However, taking this approach keeps organizations locked in a cycle where perfect data is unattainable. Here’s why: the truth of the matter is that the criteria underlying an organization’s pursuit of ‘perfect’ data varies dramatically. For example, when an organization goes through a highly-public product launch, being able to report on the initial number of units sold is critical. As a result, perfect data is defined largely as being ‘rapidly accessible’. In contrast, if an organization is preparing for a merger, collecting information from across all of its departments and locations will take prominence. In that case ‘completeness’ factors heavily in defining the quality of data.

Ultimately, while every organization emphasizes different elements, organizations place a greater reliance on data when it blends several criteria, including: 1) relevance given the context; 2) the ability to access it when it is needed; 3) the ease with which it can be understood; and; 4) the degree to which it can be used to drive meaningful action.

Recognizing the variable nature of ‘perfect’ data actually enables organizations to embrace data analysis without feeling that they need to solve all of their data quality issues. That’s because one of the positive outcomes of conducting rigorous analyses is data that more closely approaches the organization’s definition of ‘perfect’ data. In other words, achieving the data quality you need is a consequence of data analysis rather than a prerequisite. As a result, any tool that helps you better understand your data ultimately improves its quality.