As AI evolves, Tableau talks direction ahead of Dreamforce
A knowledge layer and headless analytics capabilities show that the vendor is changing to meet the needs of its users as AI reduces the need for traditional BI.
As AI creates challenging conditions for analytics specialists by becoming the new interface for BI, Tableau is evolving.
And it's doing so in an appropriate manner, according to David Menninger, an analyst at ISG Software Research.
"Tableau continues to be a standard bearer," he told TechTarget. "They are not alone, but they eat, sleep and breathe analytics, and are constantly pushing the boundaries."
AI is altering the way users consume BI, making some of the chief capabilities that Tableau and competitors such as Qlik and Microsoft excelled at providing obsolete. To remain relevant, such vendors need to adjust.
Tableau, a subsidiary of CRM giant Salesforce specializing in analytics, hosted a webinar on Aug. 24 during which it previewed the features it plans to highlight during Dreamforce, Salesforce's annual user conference which will be held Sept. 15-17 in San Francisco.
None are new capabilities, though each has features that are not yet generally available. They include the knowledge layer that Tableau unveiled in May as part of its new Agentic Analytics Platform, headless BI capabilities that enable customers to analyze data while using applications such as Slack and ChatGPT, and proactive intelligence that surfaces insights that might not otherwise have been discovered.
Collectively, the capabilities demonstrate that Tableau is changing in a logical way, according to Donald Farmer, founder and principal of TreeHive Strategy. But he suggested that there is more the vendor should do to keep current, and he noted that even if Tableau does make all the right moves as it reacts to a new reality, AI's emergence drastically alters the paradigm for traditional BI providers.
"Tableau is right to focus on context, the one thing AI models do not supply," Farmer told TechTarget. "Salesforce owns as much of the enterprise context -- within its domain -- as anyone, so it's a strong position. Meanwhile, headless delivery is unavoidable and the right direction."
However, while Tableau is correct to provide headless BI capabilities, doing so makes the vendor's platform less visible to its customers by giving them a reason not to open their Tableau instance, he continued. In addition, simply providing new capabilities is likely not enough for BI providers to retain users.
"I am not sure [they] address what the 'DataFam' needs -- training, tooling and credentials for maintaining knowledge with the depth needed for AI, not just re-packaging BI insights," Farmer said.
An altered landscape
Reports, dashboards and data visualizations used to be the end point for analytics. They were how business users and expert analysts consumed data and derived insights that informed strategic decisions. Tableau excelled at providing customers with the capabilities to build such tools. So did Qlik, Strategy, ThoughtSpot, Domo and Microsoft, among others.
Tableau is right to focus on context, the one thing AI models do not supply. Salesforce owns as much of the enterprise context -- within its domain -- as anyone, so it's a strong position. Meanwhile, headless delivery is unavoidable and the right direction.
Donald FarmerFounder and principal, TreeHive Strategy
Now, rather than going into a BI environment to develop data products and analyze data, users can simply ask a question using natural language and their chatbot or agent can deliver a response in seconds that includes detailed analysis, along with a supporting report or visualization. In addition, unlike a static report or dashboard that can take hours or weeks to update, users can immediately ask follow-up questions of AI tools to get deeper insights.
"Before AI, visualizations were the holy grail of BI," Menninger said. "Now, BI is all about conversational interfaces -- ask a question, see some visualizations, ask a follow up question, ask more questions and provide some instructions to prepare a report or presentation. These things happened before, but not in the software."
Farmer likewise noted that AI has altered BI by making data artifacts a byproduct of AI-powered analysis rather than the deliverable while insights have become the product.
"The report, dashboard, or visualization used to be the deliverable and decisions followed from that," he said. "Now, and especially with agentic AI, the decision is the deliverable, and the artifact is a by-product of the process. In fact, a lot of analysis now happens with no BI tool involved at all. Someone shares a spreadsheet with an AI assistant and has a chart in seconds."
Therein lies the ongoing challenge for the vendors that specialized in BI and whose platforms empowered users to make data-driven decisions, Farmer continued.
While Tableau and its peers now provide chatbots and agents that drastically simplify insight-generation and enable any user with proper authorization to query and analyze data, so do broader-based data platform providers that previously didn't directly compete with BI vendors.
"Some of the problems BI vendors specialized in are now pretty much solved," Farmer said. "Most BI vendors are still trying to sell the interface, but that has disappeared into more-or-less universal chat experience or agent framework."
Beyond data platform vendors such as Databricks and Snowflake, analytics specialists now face further competition from major application providers such as ERP software vendor Workday, as well as AI vendors including OpenAI that provide chat interfaces, according to Menninger.
