Analyst Perspectives

Natural language interfaces for business intelligence products existed long before the emergence of generative artificial intelligence. Large language models have allowed BI providers to accelerate the delivery of functionality to convert natural language questions into analytic queries and generate summarizations and recommendations from data and charts. Features that enable natural language query and natural language generation are now ubiquitous.

As I previously noted, however, facilitating business user access to data is more challenging than adding GenAI interfaces to existing BI products. BI software providers previously differentiated by NLQ and NLG, such as ThoughtSpot, have sought to maintain an advantage by developing new products with GenAI-first interfaces to democratize access to data across the enterprise.

ThoughtSpot was founded in 2012 with a mission to democratize access to data by bringing business intelligence capabilities to line-of-business personnel in addition to business analysts and decision-makers who traditionally use business intelligence products. The company’s approach was built on a combination of search and AI, with its search-based analytics engine designed to generate insights through machine learning and reinforcement learning algorithms. ThoughtSpot’s search interface lowered barriers to analytic insight, and the enterprise was an early adopter of NLQ and NLG to democratize access to data.

In addition to investing in internal development, the company has boosted its capabilities through acquisitions. ThoughtSpot added data integration capabilities with the purchase of Diyotta in 2021 and collaborative BI features with the acquisition of Mode Analytics in 2023. The software provider was rated as a Vendor of Assurance in the ISG 2024 Buyers Guide for Analytics and Data and the Buyers Guide for Embedded Analytics and was rated as Exemplary in the 2024 Buyers Guides for Mobile Analytics, Collaborative Analytics and GenAI Analytics.

Many enterprises seeking to increase data-driven decision-making are investing in strategic data democratization initiatives to provide self-service access to business users and data analysts. This has long been a goal of many enterprises, but few have achieved it. Our research shows that most workers are not using analytics and business intelligence tools. Only 15% of participants in ISG’s Analytics and Data Benchmark Research say their organization is very comfortable allowing business users to work with data that has not been integrated or prepared for them by IT.

As covered in my recent Analyst Perspective, data democratization encompasses several functionalities, notably self-service data discovery using natural language interfaces to lower the barriers to working with analytics software. While the adoption of GenAI for analytics is still in its infancy, early signs are encouraging. For key analytics use cases, 99% of participants in the ISG Market Lens AI Study have seen positive outcomes from natural language search queries, for example. And 97% have seen positive outcomes from the interpretation of data. Also critical for data democratization is ensuring access does not depend on traditional analytics software. This is the goal of ThoughtSpot Spotter, which was launched in late 2024 to make it easier for more people to work with data through an interactive, intuitive dialogue-based interface that enables natural language conversations.

Agreement on data definitions becomes more important as more business users interact with and analyze enterprise data through GenAI interfaces. As I previously explained, this reinforces the importance of semantic data modeling to standardize metrics and definitions, as well as reasoning to enable agentic AI. Under the covers of Spotter is an agentic reasoning layer and the provider’s semantic modeling language capabilities, which combine to classify user queries in the context of an enterprise’s business definitions and match them with appropriate agents to provide information and recommendations. In addition to being available via ThoughtSpot Analytics, Spotter is also incorporated into ThoughtSpot Embedded, enabling access through business applications, including Salesforce and ServiceNow.

While ThoughtSpot Spotter enables business users to access data-driven insights, ThoughtSpot has recently delivered enhancements for data experts with the launch of Analyst Studio. This collaborative analytics environment provides capabilities for ad hoc analysis and advanced analytics using a combination of SQL, R and Python. ThoughtSpot Analytic Studio is an add-on option for ThoughtSpot Cloud users of ThoughtSpot Analytics and ThoughtSpot Embedded, delivering functionality for data preparation and modeling plus iterative and advanced analytics.

I assert that by 2027, more than one-half of enterprises will deploy chatbots and other conversational experiences as standard BI interfaces, making it easier for line-of-business workers to derive value from analytics. However, more widespread interaction with analytics could stem from new products with GenAI interfaces as the primary means of interacting with data, supported by charts and tables. This contrasts with GenAI interfaces bolted onto or alongside the charts and tables delivered by traditional reports and dashboards.

Tapping into the opportunities for Spotter will require ThoughtSpot to sell to a new set of users. Working with business application partners to drive the adoption of ThoughtSpot Embedded will be critical. I recommend that enterprises investigating analytics software with GenAI capabilities for data analysts and line-of-business workers include ThoughtSpot in evaluations.

Regards,

Matt Aslett