Fit-for-purpose data quality: A pragmatic take on governance

Organizations have long recognized the critical importance of data quality in enabling effective decision-making, operational efficiency and stakeholder confidence. Yet despite this awareness, many data quality initiatives struggle to gain lasting traction. Poor data quality can erode trust, compromise strategic insights and introduce inefficiencies across core business processes. Too often, efforts are derailed by an unrealistic pursuit of perfection, leading to stalled projects, frustrated teams and unmet expectations. 

In practice, perfect data is rarely achievable and frequently unnecessary. A more effective approach is to pursue fit-for-purpose data quality — data that is sufficiently accurate, complete and timely to meet specific business needs. By shifting the focus from perfection to purpose, organizations can implement more pragmatic and sustainable data governance strategies. This alignment not only drives better outcomes but also fosters a more resilient and responsive data culture across the organization.  

The pitfalls of pursuing perfection 

There’s a cultural challenge in many data teams: a tension between idealism and pragmatism. Data professionals and business leaders alike often fall into the trap of believing that all data must be flawless. But perfection requires significant time, budget and effort — resources that most organizations can’t spare. 

Leadership enthusiasm decreases when the path forward appears overwhelmingly broad or resource intensive. Proposals that require hundreds of hours to achieve 100% quality rarely get approved. Instead, they receive zero attention, leaving broken data untouched and limiting business value. 

Reframing the goal: Define “enough” 

Rather than striving for ideal quality across the board, organizations should define what level of data quality is truly enough. This approach, often referred to as fit-for-purpose data quality, aligns expectations with the impact and risk associated with each data set. 

Some data must be perfect — regulatory reports, financial statements and other high-stakes assets leave no room for error. But most of the data that organizations use daily, like survey results, marketing enrichment data or early exploratory datasets, just need to be roughly accurate and point in the right direction.