Net Score

The intensity of spend for a vendor. Higher Net Scores indicate a positive spend trajectory, while lower Net Scores indicate a flat or negative spend trajectory.

The Technology Spending Intentions Survey (TSIS) is our quarterly survey (Jan, Apr, July, Oct) of technology decision makers capturing forward-looking spending intentions for enterprise technology vendors across the global market. Our surveys are standardized, multiple choice format. For each survey question, the technology decision maker will select one of the following answers:

Adoption
Increase
Flat
Decrease
Replacing

The Emerging Technology Survey (ETS) is our quarterly survey of technology decision makers capturing the enterprise’s appetite for emerging technology vendors across the global market. Our surveys are standardized, multiple choice format. For each survey question, the technology decision maker will select one of the following answers: Allocating further Evaluated, plan to utilize Currently evaluating Plan to evaluate Aware of, no plan to evaluate Evaluated, no plan to utilize Replaced or in containment

Pervasion

How widespread a particular vendor is utilized, allowing users to gauge declines or growth over time and benchmark peers against each other.

Technology Spending Intentions Survey

Our quarterly survey of technology decision makers capturing forward-looking spending intentions for enterprise technology vendors across the global market. Our surveys are standardized, multiple choice format. For each survey question, the technology decision maker will select one of the following answers: • Adoption • Increase • Flat • Decrease • Replacing

Net Score

The intensity of spend for a vendor. Higher Net Scores indicate a positive spend trajectory, while lower Net Scores indicate a flat or negative spend trajectory.

Emerging Technology Survey

The Emerging Technology Survey (ETS) is our quarterly survey of technology decision makers capturing the enterprise’s appetite for emerging technology vendors across the global market. Our surveys are standardized, multiple choice format. For each survey question, the technology decision maker will select one of the following answers:

Allocating further
Evaluated, plan to utilize
Currently evaluating
Plan to evaluate
Aware of, no plan to evaluate
Evaluated, no plan to utilize
Replaced or in containment

The Technology Spending Intentions Survey (TSIS) is our quarterly survey (Jan, Apr, July, Oct) of technology decision makers capturing forward-looking spending intentions for enterprise technology vendors across the global market. Our surveys are standardized, multiple choice format. For each survey question, the technology decision maker will select one of the following answers:

Adoption
Increase
Flat
Decrease
Replacing

The Emerging Technology Survey (ETS) is our quarterly survey of technology decision makers capturing the enterprise’s appetite for emerging technology vendors across the global market. Our surveys are standardized, multiple choice format. For each survey question, the technology decision maker will select one of the following answers:

Allocating further
Evaluated, plan to utilize
Currently evaluating
Plan to evaluate
Aware of, no plan to evaluate
Evaluated, no plan to utilize
Replaced or in containment

Net Score represents the intensity of spend for a vendor.

Higher Net Scores = a positive spend trajectory
Lower Net Scores = a flat or negative spend trajectory

Pervasion represents how widely a vendor or product is utilized relative to a given sample.

Mind Share represents a vendor’s overall enterprise awareness within ETR’s sample.

Net Sentiment represents the overall opportunity for an emerging technology.

Net Score represents the intensity of spend for a vendor. Higher Net Scores = a positive spend trajectory   Lower Net Scores = a flat or negative spend trajectory

Pervasion represents how widely a vendor or product is utilized relative to a given sample.

Business Intelligence and Reporting Tools

Robust Self-Service Platforms Dominate the BI and Reporting Market

Based on data collected October 2023

24-minute read

A scientific, data-driven approach to business decision making is hardly new. Since the Industrial Revolution, organizations have looked to empirical evidence to drive performance and inform strategy. But after the birth of computing in the mid-20th century, early relational databases and means to query large, organized stores of data for analytical insights paved the way for business intelligence (BI) and reporting. Early tools in the 1970-90s focused on generating well-formatted paginated reports that could be run from queries across data sources and printed on a regular cadence for use by various business departments. Given the technical complexity of this work, early reporting activities were the domain of IT professionals who built reports based on specifications given to them by business units. The 2000s marked an important sea change for BI. Organizations longed to empower business units to explore data for themselves and build their own reports and dashboards. This era saw the birth of self-service BI, a shift away from reliance on IT staff to construct reports and instead deploy subject matter experts in business units to engage in the BI and reporting process. This, for instance, meant a BI specialist in the finance or marketing department could query data sources themselves and generate reports and dashboards for departmental colleagues. IT’s role in this vision was to be infrastructural, improving the quality and discoverability of data and connecting sources to data warehouses that were accessible to less tech-savvy business users.

