Question

When making decisions business people are searching for accurate data to base their models on. However, sometimes implicit and explicit biases can affect not only what data sources are selected but also how that data is interpreted. Considering this please address the following prompts in your discussion: Think of a business example of how a bias created a poor analysis, decision or outcome. How could this have been avoided?

Answer 1

A suitable business example that created a poor analysis, decision or outcome is the monthly performance data of the employees. It is important to mention that companies always focus on employee performance so that it can be improved. Hence, an example of a company that follows the same strategy of measuring employee performance and using the same to gauge an employee's performance. However, the parameters that were used to measure employee performance were based on some biases that often fail to recognize the hard and efficient work of some employees. This leads to poor analysis and a poor reflection of employee performance. Due to this false outcome, the often undeserving employee is promoted and efficient workers leave the company out of frustration.

This can be avoided if all the parameters are considered fairly and equitably. This way employees can be provided with fair performance appraisal. Furthermore, effective and efficient employees can also be retained through this procedure.

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