ThoughtSpot AI-Powered Analytics

My main use case for ThoughtSpot is to provide self-service analytics capabilities for our company and the business stakeholders. As the manager of the data analytics team, we are managing ThoughtSpot and the models so that the end users can get their answers quickly.

A quick specific example of how my team or stakeholders use ThoughtSpot day-to-day is that almost all business domains within the company use it to check the daily performance of our operations.

Regarding my main use case, we started using Spotter, the AI agent, for our account managers so that they can ask Spotter to evaluate their clients' performance and provide more action points or recommendations for their clients and also identify the risky ones.

Before ThoughtSpot, we had limited exposure of data to end users with some more static dashboards or reports. We basically achieved 80% monthly active users and 65% weekly active users in ThoughtSpot in a consistent way, so those numbers speak for themselves in terms of how the data now is embedded within the day-to-day and also more strategic operations.

Regarding how this shift to data-driven decisions has affected my team's workflow or business outcomes, the positive impact for the data analysts is that with the increase of self-service usage, the end users are able to meet their own needs. This means that the data analysts now have more time to work on more complex tasks instead of just adding columns or filters to the existing dashboards.

The best features ThoughtSpot offers are definitely the overall self-service capabilities, such as how you can easily create your own reports or answers using the search field.

On top of that, the Spotter tree is also excellent.

The search field and Spotter tree help my team or business stakeholders by allowing users to use the self-service analytics to drill down or slightly change the existing reports to find their specific needs without asking the data team to provide them the reports.

For Spotter tree, the main benefit is the ability to answer not just what questions, but also why type of questions to analyze the data and create insights.

ThoughtSpot could be improved in that the only part where ThoughtSpot is lacking compared to the more traditional players such as Tableau and Power BI is regarding the options with the visualizations and the customizations abilities for those visualizations. They provide Muse Studio now, which means that we can also build any custom visual that we want.

However, it is not really the same as having the native visualization abilities with the added features, so that is the part where ThoughtSpot could improve.

Regarding needed improvements from the visualization and customization options, I cannot think of anything else.

I have been using ThoughtSpot for close to three years.

ThoughtSpot is stable.

Regarding ThoughtSpot's scalability, I would say it is more about the scalability of the warehouse that you are working with, so there is nothing worrying about the scalability on ThoughtSpot's end.

My experience with customer support is that it is great. Whenever I have any question or problem, I can create a ticket, which gets answered quickly and also resolved in a short time. The account manager or the overall team is very helpful with everything. The solutions engineers, even after the POC period, just kept supporting us, even though it was not really their responsibility to keep supporting us.

When I joined the company three years ago, they were using Preset, and we started with ThoughtSpot POC on my second week. From what I know with my limited experience with Preset, it was very hard for the analyst to create the reports. It also was not the greatest experience for the end users, and we had user-based licensing, so the adoption was also low. There was also not really a capability of tracking the adoption, and ThoughtSpot was a huge improvement over Preset.

I would describe the integration process between ThoughtSpot and our existing data sources as pretty consistent and native. We are using Snowflake, and the connection is seamless.

I have not seen a return on investment as I do not have any metrics available, but it has really helped the company's overall very strong growth numbers for the past two years.

My experience with pricing, setup cost, and licensing is that I was not part of the pricing discussions. One thing I can provide is that they are being very flexible with the limits, and they also do not necessarily provide user-based licensing, which I think is a big positive on their side.

The advice I would give to others looking into using ThoughtSpot is that I think it is a really great tool, especially if they are considering more self-service analytics capabilities around the organization. If they want to also start utilizing AI without developing something internally, Spotter is very easy to build, optimize, and start using on top of the existing models. I would rate this product a 9 out of 10.

My main use case for ThoughtSpot involves building a self-service ThoughtSpot environment for our clients, focusing on both on-premise and cloud solutions. We emphasize ThoughtSpot as a self-service tool for our clients so they can discover their data.

A specific example of how a client uses ThoughtSpot for self-service is that it has an AI-driven natural language processing chatbot, which allows users to ask questions and receive visualizations based on their data. We enabled this functionality for our client, allowing users to directly interact with the data models we published to ThoughtSpot and create their own visualizations by speaking to ThoughtSpot in natural language.

Regarding my main use case, I believe it is very important that a central data team approves and develops all data models because these data models will be used by the end users. It is crucial for the data team to approve these data models, which can be organized by domains, making it easier for users to select the data model they want to use. For example, if they are in the HR department, they simply select the HR data model and can ask their questions.

The best feature that ThoughtSpot offers is its AI chatbot, which creates visualizations and data explorations by talking with the chatbot. You can inquire about product sales from last year, and ThoughtSpot analyzes your questions, words, and sentences to create a visualization based on what you said. This functionality is excellent because you do not need to be data savvy or have extensive knowledge about data analysis; you simply ask questions as you would to a colleague, and ThoughtSpot generates all the visualizations you need, which you can customize later.

ThoughtSpot has positively impacted my organization as it is very user-friendly, which is essential since most clients desire something easy to use and may not always have a trained data team. ThoughtSpot saves time by allowing users to create their visualizations manually because it can generate them automatically. The tool is very interactive, enabling users to build a dashboard in just ten to twenty minutes by asking questions, and ThoughtSpot provides the necessary visualizations to assemble into a dashboard. Everything about it is very user-friendly, which clients appreciate.

Productivity has increased significantly and is very measurable. For example, some of our clients used other tools previously that required everything to be built manually, but with ThoughtSpot, we could replace all of the dashboards in less than a month, which would have taken up to a year before.

ThoughtSpot needs to improve because all visualization tools such as Power BI and other BI tools are also using AI to make dashboard creation more efficient. The strength of ThoughtSpot lies in its user-friendliness as a visualization tool and data discovery tool, allowing users without technical knowledge to utilize it. I think if they focus on making it more accessible, user-friendly, and available for any user without a data background, that would be perfect.

On support and integration, in general, it is good, but it would be beneficial if they developed more connections to accommodate the various options for storing data, whether on-premises or in the cloud. There are always new connections becoming available every day, week, and year, which is an area that ThoughtSpot needs to improve.

I have been using ThoughtSpot for the last three to four years.

ThoughtSpot is stable.

In terms of scalability, I think ThoughtSpot is great and we have never had any problems with scalability.

Customer support at ThoughtSpot is excellent; they respond very quickly when we reach out, so I am very happy with them. I would rate the customer support a nine out of ten because they are always very responsive.

We used Power BI before switching to ThoughtSpot.

Overall, I find the pricing and setup cost of ThoughtSpot to be great considering what it can do for your organization, so I would rate it very positively.

ThoughtSpot is not installed in our environment; we are installing it for clients and providing both cloud and on-premises solutions.

I do not have specific metrics on return on investment, but generally, it is very positive since we need fewer employees for ThoughtSpot because the AI contributes significantly.

Before choosing ThoughtSpot, I evaluated other options, primarily Power BI and Qlik Sense.

For others looking into using ThoughtSpot, I think it is a great tool and very user-friendly, so I recommend it. I give this product an overall rating of eight out of ten.