Modern self-service BI platforms provide user-friendly interfaces, often with low-code drag-and-drop functionality. Importantly, though, modern platforms also feature data governance capabilities, security and role management functions, native integrations and data connectors, large libraries of data visualization types, and painless management of development-testing-production phases. Today, these tools have substantial artificial intelligence (AI) built in, providing a bridge from run-of-the-mill BI to more advanced analytics, data science, and machine learning capabilities. This includes easy integration with languages like Python and R, as well as natural language processing (NLP) and generative AI capabilities to help users explore data and explain data visualizations with simple chat boxes or voice commands.

By now, many organizations find themselves supporting several tools with overlapping capabilities. The cost and complexity of supporting redundant tools weighs heavily, especially in times of IT budget cuts, leading many organizations to undergo BI tool rationalization processes to trim their portfolio. Complete, self-service BI platforms are likely to survive the cull. Vendors such as Microsoft Power BI, Salesforce’s Tableau, Oracle Analytics Cloud, Google Looker Studio, SAP Analytics Cloud, and Amazon QuickSight offer a wide array of governance features, native data connectors, visualization types, and cloud-native infrastructure that make them prime to capture market share in a self-service BI world. But self-service BI platforms cannot do everything, and most organizations still have traditional reporting needs that remain stubbornly on-premises, tailored to nuanced compliance requirements, or too difficult to rebuild elsewhere. As such, legacy tools like Microsoft’s SQL Server Reporting Services (SSRS), Oracle Analytics Server, and QlikView remain present in many tech stacks. Tools like ThoughtSpot and Tellius were early movers in augmented analytics, using AI and NLP to enable easy business user interactions, but complete platform vendors like Power BI, Tableau, and Qlik Sense have all integrated similar capabilities into their core products by now. Finally, a handful of vendors continue to hold on to reputations of strength in particular use cases. These include GoodData, known for embedded and white label BI use cases; Domo, a tool embraced by and well-suited to marketing departments; and SAS Viya, with a data scientist-friendly advanced analytics suite of tools.

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Positioning for the ETR Observatory for Business Intelligence and Reporting was determined by a Market Array survey. Full methodology and graphic explanation are available on our About the ETR Observatory page.

This ETR Observatory report examines the vendors within a subsector grouping by triangulating data from ETR’s Market Array surveys, Technology Spending Intentions Survey (TSIS), Emerging Technology Survey (ETS), commentary from ETR Insights interviews with IT decision makers (ITDM) from the ETR Community, and proprietary industry analysis by our research staff.

TSIS and Market Array data measure spending velocity on a vendor or product. ETS data measure awareness and utilization on a vendor or product. ETR Insights interviews provide qualitative context and vendor evaluation to complement quantitative data. This report encompasses business intelligence and reporting vendors tracked within the Analytics / B.I. / Big Data sector of the TSIS and smaller, private players tracked in the Data Analytics / Integration subsector of the ETS.

Glossary of ETR Terms

The Business Intelligence and Reporting Market

In the October 2023 TSIS, the Analytics / B.I. / Big Data sector has the highest Pervasion of all the sectors at 75%. The sector’s overall Net Score, however, is 21%, ranking it 10th out of 29 sectors in the TSIS. In the ETS, the Data Analytics / Integration subsector has a 12% Net Sentiment and 24% Mind Share. According to the October 2023 Macro Views Survey, BI and reporting remain the top priority for organizations in the domain of analytics, with data preparation and transformation and analytics and big data platforms tied for second (see Figure 2).

Analytics/B.I./ Big Data Priorities

Figure 2. Business intelligence and reporting remains the highest priority area of analytics for organizations, according to the October 2023 Macro Views Survey (N=1,095).

I. Complete Business Intelligence Platforms Geared Toward Self-Service

The most robust BI platforms are much more than tools for generating colorful charts and graphs from data sets and disseminating them across the organization as full dashboards. Today’s complete BI platforms also include sophisticated data management and governance capabilities that enable reuse of queries, analytical models, and visualizations all hemmed in by fine-grained administrative controls over security and user access. Platforms such as Power BI, Tableau, Qlik Sense, Oracle Analytics Cloud, Amazon QuickSight, SAP Analytics Cloud, Looker, MicroStrategy, and others fall into this category (see Figure 3). The VP of BI and Analytics for a midsize financial services firm explained that his company is “making that shift to being an information-based business,” and a main focus in this journey is that “business cases … or initiatives are driven by the line of business.” As such, his team is “working hand-in-hand with the lines of business to help them, to provide them with self-service” by providing the data assets and tools necessary to enable decision-making in the business units rather than routed through IT.

