In business analytics, what is the effect of poor data quality?

Poor data quality significantly hampers an organization's ability to make informed decisions. When data is flawed—whether due to inaccuracies, incompleteness, or inconsistencies—the insights derived from that data become unreliable. This can lead to misinterpretations of trends, customer preferences, and operational efficiencies, resulting in decisions that may not align with reality.

For instance, a company might misjudge market demand based on inaccurate sales data, leading to overproduction or underproduction of goods. Similarly, strategic planning initiatives based on faulty customer demographic data could misdirect marketing efforts, wasting resources and potentially damaging the brand's reputation. All of these factors contribute to negative outcomes, both financially and operationally, illustrating how critical it is for businesses to prioritize data quality in their analytics processes.