Data Quality Starts in Data Engineering
Why Fixing Reports Never Fixes the Real Problem Ask any CXO about data quality, and the response is usually immediate. Numbers don’t match. Reports require adjustments. Dashboards need explanations. Teams debate definitions. Confidence erodes. Most organizations respond by adding controls at the end of the process—more validations, more reconciliations, and more governance forums. The intent is right. The outcome rarely is. The uncomfortable truth is this: data quality problems are almost never created where they are detected. They are created upstream, in how data is engineered, moved, and shaped long before it appears in reports. Until this is understood at a leadership level, data quality efforts will remain expensive, reactive, and incomplete. Why Data Quality Is So Commonly Misdiagnosed In most organizations, data quality becomes visible only when it reaches decision-makers. Finance flags discrepancies. Operations challenges numbers. Executives lose confidence. At that point, the natural reaction is to “fix the data” at the reporting layer. This is logical—but misguided. By the time data reaches dashboards, quality issues are already embedded. Corrections at this stage are cosmetic. They may improve appearance, but they do not address root causes. This is why organizations feel trapped in an endless loop of fixes without lasting improvement. The Core Misconception: Quality as a Control Problem Many data quality initiatives are framed as control problems. Rules are added. Exceptions are logged. Ownership is discussed. Governance structures are created. While these mechanisms are necessary, they are insufficient on their own. Controls assume that errors are anomalies. In reality, most quality issues are systemic. They arise from how data is sourced, transformed, and combined. If pipelines are inconsistent, definitions ambiguous, and transformations opaque, no amount of downstream control will create trust. Explore our latest blog post, authored by Dipak Singh: Why Data Engineering Is the Backbone of Digital Transformation Where Data Quality Is Actually Created—or Lost From an engineering perspective, data quality is shaped at three critical moments. First, at ingestion.If data is extracted inconsistently, without context or validation, errors propagate silently. What enters the system matters more than what is corrected later. Second, during transformation.Business logic embedded in pipelines determines how raw data becomes meaningful information. When this logic is duplicated, undocumented, or constantly modified, quality deteriorates quickly. Third, at integration.Combining data from multiple systems introduces complexity. Without disciplined modeling and standardization, inconsistencies become inevitable. These are engineering design choices—not reporting issues. Why “Fixing It Later” Becomes a Permanent Strategy One of the most damaging patterns in low-maturity organizations is the normalization of downstream fixes. Manual adjustments are made “just for this report.” Exceptions are handled “this time only.” Over time, these fixes accumulate into shadow logic that no one fully understands. For CXOs, this creates a false sense of progress. Reports appear accurate. Meetings move forward. But the underlying system becomes more fragile with each workaround. Eventually, the cost of maintaining appearance exceeds the cost of fixing foundations—but by then, change feels risky. The Link Between Data Quality and Trust Data quality is often discussed in technical terms, but its real impact is psychological. When leaders repeatedly encounter discrepancies, they stop trusting the system. They hedge decisions. They seek confirmation from other sources. They revert to intuition. Once trust erodes, even genuinely accurate data struggles to regain influence. This is why data quality is not just an accuracy issue—it is a credibility issue. And credibility is built through consistency over time, not isolated fixes. What High-Quality Data Looks Like in Practice In organizations where data quality is strong, a few patterns consistently appear. Errors are detected early—not at the point of consumption. Transformations are transparent and reusable. Definitions are stable. Exceptions are rare and explainable. Most importantly, teams spend less time explaining numbers and more time interpreting them. This does not happen by accident. It happens because quality is engineered into the flow, not inspected at the end. The CXO’s Role in Improving Data Quality Improving data quality is not about asking teams to “be more careful.” It is about changing what is valued and funded. When leadership signals that quality matters upstream, priorities shift naturally. A Practical Reframe for Senior Leaders Instead of asking, “Why is this report wrong?”, a more productive question is “Where in the pipeline could this inconsistency have been prevented?” This redirects attention from blame to design. It surfaces structural issues rather than individual mistakes. Over time, it changes how teams think about quality. The Core Takeaway For CXOs, the essential insight is this: Organizations that shift their focus upstream experience a gradual but powerful change. Trust rebuilds. Reconciliation declines. Analytics becomes quieter and more reliable. Data quality stops being a recurring problem and starts becoming an embedded property of how the organization operates. Get in touch with Dipak Singh Frequently Asked Questions 1. Why don’t data quality tools alone solve these problems?Most tools focus on detection and monitoring, not prevention. They identify issues after they occur rather than addressing flawed ingestion, transformation, or integration design. 2. Isn’t governance enough to enforce better data quality?Governance is essential, but it cannot compensate for poorly engineered pipelines. Without strong engineering foundations, governance becomes reactive and burdensome. 3. How long does it take to see improvements from upstream fixes?Many organizations see measurable reductions in discrepancies within weeks. Trust and stability improve progressively as fixes compound over time. 4. Do upstream data quality improvements slow down delivery?Initially, they may require more discipline. In practice, they reduce rework, firefighting, and manual fixes—speeding up delivery over the medium term. 5. Who should own data quality in an organization?Data quality is a shared responsibility, but leadership must fund and prioritize upstream engineering. Without executive support, ownership becomes fragmented and ineffective.