Vendor Timeline

Figure 3. Net Scores over time for major vendors in the BI and reporting market show Microsoft the front runner, followed by SAP, Tableau, Qlik Technologies (including Qlik Sense and QlikView), and Oracle (including Oracle Analytics Cloud and Oracle Analytics Server).

The well-managed reuse of models is important in self-service BI because it ensures a single source of truth in analysis, minimizing the chances that different business users might join and analyze data based on slightly different business definitions of the data, resulting in inconsistent insights. Reuse also keeps business users from operating in silos and “reinventing the wheel” or duplicating past work already executed by colleagues while also allowing users to develop and propagate efficient queries optimized for performance in an organization’s tech stack. Together, this unified, reusable analytics landscape is known as the semantic layer. Popular self-service BI tools like Tableau, Qlik Sense, and Power BI all support semantic layers and associated data management practices, though organizations that embrace semantic layers within a BI tool run the risk of long-term lock-in with a single vendor and often have difficulty extending the semantic logic to other BI tools in the organization. Locating the semantic layer further upstream in the data and analytics pipeline, such as via views off a data warehouse or in third-party governance tools, is often preferred in organizations with multiple BI tools. The VP of BI and Analytics for a midsize financial services enterprise explained that “the idea of pulling data out of our data warehouse into a BI tool … just didn’t resonate with me.” Instead, his organization locates its semantic layer in a third-party tool that “sits right on top of Snowflake.”

Indeed, some BI vendors have begun to embrace a spirit of openness and connectivity outside their own semantic logic to attract customers who balk at BI-centered semantic layers. One VP and Manager of Data Analytics for a large financial services enterprise praises Qlik Sense for this, seeing them as “a huge up-and-coming competitor in this space, because they have a philosophy of an open ecosystem” which he thinks will “start driving more adoption.” Further to that point, the Co-Founder and CTO for a small health technology enterprise doted on Google Cloud Platform’s “great job with acquisitions” such as Looker, “tightly coupling analytics with its cloud platform to save so many man hours in development time, integrations, and DevOps.” He called this a particular “strength around connectivity between different tools” on Google’s part.

ETR Data: Major self-service BI platforms are seen as long-term investments by organizations. The technical complexity and reach of their integration within broader data and analytics pipelines, combined with the long-term “people and process” commitments like data literacy training and data governance policies that support their success, make self-service BI tools quite sticky in organizations’ tech stacks. Data from the ETR Market Array for BI and Reporting Tools shows that nearly half (49%) of organizations with Tableau expect to continue using it at least three more years. Commitments of three or more years are even higher among organizations with Qlik Sense (55%), SAP Analytics Cloud (55%), MicroStrategy (57%), Oracle Analytics Cloud (58%), and Power BI (69%).

It is perhaps no surprise that the most dominant BI tools in the market are part of larger suites of tools offered by major public cloud providers. Easy connectivity to major data sources is one of the most important criteria for an organization’s choice of BI tool, and the BI tools associated with major cloud providers naturally have the most seamless native integrations with their data sources. An organization with a large Salesforce footprint might find Tableau appealing, for instance, or an organization with an Azure Synapse data warehouse might prefer Power BI. A VP of IT and Services for a large agricultural enterprise noted that his organization consists of “predominantly SAP data, everything from sales information to inventory information.” Because SAP S/4HANA is where much of that data lands, his organization naturally gravitated toward SAP Analytics Cloud for dashboarding and visualizing business data, and they are now “getting more into self-service” with the tool.

In this vein, many ITDMs point to Microsoft’s licensing strategy as a reason for Power BI’s broad appeal. “As everyone knows,” said the IT Manager for a midsize municipal government office, “Microsoft’s approach to their 365 suite – whether that’s their Power Platform, Dynamics, or everything from there – they’re just taking the business world by storm.” He points out that Power BI’s bundling into Microsoft licenses, coupled with it being “very friendly to use,” has helped make Power BI a dominant tool in his organization and across other industries. According to ETR’s Market Array for BI and Reporting Tools, Power BI’s top vendor strength is that it integrates easily with an organization’s existing ecosystem. In the Market Array data, Power BI also has the highest Net Promoter Score and the fastest expected return on investment (ROI), with 48% of respondents expecting ROI within 12 months. Respondents in this survey also named Power BI the most innovative BI vendor and the most desired vendor to prioritize if given the opportunity to rebuild their data and analytics tech stack, both of which were open-ended questions. Tableau ranked a distant second in write-in response for both questions.

