Data.
Not opinion.

Self-Service Business Intelligence Market Study 2025

Self-service BI remains an important topic for organizations seeking to better leverage both information resources and scarce human experts to drive improved group-based decision making in a governed fashion. Widespread leverage of artificial intelligence and generative AI has renewed interest in self-service BI for its potential to make BI more accessible to a larger community of users.

According to the study, now in its 14th edition, end-user self-service ranks 11th of 65 technologies and initiatives strategic to business intelligence, slightly higher than in 2024. Sixty percent of respondents say self-service is critical or very important. Importance increases with global headcount and shows a correlation with overall successful BI implementations.

Multiple traditional avenues for collaborating with business intelligence are widely used in 2025, led by email, virtual meetings and face-to-face meetings. Methods and usage are highest and broadest in very large organizations, while younger organizations are more collaborative.

2025-04-30

Active Data Architecture® Report 2025

Active Data Architecture supports a platform-independent layer located between physical data stores and points of data consumption. It is comprised of various data management capabilities including virtualized and distributed data access (e.g., mesh, fabric), data governance, and security.

The 2025 report shows a strong and growing majority of the market is interested in active data architecture. Larger organizations are more likely to embrace the vision for active data architecture to combat the weight of increasing data volumes, data source types, and data distribution.

2025-04-15

Cloud Computing and Business Intelligence Market Study 2025

Cloud business intelligence (BI) is comprised of the technologies, tools, and solutions that employ one or more cloud deployment models.

Cloud and software-as-a-service (SaaS) ranks sixth in importance out of 65 topics under study by Dresner Advisory, and near the all-time high level of user importance.  Scalability, administration, reliability, and ease of use are the perceived most important advantages of cloud.

"We started tracking and analyzing dynamics within the cloud computing and BI market in 2012 when adoption was nascent, and now have 14 years of detailed data on the topic," said Howard Dresner, founder and chief research officer at Dresner Advisory. "It's exciting to see the changes since the early days of cloud-based solutions and not surprising to see cloud now ranked as a top initiative. Traditional organizations have been adopting cloud services, even as many new enterprises have shifted to cloud-native or cloud-first, moving the topic from fringe to strategic, to ultimately, competitive."

2025-03-26

Engineering BI Success–Growing Data-Engineering Capabilities for Maximum Value

Data engineering now represents a critical core competency for success in business intelligence (BI), as well as all forms of analytics and artificial intelligence (AI). Data leaders unable to develop broad-based data-engineering skills and technology capabilities risk degrading the value of their organizations’ BI investments. Data-engineering services and the ability to apply them in support of frequently changing business requirements (including new data sources, complex data types, and bulletproof delivery to the point of analysis) will make or break the ability to achieve desired outcomes from new BI use cases. Organizations that develop strong data-engineering skills and capabilities in support of BI will outperform their peers.

Success with BI, enabled by data-engineering capabilities, breeds future success. Organizations reporting that their BI initiatives are highly successful tend to have data-engineering capabilities in place and also routinely plan to expand those investments. This approach helps them further outpace peer organizations that do not consider data engineering strategic, and that often apply these capabilities via ad hoc, reactive, and project-specific approaches, rather than capitalizing on core investments in this area (such as standard tools and approaches, reusable components, and skills).

2025-05-01

Packaged BI and Analytics Solutions Are Table Stakes; Solution Augmentation Could Be the Jackpot

A majority of deployed business intelligence (BI)/analytical solutions are now packaged offerings sourced from a software vendor. However, our data also shows that customized “partial solutions”—accelerators/templates that fill gaps in functionality or address specific industry or company-size needs—most often deliver the highest perceived value. Therefore, data leaders need to focus on the innovation and business insight discovery enabled by this trending twist on a hybrid architecture.

Packaged BI/analytical solutions—whether sourced from a software vendor or systems integrator (SI) or consultant—have become the ante for most organizations to play in the analytics game, with 82% of organizations using them. Winning the game depends on holding and drawing the right cards—highly leveraging “good enough” functionality in packaged BI/analytical solutions for most use cases, but augmenting these solutions with specialty capabilities provided through accelerators/templates. The result is a BI/analytics architecture that offers the least-cost access to baseline capabilities for a majority of users and use cases, and optimally deploys additional capabilities through accelerators/templates that address unique business needs and requirements, and help build competitive advantage. Data leaders need to focus on guiding their organizations to the optimal mix of packaged and custom solutions.

2025-04-24

It’s Not a Packaged-Only World—BI Success Requires Strong Development Skills

The availability of packaged analytical solutions has increased dramatically in recent years, with many vendors of BI tools, applications, and services now offering out-of-the-box products that address common process- and industry-specific analytic requirements. However, despite this easy access, organizations find that these packaged solutions “as is” meet business needs only about half the time. Data leaders and their teams frequently must determine when packaged options are the right choice, and when they are not, how to fill the gap with alternatives.

When packaged solutions do not fit or fully address business needs, the most common choices are custom development and open-source offerings. Although open-source capabilities sometimes provide a reasonable fit, more often than not they also require extension, enhancement, or embedding within more complete solutions. So, whether or not open-source options provide some advantage, closing the gap left by the shortcomings of packaged solutions requires data leaders and their teams to leverage development skills and resources.

2025-03-26

Vena vs. Workday EPM

This special report examines the offerings and positioning of two major players in the EPM market: Vena Solutions (Vena) and Workday Adaptive Planning (Workday) and compares them on key aspects such as usability, integration capabilities, performance, scalability, and support. The goal is to provide a clear understanding of how each platform meets the needs of modern enterprises and supports their overall performance management initiatives.

2025-01-01

Anaplan vs. Pigment EPM

This special report examines the offerings and positioning of two major players in the EPM market: Anaplan and Pigment and compares them on key aspects such as usability, integration capabilities, performance, scalability, and support. The goal is to provide a clear understanding of how each platform meets the needs of modern enterprises and supports their overall performance management initiatives.

2025-01-01