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

The processes and technology of the analytics and data software industry continue to play an instrumental role in enabling an enterprise’s business units and IT organization to optimize data in tactical and strategic ways. To accomplish this, organizations must provide technology that supports a variety of personas, enabling them to access data, generate and apply insights from analytics, communicate the results and support collaboration as needed.

ISG Research defines analytics and data technology as the use of mathematics to create measurements and metrics that enable the evaluation of any form of data to provide insights and guide decision-making. It includes the collection and preparation of data needed for analyses, often one of the most time-consuming aspects of analytics. It also includes the various forms of visualization and dissemination of information, such as dashboards, reports, emails or text messages. Analytics range from simple ratios and percentages to forecasting, optimization and simulation. Increasingly, analytics must also include artificial intelligence-based insights using predictive, descriptive and prescriptive models.

In today’s data-driven world, enterprises must use analytics to understand and plan the details of operations across every department and the lines of business and IT. Organizations use analytics to track costs, create staffing plans, assess worker and supplier performance, identify variances and plan corrective actions. Analytics also helps inform workers and facilitates communication throughout the organization to coordinate actions toward a common mission and specific objectives. Operating without analytics would be like flying a plane without an instrument panel.

While analytics as a contemporary business tool dates back more than four decades, today’s business intelligence requirements have expanded well beyond query, reporting, analysis and publishing. Enterprises need tools for sourcing and integrating data and the use of analytics for planning and forecasting, as well as dashboards that present analytics in a variety of visualizations. Now, analytics requires presentations in the form of natural-language narratives using generative AI with an increasing number of software providers able to support multiple languages. The collaborative sharing of insights helps reduce the time to take action and make decisions.

Artificial intelligence and machine learning extend analytics, enabling it to classify, predict and suggest behaviors that help improve business operations. Enterprises need software providers to use ML to analyze product usage data to enhance and streamline interactions, anticipate the best next step in the analytical process and perform or recommend that step. In addition, GenAI is being applied to all aspects of data analytics software to make products easier to use by recommending data preparation steps, suggesting visualizations of data and documenting analytics processes. Advanced analytics, which incorporates AI and ML, has become a staple in analytic processes, with BI tools acting as delivery vehicles for this important information. Organizations that analyze data using machine-learning technology report gaining a competitive advantage, improving customer experiences, increasing sales and responding faster to opportunities. In light of these benefits, it is no surprise that nearly two-thirds of organizations report using machine learning today, and three-quarters of organizations in our research plan to increase the use of machine learning.

The ultimate goal of analytics is to help enterprises make and implement decisions that improve operations and the bottom line. The range of analytics capabilities has become known as decision intelligence. Decision intelligence involves analyzing historical performance, determining potential courses of action, evaluating the results of those actions, identifying the best path forward and coordinating the activities that support the implementation of the decisions.

Ideally, these processes can be conducted on a self-service basis. Self-service analytics continues to be a goal for most enterprises, and those that achieve it report greater satisfaction with the use of analytics. Organizations that access analytics without IT assistance are more satisfied than those requiring IT assistance.

Enterprises seeking to provide self-service analytics also need self-service data preparation. We have seen significant additional capabilities in this area resulting from the application of GenAI. In many of our research studies, preparing data is reported as the most time-consuming part of the analytics process, and our research finds that a majority of organizations are not comfortable allowing business users access to data that has not been integrated or prepared for them by IT. We expect GenAI-based self-service data preparation capabilities will reduce the often-necessary involvement of IT. Semantic models can also enhance self-service data preparation processes, especially when coupled with GenAI.

While AI and ML still require highly specialized skills, software providers have used elements of AI and ML to provide augmented intelligence capabilities such as automated insights and key driver analyses that require little or no input from line-of-business workers using the tools. These augmented intelligence capabilities, often delivered as narratives using GenAI, make it easier for a larger segment of the workforce to gain insights they might not see otherwise. ISG Software Research asserts by 2026, almost all business intelligence software providers will include augmented intelligence based on GenAI to make analytics easier to use. Augmented intelligence enables enterprises to change the analytics processes from a “pull” model, where analysts must create analyses, to a “push” model, where many analyses are created automatically. This push model also ensures more consistency in an enterprise’s analytics discipline because many of the analyses are automated, ensuring the same types of information are available to all.

For analytics to be effective, they must be accessible. Nearly all software providers now use GenAI to enable natural language processing, making it easier to access and find information via search and understand information through narratives explaining the analyses. Line-of-business workers also need access to analytics to conduct business, which means providing rich mobile access to analytics to support a workforce seeking to conduct business in any location at any time. Workers today expect these mobile capabilities, and organizations must choose analytics and data platforms that can deliver on these needs.

Collaboration in conjunction with analytics has finally become much more commonplace. Two-thirds of organizations report using or planning to use collaboration with analytics. Software providers now offer many options to enable collaboration, ranging from commenting on analyses to rating data sources. Other applications enable organizations to assign tasks and track them to completion, ensuring that the enterprise takes specific actions.

Analytics should lead to action. Enterprises use a variety of operational applications to ensure the implementation of decisions resulting from analyses. Embedding analytics directly into these systems makes it easier for line-of-business workers to access the information they need without using a different system, reducing the need for additional training.

As software providers continue to build out rich application programming interfaces that provide access to nearly all of the functionality in products, some providers offer prebuilt connections, delivering analytical outputs into operational systems. ISG Research asserts that by 2026, more than two-thirds of line-of-business workers will have immediate access to cross-functional analytics embedded in activities and processes, helping to make operational decision-making more efficient and effective.

Analytics must also be timely. Enterprises often operate 24/7. Information streams into business operations from a rapidly growing number of devices and sources. Without the ability to analyze this information as it occurs, organizations risk missing an opportunity to respond in the moment. Our research shows that one-half of organizations consider it essential to process streaming data and event information in seconds or milliseconds.

As organizations seek to expand the spectrum of analytic requirements, transitioning to enterprise-class analytics is an essential step forward. Software providers have responded to these broadening needs with additional capabilities. In some cases, providers have invested in developing additional capabilities. Others have acquired software companies that offer capabilities complementary to the existing portfolio. Not only have the capabilities expanded, but the number of providers has proliferated. Despite this expansion, there are still few software providers attempting to deliver the entire spectrum of capabilities we evaluate in this assessment. You will likely need more than one provider to meet your analytic needs.

This research evaluates the following software providers that offer products to address key elements of analytics and data 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.

The ISG Buyers Guide™ for Analytics and Data evaluates software providers and products in three key capability areas: data, analytics and communication. Data capabilities evaluated include defining and managing data models, accessing, integrating and governance data and managing master data. Analytics evaluation criteria include data discovery using computers, browsers and mobile devices; query; visualization; presentation; calculation; and advanced analytics, including predictive capabilities. Software providers are evaluated for communications capabilities, including informing audiences of the results of analyses by defining and delivering relevant information via any device, supporting collaboration among those audiences with sharing and social media-style interactions and automating delivery and action on the results of analyses with alerting and agents.

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

• Oracle
• Microsoft
• SAP

The Leaders in Customer Experience are:

• Oracle
• SAP
• IBM

The Leaders across any of the seven categories are:

• Oracle, which has achieved this rating in six of the seven categories.
• SAP in five categories.
• Microsoft in four categories.
• AWS, MicroStrategy and Google 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 analytics and data 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 analytics and data 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.