Data quality within supply chains continues to be a critical issue for supply chain directors and operations leaders. Despite ongoing technological advancements, the accuracy and timeliness of data exchanged between external trading partners often fall short of expectations, as highlighted by recent industry findings. According to a June 2024 survey by Indago, 75% of supply chain professionals rated the data quality they receive as “Average” or “Poor.” With no respondents rating the data as “Excellent,” it’s clear that many companies are grappling with the consequences of unreliable data.
While these findings provide a snapshot of current challenges, the discussion needs to go deeper. The problem of data quality isn’t new, but it’s become more urgent as supply chains grow in complexity. The solution isn’t simply adopting new technologies; it lies in addressing systemic issues such as fragmented accountability and governance structures that leave data management in a state of disrepair.
The Real Issue: Ownership and Accountability
One of the most glaring issues contributing to poor data quality is the lack of clear ownership within organizations. Many companies have yet to formally designate who is responsible for managing the integrity of their supply chain data. This vacuum allows data management to remain siloed across departments—procurement handles its own data, logistics manages another set, and so on—without coordination or standardization. This fragmented approach inevitably results in incomplete or inconsistent data, undermining supply chain visibility and decision-making.
This is not just an internal issue. External partners often do not share a uniform approach to data quality, and many companies struggle to ensure that data exchanged with third parties meets necessary standards. Without clearly defined accountability, both within and between companies, supply chain leaders are left reacting to problems rather than preventing them.
Addressing Governance: More Than Just Technology
While many organizations look to technology as the solution to data quality issues, technology alone cannot fix the problem. Systems like blockchain, AI-driven analytics, and IoT sensors can offer significant enhancements to data visibility and accuracy, but without a strong governance framework, they are merely tools in a poorly managed environment. The absence of a formal, centralized data governance structure allows poor practices to persist, even with the best technology in place.
A centralized data governance model is essential for creating a single source of truth across the supply chain. This involves appointing a dedicated team responsible for setting data standards, ensuring compliance, and continuously monitoring data quality. This governance structure needs to be cross-functional, cutting across procurement, logistics, finance, and IT to ensure alignment throughout the organization.
Supply chain leaders must realize that data governance is not simply an IT responsibility. It is a strategic function that directly impacts operational performance, risk management, and ultimately the company’s bottom line. In the absence of this, even the best technology investments will fall short.
The Role of Centralized Governance in Improving Data Quality
To overcome these challenges, centralized data governance must be a top priority for supply chain leaders. A governance framework that spans across the organization ensures that all departments are aligned in their data practices, from how they collect and store data to how they share it with trading partners. Here’s what an effective governance structure should look like:
- Establish a dedicated data governance team: This team is responsible for creating and enforcing data quality standards across the organization, ensuring that each function adheres to these guidelines.
- Set cross-functional standards: Data quality management cannot be confined to one department. A governance framework must set consistent data standards that apply across procurement, logistics, finance, and sales to eliminate silos and ensure a unified approach to data handling.
- Integrate external partners: Data governance should extend beyond the company’s internal operations to include its external trading partners. Standardizing data exchange protocols and ensuring accountability on both sides of the transaction is crucial for improving the overall data ecosystem.
Beyond Governance: The Importance of Continuous Improvement
Supply chains are dynamic by nature, and data quality issues evolve along with them. A static governance model will not suffice. Instead, companies need to adopt a culture of continuous improvement in data quality management. This requires ongoing investment in monitoring tools, auditing processes, and training programs to ensure that data quality improvements are sustained over time.
For example, AI and machine learning tools can be leveraged not just to identify anomalies in real-time, but to predict potential issues based on historical trends. This proactive approach enables supply chain directors to mitigate risks before they impact operations.
Moreover, continuous engagement with external partners is essential. As supply chains become more collaborative, the quality of shared data is only as good as the weakest link in the chain. Supply chain leaders must hold their partners accountable for meeting data quality standards, creating transparent mechanisms for resolving discrepancies and ensuring consistency across the board.
Strategic Alignment: Data as a Competitive Advantage
At the executive level, data quality is not just a tactical issue; it is a strategic imperative. Companies that succeed in creating reliable, actionable data streams will have a distinct advantage in a market where agility and responsiveness are key. High-quality data allows for better forecasting, optimized inventory management, and more efficient production planning—all of which lead to reduced costs and improved service levels.
In contrast, companies that continue to tolerate poor data quality will find themselves at a competitive disadvantage. Decision-making will remain reactive, and inefficiencies will persist, leading to higher costs and slower response times. As supply chains face ongoing disruptions—from geopolitical shifts to climate-related risks—those with robust data management systems will be far better positioned to adapt.
Time for a Paradigm Shift
Improving data quality in supply chains requires a fundamental shift in how companies approach the issue. It’s not enough to simply implement new technologies or update processes piecemeal. Supply chain directors must champion a holistic approach that incorporates governance, accountability, and continuous improvement.
By establishing clear ownership of data quality, integrating technology into a structured governance framework, and fostering a culture of continuous improvement, companies can significantly enhance the reliability of their supply chain data. In doing so, they not only mitigate risk but also unlock new opportunities for efficiency, agility, and competitive advantage.
