Business intelligence has evolved far from its traditional focus on historical analytics and batch operational reporting.

The latest generation of BI products are focused on self-service predictive capabilities to enable business users to do many things that once required either a BI specialist or a highly skilled data scientist.

In addition, most BI solutions now offer their functionality primarily through subscription-based software as a service and deployment into public-cloud platforms as a service. Not least, most BI solutions now are entirely self-service, in-memory, interactive, guided, browser-oriented and otherwise amazingly searchlike in user experience.

Staying in the thick of AI-driven BI differentiation

At its 12th annual customer conference this week in Las Vegas, BI powerhouse Tableau Software advanced its value propositions and roadmaps in all of these areas. It also deepened its relationship with Amazon Web Services Inc. and added support for Alibaba Cloud, giving Tableau customers the ability to run their analytics in the public clouds of their choice.

To stay ahead in the dynamic BI and data visualization market, Tableau has been investing deeply in products, partnership, and integrations. This week, it provided eye-chart lists that enumerated hundreds of new features that it has introduced across its portfolio in the past year.

Chief among these new features are those that pivot on artificial intelligence and machine learning. Tableau is not the only BI company to go deep on embedding of AI and machine learning to automate the distillation of predictive insights from complex data. But it has been one of the most aggressive BI providers using data-driven algorithms to improve its self-service functionality and thereby accelerate user productivity.

Keeping pace with AI-centric BI rivals such as ThoughtSpot Inc., Tableau highlighted several AI- and machine learning-related announcements at this week’s conference:

  • Cross-portfolio AI/ML capabilities: Most important, Tableau decisively positioned its AI/ML R&D as central to its product evolution going forward. It discussed the AI/ML-driven features that it has embedded in Tableau Server Data Engine to support self-service data management across its BI solution portfolio. It presented a roadmap under which AI/ML-driven “Smart Recommendations” will permeate the platform, providing guidance on data cleansing in Tableau Prep, as well as join and view recommendations in Tableau’s visualization and modeling tools, including Tableau Desktop, Tableau Prep and Tableau Online.
  • Beefed-up metadata catalog for AI/ML-accelerated BI: Tableau discussed how features of the recently released Tableau Catalog contribute to improved data discovery, data preparation and cleansing, and metadata management, thereby accelerating AI/ML-accelerated user productivity across its BI portfolio.
  • AI/ML-automated plain-language explanations of data visualizations: Tableau demonstrated the recently released Explain Data solution, which uses AI/ML to help users quickly find relevant visualizations in complex data sets. Explain Data can auto-generate plain-English explanations for data-driven insights uncovered by Tableau applications.
  • AI/ML-interpreted natural-language queries: The vendor enhanced its recently released Ask Data solution, using embedded AI/ML to automate interpretation of complex natural-language data queries, such as those involving year-over-year and geospatial comparisons. The product currently only supports automated interpretation of English-language queries, but Tableau says it plans to support other natural languages eventually.
  • AI/ML-generated data statistics: Tableau laid out a roadmap for using AI/ML to automate generation of statistics on data across its solution family. This will involve automation of forecasts, anomaly detection, expected values and what-if analyses.

Still no clear joint Salesforce-Tableau go-to-market approach

Though product and roadmap announcements were the focus of this week’s Tableau Conference, the audience was keenly interested in learning whether the vendor would reveal how it will align its strategies with those of its new parent, Salesforce.com Inc.

Unfortunately, the combined companies were not forthcoming with many details of their joint roadmaps for products, partnerships and other matters. Salesforce Chief Executive Marc Benioff appeared at the Tableau keynote, where he promised to provide details in his Dreamforce keynote next week.

Tableau remains an independent solution provider under Salesforce, a fact that helps it continue competing in the market for standalone BI tools, in which it remains an industry leader. Tableau Chief Product Officer Francois Ajenstat promised that it would continue to execute its pre-acquisition product roadmap with “zero distractions.” However, that is highly unlikely, considering that Tableau will almost certainly need to, at the very least, harmonize its offerings with Salesforce’s Einstein Analytics solution and with its corporate parent’s established AI/ML and analytics capabilities.

