Data is the most valuable asset in the modern enterprise, yet ironically, it's the most neglected. Time and again, organizations invest millions into data platforms, analytics tools, and talent—only to realise that poor data quality undermines everything.
In a recent conversation with Carlos Bossy, CEO of DataLere, and Sean Hewitt of Succeed Data Governance Services, we explored how organizations can navigate today’s “nasty” data quality issues.
Why Data Quality Is Still So Hard
Despite decades of effort, data quality remains elusive for many. It’s a people, process, and culture problem—not just a technical one.
Organisations underestimate the effort required to maintain high data quality. This translates to a lack of clear ownership, sustainable processes, or lack of alignment among business and technical stakeholders.
Data professionals are stuck in firefighting mode. They spend the majority of their time fixing bad data reactively rather than preventing issues. This leads to a cycle of mistrust, manual workarounds, and wasted investments in otherwise powerful data tools.
The Four Dimensions of Data Quality Success
Success comes from aligning strategy, governance, architecture, and implementation. Here are four key principles we’ve seen work consistently across industries:
- Start with a Use Case:
Identify a tangible business problem caused by poor data quality. This could be a failed marketing campaign, regulatory non-compliance, or inconsistent financial reporting. Solve for that case specifically, and use it to build momentum.
- Define Accountability:
Data needs owners. One of the biggest gaps we see is the lack of clear responsibility for data domains. Whether it’s product data, customer records, or financial metrics, someone must be accountable for quality—and empowered to act.
- Embed Data Quality into the Workflow:
Data quality can't be a forgotten side project. It has to be integrated into existing systems and processes. We call this data “governance by design” where governance tasks are steps in the lifecycle of any project.
Final Thoughts
Data quality is a persistent organisational challenge that touches every part of the business. Successful approaches to data quality require more than tools or quick fixes; they involve strong governance and cross-functional collaboration.
The good news is that organisations don’t need to solve everything at once. By anchoring efforts in business needs, assigning ownership of and accountability for data domains, and embedding quality into day-to-day operations, data teams can start turning the tide.
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