Market Perspectives

ISG Research is happy to share insights gleaned from our latest Buyers Guide, an assessment of how well software providers’ offerings meet buyers’ requirements. The Master Data Management: ISG Research Buyers Guide is the distillation of a year of market and product research by ISG Research.

Despite efforts made by enterprises to be more data-driven, some of the most fundamental questions about an enterprise—such as how many customers it has—remain difficult to answer. Trust in data is foundational for an enterprise to make data-driven business decisions. The problem lies not just in being able to accurately count how many customers the enterprise has by combining data from multiple business entities, regions, departments and applications, but also in ensuring those various entities, regions, departments and applications are using the same definition of what constitutes a customer.

ISG Research defines master data management as the practice of establishing and protecting foundational reference data used by an enterprise to provide an agreed list of entities that can be shared throughout the organization, including categories such as parties (customers or workers), places (addresses or regions) and things (products, assets, financial instruments). Master data management encompasses data validation, matching and merging duplicate records and enriching data with related information. Another important component of MDM is data modeling, which documents the relationships between data elements. This results in the generation of data catalog entries or enterprise glossary information that can be shared across the enterprise, as well as with partners and suppliers.

Creating a “single version of the truth” that provides an agreed definition of customers, products, suppliers or workers is a perennial challenge for many enterprises. One-half of participants in Ventana Research’s Data Governance Benchmark Research say disagreement on the definitions of data is a primary concern in managing data effectively. Master data management products enable enterprises to ensure data is accurate, complete and consistent to fulfill operational business objectives.

While MDM is a dedicated business process, it is also an important aspect of a larger data governance strategy that includes policies and rules to govern accessing and editing master data. Enterprises must be able to trust the data to deliver operational efficiency and analytics insight. Ensuring the integrity of data used for business decision-making can be difficult, given that enterprises have an increasing volume and range of data sources to contend with. More than 8 in 10 participants in Ventana Research’s Data Governance Benchmark Research use MDM technologies for data governance and those that do have greater confidence in the use of data. Almost three-quarters of those that use MDM for data governance are confident in the enterprise’s ability to govern and manage data across the business, compared to only 27% of those that do not use MDM for data governance.

The benefits of MDM are well understood, and MDM as a discipline has been an important aspect of data management for decades. However, MDM is also traditionally seen as a complex, costly and manual task that requires expert users and can slow innovation by failing to move at the pace of change necessary for contemporary enterprises. While this may have been true of legacy MDM products, the use of artificial intelligence and machine learning in today’s MDM software—as well as cloud consumption—increases automation, accuracy, agility and speed.

While it is an established and mature sector of the market, MDM is also a primary focus for innovation in data management. MDM software was initially developed to target two key domains: customer data integration and product information management. These remain natural starting points for MDM initiatives. Enterprises can be negatively impacted by the lack of processes to track customers, customer service and retention. Cross- and upselling opportunities could also be missed. Similarly, if enterprises cannot track the bills for materials, the ability to produce, market and sell products can be negatively impacted, along with product maintenance and customer engagement.

Some enterprises still focus MDM efforts solely on customer or product data, but this could undermine the broader purpose of MDM to ensure smooth and efficient operations. Data-savvy enterprises seek out MDM products with multi-domain capabilities, providing the functionality to address customer and product data alongside data about workers, assets, suppliers, locations and other pertinent business data. Managing data from across multiple domains can be easier said than done, given the increasing range of data sources and formats as well as growing data volumes.

MDM as a discipline has been an important aspect of data management for decades, but the tools and platforms used for MDM initiatives have evolved rapidly in recent years. MDM has traditionally involved complex, manual processes and expert users. The current generation of MDM products incorporates artificial intelligence and machine learning to automate approaches to mastering data that have traditionally been manual and time-consuming. This facilitates improvement in operational efficiency and time to value from data-driven initiatives.

AI/ML enables automation to improve efficiency and lowers barriers to collaboration across domains. Through 2026, more than three-quarters of enterprises’ data management processes will be enhanced with AI and ML to increase automation, accuracy, agility and speed.

Utilizing AI/ML in MDM software can make data more accessible and usable in several ways. For example, AI/ML can support personalization by identifying and providing access to information most likely relevant to a specific user and their role. AI/ML-guided authoring and assistance, including usage recommendations, can automate data profiling processes. Recommendations may also highlight related information from multiple domains in the data governance process.

