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.

When teams clearly define the distinction between perfect data and data that is fit for purpose, and communicate expectations early in the process, they not only strengthen trust in the data but also establish a practical and achievable path forward.

Approach 1: Minimum viable quality

Minimum viable quality (MVQ) is the lowest level of data quality required to meet a specific business need. It helps data teams define thresholds that are tailored, not arbitrary.

Key criteria to assess MVQ include:

• Frequency of use: How often is the data accessed or updated?
• Impact of errors: What are the downstream effects if the data is wrong?
• Regulatory or financial relevance: Are there compliance or reporting requirements?
• Tolerance for risk: How much variance can the organization accept?

For example, product master data likely requires higher quality standards than ad-hoc survey responses. MVQ allows teams to focus their quality checks where it matters most, so they don’t waste time or resources putting too much effort into less important data.

The result: more focused efforts, better resource allocation and conversations rooted in business value — not technical purity.

Approach 2: Stack ranking for prioritization

When organizations are overwhelmed by the sheer volume of low-trust data, a stack-ranking approach can clarify priorities quickly.

This method involves evaluating two dimensions:

1. Need for high-quality data (based on business importance)
2. Current level of trust in the data

Mapping data assets across these axes creates a 2×2 quadrant:

By making teams rank their datasets without allowing ties, organizations can focus on what matters most — ensuring that high-value, low-trust datasets receive attention first. This model also helps justify resourcing decisions to leadership and avoid expending effort on low-impact areas.

Governance: The enabler of pragmatic quality

A fit-for-purpose mindset requires more than a new framework; it requires governance that’s collaborative, adaptive and embedded in business workflows.

Effective governance:

• Establishes clear ownership and stewardship
• Sets realistic quality expectations
• Enables ongoing evaluation and iteration
• Fosters alignment between data teams and business users

Critically, governance isn’t about enforcing rigid rules. It’s about creating a culture of partnership, where quality is defined in terms of relevance, not perfection. This shift in mindset helps organizations break free from outdated ideals and embrace a more strategic, outcome-driven approach to data.

Conclusion: Build trust through relevance

The smartest data teams aren’t chasing flawlessness — they’re making informed trade-offs, prioritizing business outcomes and building trust through relevance. By adopting a fit-for-purpose mindset, using structured prioritization methods and embedding governance as a strategic partner, organizations can finally unlock the value of their data, without burning out their teams in the process.