However, Tableau might be playing catch up with other vendors, analysts said.
“A number of vendors have developed and are refining ways to look at the graph of individual and user behaviors and interactions with data and then glean insights and make recommendations based on changes,” said Doug Henschen, principal analyst at Constellation Research.
Cloud-based products, according to Henschen, tend to have a leg up in analyzing user behaviors and data interactions at scale.
“Products that started out as server-based products, like Tableau, have typically taken longer to develop graph and personalization capabilities that can be delivered consistently across the both cloud and on-premises deployments,” Henschen said.
Though many vendors offer automated insights, the addition of generative AI-produced narratives “will help make these insights more complete and more easily delivered in multiple languages,” Ventana’s Menninger said.
Tableau Pulse is expected to be available in pilot later this year, the company said.
Data Cloud for Tableau to unify data for analytics
In addition to Tableau Pulse, Salesforce is offering Data Cloud for Tableau to unify enterprises’ data for analytics.
The plan is to layer Tableau on top of the Data Cloud, which was released last year in September at Dreamforce under the name “Genie.
“With Tableau, all of a company’s customer data can be visualized to help users explore and find insights more easily. Data Cloud also supports zero-copy data sharing, which means that users can virtualize Data Cloud data in other databases, making it instantly available to anyone,” the company said in a statement.
Data Cloud for Tableau will also come with data querying capabilities, the company added.
There are many business advantages that Data Cloud for Tableau can provide, according to Henschen.
“Advantages include bringing together all your disparate data, separating compute and storage decisions, and enabling many types of analysis and many different use cases against the data cloud without replication and redundant copies of data,” Henschen said.
Salesforce’s move to combine its Data Cloud with Tableau can be attributed to Tableau having reaching a ceiling in its core analytic discovery capabilities, according to Park.
“It is being pressured to increasingly support larger analytics use cases that push into data management and data warehousing. Although Tableau is not going to be a full-fledged data warehouse, it does want to be a source of master data where analytic data is accessed,” Park said.
Data Cloud for Tableau, however, is part of a strategy to compete with data lakehouse, data warehouse vendors, and an effort to own or control more data, Menninger said. The integration of Tableau and Data Cloud will lead to direct competition with the likes of Qlik, Tibco IBM, Oracle, and SAP, analysts said.
Data Cloud for Tableau is expected to be made available later this year.
Other updates includes a new developer capability, dubbed VizQL (visual query language) Data Service, that allows enterprise users to embed Tableau anywhere into an automated business workflow.
“VizQL Data Service is a layer that will sit on top of published data sources and existing models and allows developers to build composable data products with a simple programming interface,” the company said.
Salesforce woos new users with Tableau generative AI
Generally, the addition of generative AI features to Tableau can be seen as an attempt to attract customers who are not analytics or data experts. Business intelligence suites face a problem of adoption as at least 35% of employees are not willing to learn about analytics or data structures, Park said.
“To get past that, analytics needs a fundamentally different user interface. This combination of a natural language processing, natural language generation, generative AI, and jargon-free inputs that translate standard language into data relationships provides that user interface,” Park added.
Another reason why the new features could attract customers is the disinterest of business users in using dashboards. “These users would rather use natural language which has context. Up until now, NLP was very difficult for computers to handle but the new LLMs changed that,” Mohan added.