The core processes involved in master data management can also be enhanced with AI/ML. Multiple matching algorithms combined with ML scoring capabilities can help improve accuracy, while AI/ML can also accelerate dynamic data classification, data profiling and in-line data enrichment.

ML techniques can also automatically identify missing or inaccurate relationships in data that might have otherwise been overlooked in manual processes. Examples include identifying whether individual customers are members of the same household or whether businesses are related entities. AI/ML can also be used to automatically identify rules for data quality, standardization, enrichment and matching based on previous processing outcomes as well as facilitating automated enforcement as data is processed.

These are not theoretical examples of how AI/ML could be applied to MDM but practical examples of how AI/ML is employed in the current generation of MDM products, lowering the barriers to successful adoption and accelerating time to value. MDM is not a new concept, but while it does not get the same attention as other aspects of data management and operations, it is also a hotbed of innovation.

Enterprises looking to make more data-driven decisions should evaluate the new breed of MDM products to increase trust in data and data management processes. Enterprises with greater confidence in data can move more quickly to make data-driven decisions and respond faster to worker and customer demands for more innovative, data-rich applications and personalized experiences, gaining competitive advantage.

Our Master Data Management Buyers Guide is designed to provide a holistic view of a software provider’s ability to deliver the combination of functionality to provide a complete view of MDM with either a single product or suite of products. As such, the Master Data Management Buyers Guide includes the full breadth of MDM functionality. Our assessment also considered whether the functionality in question was available from a software provider in a single offering or as a suite of products or cloud services.

The ISG Buyers Guide™ for Master Data Management evaluates products based on data modeling, data stewardship and master data rules. To be included in this Buyers Guide, products must also include capabilities to facilitate the configuration of MDM software. The evaluation also assessed the use of AI to automate and enhance MDM.

This research evaluates the following software providers that offer products that address key elements of master data management as we define it: Ataccama, Boomi, Cloud Software Group, IBM, Informatica, Oracle, Precisely, Reltio, SAP, Stibo Systems, Syndigo and Syniti.

This research-based index evaluates the full business and information technology value of master data management software offerings. We encourage you to learn more about our Buyers Guide and its effectiveness as a provider selection and RFI/RFP tool.

We urge organizations to do a thorough job of evaluating master data management offerings in this Buyers Guide as both the results of our in-depth analysis of these software providers and as an evaluation methodology. The Buyers Guide can be used to evaluate existing suppliers, plus provides evaluation criteria for new projects. Using it can shorten the cycle time for an RFP and the definition of an RFI.

The Buyers Guide for Master Data Management in 2024 finds Informatica first on the list, followed by IBM and Oracle.

Software providers that rated in the top three of any category ﹘ including the product and customer experience dimensions ﹘ earn the designation of Leader.

The Leaders in Product Experience are:

• Informatica
• Oracle
• IBM

The Leaders in Customer Experience are:

• SAP
• Oracle
• IBM

The Leaders across any of the seven categories are:

• Oracle, which has achieved this rating in six of the seven categories.
• Informatica and SAP in five categories.
• IBM in three categories.
• Stibo Systems and Syndigo in one category.

The overall performance chart provides a visual representation of how providers rate across product and customer experience. Software providers with products scoring higher in a weighted rating of the five product experience categories place farther to the right. The combination of ratings for the two customer experience categories determines their placement on the vertical axis. As a result, providers that place closer to the upper-right are “exemplary” and rated higher than those closer to the lower-left and identified as providers of “merit.” Software providers that excelled at customer experience over product experience have an “assurance” rating, and those excelling instead in product experience have an “innovative” rating.

Note that close provider scores should not be taken to imply that the packages evaluated are functionally identical or equally well-suited for use by every enterprise or process. Although there is a high degree of commonality in how organizations handle master data management, there are many idiosyncrasies and differences that can make one provider’s offering a better fit than another.

ISG Research has made every effort to encompass in this Buyers Guide the overall product and customer experience from our master data management blueprint, which we believe reflects what a well-crafted RFP should contain. Even so, there may be additional areas that affect which software provider and products best fit an enterprise’s particular requirements. Therefore, while this research is complete as it stands, utilizing it in your own organizational context is critical to ensure that products deliver the highest level of support for your projects.

You can find more details on our community as well as on our expertise in the research for this Buyers Guide.