MDM Landscape

MDM Landscape Q2 2024

Master data management (MDM) is the software and supporting processes to handle data that crosses application boundaries, such as data about products, customers, assets and locations. In a large enterprise, such data is typically stored multiple times, scattered across a range of applications. An average large enterprise has hundreds of applications, only one of which is its ERP system, so the careful management of master data is necessary in order to support business performance management. Any question that is asked by management like “Who are my most profitable customers?” or “Which are my most effective sales channels” can only be answered if there is a consistent set of data about those customers and channels, and the revenues and costs associated with them. A master data hub is a place where this kind of data is brought together and where duplicates are removed and inconsistencies are resolved. The quality and completeness of the data must be checked, and potential duplicate data records resolved by deciding which source systems are the most trustworthy. The latter is done via “survivorship rules” defined in the master data hub, where rules are defined that take into account things like the completeness of the records, how recently they have been updated and the trustworthiness of the source systems. Once a trusted “golden record” of each item of master data is produced, this hub can be used to feed other systems, for example, populating the dimensions of a data warehouse.

These days some companies have tried to implement a newer type of data architecture called a data fabric (a data mesh is essentially a variant on this idea) whereby data is left in place in source systems, with business users accessing the data dynamically for analysis purposes via a semantic layer of metadata. In such cases, a master data hub can be a major asset, since without it the semantic layer will have to somehow carry out all the work that was previously done in the master data hub in order to decide which records to actually access and serve up to the business user.

A widespread trend is the gradual migration of systems from on-premises to the cloud, whether that be public clouds like AWS, Azure or GCP, or to privately managed clouds. Master data management software has to adapt to this changing world, since the source systems that contain underlying master data will themselves be gradually moving from one platform to another. Master data platforms need to be able to seamless access both, and themselves be deployable in either environment.

Experience has shown that master data projects work best when there are supporting business processes that define clear responsibility for data ownership. Who exactly owns “customer” and “product” within an enterprise, and can decide on the classification of these entities? Changes to things like product hierarchies or customer classification structures need to be proposed, reviewed, agreed and signed off, and so an MDM system should ideally have workflow capabilities that can support such business processes, enabling the data stewards within an enterprise to work effectively.

As enterprises try to implement generative AI applications in a variety of fields, from customer chatbots to a plethora of other areas, it turns out that MDM still has a role. A generic customer chatbot will be a lot less effective than one that is familiar with a customer’s sales history and buying preferences, so companies have been training raw large language models on their own data, a process called retrieval augmented generation. A large language model is only as effective as the data it is trained on, so it is important that data be consistent and of high quality in order for it to be effectively used by a large language model. In this way a master data hub can be a key underpinning of an AI initiative. The widespread interest in generative AI should be a boost for both the data quality and master data management industries. Other broad business initiatives like greater digitization and automation ultimately depend on a sound basis of core corporate data.

Various analyst firms expect that the MDM software industry will grow at a rate of around 16% in the coming years, faster than the enterprise software market as a whole.  Information Difference research shows that, on average, the people-related costs of an MDM project are four times that of the software license cost, so there is clearly a large and separate growing consultancy market associated with MDM. Although there has been much consolidation in the industry over the years, newer players are still emerging and there is still some merger and acquisition activity in the sector, suggesting that master data management has a secure future.

The main vendors in the market are summarised in the diagram below.

The landscape diagram represents the market in three dimensions.  The size of the bubble represents the customer base of the vendor, i.e. the number of corporations it has sold MDM software to, adjusted for deal size.  The larger the bubble, the broader the customer base, though this is not to scale.  The technology score is made up of a weighted set of scores derived from the following elements: customer satisfaction as measured by a survey of reference customers, analyst impression of the technology, maturity of the technology in terms of its time in the market and the breadth of the technology in terms of its coverage against our functionality model.  Market strength is made up of a weighted set of scores derived from: MDM revenue, growth, financial strength, size of partner ecosystem, (revenue-adjusted) customer base and geographic coverage.  The Information Difference maintains profiles on each vendor that go into more detail.  Customers are encouraged to carefully look at their own specific requirements rather than high-level assessments such as the Landscape diagram when assessing their needs.  We maintain a comprehensive MDM functionality model and evaluation approach (MDM Select) that we offer to customers.

A significant part of the “technology” dimension scoring is assigned to customer satisfaction, as determined by a survey of vendor customers.  In this research cycle, the vendor with the happiest customers was Viamedici, followed by Informatica, Reltio and Stibo Systems. Congratulations to those vendors.

Below is a list of the significant MDM vendors.

________________________

(*) In the absence of sufficient completed references, a neutral score was assigned to this factor.

Below is a list of the significant MDM vendors.