Data Deception: Dispelling the Top 10 Myths about MDM
Master Data Management, or MDM, isn’t a new player in the realm of IT solutions. Its roots trace back over two decades when the software promised to streamline, unify, and optimize an organization’s core data assets. However, like many pioneering technologies, its early iterations were challenging. Initial versions often came with cumbersome implementations and less-than-ideal outcomes.
As the trauma from challenging implementations lingered, many myths have shaped perceptions of MDM. Among the most persistent beliefs were that MDM solutions were prohibitively expensive, deployment timelines too lengthy, and they often fell short of delivering desired business-related outcomes. These criticisms, stemming from genuine past experiences, became entrenched in the collective consciousness of the IT community, casting a long shadow over the product’s subsequent evolutions.MDM has undergone transformative changes in recent years, with modern approaches addressing the shortcomings of their predecessors. Today’s advanced MDM solutions offer more agile implementations, cost-effective structures, and results aligning with business objectives. Reltio recently had the opportunity to discuss the common myths about MDM and how modern, cloud-based approaches are changing the game on a Dataversity webinar. Below is a summary of that discussion and busting of the top 10 myths about MDM.
Myth #1: MDM is only about technology: While technology is a critical component, MDM is equally about processes, governance, and people. MDM is about having the right organizational mindset, one where departments and team members are aligned with the mission and vision surrounding MDM. Even the most sophisticated MDM technology solution can fail without the right processes, team, and buy-in.
Myth #2: MDM is only for large enterprises: Large enterprises undoubtedly have more complex data challenges than small companies and require MDM to unify fragmented, siloed data. However, mastering data and unifying it is not a problem exclusive to large enterprises. Companies of all sizes can benefit from MDM to improve data quality, consistency, and efficiency and ultimately get to the single source of truth. Companies of all sizes are becoming “digital first,” which means they need clean, connected, trustworthy data to reduce risk, save time and money, improve customer experience, operate, and make data-driven decisions.
Myth #3: MDM is a “one and done” project: Many perceive MDM as a one-off project rather than an ongoing program. Maintaining master data quality and governance requires continuous effort and reviews, however. This helps provide the true benefit of MDM and drive long-term business value. MDM is not a “set it and forget it” proposition. MDM requires regular monitoring, updating, and maintenance. New data sources constantly emerge; companies can merge or be acquired, creating many changes. This makes it even more essential to monitor and update the MDM solution continuously.
Myth #4: MDM and data governance are the same: There is some confusion around MDM and data governance, as the two concepts are frequently conflated. Although they are closely related, they are not the same. MDM focuses on mastering, unifying, and consolidating data, while data governance is a broader discipline concerning the overall management of data’s availability, usability, integrity, and security. Data governance is more about the policies established around data to ensure its usefulness to the organization.
Myth #5: MDM is only about data de-duplication: While deduplication is one aspect, MDM encompasses much more. This includes integrating various data sources, cleansing and standardizing data, managing hierarchies, enriching the data, and ensuring data consistency across systems.
Myth #6: MDM is only for analytical use cases: MDM initiatives used to be all about pushing data to a warehouse. Expensive operational data stores and complex service layers needed to be built and deployed to support real-time access, but performance was always an issue. Today’s MDM is all about solving operational use cases without creating yet another data silo. Response times of under 300 milliseconds (ms) are standard for using your master data in real-time across your business.
Myth #7: MDM is too expensive: We can forgive the eye-rolling from IT pros familiar with traditional MDM and on-premises implementations–as historically, most of these ran way over budget. Initial up-front investments for MDM implementation are typically the most significant financial commitment in the project. The long-term benefits, like improved decision-making, operational efficiencies, and reduced risks, can provide a significant return on investment (ROI). Over a longer period, the business value and benefits become clear and more than cover the upfront costs required. Modern SaaS MDM further reduces costs by eliminating the need for upfront hardware investments and reducing the initial setup costs.
Myth #8: MDM is a multi-year implementation journey: Much like the cost overruns noted above, those with previous MDM implementation experience are all too familiar with the pain from projects that never seem to end. Some MDM projects take years to implement. However, that’s not always the case. With simpler use cases and quicker out-of-the-box solutions, MDM can be implemented in just weeks and months versus the years it has taken in earlier versions.
Myth #9: MDM is only about customer or product data: To the contrary! Master customer or product data are common use cases, but MDM spans a breadth of data domains like location, supplier, and asset data, among others. For example, Reltio customer Radisson Hotel Group uses location data for its hotels. There was a lack of accuracy in the contact numbers of the hotels as well, poor information about which hotels were part of the Radisson Hotel Group, and general issues with invoicing. Removing inefficiencies and improving data accuracy freed up the time it took to maintain this list, improved invoicing, and sped up cash flow. An added benefit was improved customer satisfaction due to more accurate phone numbers for the locations.
Myth #10: MDM is the same as data warehousing: MDM is about creating a central repository for core data, ensuring its accuracy and consistency. Data warehousing is about consolidating and aggregating all data, with the primary use case being analytical. Data warehousing is not about unifying data and getting to the single source of truth – which is the primary function of MDM.
The Evolution of MDM: Correcting past missteps
MDM has come a long way in just a few short years. From on-prem software to a cloud-based SaaS offering, the solution has become cost-effective, nimble, flexible, and offers fast time-to-value. Companies seeking to modernize and realize efficiencies through exciting new technologies such as AI/ML must build upon a foundation of trusted core data that modern MDM delivers. Core data feeds operations and analytics – any new tool or need for data must be met with reliable “always-on” capabilities that current MDM can provide. This is not to say MDM is a universal solution for all things data–it is not. Different data problems may require different data solutions. MDM focuses on mastering the data, but there are other solutions for specific issues surrounding data, like data governance and observability.
In the dynamic world of technology, stagnation is seldom an option. However, the trauma of past challenges means that some myths persist, even when they no longer hold. Organizations on the path to digital transformation must distinguish between outdated beliefs and the current realities of MDM, ensuring they harness the full potential of what modern MDM can offer.