Power BI is often compared directly to Tableau. Tableau was an early pioneer in visually stunning dashboards and is still seen by many as a leader, even as Power BI has proliferated. The Sr. VP for a large financial services enterprise noted that “we’ll do best of breed” when explaining her organization’s rationale for choosing Tableau. She adds that “the capabilities of Tableau were just a really strong match” for her organization, and “Tableau seems to be outrunning” other BI tools “just in terms of the flexibility, the expansion, and their roadmap.” At the same time, however, her organization is expanding its Microsoft footprint, and they are starting to explore how weaving in additional BI tools like Power BI might work alongside Tableau. Ultimately, her goal is how to get her organization to “work ‘with,’ not ‘instead’” when it comes to embracing multiple BI tools.

Controlling cost is important, however, and some of the leading BI tools in the market are notoriously expensive. With lower cost options like Power BI expanding through Microsoft licensing and modern BI platforms now having many overlapping capabilities, many organizations are questioning the added expense of maintaining multiple, pricey tools. The Global Head of Enterprise Data Platform for a large financial services organization is using their move to the cloud as a catalyst for BI tool rationalization, describing how they ended up with so many tools in the first place. “At least historically,” he said, “different departments have picked different tools. That’s why we ended up having a dozen or more of them. Now at least we’re trying to consolidate a bit as part of the cloud migration.”

One CTO for a midsize nonprofit enterprise called out Tableau in particular for its high price tag: “Back 15 years ago I used Tableau in the industry where I was working, and it was a very powerful tool for reporting and analytics. It’s expensive, so we’ve gotten away from the use of Tableau.” He questioned the cost today, stating it is “expensive for what you get now.” He thinks “other tools have been able to provide similar functionality at a far lower price,” and if there is not much difference between BI tools today, how can you justify “a Porsche” when another car “works for what I need.” To further illustrate the point, data from the ETR Market Array for BI and Reporting Tools finds that “this product offers good value for the money” is the third-highest strength for Power BI, with 81% of respondents agreeing with the statement, while it is the third-lowest strength for Tableau, with just 51% of respondents agreeing with the statement. Also, just 31% of respondents expected ROI within 12 months for Tableau. For comparison, 48% of respondents using competitor Power BI and 34% using Looker report ROI within 12 months. ITDMs regularly cite product cost and ROI concerns as the top reason for replacing Tableau (see Figure 4).

II. Legacy Systems for On-Premises and Paginated Reporting

For all their strengths, however, modern self-service BI platforms have some blind spots that more traditional, legacy tools like SSRS, Oracle Analytics Server, and QlikView fulfill. Born of a newer generation in computing, self-service BI tools tend to be either cloud-only or offer the bulk of their features only when deployed in the cloud rather than on-premises. Connecting to on-premises data sources is often doable by these platforms, but it can be clunky depending on the tool and can involve essentially importing on-premises data into the BI tool’s cloud instance before the data can be used. A BI tool’s cloud may also only exist in certain geographic zones, which complicates regulatory requirements or technical performance standards for some. For organizations that have technical or legal reasons for keeping data on-premises and in certain geographic areas, on-premises reporting tools may still be necessary.

Furthermore, some of the most popular self-service BI platforms struggle to deliver seamless paginated reporting options or pixel-perfect PDF layouts for reports. Organizations in highly regulated industries often have requirements to produce reports laid out in very specific ways with elements positioned in exact locations on certain pages. Modern self-service BI platforms emphasize web and intranet-based dashboards with interactive capabilities, not traditional paginated reporting. Add-on licenses, third-party integrations, or considerable bespoke technical effort are usually required to execute paginated reporting via a modern self-service BI platform on par with legacy reporting tool outputs. As with the need for on-premises reporting options, many organizations also have technical and regulatory requirements for paginated reports that necessitate certain reporting tools.

Replacing Reasoning

Figure 4. In the October 2023 TSIS, 28 respondents indicated plans to replace Tableau. More than half (54%) cited product cost and ROI as the reason for their replacement decision.