"AI has significantly reduced the barriers to entry in the analytics market … so the biggest challenge is a barrage of new competition," he said.
In response, Tableau is changing.
A new emphasis
Once renowned for its pioneering data visualization capabilities, Tableau is now touting tools that aid customers building AI tools and others that use AI to deliver insights to users without forcing them to use Tableau's environment or even do the analysis on their own.
In May, the vendor unveiled a knowledge layer as part of its new Agentic Analytics Platform.
The knowledge layer is designed to be a data foundation that agents and other AI applications can tap into to access the contextually relevant information that enables them to carry out their prescribed tasks. Included is a data knowledge engine based on Tableau's decades of semantic modeling, a natural language interface, and built-in security and governance.
Given that AI outputs are often misleading because they are not accessing the appropriate data, Tableau's knowledge layer is built to improve the accuracy of AI outputs so that agents can be trusted in production environments, according to Emma Annand, Salesforce's director or product marketing for agentic analytics.
"Data leaders are finding that the quality of data is going to be what [enables] successful agentic analytics," she said during the webinar. "We have to think of data as a valuable resource, and knowledge is the unlock."
Tableau is not alone among analytics specialists in developing a knowledge layer.
In addition to Tableau, vendors such as ThoughtSpot and GoodData have also pivoted to transform their expertise in data analysis into fuel for AI. Similarly, data management providers including Alteryx, AWS, Databricks, Goole Cloud, Informatica, Microsoft, Snowflake and Teradata have all introduced tools aimed at connecting agents with context.
Nevertheless, Menninger noted that it is important for Tableau's futureto provide such capabilities, as dedicated BI environments disappear and AI becomes the interface for generating insights.
"As an industry we need to solve the problem of context and semantics," he said. "We still don't know all the information that an enterprise collects and what it means. That knowledge is necessary to correctly interpret the data and perform the appropriate actions."
Farmer likewise noted that, as enterprises continue to invest in AI development and strive to move pilots into production, building a knowledge layer is significant and could help Tableau stand out from competitors that aren't evolving away from traditional BI.
"[Rather than] just repackaging BI insights, the knowledge layer is a differentiator," he said.
Adding headless BI and proactive intelligence capabilities are also wise ways for Tableau to evolve and remain relevant to its users, according to Menninger.
Historically, Tableau was its own environment. Through APIs and software development kits, headless analytics enables users to export Tableau into agents, collaboration platforms such as Slack and Teams, AI tools including Claude and ChatGPT, and applications where they do much of their work.
Headless BI is not a new concept. For example, GoodData launched headless analytics capabilities in 2021. But new or not, Menninger noted that headless BI is a valuable addition for Tableau users.
"Salesforce has made a big bet on headless," he said. "We had a previous round of headless in analytics and it didn't go over very well. … Conceptually though, it is the right thing to do. Analytics should never have been a separate discipline. It needs to be integrated with the core business processes it supports."
Proactive intelligence, meanwhile, is AI-driven analysis that surfaces insights so users don't have to constantly comb data to discover new ideas.
Competitive standing
While adding and emphasizing capabilities that are useful as AI makes traditional BI less necessary, Tableau is largely just keeping pace with its competitors rather than pacing the market, according to Menninger.
However, even though other vendors are providing knowledge layer and headless BI capabilities, quality is way that Tableau can continue to distinguish itself, he continued.
"Tableau continues to be a standard bearer," Menninger said. "They are not alone, but they eat, sleep and breathe analytics, and are constantly pushing the boundaries."
Farmer likewise noted that Tableau is in step with its peers rather than launching market-moving new capabilities.
For example, Qlik has made the data foundation its focus rather than the analysis layer while ThoughtSpot, which built its platform around AI-powered search from the time it first launched, continues to make analytics part of an AI workflow rather than a set of standalone data products.
However, whether Tableau and other BI specialists can further evolve to remain critical to their customers is unclear, Farmer continued.
"Every BI vendor is moving in the same direction, and it's mostly downhill from here," he said.
One way that Tableau could potentially grow is by breaking its knowledge layer out from the Agentic Analytics Platform, Farmer suggested.
By doing so, Tableau would enable customers to use their Tableau semantic models to ground any agent, whether built using Salesforce's development tools or not, while their governance and audit trails remain in Tableau.
"That would turn … risk into revenue, where Tableau gets paid when its knowledge and context is used somewhere else, instead of losing a seat," Farmer said.
"Tableau, and other analytics providers, need to learn to live in an agentic world," he said. "As agents evolve, they will incorporate more and more decision making, [so] I expect to see more decision intelligence in analytics products. To make informed decisions, you need to evaluate multiple scenarios and alternatives. We still don't see enough of these capabilities in most BI products."
Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.
