MDM Landscape

MDM Landscape Q2 2023

Master data management (MDM) is the software and set of supporting processes to support the business entities data that span across an organization, such as customer and product data. There are several master data domains in addition to these, such as data on locations, suppliers, assets, staff, contracts etc. A global enterprise may have many dozens or even hundreds of major applications spread across many subsidiaries, including multiple instances of ERP systems, so this kind of master data is almost inevitably duplicated across different enterprise software applications. Even if you started with a clean slate of perfectly consistent master data, as soon as you make an acquisition of another company then that acquired company will bring with it its own systems and master data, and this will take time to integrate. This data inconsistency makes it hard to answer even simple questions about business operations. For example, suppose you decided that you want to understand the profitability of your different product brands. To do this you actually need to have data about the revenues by brand and consistent cost allocation rules so that you can calculate brand profitability. If revenue, product and cost data are spread amongst multiple systems and cost allocation rules are inconsistent across the enterprise then you have a problem.

Such problems led to the development of the master data management industry, originally concentrated around the common domains of customer and product data, but later expanding to cater for other data domains like asset, location and legal entity. Such data is either co-ordinated in a virtual form or physically copied into master data hubs, where business rules are applied to ensure that the most reliable and up to date version of master data is identified. This data may be fed into other systems for analytic purposes, or used to correct data in operational systems. MDM software needs to be able to handle issues such as hierarchy management and be able to resolve inconsistencies between classification schemes used in different operational systems. There is an inherent data quality element to any MDM project, as reliable master data should be complete, accurate and up to date. MDM software typically has its own data quality components such as merge/matching.

These days, MDM software has to deal not just with data held in on-premise operational systems, but also data held in public or private clouds. As well as traditional numeric values, MDM systems need to be able to deal with text, images and video, for example descriptions of consumer products that are advertised on a corporate website. Such product data may very well be imported from suppliers or exported to channel partners. A modern MDM system should be able to handle relatively static reference data such as currency and country codes, but also regularly changing data such as customer addresses, product images and may be required to deal with near real-time data from eCommerce systems and websites. Customer data records may be large in volume, and product data may have very complex classification schemes for different marketing purposes, with variable depth hierarchies.

There has been an explosion in the amount of data that corporations have to deal with in recent years, with 90% of the world’s data being created in the last two years. Consequently, there is understandable demand for more automation of handling this data, including ensuring the quality of this data. Artificial intelligence has been used for some time in the form of expert systems to help with resolving potential duplicate records. Business rules can be set up to validate records e.g. to check for duplicate customer accounts, and in some cases it is obvious where there is a match or not, and the system can handle that automatically. However, in borderline cases a human domain expert is needed to assess things, and an expert system can be trained to observe the decisions of these domain experts and to improve the quality of the MDM system based on these observations. In 2023 there has been an explosion of interest in AI as “generative AI” has caught the public imagination through products such as ChatGPT, Bard and Midjourney, and companies around the world have been assessing how they can use this new technology to help improve their productivity. MDM vendors are similarly scrabbling to apply these new technologies to areas such as generating content for business glossaries, detecting data anomalies, generating dashboards, enriching data such as product descriptions or even using AI generated images to illustrate products. This is in addition to potential usage in internal processes, from providing support Chatbots to customers through to coding software.

Various analysts project the MDM software industry will continue to 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.
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 Informatica, followed by Stibo Systems. Congratulations to those vendors.

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

Below is a list of the significant MDM vendors.