III. Augmented Analytics Tools and Capabilities

In a race to further “democratize” access to BI by business users, a niche of tools in the early 2010s were developed that focused on AI-driven approaches to data exploration, analysis, and visualization. ThoughtSpot, in the Advancing vector, and Tellius, in the Trailing vector, are two prime examples of this new class of tools that exploit what is called “augmented analytics.” ThoughtSpot and Tellius both emphasize a search-based interface for exploring data. Users can type questions in natural language in a search bar – such as “what branch offices have the highest revenue in Q3?” – and the systems comb through the organization’s data to produce relevant visualizations to answer the question. Users can adjust their questions, ask follow-up questions, and make their outputs discoverable and reusable by colleagues in the organization in a governed way. The NLP engine that helps users find and analyze data can also power natural language explanations of the data, helping business users understand in plain language what visualizations mean, too. The CTO for a large travel enterprise is a fan of ThoughtSpot in his organization, calling it “a fantastic tool.” He thinks “more and more biz folks are going to be just looking for natural language query capabilities to drive analytics, because today the playing field is still very difficult for the business to get insights.”

Major modern BI platforms were quick to embrace NLP technologies and include new feature sets in their products. These features include Power BI Q&A, Tableau’s Ask Data and Explain Data, Qlik Sense’s Insight Bot, SAP Analytics Cloud’s Search to Insight, and many other examples. Though highly desirable for their business user-friendliness, augmented analytics capabilities can raise at least two causes for concern among IT professionals. First, the outputs of augmented analytics tools are only reliable if the underlying data are well-organized and high quality, and many organizations find that AI-powered features like these force IT teams to revisit data quality and data warehousing initiatives. In the October 2023 Macro Views Survey, for instance, improving data quality was by far the top data and analytics priority for organizations seeking to better support generative AI goals, followed by improving data warehousing and data lakes.

A second concern raised by augmented analytics capabilities is where data can reside in order for the augmented analytics tools to perform properly. Early on, tools like ThoughtSpot required data to be loaded into the tool in order to function, a less-than-ideal arrangement for many organizations’ data and analytics programs that involved data warehousing, federated governance, or upstream semantic layer strategies. In time, tools like ThoughtSpot developed native integrations with select data warehousing tools to allow organizations to keep from having to load data into the BI tool. But the concerns about where and how tools with augmented analytics capabilities access underlying data in a performant way persist.

ETR Data: Tellius and ThoughtSpot are both tracked in the ETS in the Data Analytics / Integration subsector. In the November 2023 ETS, Tellius had a 13% Net Sentiment, down four percentage points from a year prior. Its Mind Share of 8% has remained consistent for several surveys. ThoughtSpot’s Net Sentiment has dropped consistently for years, hitting an all-time low of 9% in the November 2023 ETS. Its Mind Share is also declining, down to 13% in that same survey. ETR also tracks ThoughtSpot in the TSIS, however, where its October 2023 Net Score is 17%, up four percentage points since the July 2023 survey but down six percentage points since October 2022.

Ultimately, as the VP of BI and Analytics for a midsize financial services firm summed up about augmented analytics tools: “all paths lead back to governance. The governance of metadata and governance of your data. The better poised you are from that perspective, the more ready you are to take on these new, exciting technologies.”

When generative AI burst onto the scene a year ago, technology vendors scrambled to figure out how to incorporate it into their product offerings. For the vendors reliant on NLP and other AI techniques to differentiate themselves in the market, generative AI raises new questions about how this new enthusiasm may enhance or possibly cannibalize their business. The VP of BI and Analytics for a midsize financial services firm said “some of those natural language capabilities” in tools like ThoughtSpot and Tellius “kind of feel like I’m interfacing with my stove compared to ChatGPT now. The leaders in that area will probably figure out how to weave it into what they do. It’ll be exciting to see where it goes, but whatever it is, it seems like it’s going to happen pretty fast.”

IV. Tools Tailored to Niche Use Cases

Some BI tools entered the market carving a niche for themselves around specific business use cases or emphasizing certain business functions or analytics roles. Over time, these tools have grown their products to offer more robust end-to-end BI platforms that enable self-service, but they retain their notoriety for having certain strengths. One example is GoodData, a complete self-service BI platform that is still widely known for its embedded analytics prowess. Embedded analytics allow organizations to place interactive dashboarding components on web pages and apps as part of a seamless user experience rather than force users to open a BI tool. Vendors like GoodData also offer embedded analytics in a “white label” arrangement, allowing companies to apply their own branding to the embedded dashboard components.

