logo

The significance of Master Data Management (MDM) solutions for data-driven organizations is a no-brainer. A large-scale MDM solution allows businesses to manage all business data from a single point of access. High integrity, consistent data with no duplication or errors, well aligned teams, easy access to data across departments – the list of advantages goes on.

While this data utopia is what every business aims for, the journey to get there is never the same. And it is in this expectation of a one-size-fits all MDM solution that many organizations lose the plot and land up with MDM implementation horror stories.

Here are the top 5 reasons for failed MDM implementation; avoid them and your business improves its odds at striking MDM gold:

  1. Lack of Vision

    Think "master data", and it won’t be wrong to think large! But this is where thinking of MDM as a "big bang" solution can be fatal. While businesses strategy is, and should be long term, the approach can lead businesses to bite of more than they can chew, initiating MDM projects with a scope too large and daunting to achieve.

    Similarly, you don't want to be starting out with a myopic approach, looking at low hanging fruits while failing to plan for future developments. The ideal approach would be to start small, focusing on a single domain, and planning for the future with all “what-ifs” taken into account. Ensure that your business has a viable plan for changes, and have these plans reviewed by business domain experts and stakeholders. Once you have the first few steps laid out, things will fall into place as you build from the ground up and expand MDM capabilities.

  2. A Lift-and-Shift Approach

    Another peril of the “big bang” expectation is rushing towards a lift-and-shift approach. Organizations that rush into such deployment do so with little or no foresight, and land up with tons of technical impediments. Then again, there is also the risk of pre-existing inaccuracies and duplication finding their way into newly setup systems, rendering MDM ineffective. If you’re looking at MDM to resolve data-related issues, lift-and-shift could just mean that you’re migrating your limitations, problems and poor practices to a new ecosystem.

  3. Business vs. Tech Misalignment

    Why do you need MDM? Ask this question to teams across departments, and you’ll get a range of answers. From the tech point of view, MDM, most obviously, improves data quality, data compliance and so on. From the business point of view, it helps reduce time and costs, enables better purchase decisions, improves business processes etc. But the real question is, is MDM serving larger objectives. Many organizations fail to strike a balance between technology and business objectives. "How do I improve data quality?" vs. "How do I improve customer experience?" exist in silos, and the twain never meet. And this is where it takes strong leadership to ensure collaboration across the board, and achieve overall MDM efficacy.

  4. Poor Execution Methodology

    Failing to set the right data standards can lead to poor adaptation to differing data types. Similarly, the lack of structured data models renders master data hard to understand, and even unusable. Ambiguous data models further burden existing systems. Then again, even with data standards and models in place, the lack of data governance can lead to non-compliance with business rules.

    Managing master data is not just about managing data across multiple departments, it entails integration of the MDM system with other data applications. Flawed integration practices can lead to errors and also extend transfer time tremendously. Finally, without the required data stewardship, all the above systems can still fail, leading to poor data quality and long-term problems in data management.

  5. Lack of End-User Participation and Preparedness

    End-users are the most basic, yet most integral drivers of MDM implementation success. And successful end-user adoption is driven by seamless initiation. Yet, many businesses fail to streamline end-user adoption by overlooking the basics – training end-users on the technology in use, documenting processes and day-to-day tasks, creating a feedback loop pertaining to the challenges and benefits of using the system, including users in testing, validation and training etc.

    At the end of the day, even if you’ve identified a solution that fits your business requirements like a glove, if your teams don’t use it (appropriately), the MDM solution is a lost cause. Just like data itself, the success of an MDM solution doesn’t work in isolation. It is a heady mix of vision, scope, execution and adoption that will unleash MDM as the magic wand your business is looking for.