As a lot of improvements are still being made to MDM, there shall be many best practices coming in the years to come. So, some of the below mentioned might not be the same after a few years.
- Need to emphasize the change aspect: The outside strategy can many times help a lot to MDM. So, the change aspect is critical to the success of large transformation projects involving MDM. If this is not adjustable, it is good not to go towards MDM.
- Planning is the most important metric: Great to start planning a few months ahead of time. Developing a foundational data model is not a simple task. It needs more effort and experience. Also the planning phase need to be continuous and must be able to accommodate new changes into the job. A few companies even do a small similar project before the big start.
- Needs data governance: One of the main goals of data governance is to give data management practices a broader reach and visibility. Thus, data governance is necessarily not appropriate to operational Master Data Management or analytic MDM. Anyhow, enterprise MDM is difficult to pull off without data governance's central organization representing all data stakeholders.
- Fragmentation and Defragmentation approach: Keep in mind the ultimate goal, but limit the scope of the initial deployment. After you get the basic framework to work, extend it step by step, they advised. Business processes, rather than technology, are often the mitigating factor, they said, so it's important to get end-user input early in the process.
- Take up long term: Never take it up as a short term program. Instead it can be broken into short term programs that can be assembled with time and progress. Unless the whole team spends their best available time, it is not easy to achieve the main MDM goal.
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