Reference data management is the process of creating and maintaining a single, consistent view of reference data across an enterprise. It includes the processes of data cleansing, data enrichment, and data governance, and it’s a critical part of any data management strategy. Keep reading to learn more about what is reference data management.
What is reference data?
Reference data is a critical component of most business processes, yet it’s often difficult to manage. Reference data is used in many industries to describe various data types. In some cases, reference data is used as a term for master data, while in other cases, it may refer to different types of data. The quality of reference data is essential for ensuring the accuracy of data sets built on top of it. It must be accurate and up-to-date to ensure that the data sets that rely on it are also accurate. In some cases, reference data may be used to verify the accuracy of other data sets. Reference data can include customer addresses, product SKUs, and account numbers.
The purpose of RDM is to ensure that all users have consistent and up-to-date information about the entities they are working with; and to make it easy to find, use, and maintain the correct version of data. There are several ways to manage reference data sets: centralized, decentralized, or distributed.
Centralized management is when all the data is stored in one place. Centralized management can be helpful if you have a lot of reference data and need to control who has access to it.
Decentralized management means that each department manages its own reference data. Decentralized management can be useful if you have departments that work independently from each other.
Distributed management occurs when different parts of the organization manage their data, but there is some coordination between them. Distributed management can be helpful if you want more autonomy for different parts of your organization but still need some coordination between them.
What features does a sound RDM system have?
A sound RDM system should have the ability to:
- Organize and manage reference data across different applications, platforms, and data stores
- Integrate with existing systems and processes
- Ensure accuracy, completeness, and timeliness of reference data
- Facilitate collaboration among users
- Support self-service access to reference data
How does RDM help reduce risk in your organization?
RDM can help minimize risk in an organization by ensuring that the correct and most up-to-date information is being used. This can help prevent mistakes or misunderstandings due to incorrect data. Reference data management can also help ensure compliance with regulations and industry standards. Having accurate reference data can also help avoid costly fines and other penalties.
How can you ensure compliance with your reference data management strategy?
RDM can be done through various methods, including manual updates, automated updates, and third-party verification.
One way to ensure compliance with your RDM strategy is to create and enforce policies and procedures for updating reference data. This includes specifying who is responsible for updating different types of reference data, setting update schedules, and establishing criteria for verifying the accuracy of reference data.
Another way to ensure compliance is to use a dedicated reference data management system or tool. This can automate many tasks in maintaining accurate reference data, such as identifying changes, comparing new versions against old versions, and propagating changes across all relevant systems.
Finally, you can also supplement your RDM strategy with third-party verification services. These services can help ensure that your reference data meets industry standards and complies with regulatory requirements.
Reference data management allows organizations to keep track of their data and keep it organized. This allows for easier access to the data when needed and helps ensure that the data is accurate.