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MicroStrategy launches new suite of generative AI tools

The longtime analytics vendor's suite incorporates LLM technology to make users more efficient by enabling them to use natural language to query, model and analyze data.

MicroStrategy launched MicroStrategy AI, a portfolio of features designed to make data workers more efficient by enabling them to interact with data using generative AI.

The suite, which the longtime independent analytics vendor made generally available on Oct. 3, includes natural language processing (NLP) capabilities that enable users to engage with data without writing code, automated dashboard development, and text-to-code translation tooling designed to help data engineers and other experts model their data.

Unlike many of its competitors, MicroStrategy did not previously unveil its generative AI capabilities in preview, making it one of the last major analytics vendors to reveal how it plans to incorporate large language models (LLMs). It is, however, one of the first to make its generative AI capabilities generally available, according to Donald Farmer, founder and principal of TreeHive Strategy.

Most analytics vendors have revealed plans for generative AI in the 11 months since OpenAI launched ChatGPT in November 2022, which represented significant advancement in generative AI and LLM capabilities.

Many, including ThoughtSpot and Tableau, have advanced those tools to the preview stage. A few others, such as Domo and Qlik, have even made certain tools generally available. But most have not yet released entire portfolios of generative AI capabilities to all customers.

"Most vendors have been -- [perhaps] deliberately -- vague about the extent of the support they offer for their generative AI features," Farmer said.

However, being first or among the first is not critical, he continued.

Given that most organizations aren't yet prepared to move generative AI models into production, vendors still have time to hone tools that enable generative AI modeling and analysis.

"MicroStrategy may be first to move their portfolio into production, but I don't think it matters very much," Farmer said. "Everyone is still kicking the tires and very few customers are ready to move into production, so GA products are less necessary at this stage. This is changing. Next year we will see more production uses of AI and therefore more demand for fully supported products."

Before launching MicroStrategy AI, the vendor's only public revelation related to generative AI was unveiling a partnership with Microsoft in June aimed at adding generative AI to MicroStrategy's platform. The partnership includes an integration with Azure OpenAI Service.

New capabilities

MicroStrategy AI is designed to help make employees more productive, according to the vendor.

NLP has long held the promise of making data workers and business analysts more productive by enabling them to interact with data using natural language rather than code.

The NLP tools developed by analytics vendors, however, did not fully deliver on that promise due to their limited vocabularies. They enabled some natural language interaction, but they still required users to phrase queries and commands in specific ways that required data literacy training.

Generative AI changes that. LLMs are trained on massive amounts of public data, including extensive vocabularies that enable true natural language interaction -- including different phrasing that may have the same or similar meaning -- rather than the use of business-specific terminology.

In addition, NLP has held the promise of enabling developers and data engineers to build applications and data pipelines by translating text to code. Again, however, the reality of what individual vendors could build on their own fell short.

LLMs change that as well, automatically converting text commands to code that computers can understand and execute.

MicroStrategy AI includes LLM capabilities that enable both data analysis as well as interactions with databases using natural language, according to the vendor.

Auto Answers lets self-service analytics users essentially converse with their data in natural language, asking questions of their data and receiving answers. In addition, the feature can suggest further questions based on users' initial questions and the dashboards they're using.

Auto SQL, meanwhile, is aimed at developers and engineers, enabling them to query data and create tables using natural language. In addition, by eliminating the need to write code, the tool enables business users to go beyond self-service analysis to work with data within databases.

This is really about enabling their customers to get the most out of their data and to make better decisions in a fast and trusted way.
Mike LeoneAnalyst, Enterprise Strategy Group

Other tools include Auto Dashboard, which automates portions of dashboard development and suggests visuals and other data assets to include in dashboards, and Auto Expert, a chatbot that surfaces answers and resources to assist users as they navigate MicroStrategy's platform.

In addition, the portfolio connects with MicroStrategy's governance capabilities, such as its semantic graph to ensure accurate AI model results as well as security measures that aim to keep customers' data both compliant and private.

The MicroStrategy AI architecture, meanwhile, is multi-cloud to make it flexible as technology continues to advance and composable. Users can choose the integrations they want to develop their AI ecosystems.

In sum, as with other vendor's generative AI capabilities, the portfolio will make users more efficient, according to Mike Leone, an analyst at TechTarget's Enterprise Strategy Group.

"This is really about enabling their customers to get the most out of their data and to make better decisions in a fast and trusted way," he said.

In fact, the trust aspect is critical, Leone continued.

"MicroStrategy is well known for its enterprise-readiness and enhanced governance capabilities," he said. "This creates a fantastic scenario where customers can accelerate the use of AI with confidence."

Measuring up

While MicroStrategy AI stands to benefit the vendor's existing customers, the portfolio of tools doesn't stand out from the generative AI capabilities unveiled by MicroStrategy's competitors, according to Farmer.

Instead, it is essentially in line with what others have planned, with the main difference being that MicroStrategy has been able to deliver its generative AI tools sooner than most.

For example, Domo AI similarly includes text-to-code translation capabilities and a composable architecture. Amazon QuickSight includes Ask Me and Build For Me, enabling users to converse with their data and develop dashboards using natural language.

ThoughtSpot, meanwhile, made natural language search the foundation of its platform when it was founded in 2012 as a means of broadening the use of analytics within organizations. Its Sage system, which was first unveiled in March and is built on an integration with OpenAI, is designed to finally enable true natural language processing throughout the entire ThoughtSpot platform.

"I don't see much differentiation here," Farmer said of MicroStrategy. "There's an emphasis on security and compliance, which will be welcome to their customers. But it's not so different from what ThoughtSpot, Domo and Tableau have spoken about."

Whether differentiated or not, the tools still stand to benefit MicroStrategy's customers, according to Leone.

As the vendor adds more generative AI functionality going forward, it would be wise to continue developing capabilities with its customers in mind.

"This is a big launch for MicroStrategy, and their starting use cases align with what organizations are asking for when it comes to generative AI and BI," Leone said. "Staying attached to their customers proverbial hip as they execute and deliver value will be critical in helping not only optimize their current use cases but, more importantly, shape the next set of use cases."

Eric Avidon is a senior news writer for TechTarget Editorial and a journalist with more than 25 years of experience. He covers analytics and data management.

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