If you are an organization looking to improve data quality and business operations through the development of Master Data Management (MDM), then confusion about how to implement it can have drastic consequences. While business leaders see and live with the challenges, they are likely unclear about the details, and turn to technology leadership for guidance. Technology leaders are more apt to see immediately how MDM transforms an organization from one with costly Information Gaps, to one which operates more seamlessly through its ability to create Data Enabled Value.
But if you have never implemented MDM, your preconceived notions about data quality can become a critical obstacle to realizing the benefits on a timeline which the business would accept. Alternatively, perhaps you simply don’t know where to start. Phase 1 is the right time to introduce your organization to fundamental MDM concepts, setting expectations for what the ownership framework for master data will look like. To ensure a successful introduction of MDM and Data Quality practices to your organization, follow these steps.
Don’t Create Another Information Silo – The goal of MDM is to bring break down barriers to clean, high value data assets. But many technologists are tempted to see the MDM system as “just another database,” which can be designed like any other custom databases in the organization. MDM is not just another database. It has its own set of rules and best practices for how to design a simple, clean data model that can be managed by the business. Finally, it is not necessary to spend big bucks on an MDM database — Microsoft’s solution comes bundled into two editions of SQL Server. Really, don’t build your own.
Don’t Confuse CRM with MDM – or any other business system you currently uses, for that matter! Many CRM software companies (and some ERP’s) like to promote MDM capabilities in their software. But another goal of MDM is to improve the overall quality of the Master Data and create a space for the Data Governance group to manage that data quality. If you do not extract master data from its source, then the governance group must manage the data in a process oriented system. This is akin to tying one arm behind their back. In the end, governance becomes hindered by the process requirements of a source system. Instead, create a “data jurisdiction” (which is MDM), extract the data from sources into that jurisdiction and govern inside that jurisdiction. This brings me to my next point.
Flip the Script – a common frame of reference for data quality is “garbage in, garbage out.” This frame of reference helps technology leaders explain to the business why data assets have historically been ill-suited for future use: the data entered the system in poor form, and now exits the system in poor form. The conclusion many draw from this is that in order to improve the data quality, one must enforce better data quality rules at the outset, or “scrub” the data in transit from the source. Wrong answer! Flip the Script: bring the data “as-is” from the sources. Permit your data stewards and governors to see the data as it exists in the enterprise. This will lead to a deeper understanding of the very real process “frictions” in play. Then, use an MDM toolset to enrich, match and harmonize the data in MDM solution itself. By switching the frame of reference, you can accelerate the project plan and get the solution and data into the hands of stakeholders who are empowered to take action.
Formulate the Data Governance Program – Standing up an MDM system is a project, and should be managed as such. Data Governance is an ongoing program, and when the project is successfully concluded, the organization must now take ownership of the “Data as an Asset.” Master Data, by its very nature, implies Shared Ownership. Each of those data stakeholders are accustomed to managing their own piece of the larger whole. Differences will inevitably arise. If you don’t know how to get started, work with an experienced MDM delivery team or borrow an existing framework.
Save Operational MDM for Phase 2 – Operational MDM refers to a solution’s capability to distribute and synchronize high quality data to the sources of that data. Data cleanup of a source system, such an ERP system, is a common goal, one which is widely articulated by business and technology leaders. I see the virtue here, and indeed have completed very successful projects where master data flows back to operational systems to drive even more value. However, these types of projects are lengthy, because a Data Bus Architecture must be implemented alongside MDM to route and transmit data back to systems reliably. Further, they tend to neglect a crucial aspect of MDM — all of the Unsanctioned Master Data that resides in spreadsheets, databases or in people’s heads. There is a ton of value created simply by establishing the MDM solution as an authoritative source of high quality, high value data assets. Don’t make ERP cleanup part of your initial goal. But, do create a data model that will make it easy to do so in a later phase. MDM projects which defer this requirement show value to the business very quickly, and get funding for later projects more easily.
Master Data Management is a powerful remedy for a number of broad information gaps in an organization. The challenge of implementation is understanding what the goals are, managing expectations, and building a Data Governance and Stewardship culture. Make sure you understand what can be accomplished quickly with MDM, and focus on those goals in the early stages.