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Who Owns Data Quality? 3 Models for Supply Chain Data Management

Effective data management is at the foundation of any successful business operation; however, the importance is even more imperative within the intricacies of supply chain management. There are many benefits to maintaining clean data, such as improved decision-making, increased efficiency, enhanced productivity, and better customer service, just to name a few.

Successful data management will also position your team to more quickly and effectively use existing automation and AI technologies in supply chain. On the other hand, it's likely that problematic data management will derail progress (and demoralize the team).

Data Governance for Supply Chain Success
supply chain leader with pen and graph

Establishing effective data governance is essential for ensuring that your organization can capitalize on the benefits of successful data management. Data governance are the policies, rules, and standards defined by an organization for how data will be effectively managed. Simply said, it is how you will keep your data clean and protected so that it drives the most effective use of systems and analytics that drive value for your organization. 

One of the key facets of data governance is who will have responsibility for the data quality, accessibility, and reporting cleanliness metrics.  

  • Data Quality: Ensuring consistency in spelling and master data setting use
  • Data Control: Defining who has access to change settings and what approvals are required
  • Data Ownership: Determining who is responsible for ensuring cleanliness, and how it will be tracked and measured

For a supply chain organization, this would include:

  • Item Master Setup
  • Vendor Master Setup
  • MRP / Planning Settings


3 Models for Supply Chain Data Management

As supply chain consultants across all industries, we've seen the following three models used for data management within an organization. All require definitions for who owns what and who can change what. Additionally, all have their pros and cons.

The Centralized Model

In this model, all data management is owned by a central team. All Item Master, Customer Master, Vendor Master, BOMs, etc, must be set up in the system through a central team of data conduits. There is generally a partnership with the business data owners, such as planners, schedulers, or engineers, to help ensure the central data team knows the best settings, but all changes can only be made through these centralized conduits.

The Decentralized Model

In this model, data is owned within the business. You'll generally see defined ownership workflows for highly cross-functional setups such as item master data. Since departments of the organization, such as sales, supply chain, accounting/finance, etc, may own parts of the item master data setup, there should be a defined workflow with sign-offs as the setup moves from person to person. Additionally, all MRP / planning data settings may be owned by planners/buyers for their specific products or for particular data parameters.

The Hybrid Model

In this model, certain data management is centralized while other management is not. For example, item master or vendor master data setup would likely be centralized since it is so critical and can be unwieldy when decentralized. However, an organization may choose to decentralize data ownership, such as MRP and planning/scheduling settings and push that ownership to the ground level of buyer/planners.

supply chain model graph

Impact of Poor Data Quality on Supply Chain Efficiency

If your organization is struggling with keeping the data clean enough to be effectively used for analysis and, eventually, AI technology, it may be good to initiate this conversation with the IT or finance teams and see if you can get your arms around the best approach. The costs of not doing so will come in the forms of:

  • Additional headcount to manage tasks that could be performed by the system
  • Inability to leverage data-driven insights that could lead to lower costs, lower inventory, and higher margins
  • The inability to utilize technologies to solve issues like worker availability
  • The inability to leverage future technologies, such as AI and automation


Related: 3 Ways to Prepare for AI to Make Your Supply Chain More Successful

Every business is unique, and what model works for one will be different than what works best for another. If you are looking for expert advice on improving your data management and leveraging supply chain systems, the experienced team at Waypost Advisors can help. Take advantage of our free, no-obligation consultation and learn more about how we assist organizations in finding and implementing solutions within their supply chain.

Waypost Advisors is an end-to-end supply chain and resourcing solution. We offer expertise in procurement, inventory, project management, planning, transportation & warehousing to fit the needs of your B2B manufacturing or distribution company. Our advisors can provide you with the resources and expertise to tackle your supply chain challenges while allowing you to still focus on running your business.