Most of us working in this area of information management know that MDM implementations have a high failure rate. According to a study, only 24% MDM programs end successfully. There are many reasons why these projects sink or don’t deliver what was initially promised. Poor management, lacks of organization support, unclear objectives are some of the common reasons to blame.
I have been helping customers with MDM implementation for almost a decade now. Keeping aside aforementioned reasons, my experience has shown me that there are two key organizational traits that trigger failure.
The number one reason is, the absence of a holistic approach to MDM. Many organizations think implementing MDM in a silo environment, purely as a technology endeavor is a good way to go. But as most of us know, this approach as often proved to be catastrophic.
Second important reason for MDM failure is the missing agility, the inability of organizations to adapt to the change. MDM requires a nimble methodology, which helps in looking at data quality issues not just as an afterthought, but also to fix the complications in an incremental and iterative manner.
MDM implementations are unique in that they are not run in a silo setting delivering value to only a department or a small business area. MDM impacts large area of an organization. As you start consolidating master data records, which have multiple touch points and application specific usage, you soon will realize that you not only need technology, but also require changes to the way people and processes use master data.
Being holistic is about being business driven.Being holistic is about being business driven. Implementing a solution to address a specific problem occurring in a business function is a great way to start MDM. At the onset this might sound tactical, but the strategic aspects can happen in following ways and help you implement MDM holistically.
- As you plan to integrate master data from different sources, ensure you have done systematic data discovery process. This allows you cross check your data with your business rules and accordingly tune your transformation layer to ensure clean data gets loaded to MDM.
- Every entity and data element housed in MDM should go through strict standards and quality check process.
- Master data should be managed under your organization’s data governance umbrella with focus on stewardship, accountability and clearly defined roles and responsibilities.
- Different sources of master data such as order processing, customer service, billing etc. treat data in their own unique way resulting in lack of consistency. The data quality and integration effort should focus on applying standard rules to bring the data to a common, agreeable, enterprise standard structure.
Many organizations are still looking at MDM implementation from a purely infrastructure and technology standpoint. This has to stop and the only way to achieve that is to bring the business leaders to the table. As many of my blogger friends and analysts have been saying – always have the MDM owned by the business, backed by strong data governance practice with primary focus on improving the quality of master data.
Being agile is about doing things in small chunks (phases or sprints) with a vision on bigger picture. Sounds like a key missing aspect of an MDM implementation isn’t it?
In reality, agile implementation style offers more. Agile is about doing things quickly (and failing fast) so we learn from it. This works perfectly with the data management efforts where the chance of failures is high. You want to try different approaches, which are precise and short to see if they deliver expected result. And if you know that a particular approach is not a right option, you take a different route.
One of the examples I can provide you here is the de-duplication process which is a key feature of MDM. Although the matching & merging rules change across organization we implement MDM, I spend couple of weeks (a sprint) with customers to use out of the box rules delivered with our MDM product to identify and remove duplicated records. One key advantage of this is, these rules are tuned & enhanced with years of experience. Most of the time, these exercise prove to be very beneficial as the rules we are providing with product turn out to be right fit (with minor tweaks off course), many times much better than what clients tend to come up with on their own.
Agile approach helps you achieve your enterprise master data management goals in a sustainable wayAgile provides a lean approach to MDM implementations. It helps you achieve your stated enterprise master data management goals in a sustainable way. With this approach, you can greatly increase your chance of success.
One of the things we don’t often hear is the aspect of technology being a reason for MDM program failure. Tools and technical acumen are sure necessary, but what matters the most is, how your organization approaches MDM culturally. Winning organizations succeed because of their disruptive thinking. They are agile to changes and challenge the norms, which help blur the organization boundaries. This is key aspect of agile and helps you succeed with MDM.
Do you agree? What do you think are the success factors for MDM? I would love to hear your feedback.
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