Data-averse

Data-averse describes an individual, organization, or system that exhibits a reluctance, avoidance, or resistance towards the collection, analysis, interpretation, or utilization of data. This aversion can stem from various factors, including a lack of understanding of data's potential benefits, discomfort with statistical concepts, distrust of data accuracy or privacy concerns, perceived high costs associated with data projects, or a preference for intuitive decision-making over data-driven approaches. A data-averse entity might downplay the importance of evidence-based conclusions, favor anecdotal information, and be skeptical of quantitative findings. The condition may manifest as neglecting data altogether or actively hindering its integration into workflows.

Data-averse meaning with examples

• Despite clear evidence from customer surveys, the marketing team remained data-averse, choosing instead to rely on gut feelings for campaign strategies. They believed that the surveys were too complicated to interpret and would only confuse their decision-making process. This resulted in underperforming campaigns and ultimately hindered sales growth. The reliance on anecdotal evidence rather than collected data held them back significantly.
• The old-school CEO was famously data-averse, stubbornly rejecting suggestions to implement a data analytics platform to improve operational efficiency. He voiced concerns over the costs and security, claiming his years of experience and intuition were sufficient for success. This reluctance to embrace data led to missed opportunities for streamlining processes, optimizing resource allocation, and staying ahead of competitors.
• Because the company's data privacy policies were poorly defined and inconsistently applied, many employees were naturally data-averse. They avoided entering or accessing critical information due to fear of potential breaches. This lack of data accessibility and quality severely limited the team's ability to make data-driven decisions and improve the team's efficacy on new projects.
• In response to several past failures, the company had been historically data-averse, prioritizing qualitative feedback from salespeople. The company was very uncomfortable with data analysis, but eventually the company sought a consultant to improve its decision-making and use of information, finding it now helpful, but not before losing market share to their more data-driven competitors.