Too often, organizations treat data management as a technical or back-office task—disconnected from the business outcomes that matter most, like revenue growth and operational efficiencies. But when data is inaccurate, siloed, or poorly understood, it creates friction across the business. Planning becomes unreliable, decisions are delayed, and realizing the full value of digital tools remains out of reach. Data management must be aligned with operational objectives—not just managed but managed with purpose.
Why Data Management Matters
When Data Doesn’t Align: A Real-World Example
We recently supported a large supply chain organization where unreliable data quality adversely affected nearly every aspect of supply chain performance and management decision-making. Forecasting and planning processes required extensive manual edits and offline models because the data couldn’t be trusted. Financial leadership couldn’t effectively set budgets or pricing strategies without significant manual review from subordinate groups.
Meanwhile, data maintenance in the organization was often performed by personnel disconnected from day-to-day supply chain operations, further reinforcing silos and inconsistencies. This wasn’t a technical failure—it was an organizational one. The core issue was that data management was not aligned with operational objectives. This problem was amplified by the lack of accountability around data maintenance.
To address this, we helped the client build a data management approach centered on the needs of the business, not just the systems they were using. We started by prioritizing the most impactful data to fix based on its impact on operations. This was followed by establishing a cross-functional team, conducting research, building a system for governance, and leading change management for the client. This effort saved over 80,000 man-hours of manual reviews (annually) by enabling process automation. Additionally, 3,500 man-hours were saved in supply chain planning by correcting incorrect data.
Six Fundamentals for Purpose-Driven Data Management
The six fundamentals detailed below come from a central premise that data should be managed as closely to the business area that uses it as possible. Data management is a cross-functional effort, and those who need accurate data must have direct, active involvement in making it right. Executive leadership must take an active role in understanding how data quality affects business processes and decision-making. Too often, senior leaders engage only after poor data quality has already caused a serious problem instead of proactively building in data quality across the organization.
Here are six foundational steps organizations should take to align data management with desired operational outcomes:
Identify the key outcomes your organization is trying to achieve—such as on-time delivery, inventory optimization, cost control—and ensure your data strategy supports those goals directly.
Ensure data aligns with real-world use cases and drives business outcomes by forming cross-functional working groups that include operations, IT, finance, and other key stakeholders to define clear business rules for setting and maintaining data.
3. Implement Strong Governance
Implement systems that enforce business rules, with accountability structures to maintain consistent and transparent data management practices across the organization.
Train data stewards and operational users so both understand the meaning, source, use, and business impact of key data fields.
Create channels for operational users to raise issues, suggest improvements, and help refine data practices; ensure data teams can share context about why certain rules exist. These feedback loops should flow through the governance processes.
Ensure the goals of the data management team reflect the broader operational goals of the business. Everyone must be working toward the same business outcomes.
Data as an Operational Asset
When data is managed with purpose and tied directly to the needs of the business, it becomes a strategic asset rather than a liability. In the supply chain, more accurate forecasting, accelerated planning, and more informed decisions and investments in automation and AI begin to drive results.
For leaders committed to maximizing the value of their business and achieving operational excellence, don’t start with technology. Start with data—ensuring it is structured, governed, and managed to enable the outcomes that matter most.
Authors
Engagement Director