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

For decades, enterprises have focused on using analytics and data software—or business intelligence—to improve operations. Software providers have made dramatic improvements in BI products by incorporating highly interactive visualizations and the ability to quickly process and display very large volumes of data. However, the quest to make analytics accessible to more of the workforce has led to generative artificial intelligence, applying it to all aspects of data analytics software to make the products easier to use. ISG Market Lens research shows that 87% of participants are AI-enabling analytics and BI applications.

ISG Research defines GenAI Analytics as the use of generative AI and other AI and machine learning techniques to enhance analytics processes. It includes providing conversational interfaces, recommending data preparation steps, suggesting visualizations of data and documenting analytics processes. It also includes using AI/ML to provide automated insights and natural language generation.

Adopting AI/ML has proven more complicated than many had expected. Ideally, BI software products would have full AI/ML capabilities. That has not happened, and AI/ML functions remain independent of BI software. AI/ML requires skills beyond the reach of many analysts, and organizations have had difficulty finding skilled resources. As a result, we expect through 2026, three-quarters of organizations will maintain multiple, separate skill sets.

Faced with this separation, BI software providers have invested in making AI/ML more accessible by augmenting the products’ capabilities. With the advent of GenAI, elements of AI/ML are more easily incorporated into analytics experiences. For example, AI/ML can drive automated insights to identify and explain relationships in the data as well as recommend actions to take. One of the most common and beneficial uses of GenAI is natural language processing to support conversational analytics with natural language queries and narrative responses. Creating ML models is made more efficient by automated machine learning, making more sophisticated analytics—such as customer segmentation using clustering techniques—accessible to more individuals. And GenAI can be applied to many tasks in analytics and data processes to make those tasks easier to design and perform.

In addition to conversational analytics, one of the greatest opportunities for GenAI is to assist with data preparation. Data preparation continues to be where organizations spend the most time in analytics processes. GenAI can be used to suggest which data tables to combine and how to combine those tables. It can automatically construct a logical data model from a physical data model. AI/ML can augment data quality processes, identifying outliers and anomalies in the data, even recommending potential corrections for those data points.

While efforts to apply AI/ML have been underway for some time, the sudden explosion of GenAI capabilities has fueled more interest in how to augment BI. GenAI is also being used to generate SQL to access data sources, and, in some cases, GenAI produces documentation of data pipelines used in analytics processes, enhancing the understanding and lineage of the data. In some ways, it is the Wild West, with providers racing to outdo each other in the application of GenAI. The technology holds much promise, and we expect it will have a significant impact on the analytics market, but it is still early days.

GenAI analytics will continue to evolve. Many features are still under development or in pre-release mode. GenAI is making conversational analytics more common and more capable than it is today. It will enable better support for multilingual capabilities currently lacking in most analytics products and likely lead to increased automation in data preparation processes and in creating initial analyses, making analysts much more productive.

More products will also offer AutoML capabilities. Among the software providers we evaluated, AutoML is most often used to generate forecasts and perform customer segmentation analyses. Over time, AutoML capabilities will expand to support more types of analyses and produce models with improved accuracy. The exact intersection between AutoML in GenAI analytics products and the models produced from more sophisticated AI/ML products remains to be seen. Today, some GenAI analytics products can work with these models, but it is still a loosely coupled process.

Enterprises should be aware of the changes going on in the market. Understand the capabilities and compare current software with what other providers have available. In evaluating GenAI analytics, one must consider the underlying analytics capabilities. GenAI can only do so much if the foundation of underlying analytics is weak. Consequently, this Buyers Guide combines an assessment of GenAI analytics capabilities with core analytics capabilities to determine the provider’s overall rankings. Organizations can then use this report to help guide purchasing decisions but also to guide conversations with software providers about the roadmap for GenAI analytics. The market is still evolving rapidly, but organizations can realize value today that will improve analytics processes.

The ISG Buyers Guide™ for Generative AI Analytics evaluates software providers and products in the three key areas of data, analytics and communications. It includes a wide variety of the criteria used in our Overall Analytics and Data Buyers Guide but places emphasis on assistance and automation in data and analytics processes.

This research evaluates the following analytics and data software providers that offer products that include key elements of GenAI analytics as we define it: Alibaba Cloud, Amazon Web Services, Cloud Software Group, Domo, GoodData, Google Cloud, IBM, Idera, Incorta, Infor, insightsoftware, Microsoft, MicroStrategy, Oracle, Qlik, SAP, SAS, Sisense, Salesforce (Tableau), ThoughtSpot and Zoho.

This research-based index evaluates the full business and information technology value of GenAI analytics 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 GenAI analytics 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 GenAI Analytics in 2024 finds Oracle first on the list, followed by SAP and Domo.

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:

• Oracle
• Domo
• SAP

The Leaders in Customer Experience are:

• Oracle
• SAP
• IBM

The Leaders across any of the seven categories are:

• Oracle and SAP, which have achieved this rating in five of the seven categories.
• Microsoft in four categories.
• AWS, Domo, Google, IBM, Qlik and Zoho 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 GenAI analytics, 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 GenAI analytics 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.