Inform Decisions with Master Data Management: A Primer

Master data represents some of the most valuable information shared across an organization such as customer, vendor, product, and employee data. It tends to be static and non-transactional in nature, meaning it doesn’t change very often. Master data may also include reference data such as zip codes and U.S. states as part of address data for customers, vendors, or employees.

Organizations typically look for master data management (MDM) solutions to have 360-degree views of customers, vendors, products, and employees. However, there is rarely a single source or consistent view for this master data. It’s common for global organizations to have multiple instances of ERP (Enterprise Resource Planning) software, in which the lifecycle of master data is managed without having a single view of master data. As a result, many organizations tend to spend a lot of unnecessary time and effort attempting to validate data sources and aggregate data to meet the organization’s needs. The results of these efforts may or may not produce accurate and reliable analytics for the organization, in order to inform decisions, improve the bottom line, and decrease operational costs.

MDM is one of Impact Makers’ capabilities, part of our overarching data governance framework. Our focus is on the people, process, and technology to design and build solutions that deliver business value.

• People: In order to understand the current state, identify which people, groups, entities, and applications produce/consume master data. Gather their pain-points and requirements. As master data processes are defined and technologies are implemented, assign people as requestors, reviewers, and approvers of master data.
• Process: Since many organizations manage separate master data across departments or regions, it is critical to work with all stakeholders to properly understand and inventory master data processes and determine the degree of variation and opportunities for consolidation. The goal is a set of common processes to roll out across the organization. The future state often includes common request forms with required information and standard reviews and approval workflows. This approach can significantly improve accuracy and completeness of master data. Some organizations elect to embed data quality management in the process, for example a requestor may be asked to check for existing master data before attempting to create a new master data record in the system. Data quality reports can check for the quality of master data on a regular basis to maintain information. Stakeholders close to the master data lifecycle should inform a data dictionary including common definition of customers and other elements in support of metadata and master data usage.
• Technology:Technology is an enabler allowing people to effectively manage master data and streamline newly designed holistic processes. Many organizations implement different MDM vendors for different master data (e.g., customer, vendor, and product). Some vendors package MDM solution with built-in data quality and metadata capabilities to improve master data value.

As MDM affects master data with far reaches across organizations, it is strongly recommended to look at change management capabilities early to establish clear and transparent communication channels at the organizational and individual level.

Master Data Management Approaches

 There are three tactics to implementing MDM:

1. Consolidation:Assuming that there is more than one system creating and managing master data, this approach focuses on consolidating different versions of master data. Duplicate records are identified and merged into one record. An example is formatting of names; different systems may have “Jane Doe,” “J Doe” or “Jane K Doe.” Consolidation results in one version of a customer, vendor, or employee name (“Jane K Doe”) in a single central location.
2. Harmonization: Harmonization is the syndication of consolidated master data back out to sources which have different versions of master data. It is the pushing back of cleansed data to the other sources. With the example above, all three systems would now have “Jane K Doe.”
3. Centralization: Centralization positions MDM software as a central place and platform to create and manage master data. It can serve as a central hub, and technology such as middleware, APIs, or web services can push master data downstream to other systems and analytics systems.

It is advised to start Consolidation, followed by Harmonization and Centralization to achieve optimal results and to gain gradual buy-in and commitment. Also, it is recommended to pick one master data domain (e.g. customer, vendor) at a time to implement MDM, as it will ensure more successful outcomes and ultimately greater value to the business.