Spotter, ThoughtSpot’s AI-powered analyst, has redefined how users interact with data by answering natural language queries in seconds. While Spotter works efficiently right out of the box, taking the time to train it can significantly enhance its accuracy. For organisations with complex data structures or unique business definitions, this step ensures Spotter delivers highly relevant, precise, and actionable insights.
In this guide, we’ll explore why training Spotter is so important, the benefits it offers, and how to fine-tune it to align perfectly with your organisation’s needs.
Why Train Spotter?
While Spotter is capable of connecting to data and providing insights immediately, training it takes things a step further. It ensures Spotter understands the specific logic, filters, and definitions unique to the organisation’s datasets. This means more accurate answers, reduced manual effort, and smoother processes.
Here are the key reasons why training Spotter is worth the investment:
- Improved Data Accuracy: Training helps Spotter learn the specific nuances of an organisation’s data. For example, if "Revenue" always requires a particular filter to exclude refunds or discounts, Spotter can be trained to apply that logic automatically. This eliminates inconsistencies and ensures users always get the correct results.
- Simplified User Experience: Without training, users may need to have a deep understanding of the data structure to phrase their queries correctly. Training Spotter removes this burden, allowing even non-technical users to get accurate insights without worrying about the underlying data intricacies.
- Streamlined Workflows: Spotter reduces the back-and-forth with analysts by providing accurate answers upfront. This not only saves time but also empowers users to access insights independently, freeing up analysts to focus on more strategic tasks.
Now that the benefits are clear, let’s move on to the actual training process and how it can be implemented effectively.
The Training Process: Step-by-Step
Training Spotter involves refining how it interprets questions and connects them to the organisation’s data. This includes setting up reference questions, defining key business terms, and adjusting tokens to ensure Spotter understands queries the way the organisation intends.
Step 1: Set Reference Questions
The first step in training Spotter is creating reference questions. These serve as examples for Spotter to learn how to interpret and respond to queries correctly.
In the Data Workspace, navigate to the “Spotter Reference Questions” section. Here, users can input sample queries that represent common questions asked by their team, such as “How many jackets were sold last year?”
When Spotter receives a question, it breaks it down into components known as tokens. For example, in the query above:
- Filters: Spotter identifies “last year” as a date filter and “jackets” as a product filter.
- Measure: It recognizes “How many” as a request for the sum of the quantity sold.
By validating and refining these tokens, Spotter learns how to handle similar questions in the future. For instance, once trained, it will consistently interpret “What’s the total jacket sales from last year?” in the same way.
This step is critical because it builds the foundation for Spotter’s ability to deliver accurate results.
Step 2: Define Business Terms
The next step is to define key business terms within the platform. This ensures Spotter understands the specific meaning of phrases and words used in the organisation.
For example, consider the phrase “sold.” In one organisation, it might refer to the sum of quantity purchased, while in another, it could mean net sales revenue. In the “Spotter Business Terms” section, these definitions can be set so Spotter automatically applies the correct logic.
Similarly, common phrases like “last year” or “this quarter” can be mapped to specific date filters, ensuring Spotter applies these filters accurately without requiring additional user input.
By defining these terms upfront, Spotter eliminates ambiguity and ensures consistency across all queries.
Step 3: Refine Tokens
Sometimes, Spotter may need further adjustments to handle more complex data structures. This is where token refinement comes in.
For instance, if a dataset contains both “order date” and “delivery date” fields, Spotter might need guidance on which one to prioritize based on the context of the query. Users can refine these tokens within Spotter by specifying preferences or resolving conflicts.
Refinement also allows adjustments to handle edge cases, such as when a dataset has overlapping field names or ambiguous relationships. By resolving these issues during training, Spotter becomes much more reliable when handling real-world queries.
Step 4: Test and Save
Once the reference questions are established, business terms are defined, and tokens are refined, the final step is to test the changes and save them.
Testing involves running sample queries to ensure Spotter responds as expected. For example, after training, running a query like “What’s the revenue for jackets last year?” should return a precise result, automatically applying all the defined logic, filters, and measures.
If any discrepancies arise, further adjustments can be made to fine-tune Spotter’s understanding. Once everything works as intended, the updates are saved, and Spotter begins applying the refined definitions to all future queries.
Summary: A Smarter Spotter for Smarter Decisions
Training Spotter is more than a technical task—it’s about making the AI smarter and more aligned with the organisation’s needs. By establishing clear reference questions, defining important business terms, and refining tokens, Spotter becomes a tool that provides consistent, precise, and actionable insights.
This not only simplifies the user experience but also empowers teams to explore data independently, driving better decisions across the organisation. With a well-trained Spotter, organisations can ensure their data works for them—not the other way around.
Need help training Spotter in ThoughtSpot?
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- Accelerators for ThoughtSpot: Check out our Dbx2Spot and Snow2Spot solutions to fast-track your ThoughtSpot implementation and make Spotter more accurate .
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- Data Readiness Expertise: We will support you through modernisation, augment with your organisation with a view to handover and partner as advisors over a longer period of time.
Whether you’re just getting started with ThoughtSpot or looking to optimise your analytics strategy, 7Dxperts is here to help you achieve your goals faster and more efficiently.
