Why Data Quality Is the Biggest Key to MDM Success

While master data management (MDM) programs begin with the right goal of creating and maintaining a single, trusted source of truth for an organization’s most important business data, troubling patterns can emerge as the go-live date approaches. An increasing backlog of data issues, declining business trust, and downstream team performance often call into question the quality of what is being published.

The root cause of this uncertainty is rarely the MDM platform itself. The problem typically lies with data quality, which is almost always treated as a follow-up phase rather than a foundational design decision.

The failure to address data quality during design ultimately leads to defective data spreading across enterprise resource planning (ERP), analytics, and planning systems, sharply increasing the cost of correction. Native data quality capabilities in most MDM platforms encompass the conventional domains, including customer, vendor, material, and product, but the master data that disrupts operations sits outside that coverage. Organizations that treat data quality as a design decision rather than a remediation activity can avoid downstream costs, adoption failures, and operational disruption.

Data Ownership Has Shifted

For the majority of the first generation of MDM adoption, data governance was organized as an information technology (IT) responsibility. Technical teams defined quality rules, enforced validation logic, and managed exceptions. Business users received the output. The accountability for what the data said, and whether it was correct, resided with the people who managed the systems, not with those who relied on that data to run the company.

That model did not survive contact with operational reality. It produced data governance frameworks that were technically enforced but organizationally unsustainable. Rules defined by IT without meaningful business input were routinely bypassed under operational pressure. Stakeholders who were excluded from the design of preventive controls had little reason to trust them and every reason to work around them. Additionally, stewardship roles assigned to people without the domain knowledge to make sound quality judgments typically produced inconsistent outcomes. Then, when quality failures trigger real disruptions, such as a production stop, a financial reporting discrepancy, or a missed customer commitment, the question of who is responsible rarely produced a satisfying answer.

As a result of these circumstances, business leaders are now asked to own data domains, set quality standards, and accept accountability for data accuracy within their functions. In many leading organizations, responsibility for planning and sourcing data has been formally transferred to sourcing and procurement managers, those closest to the decisions that data supports. That accountability shift changes how quality rules are written, how exceptions are resolved, and how stewardship is practiced daily.

In this new environment, the overnight data run is no longer a reasonable solution. Batch-cycle MDM made sense when it was designed. Synchronize it overnight, consume it the next morning, repeat. That rhythm worked because everything downstream operated on the same schedule, and everyone followed the same clock. But organizations aren’t standing still. Business processes are growing more complex, cross-functional, and interdependent. The tolerance for stale or defective master data has shrunk.

The old reactive model of catching the error in a morning report, fixing it by the afternoon, and hoping that nothing broke in between no longer fits the environment it’s supposed to serve. Businesses need near real-time visibility into data quality to make active decisions, such as whether to update a material master, flag a vendor record, or hold a planning run until the data is clean. The data quality (DQ) report has shifted from an audit artifact into an operational tool.

The importance of high-quality data is evident in an incident at Unity Technologies in 2022. The company disclosed that inaccurate data ingestion had corrupted datasets used to train advertising machine learning (ML) models, resulting in approximately $110 million in lost revenue tied to underperforming models, delayed initiatives, and the cost of retraining affected datasets. The defect entered the data pipeline and went undetected until it had already propagated through every model and campaign built on top of it.

More recently, at a global enterprise offering cloud services, a purchase order was generated to procure a single cable for $300,000 due to bad data quality. The root cause was not a system failure; it was a text-based purchase order (PO) that bypassed every structured master data control in place. A material master did not validate the item, a source list did not confirm the approved vendor, nor did a purchase info record check against a negotiated price. Nothing in the architecture was designed to stop it.

The result was that the system processed the transaction exactly as instructed. Both incidents demonstrate that when reactive DQ is the model of choice, defective data can enter a system, propagate undetected, and surface only after it has shaped decisions, disrupted operations, or even triggered legal exposure.

Moving Beyond Reactive DQ

For companies eager to move beyond reactive DQ, it is essential to define data quality by their governance requirements, not by the boundaries of a vendor’s out-of-the-box object model. It’s also crucial to commit to “design quality in from day one,” which means treating quality requirements as first-class design inputs considered at the beginning of every architecture decision, every workflow design, and every governance conversation, rather than as a compliance checklist.

Every decision made during data modeling is a quality decision, even when not framed as one. When an architect defines a field as mandatory, a reference data domain as the set of permitted values, or a relationship as a required attribute, they are encoding quality requirements into the system’s structure. In day one quality design, these decisions are made collaboratively by the technical architect and the business domain owner, with the explicit goal of reflecting operational quality requirements. A key decision is to include the data quality monitoring dashboard as a launch deliverable. The first weeks of MDM operation are the program’s highest-risk quality window. New workflows run under real pressure for the first time. Edge cases that never appeared during testing now appear. The dashboard shows all of this and allows users to see quality scores updating in real time. Without dashboard monitoring, failures and inefficiencies can go unchecked, weaken data, and accumulate downstream.

To ensure high-quality data, a company can stop treating data quality as a technical discipline and instead consider it a financial risk-management decision. That means building a structured accountability model that distributes responsibility across four distinct organizational layers:

• Data domain owners. These individuals are accountable for the quality of data in a specific domain, such as materials, vendors, customers, source lists, bill of materials (BOMs), or pricing conditions.
• Data stewards. Data stewards understand the business meaning of the data they govern, not just its technical structure, because the judgment required to resolve a quality exception is always a business judgment.
• Technology talent. These employees have MDM platform expertise, such as the ability to configure, govern, and maintain the specific platform in use at a meaningful depth.
• Change management overseers. Experienced professionals who help organizations manage complex data environments will resist new controls unless the transition is explicitly framed as a capabilities upgrade rather than a displacement.

In addition to building a structured accountability model and hiring the right talent for the right roles, it’s important for companies to avoid common missteps that can derail effective MDM strategies. Those missteps include starting with technology before governance design, treating migration cleansing as a data quality program, scoping DQ coverage to platform-native objects only, and deferring the DQ dashboard to a later phase.

Additional mistakes include measuring governance activity instead of governance outcomes, over-relying on technology while underinvesting in the human governance layer, and positioning MDM as an IT program. McKinsey determined that only 16% of MDM programs are funded as strategic business initiatives. IT can build and maintain the platform. It cannot own the data. Until business executives accept accountability for the accuracy of the data their functions depend on, MDM will deliver infrastructure without governance.

The Data Problem That Can No Longer Wait

For years, organizations have had more urgent priorities, like new systems to implement, models to fine-tune, and dashboards to build. Data quality was the unglamorous work deferred to the next planning cycle. Gartner now projects that through the end of 2026, organizations will abandon 60% of artificial intelligence (AI) projects due to poor data quality.

The conversation used to be about when it made sense to invest in data quality. Now it is focused on the $2.59 trillion in AI investments already on the books this year, which are not going to deliver unless the data underneath them is sorted out. It is imperative that data quality now be a foundational architectural decision, not an operational afterthought.

The MIT-validated 1-10-100 rule captures the cost of poor data precisely: one unit to prevent a defect at the point of entry, 10 to correct it within the system, and one hundred to remediate it once it has reached downstream operations. Organizations that embrace data quality lay the foundation for the AI infrastructure of tomorrow. Agentic MDM, real-time quality monitoring, regulatory data as an emerging MDM domain, and federated governance with AI control loops are the next design decisions to be made. Companies that embrace the importance of data quality now and keep abreast of innovations will position themselves to gain an advantage over the competition and grow their profits.