There are a lot of definitions of Master Data Management out there today:
“Master data management (MDM) is a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise’s official shared master data assets.” – Gartner
“Master data management (MDM) is a comprehensive method to define and manage an organization’s critical data. It provides a single, trusted view of data across the enterprise, agile self-service access, analytical graph-based exploration, governance and a user-friendly dashboard.” – IBM
“Master data management (MDM) is the effort made by an organization to create one single master reference source for all critical business data, leading to fewer errors and less redundancy in business processes.” – Informatica
My definition of MDM is an organizational culture focused on enhancing and enriching the data critical to the success of the enterprise.
Let’s break down this definition to see why this works better than the other definitions I have listed above or seen/heard in the past.
First, MDM is an organizational culture. What I am speaking of here is that the whole organization must be a part of the MDM process. MDM isn’t an IT problem or a Marketing challenge; it is an initiative deemed to be critical by the leaders of the organization, and all employees and processes play a crucial role. Therefore, this program, like all culture-based initiatives, must come from the highest parts of the organization and trickle down through everything the company does. I still remember an initiative from Ford Motor Co. from the early ’80s through the ‘90s – “Quality is Job 1”. The understanding is that everything that anyone who worked for Ford would focus on would be enhancing the quality of the cars they made. It didn’t matter if they were designing a new vehicle or installing breaks on the assembly line; the focus was on quality. In the same vein, leaders must infuse their organizations with a culture of data quality. There are no short cuts to quality; in fact, it sometimes comes at a cost. That cost should be considered an investment because the results will pay off multiple times over via increased profits and enhanced business opportunities.
Now that we have set up a data quality culture, we now turn to the second half of the definition, focus on enhancing and enriching data critical to the success of the enterprise. Not all entities are created equal; therefore, the organization needs to concentrate our efforts only on those entities that will bring profit and long-term success to the enterprise. This means we have to be selective on the data we choose to enhance and enrich. What data holds strategic value for the enterprise? Again, you can see this question cannot be answered by individuals managing tactical lines of business operations, but rather by the corporate executives of the organization.
OK, now that we have a definition, let’s discuss how to implement a successful MDM initiative.
First, there must be agreement and commitment from the leaders of the enterprise that this initiative is critical to the success of the organization. If the determination is lacking, stop and forget the whole plan. One will undoubtedly run into issues that will test the resolve of the leader’s decision to move forward with this endeavor. If it were easy, everyone would be doing it or have it dome as a matter of course. I can assure you that in the end, if you “trust the process,” you will be happy with the results, and your company will be the other companies in your economic sector.
Now that you have established the corporate fortitude to move forward, you need to set up a Data Governance structure. The most successful model is a three-tiered structure. The top of this model is the Steering Committee. The Steering Committee is comprised of the senior leaders of the organization who are tasked with providing guidance and making high-level decisions. The next tier is the Data Governance Office, a collection of data owners who have policy-level managerial responsibility for data within their functional areas. The duty of the Data Governance Office is to create and establish a Data Governance Framework and provide strategic and tactical guidance to the Data Governance Working Group. The final piece of this model is the Data Governance Working Group. The Data Governance Working Group is comprised of data owners, data managers, data consumers, and information technology managers who understand and can address the information and reporting needs of the organization. The primary focus of this group is the tactical implementation of rules and policies that will enhance the data-driven decision-making of the enterprise.