6 min Analytics

Ambidextrous analytics: Ad hoc or advanced, ThoughtSpot Analyst Studio

Ambidextrous analytics: Ad hoc or advanced, ThoughtSpot Analyst Studio

AI-native intelligence platform company ThoughtSpot has extended its efforts to get every user touching data analytics. Already known for its natural language data search capabilities designed to enable data engineering and business teams to generate answers from business data at every point of the “decision lifecycle”, the organisation has now launched Analyst Studio, a “creator space” that lets data teams get data ready for AI and analytics, manage cloud costs while also being able to switch between ad-hoc analysis and advanced data science.

Ambidextrous analytics

While we might define left-hand analytics as ad hoc (perhaps less complex queries typically carried out by the business function, or indeed quicker queries from the data analytics team), in this story we can denote right-handed analytics as those data workflow processes more directly emanating from the data engineering team for more advanced complex tasks. No hate intended to left-handed individuals, being able to apply both streams from within a unified, flexible and integrated platform could mean a more democratically balanced approach to data science inside working organizations.

For data analysts, Analyst Studio is said to provide ways to work around data silos (the high cost of analytics when using multiple tools) and… crucially, its ease-of-use functions are supposed to help teams still perform analytics where there is a lack of advanced technical capabilities in technologies such as SQL, Python and R.

A creator space works to amplify business user capabilities such as Liveboards which offer a live and fully interactive view of data and Spotter, an agentic AI analyst that brings the analytical and reasoning skills of a data analyst to every user.

An end-to-ender bender 

“With the introduction of Analyst Studio, ThoughtSpot is delivering a complete end-to-end analytics workflow solution within the ThoughtSpot platform. Data analysts are empowered to independently prepare and explore data, data scientists can produce and distribute models to the broader organization and business users can consume insights in natural language, all within the same platform. Analyst Studio is providing ThoughtSpot customers with much-needed agility in the age of AI analytics,” said Mike Leone, practice director and principal analyst at Enterprise Strategy Group.

In the age of AI, the role of the analyst has clearly evolved beyond “simply” preparing data and building dashboards and it has now become more pivotal to business operations. While AI is enabling business users to extract more insights than ever, the accuracy, relevance and integrity of the AI data foundation relies on the expertise of the data analyst and analytics engineer to provide business users and the organization the opportunity to trust the insights that are generated and make (many would also argue to “scale”) the process of business decisions.

Analysts are now ‘creators’

“At ThoughtSpot, we believe analysts are creators, and not just report builders. Analyst Studio empowers them to embrace that role fully,” said Sumeet Arora, chief development officer at ThoughtSpot. “We’re giving analysts the tools to create AI-ready data, uncover deeper insights faster and drive greater business impact. By unifying data management, clicks, code and conversations through visualisations and agentic AI, we deliver a complete intelligence platform for all your business data.”

“Self-service analytics has always been a pipe dream. Analyst Studio has finally made it a reality,” said Kishore Narahari, engineering manager at Lyft. Narahari may be short on words, but he underlines the point above, which is also underlined by ThoughtSpot’s own C-suite.

Going deeper on what’s happening here, ThoughtSpot CEO Ketan Karkhanis suggests that as AI becomes an integral driver of business processes, data teams face mounting pressure to ensure accuracy, speed up decision-making, deepen analysis and optimize cloud costs.

But those efficiencies all need to be applied across disparate solutions. 

This is where we might be able to see the DNA of ThoughtSpot (or at least glean an understanding of what it is trying to do come to the fore) i.e. because we face the reality of tedious and time-consuming data preparation which stresses analysts’ resources, these teams need a way to combat fragmented analytics workflows and inflexible data modeling requirements.

Mash it up style

The company says that Analyst Studio addresses these challenges by simplifying data preparation for AI. Users can connect to, join and “mash up” data from different sources using built-in connections to the most popular databases, cloud data warehouses and file types, including Google Sheets. Analysts can use the integrated SQL IDE with AI Assist for faster SQL writing using natural language commands. The workflow is optimised to deliver AI-ready data that can be used with AI agents, like Spotter, enabling business decisions.

“Analyst Studio is a fully integrated suite of analytical tools that turns data teams into a force multiplier for business growth with connected tools,” said Karkhanis and team. “Providing choices to analyse and explore in SQL, Python and R, Analyst Studio empowers analysts and data scientists with the flexibility to use their preferred tools to do their most valuable work. Analysts can iteratively explore data and perform advanced analyses in the native cloud-based SQL editor, or in industry-grade Python & R Notebooks, which can also serve as data science workbenches and automate workflows in third-party applications. Analysts can also visually explore and profile data within a grammar-of-graphics workflow that allows endless choice in visualizations.”

External connections

This technology from ThoughtSpot integrates with cloud data platforms including Snowflake, Databricks and Google BigQuery for iterative, exploratory analysis and business logic development using tools like schema browsing, a native SQL IDE with support for DDL operations, and built-in visual data exploration and data profiling tools. 

Beyond that, data teams can develop and orchestrate complex data workflows that access the full power of their data ecosystem, for example by accessing Snowpark from the built-in Python Notebook.

Despite a change at the CEO helm in September of 2024, ThoughtSpot continues its analytics ease-of-use and democracy message with extensions to its toolkits as tabled here. The company appears to be working on the next phase of its platform development by very much basing new innovations on its existing stack, but while also looking to forge extensions to the most relevant cloud hyperscaler and data services being used today. Simple, yet complicated at the back-end? The next phase may be more business user facing and be even more ad hoc than advanced, let’s see.