Products like SAS Viya offer broad functionality but find an enthusiastic audience among more advanced data analysts and data scientists. SAS is a well-known name in the data science and machine learning space and popular in scientific organizations, and its Viya product leverages that legacy. SAS Viya has focused in recent years on improving its accessibility to non-technical users by adding some drag-and-drop, low-code features, but largely the tool is seen as suited to more technically advanced and quantitatively trained analysts.

Domo is another BI tool that made its mark early among a specific user base and has since expanded its footprint. Marketing teams in particular embrace Domo’s Marketing Suite, which includes customer 360 and campaign-based analytics modules. The company also has solutions tailored to finance teams and IT operations teams, but largely the company is known for making a mark in marketing.

Finally, Sigma Computing stands out for the emphasis it places on its interface, which looks like a typical spreadsheet. Business users familiar with spreadsheets and the standard formulas used in them find Sigma Computing a comfortable platform for producing visualizations from data. The tool is web-based, and as users input standard formulas in the spreadsheet, it generates live SQL queries to cloud data warehouses to output tables and visualizations on the fly.

Conclusion: Complete Self-Service BI Platforms the New Normal

The two vendors in the Leading vector for this Observatory, Power BI and Tableau, have similar core functionality but differing appeal to customers. Tableau is seen as a high-end visualization platform bolstered by data management and governance controls and poised for growth after its acquisition by Salesforce four years ago. Power BI, on the other hand, is the lower cost option, conveniently included in Microsoft enterprise licenses that are rather ubiquitous in the business landscape. Power BI has closed the gap in functionality with Tableau and in some ways offers more sophisticated approaches to governance and administration that integrate well with diverse hybrid, multicloud, and multi-vendor tech stacks. Tableau suffers from its high price tag, and the cost is difficult for some organizations to justify in light of overlapping functionality with many other self-service BI platforms. Other major self-service BI platforms, such as Oracle Analytics Cloud, SAP Analytics Cloud, Looker, and Amazon QuickSight occupy the Advancing vector, while others like Qlik Sense and MicroStrategy linger high in the Trailing vector.

Organizations, as much as they may desire a consolidated BI tool environment, tend to resolve themselves to maintaining legacy on-premises reporting tools like SSRS in the Tracking vector, Oracle Analytics Server in the Advancing vector, and QlikView in the Trailing vector. The forces keeping these legacy tools sticky in organizations’ tech stacks are difficult to rebut, with thorny regulatory compliance requirements keeping pixel-perfect paginated reports relevant and major cloud-native BI platforms remaining resistant to engineering elegant on-premises data connectivity solutions.

On trend with the rest of the enterprise IT landscape, AI has made its presence known in the BI tool market. Vendors like ThoughtSpot in the Advancing vector and Tellius in the Trailing vector made a splash about a decade ago with NLP and search-based analytics, democratizing self-service BI even further into the hands of the least tech-savvy business users. Major platforms like Power BI, Tableau, and Qlik Sense were close on their heels, though, and continue to innovate better user experiences powered by AI. Recent developments in generative AI may further raise the profile of these NLP approaches to BI and accelerate their adoption in organizations, an effect that remains to be seen in the coming years. And finally, some smaller vendors in the Trailing vector have maintained a foothold in their particular niche areas of strength even as they have expanded their capabilities to try and rival the major self-service BI platforms. Vendors like GoodData continue to be known for excellence in embedded and white label analytics, Domo is still favored by many marketing teams, SAS Viya finds its appeal among more tech-savvy analysts and data scientists, and Sigma Computing continues to market its web-based spreadsheet interface to business users more comfortable in cells and formulas.

The future of the BI and reporting tool market is likely one of stabilization. Full-function self-service BI platforms are increasingly aligned with one or more major cloud provider, emphasizing easy connectivity to important data sources while continually improving the business user experience with better UI and AI functionality.

Contact the ETR Insights Team to discuss all the details from this analysis or request custom research. ETR Insights:

Our ETR Insights library contains transcripts and executive summaries from 360+ live and virtual events, totaling 15,000+ minutes of audio interviews. Uncover inflection points, understand what drives the decisions of enterprise technology purchasing leaders, and aggregate end-user sentiment around progressive technology trends.

ETR Insights Team:

Erik Bradley, Chief Strategist epb@etr.ai

Daren Brabham, PhD, Sr. Director Analyst dbrabham@etr.ai

Jake Fabrizio, Associate Analyst jf@etr.ai

Media Contact:

Contact our press team at press@etr.ai

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