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 Data Governance: ISG Research Buyers Guide is the distillation of a year of market and product research by ISG Research.  

Data governance is integral to an overall data intelligence strategy. Good data governance provides guardrails that enable organizations to act quickly while protecting the business from risks related to regulatory requirements, data-quality issues and data-reliability concerns. It accelerates the delivery of analytics projects, providing the confidence required to make agile business decisions.  

ISG Research defines data governance as enabling enterprises to ensure data is cataloged, trusted and protected, improving business processes to accelerate analytics initiatives while supporting compliance with data privacy, security policies and regulatory requirements. While not all data governance initiatives are driven by regulatory compliance, the risk of falling afoul of privacy (and human rights) laws ensures that regulatory compliance influences data-processing requirements and all data governance projects. Improved operational efficiency is another major benefit of data governance, along with reduced cost of existing IT support. 

The importance of data governance is well recognized and understood. Almost 9 in 10 participants in our Data Governance Benchmark Research identified data governance as important or very important to the organization. Large enterprises rated data governance as 97% important or very important, and finance, insurance and real estate organizations gave it a 93% important or very important rating. This increase in importance, alongside the growing maturity and expanded functionality available in data governance software, is a contributing factor in its accelerated adoption. 

The emergence of the data catalog drove much of the increased focus on data governance. It has become an integral component of enterprise data strategies over the past decade, serving as a conduit for good data governance and facilitating self-service analytics initiatives. The data catalog has become so important that it is easy to forget that, just 10 years ago, it did not exist as a standalone product category. Metadata-based data management functionality have played a role in products for data governance and business intelligence for much longer, but the emergence of the data catalog as a product category provided a platform for metadata-based data inventory and discovery that spans an entire organization, serving multiple departments, use cases and initiatives. 

Data catalogs facilitate a holistic view of data production and consumption. They support the requirements of data operators, with capabilities to address internal data governance policies and external regulatory requirements. Data catalogs also support the needs of data consumers with functionality to address self-service data discovery and collaboration. Both are supported by automation, with data catalogs using artificial intelligence and machine learning to accelerate metadata collection, semantic inference, tagging and recommendations. 

Initial adoption of data catalog products was led by large organizations embarking on digital transformation and data governance initiatives. Digital native startups, recognizing the importance of data democratization to create a data-driven culture, were also early adopters. Adoption has now spread to organizations of all shapes and sizes. Through 2027, enterprises will increase strategic focus on data catalogs as the intersection of data production and consumption, enabling the self-service creation and sharing of data products based on trusted and governed data sources. 

The significance of the data catalog to data governance initiatives is highlighted by our research, indicating that the more data catalog users an enterprise has, the greater trust the organization has in its data and the higher the level of confidence in its ability to govern and manage data across the business. Almost three-quarters (74%) of enterprises with more than 100 data catalog users trust the data used in decision-making and operations, compared to 66% of those with 100 or fewer data catalog users. Similarly, three-quarters (75%) of enterprises with more than 100 data catalog users are confident in the organization’s ability to govern and manage data across the business, compared to 56% of enterprises with 100 or fewer data catalog users. 

The role data catalogs play in enabling enterprises to inventory and discover data in data lake environments has contributed to adoption. Further, the evolution of these environments into data lakehouses has increased approaches to store and analyze large volumes of data from multiple applications. Data lakehouse environments based on cloud object storage offer a relatively inexpensive way to store large volumes of data. This includes structured, semi-structured and unstructured data unsuitable for storing and processing in a data warehouse. Additional functionality is required, however, to ensure that all that data is useful from a business context. Due to the complexity of storing and processing large volumes of raw data from multiple operational applications, the data catalog has emerged as a critical resource for providing an inventory of data of varied formats stored in a data lakehouse to be queried by multiple business departments for diverse analytic workloads. 

This is not to say that data catalogs are a panacea for data governance. In fact, the proliferation of data catalog functionality in the past decade could contribute to the challenge of effective data usage. Not only are there many standalone data catalog providers, but numerous software providers offer data catalog functionality in data and analytics platforms. Through 2026, more than one-half of all enterprises will continue to utilize multiple data catalog technologies, running the risk of creating silos of information knowledge. Co-opetition among software providers is critical to ensure that multiple data catalogs do not become competing silos of information knowledge. Such an outcome would impede the use of enterprise data for strategic AI initiatives and pose risks in ensuring compliance with regulatory requirements. 

Multinational organizations must be aware of the wide variety of regional data security and privacy requirements. This includes the European Union’s General Data Protection Regulation and similar regulations like the California Consumer Privacy Act. In addition to differing regulatory requirements, our research illustrates varying attitudes and approaches to data governance on either side of the Atlantic: Better regulatory compliance is seen as a benefit of investing in data governance by almost three-quarters of European enterprises, compared to 57% of North American organizations. 

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

The ISG Buyers Guide™ for Data Governance evaluates products based on the governance of real-time data in motion and data at rest, as well as the use of AI to automate and enhance data governance. To be considered for this Buyers Guide, products must include at least one of the following functional areas, which are mapped into the Buyers Guide Capability criteria: data catalog, data lineage and data stewardship. 

This research evaluates the following software providers that offer products that address key elements of data governance as we define it: Alation, Alibaba Cloud, Amazon Web Services, Ataccama, Atlan, Collibra, Confluent, Databricks, Google Cloud, Hitachi Vantara, Huawei Cloud, IBM, Informatica, Microsoft, Oracle, Precisely, Qlik, Quest Software, Rocket Software, SAP, SAS Institute, Solace and Syniti. 

This research-based index evaluates the full business and information technology value of data governance 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 data governance 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 Data Governance in 2024 finds Informatica first on the list, followed by IBM and Microsoft.  

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. 
• IBM. 
• Microsoft. 

The Leaders in Customer Experience are: 

• Databricks. 
• Microsoft. 
• SAP. 

The Leaders across any of the seven categories are: 

• Informatica and Oracle, which have achieved this rating in five of the seven categories. 
• Alation, Databricks and Microsoft in two categories. 
• AWS, Google Cloud, IBM, Qlik and SAP 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 data governance, 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 data governance 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.