Infusion of generative AI into analytics a work in progress

By neub9
3 Min Read

The convergence of generative AI and analytics is an area that is constantly evolving. A number of vendors are currently working on plans to allow customers to analyze and query data using conversational language as opposed to code or business-specific natural language processing (NLP) capabilities. AI assistants are also being developed to help users with various tasks, and tools are being created to automatically summarize and explain data products such as reports, dashboards, and models. Moreover, there are unique features such as SQL generation and automated coding suggestions that are being introduced to simplify the data modeling process.

For example, Sisense was the first analytics vendor to reveal its intentions to integrate generative AI (GenAI) capabilities, and Tableau and Qlik were among the subsequent vendors to follow suit. Tech giants like AWS, Google, and Microsoft are also working to add generative AI to their BI tools, but as we approach the end of 2023, most of these capabilities are still in development and not yet publicly available. Exceptions to this include MicroStrategy, which recently made NLP and text-to-code translation capabilities available, and Domo, which released NLP and AI model management capabilities.

There are significant challenges in integrating generative AI tools with analytics platforms, and vendors are working to ensure that these tools are both effective and secure. ThoughtSpot’s chief development officer, Sumeet Arora, notes that it is difficult to achieve 100% accuracy in using generative AI to answer natural language questions, especially in an enterprise setting. The expectation of accuracy is at the heart of the delay in making these tools available for public use.

Despite these challenges, generative AI holds significant promise for the enterprise. By simplifying complex processes, vendors hope to expand the use of BI within organizations and make data experts more efficient. Generative AI enables conversational interactions with data and reduces the need for coding, ultimately leading to a wider adoption of analytics tools within organizations. The benefits of generative AI also extend to its ability to improve communication, automating tasks, and enhancing data integration and code development.

As these generative AI capabilities continue to be refined and developed, vendors are experimenting with cutting-edge features that go beyond traditional NLP and AI assistant tools. Microsoft, Amazon, and Google are among the players at the forefront of this innovation, integrating generative AI tools with their entire data ecosystems.

Overall, while the release of generative AI tools has been delayed due to the need for accuracy and security, it is clear that these capabilities hold tremendous potential to revolutionize analytics within organizations. As vendors continue to refine and develop these tools, we can expect to see more sophisticated and efficient ways of interacting with data in the near future.

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