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ThoughtSpot’s AI-Powered Analytics for the Modern Enterprise

Data has become the ultimate differentiator in today’s rapidly evolving digital landscape. Extracting value from enterprise data can generally be accomplished through three key drivers: data quality, data infrastructure, and data analytics. In 2021, we wrote a piece that highlighted the importance of these drivers – collectively dubbed the “modern data stack” – in maximizing the value of enterprise data.

The Evolution of Business Intelligence (BI)
Enterprises have long sought actionable insights, the “holy grail” outcome of the modern data stack. To achieve this outcome, however, the field of BI and data analytics has been a translation exercise to date. Data practitioners translate business leaders’ questions into programming languages like SQL and C++, extracting data from enterprise databases and storage systems. They then translate the findings into dashboards for business leaders. However, business leaders rarely have the ability to interrogate the data themselves.

This language barrier has challenged technology providers for years and impeded the adoption of BI and analytics within the enterprise. Recognizing this, the ThoughtSpot team delved into their data and found that by putting trusted, contextual insights into the hands of every employee, businesses experience considerably faster revenue growth compared to their peers.

The uniqueness of this approach informed our investment into the company in 2021. ThoughtSpot empowers every individual within an organization to generate, explore, and engage with detailed, hyper-personalized, and actionable insights from real-time data wherever it is stored, however it is stored.

Generative AI + BI
ThoughtSpot’s focus on the business user has positioned the company well to tackle the current revolution in business analytics – the use of Generative AI. By democratizing access to highly performant analytics within the enterprise, Generative AI stands to meaningfully expand how companies can leverage their proprietary data. Building a Generative AI strategy is a growing C-Suite priority as enterprises increasingly look to outperform competition with best-in-class analytics. Business leaders ask their data teams for demonstrable Generative AI value, while data teams yearn for budgets to support data quality and infrastructure initiatives that fuel such outcomes.

To address both of these pressing needs and continue its mission of empowering business users with enhanced analytics capabilities, ThoughtSpot released Sage – a GPT-based tool that uncovers profound AI-generated insights through simple language queries. At March Capital, we applaud this strategic move because it is a natural evolution of ThoughtSpot’s core product, utilizing best-in-class new technology. Moreover, in an era of increasing Generative AI competition and diminishing differentiation, we believe the company’s extensive customer base provides a formidable distribution advantage.

Implementing a Generative AI Product Strategy – A Case Study
ThoughtSpot’s Generative AI strategy originated from one of the best forums for enterprise innovation and cross-functional collaboration: a quarterly “ship-a-thon”. Chief Development Officer (CDO) Sumeet Arora aligned incentives with employees by hosting discussions about market trends, customer feedback, and product roadmaps. They encouraged experimentation with new generative technologies and streamlined the release of new generative AI features while reiterating ThoughtSpot’s commitment to customer trust, safety, and user experience.

ThoughtSpotTeam
Fig. 1, ThoughtSpot team

 

After completing an in-product and blue sky thought process to identify the best ideas, the product teams focused on building a nuanced natural language front-end and an advanced backend that understands the enterprise context in which the data lives. Simultaneously, business leaders and GTM teams strengthened ThoughtSpot’s partner ecosystem, adding partnerships that would enhance their universal AI-driven analytics and data-centric insights across diverse data storage and tool environments.

ThoughtSpot meticulously evaluated the impact of LLMs on its architecture and user experience with rapid iteration with stakeholders, including the field, customers, and partners. The company also carefully considered the requirements of its product, data engineering, and IT programs to ensure seamless provisioning and security of new features, and governed AI with human-in-the-loop feedback to ensure product quality.

Following these considerations and months of iteration, ThoughtSpot introduced Sage to the to the world to take analytics beyond the dashboard and put data insights in the hands of everyone, regardless of the internal team they sit on. ThoughtSpot Sage combines the power of foundational language models with the accuracy of ThoughtSpot’s patented AI technology (SpotIQ) to provide enterprises with a natural language search experience that automatically creates insights from enterprise data with verifiable accuracy and trust. With ThoughtSpot Sage, enterprises have access to AI-powered search in natural language, search recommendations, AI-assisted data modeling, and AI-generated insights that improve with usage.

SpotIQ Overview
Fig. 2, ThoughtSpot’s SpotIQ Architecture

 

ThoughtSpot showcases the transformative power of customer centric products in driving successful adoption of Generative AI within the enterprise. We are proud to partner with the company on its journey to bring data and business teams together with Generative AI that has only just begun.

Last week, ThoughtSpot announced the acquisition of Mode Analytics to help data teams bring Generative AI capabilities to business users throughout their organization. According to CDO Sumeet Arora, “The cloud data warehouses brought data together and busted silos in how data is stored and analyzed. But these silos remain in analytics with point products serving different personas and UI needs. These point products don’t work with each other leading to trade-offs and lost productivity as well as value. With Mode and ThoughtSpot, businesses can go from code-first analysis to code-free data exploration and back again quickly, giving customers both the speed and flexibility of code with governed, trusted self-service analytics. The trade-off between flexibility and governance is removed and the silos between data teams and BI teams are solved by a unified platform.”

Generative AI Powered Business Analytics
The ability to leverage Generative AI for analytics presents a massive opportunity to enhance knowledge workers’ capabilities and productivity, enabling enterprises to finally become data-driven. We’re most excited about the potential of using natural language as the new interface between humans and computers, starting with conversational AI for data exploration. Several incumbents are already taking notice. In the six months since ChatGPT was released to the public, multiple familiar names in business analytics, including Microsoft, Tableau, SAS, and Sisense have also announced plans to infuse Generative AI into their platforms.

In the future, we anticipate seeing LLMs integrated elsewhere in data management and analytics processes (i.e., data enrichment for enhanced insights and efficiency for data preparation and cleansing). Several startups are building at these intersections, includingPecan, Cumul.io, Preset, and GoodData. By abstracting away complexities across the analytics stack, we anticipate all knowledge workers can focus on higher-value tasks and strategic initiatives. We’re excited to invest in the next wave of business intelligence and analytics, fueled by Generative AI.