# How to Resolve Data Quality Issues By:: [[Brian Heath]] 2022-11-10 Organizations regularly struggle with trusting data and analyses. They question it, feel uncomfortable, and get uneasy feelings. Where does this come from? Often the finger is pointed at poor data quality or timeliness of analyses. If the numbers are regularly late or change frequently, organizations begin to question whether the process is in control or out of control. It's a common heuristic in humans to see negative outcomes and assume something is deeply wrong with the system that produced it. It can certainly be that way - the heuristic exists for a reason. However, if we dive deeper, we discover that perceived quality or timeliness are the symptoms and not the cause of the problem. As discussed in [[Analytics-Induced Anxiety]], the deeper cause of concern is internally motivated and not necessarily the data itself. The organization may point to data quality as the source of poor trust, but the real issue is that the organization doesn't trust you based on their needs and perceptions of the world. In [[The niceness barrier|nice organizations]] the tendency is to not point at a person but at a process or outcome. As an analyst, you may be able to fix the process but still not address the root cause that the organization does not trust or understand you. To resolve the trust problem, it is advisable to fix the process problem in a highly visible manner. Show all the flaws, inner workings, struggles, and how the sausage is made. Trust is established in the belief that your intentions and actions are known. Having no process secrets or magical resolutions allows the organization to see your intentions. Honesty and openness along the path of fixing the process and data problem establish your credibility and trustworthiness, which will pay dividends. If done correctly and honestly, people stop questioning the data and work from your team when they probably should be taking a closer look. However, because you've established this trust, this is the perfect opportunity to remind them of your trustworthiness by highlighting the error they missed that you are actively correcting. #### Related Items [[Analytics]] [[Data]] [[Trust]] [[Honesty]] [[Business]]