Daniel Kahneman and Amos Tversky first explained this phenomenon that is largely driven by hugely optimistic bias. Such poor estimations often lead to higher budget and delayed timelines which can cause customer satisfaction dips, budget overruns, investor stress amongst other negative effects.Similarly, the sunk cost fallacy also creates poor judgement where past investment decisions influence the choices on how to move forward. Cognitive dissonance leads individuals to analyze information in a biased manner. Most of us tend to place heavier emphasis on data which supports our initial position (confirmation bias), while discounting discordant information. This means that we make decisions to pursue an unprofitable course of action, because the brain only emphasizes positive results, while disregarding negative inputs.

Once again, the effective use of robust analytic methods that can overcome such biases and fallacies, will give any business operations leader a strong edge for effective leadership and digital transformation opportunities.

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