In part 1 of this series, I've reviewed through the current state of Master Data Management and highlighted three main reasons as to why MDM initiatives have a historical record of failure, discussed the first reason about the tendency for MDM solutions to promote the idea of a "single view", and how in reality there is almost never a single view that's applicable to all use cases. In part 2, I'll cover the tendency for organisations to treat data as a first class citizen, thereby neglecting the bigger picture of what needs to be achieved.
Data isn't a first-class citizen
You may have heard the phrase "data is the new oil, the new sun" at conferences and events in recent years. But what good is all that data if it's not been strategically aligned with specific business goals?
Having a clear understanding of how data will be used and what value it will drive is crucial in order to make the most out of the data your organisation collects. Without a clear purpose, data can become a liability rather than an asset.
Data management isn't free: data storage, quality management, and integration are all expensive. Even “open-source” isn't really free. Proper data management is essential to ensure that your organisation is making the most of its data. Despite the fact that open-source options may be available, the true cost of implementation, maintenance and scalability should be taken into account. Starting an MDM project with the sole purpose of achieving a "single view" may seem like a good idea, but without clear business goals and outcomes in mind, you'll be left with a hefty bill and little to show for it. Instead, make sure to put those business goals and outcomes at the forefront of your data management efforts, treating them as first-class citizens, this will ensure that your project stays on track and delivers real value to your organisation.
Gartner predicted in their recent MQ for MDM Solutions that through 2025, more than 75% of MDM programs will fail to meet business expectations because of a failure to connect MDM value to business value. But everyone says “data should be treated as a first-class citizen”. Well, I’m not arguing with them or against that view. What they are trying to convey is the importance of data in becoming a data-driven organisation- which is absolutely the way forward. However, if you only focus on data acquisition, management, adding more technology, on-prem/on-cloud, “hybridise” this, full-stack that, you’ll end up with more trouble, silos, swamps, and liabilities than ever before, to the point where you’ll start questioning where did you start, and what exactly were you trying to achieve.
Understand your strategy, organisational impact and data landscape before selecting your drill
The point is, you need to have a clear definition, or at an least understanding, that the problem at its core isn't only a “single view” of an entity (in case of MDM), but rather something more business measurable, as an outcome. It's a data decision gap that needs addressing. It could be defined as: "enhanced customer experience", or "better marketing strategy", or "the unified bill project". Whatever you call it, it should have a tangible and quantifiable outcome. Doing this greatly helps in having proper understanding of the requirements (data, tools, people involved, architecture, risks, etc.) and the steps needed to get there, along with the investment needed and ROI. There are clear benefits, and importance, in defining quantitative business goals before solutioning any project, but I'll leave that for another blog.
In the case of MDM, doing the preceding exercise will ensure you pick up the right holistic solution from the get-go, one that "fits for purpose", rather than picking one of those "off the shelf", eventually not so useful, products, which will require you to have other products in the future to complement other functionalities and fulfil gaps, and even then, will still manage to fail on delivering real value that justifies the investment (time and money).
MDM, when implemented correctly with a solid strategy and an understanding of dynamic entity views, can and should be viewed as an enterprise-wide, strategic solution. It should not be treated as a department-specific tool. Investing in a comprehensive MDM solution facilitates for multiple use-cases and long-term return on investment for your organization. By leveraging the right data, it can drive value across current initiatives and be a powerful tool for future growth.
In short: data is important, but a properly defined vision and strategy for its usage is just as important (if not more) and will help you better purpose it for your goals.
Great, you have a clear vision, identified use cases, aligned priorities, and the buy-in from key stakeholders. But there's a significant obstacle standing in your way, traditional MDM solutions and products may not be able to fully support your vision and deliver the desired outcomes. Realising your MDM goals will require overcoming a variety of challenges that can impede progress and success. It's important to be aware of these limitations and to be prepared to address them in order to achieve a successful MDM implementation that delivers the "holy grail" of MDM.
In the final instalment of this blog series, I will delve into the last and probably most crucial reason why MDM projects tend to fall short: the use of outdated methods and inflexible frameworks when it comes to data sourcing, processing, and servicing with traditional MDM solutions. These approaches are simply not effective in today's dynamic and constantly evolving data landscape. I will explore how relying on these rigid techniques can hinder your MDM efforts and the steps you can take to avoid these pitfalls.
Next: Rethinking MDM and Our Approach to It | Part 3/3: Traditional techniques and rigid models ought to die