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The Most Common MDM Myths. Debunked.

During its 30-year history, many myths have arisen around Master Data Management (MDM), some of which may have been valid in the early days but are no longer true today. MDM has come a long way, and is experiencing something of a renaissance as progressive platforms take advantage of technical innovations like Graph databases and Generative AI, and offer increased commercial flexibility via the major CSPs.

Join us as we expose the eight most common MDM myths below, and discover how MDM has developed to become more accessible, manageable, and impactful.

1. Upfront Data Modeling is a Must

Traditional MDM systems demanded that the data model be defined before data could be onboarded. The problem is that this is only suitable for organizations with stable and pretty much fixed datasets, because this is the only way you can know ahead of time what your model needs to cater to in the future. Most organizations today operate in a far more fluid environment, and their data behaves in the same way. Graph-based MDM systems accommodate this by evolving the data model naturally as data is ingested, which means that you no longer need to model the data upfront, but can still retain control over the modeling process.

2. Data Governance must be centralized to be effective

Centralizing data governance isn’t the only way, nor is it the most effective. What is needed is a balance between a unified governance framework that everyone must adhere to, and granting autonomy to different departments to allow them to manage their data locally within those parameters. One of the barriers to this in the past was that you had to be able to code to manage data directly in the system. Not so nowadays, as MDM systems allow data stewards and business users to create and enforce data governance policies using Natural Language Processing (NLP), e.g. by integrating with Azure OpenAI.

3. MDM Systems are Solely for IT Professionals

This leads us neatly to our next point. Way back when MDM systems were first developed, the idea was to allow the business to benefit from a trusted source of high-quality data. It failed because only technical specialists were able to deploy and use the systems. We believe that domain experts should be directly involved in managing their data, and are now at the point where Generative AI has been fully baked into CluedIn. This not only means that non-IT professionals can easily navigate the platform, but with the introduction of AI Assistants, they also get help and guidance from what is effectively an MDM “helper”. It has literally never been easier for the non-techies amongst us to govern and master data.

4. MDM is Just About Managing Data

Those who work with and know data well understand that MDM is not just about managing data for the sake of having beautifully clean and consistent datasets. It’s really about how data is used to enhance decision-making processes, drive efficiencies, and provide a solid foundation for data science initiatives like AI and ML. This is why it is so critically important to involve the business in the data supply chain. Think of it as a launchpad for any number of commercial initiatives and operational improvements, all of which contribute to profitability and growth.

5. MDM Implementation Equals Disruption

MDM implementations have long been synonymous with disruption, given their breadth and depth in altering how organizations manage data. But the reality is that it doesn't have to be a strenuous shift. With thoughtful planning and strategic steps, MDM implementation can be a seamless integration into your existing systems and processes. For example, involving all relevant stakeholders - from IT specialists to department heads - in the planning and implementation process ensures that the MDM system is introduced in a way that respects each department's needs. Strategic planning is also essential, as it allows the rollout to be done in phases, making the process digestible, and managing potential disruptions proficiently.

6. It takes months to deliver your first MDM Use Case

The myth that your first MDM use case demands months of preparation and execution is ripe for debunking. Adopting an agile and focused approach allows for the rapid delivery of MDM use cases. By concentrating on specific data domains or business areas and employing iterative, agile methodologies, you can realize tangible results swiftly and incrementally build upon them. The first step is to identify and prioritize a use case that is both high-impact and feasible for quicker implementation, and can create quick wins for the business.

7. MDM Has to be Expensive

The myth that MDM is financially draining deters many businesses from attempting it. In reality, MDM solutions come in various forms and pricing models to accommodate different budgets and needs. This is why vendors like CluedIn offer both cap-ex pricing (upfront payment, yearly recurring.) and per-hour pricing (consumption-based). Hourly pricing means that you only pay when you are using the platform. This increasingly suits companies that want to avoid hefty upfront investments. A combination of both the cap-ex and op-ex models can be very powerful, particularly when you start with consumption and move to a commitment once trust has been established.

8. You must have a fully baked Data Governance Strategy Before Embarking on MDM

Embarking on MDM doesn’t require a fully fleshed-out data governance strategy in place. Both can be developed and refined simultaneously, ensuring that insights and challenges from one aspect can be immediately applied to enhance and optimize the other. MDM can reveal insightful data intricacies and challenges, feeding valuable information into shaping a more tuned and applicable data governance strategy. Similarly, preliminary data governance guidelines can ensure that the MDM implementation is aligned with overarching principles and objectives from the get-go.