When the firms start toward a convergence roadmap, it’s not at all certain they can leverage their respective strengths to boost Salesforce’s position in the software-as-a-service market or Tableau’s in BI and data visualization. There is still a vibrant market for pure-play BI tools against which Tableau faces an uphill battle, and various SaaS vendors — such as Oracle Corp. and SAP SE — have weathered the competition from Salesforce quite well.

Nevertheless, the combined Salesforce-Tableau is in an enviable market position from which to launch a joint roadmap that leverages both firms’ leaderships in their respective segments. Among all respondents to a recent ETR survey of Fortune 500 customers, Tableau (analytics sector) and Salesforce Einstein (analytics and AI/ML sectors) are experiencing year-over-year customer growth rates that are greater than their respective sector averages. Among all respondents, Tableau has seen its market share surge while its net spending score among large enterprises remains stable.

Tableau appears to have sustained the strong momentum it had achieved prior to the acquisition by Salesforce in the middle of this year. So far, the parent company has not broken out Tableau’s financials separately. But at this week’s conference, Tableau representatives stated that subscriptions in the revenue mix are now “way north of 80%,” compared to about 80% this time last year, 50% in 2017 and 20% in 2016.

Prior to the acquisition, Tableau had successfully transitioned to an annual recurring revenue model under CEO Adam Selipsky, who had formerly been with AWS and who turned around Tableau after the disastrous losses and stock value declines of 2015. In 2018, Tableau hit $841 million in annual recurring revenue, up 41 percent year-over-year, buoyed by rapid customer switchover to the subscription model. In 2019, Tableau added 15,700 new customers, growing its customer base by 23%, to more than 86,000 customer accounts.

Takeaways and recommendations

For Tableau customers, this week’s event highlighted what they already knew: It provides a top-tier solution portfolio for self-service business analytics, data visualization and decision support.

Though none of this week’s product announcements was particularly innovative, and even AI/ML-related features are becoming widespread across BI, it’s clear that Tableau has given its global customer base ample reasons to double down their investments in its solutions.

Self-service cloud analytics is the future of the BI arena. To keep its customer momentum strong, Tableau’s deeper AWS integration and newly ramped-up migration programs are critical. They enable Tableau customers that wish to do self-service analytics in that leading public cloud to migrate their data and analytics efficiently and rapidly. By the same token, Tableau’s ability to run in any of the leading public clouds — including its new support for Alibaba Cloud — should accelerate their adoption of its modeling and visualization tools.

Tableau should provide consistent data migration tooling with respect to all of the leading public clouds. Its emphasis on professional services partnerships in this regard is the right approach, and we hope that under the new Tableau Partner Network, the company will roll out these data migration partnerships worldwide.

For Salesforce customers evaluating whether they should also adopt Tableau products, it would be premature to highlight any specific synergies between the two firms’ offerings. However, Benioff hinted that a concrete roadmap for integration of its newly acquired Tableau business will be presented next week at Dreamforce. Nevertheless, it is clear that the parent company will continue to manage Tableau as a largely autonomous product group, recognizing the value of letting this leading BI brand manage itself, tend to its ecosystem and stick to its roadmap as it sees fit.

Finally, it’s clear from this week’s discussion that Tableau, though it is making deep investments in embedded AI/ML features, has no intention of becoming a data science modeling platform in its own right. For that reason, Tableau customers are encouraged by this week’s newly announced integrations with partners Alteryx, Databricks, and DataRobot, which enable customers who use these data science workbenches to publish powerful AI/ML-generated predictions and rich explanations as easily accessible Tableau data sources.

Tableau should expand this integration to a wider range of data science DevOps platforms, such as those highlighted in this Wikibon report.