ThoughtSpot AI-Powered Analytics - Enterprise Edition

Clients use ThoughtSpot  because of its search facility. It is easy for searching and does not require analysts to provide reports. There is no drill-down path on the data, so users can drill data anywhere and get answers. It has agentic AI built in with natural language processing.

It is very self-service and includes natural language search capability.

I think it is a game changer this year. They have implemented something called Spotter, which provides the end user with a Google-style analytics solution to deliver quick insights without extensive training or requiring an analyst for reports.

They work with Microsoft and AWS .

I do not think there is anything that needs improvement at the moment. It is being improved continuously. They are connecting to many AI-type tools. Their pricing has recently changed to be more price-conscious.

From the implementation perspective, it could possibly be a bit more streamlined.

Eight years.

It is absolutely stable.

ThoughtSpot  is absolutely a scalable solution.

Technical support of ThoughtSpot rates at an eight. Because we manage our customers, we solve any problems that come to us in the first instance, and then we triage the calls. If anything needs to go to ThoughtSpot, then it goes over to them.

Positive

The initial setup is medium in complexity. As an implementer of ThoughtSpot, modeling capabilities are required to get ThoughtSpot operational. We build some live boards for the customer so they can get started with it. Then we hand it over to the customer to continue and administer the platform as they see fit.

The return on investment is demonstrated through time savings and speed to insights.

I have not personally encountered any issues as I do not perform the implementations, but there could be some. I speak from a customer and sales perspective.

On a scale of 1-10, this solution rates a 10.

ThoughtSpot  is an AI-powered analytics platform that enables users to search, analyze, and visualize data easily. Here  are some primary use cases for ThoughtSpot :

Primary Use Cases

1. *Self-Service Analytics*: ThoughtSpot allows business users to ask questions and get answers from data without relying on IT or data scientists.

2. *Ad-hoc Analysis*: Users can perform ad-hoc analysis on data to answer specific business questions.

3. *Data Exploration*: ThoughtSpot enables users to explore data, identify trends, and gain insights.

4. *Data Visualization *: Users can create interactive dashboards and reports to visualize data and share insights with others.

Environment

ThoughtSpot is typically used in environments where data-driven decision-making is critical, such as:

1. *Business Intelligence *: ThoughtSpot is used in business intelligence to provide users with easy access to data insights.

2. *Data Analytics*: ThoughtSpot is used in data analytics to enable users to analyze and visualize data.

3. *Enterprise Data Warehousing*: ThoughtSpot can be integrated with enterprise data warehouses to provide users with access to large datasets.

Overall, ThoughtSpot is a powerful analytics platform that enables users to gain insights from data and make informed decisions.

ThoughtSpot has significantly improved various organizations by providing self-service analytics, enabling employees to independently explore data and generate insights. Here  are some key benefits:

- *Increased Efficiency*: ThoughtSpot reduces report requests and saves time for data analysts, allowing them to focus on high-level strategic tasks. For instance, Matillion reduced report requests by 80% and saved over £75,000 annually.

- *Data-Driven Decision Making*: ThoughtSpot empowers businesses to make informed decisions by providing real-time data insights. Austin Capital Bank implemented ThoughtSpot to enable fast, self-service analytics using natural language queries, cutting data turnaround times to under 24 hours.

- *Improved Productivity*: ThoughtSpot's platform allows users to ask questions, analyze data, and build reports in seconds, without requiring extensive training or technical knowledge. This leads to improved productivity, enabling data analysts and developers to complete projects faster.

- *Enhanced Collaboration*: ThoughtSpot facilitates collaboration by providing a centralized analytics hub for clients, employees, vendors, and partners. Its seamless integration with modern data stacks ensures that businesses can efficiently analyze and derive insights from their data.

- *Cost Savings*: ThoughtSpot's automation capabilities and reduced reliance on IT teams lead to cost savings. For example, Odido estimated saving around €1 million annually by freeing up data analyst time and reducing outsourced IT costs

.

The features I find best about ThoughtSpot include the ability to explore and comprehend data through smart data discovery. When dealing with answers in ThoughtSpot, it provided a smart function to search, as well as accessing interactive data visualizations and developing bespoke data applications to address unique needs.

I also use the natural language processing feature in ThoughtSpot, which helps reduce bottlenecks, improves decision-making, and elevates different digital experiences. I have used NLP during data analysis in ThoughtSpot and when interacting with chatbots and virtual assistants.

The support team of ThoughtSpot is excellent. When I dealt with issues regarding the creation of answers in Liveboards, I used the ThoughtSpot chatbot, which provided solutions for additional calculations and TML for Liveboards.

One of the challenges I faced with ThoughtSpot is that when the team provides the TML language of every object, such as when I was creating a worksheet, it was not giving full information about which area to debug for the TML side. It also has a steep learning curve along with some UI complexities.

Regarding ThoughtSpot's pricing, I didn't have to pay anything since the public version provided all necessary features for me. However, I think when considering the user-based plan, which is around $1000 per month, it is a bit costly and could be cheaper.

I have been using ThoughtSpot for the last three years.

I am a user of ThoughtSpot and I'm using ThoughtSpot in my current project.

After using ThoughtSpot, I find it very good compared to Tableau . I rate ThoughtSpot nine out of ten.

I would definitely recommend ThoughtSpot to others if there's a requirement for using this tool.

I am currently simultaneously working with Power BI, ThoughtSpot , Snowflake , and Postgres as the products and solutions. ThoughtSpot  is deployed on AWS .

It was implemented as a POC model because we previously used Oracle-based business intelligence, such as OAC . Due to cost considerations, we moved to Power BI, which is working fine currently. Because of recent advancements, ThoughtSpot is excellent at processing huge amounts of data. Where Power BI lacks processing compute power, we conducted a POC to check the accuracy and design capabilities of the dashboards. We worked on ThoughtSpot, tried to replicate existing reports, reconcile them, and then implemented it for one project.

The best feature is usability from the customer and client perspective. Usually, we would need a dedicated developer for creating reports in Power BI. However, ThoughtSpot is really easy to work with. The connectivity is easy with other databases, and the amount of data it consumes and the generation power is impressive. In Power BI, refreshing the same amount of data takes almost half an hour, while in ThoughtSpot it takes approximately 10-15 seconds.

Once all the datasets and configurations are set up, the usability in ThoughtSpot is excellent. Users can utilize NLQ and get quality results. The entire dashboard functionality is superior compared to Power BI's Q&A and recently introduced Co-pilot, as ThoughtSpot understands the data better.

We have seen areas where improvements are needed for predictions with natural language search. While we can derive normal Gaussian curve predictions, there are additional aspects that could be enhanced.

AI-driven insights in ThoughtSpot have not been implemented yet as our organization has not approved releasing the data to AI. This process is still pending approval.

The prediction model needs improvement. While NLQ is working perfectly fine and the data understanding capabilities of ThoughtSpot are good, there is room for improvement. Compared to Oracle's DV or Microsoft Power BI , it is more advanced. However, when implementing it for finance, general ledger, profit and loss, and balance sheet applications, we faced some challenges as these areas require very accurate data.

I have been using ThoughtSpot for approximately six months.

There were no challenges with the initial setup of ThoughtSpot. It was straightforward.

We have not experienced any glitches or downtimes with ThoughtSpot. After delivery, we haven't received any negative feedback from our brokers.

We haven't attempted to scale it up because it's being used by a small group. Scaling might be necessary when we implement it for bigger projects.

We have not yet required technical support.

Neutral

The initial setup of ThoughtSpot was straightforward. It would be beneficial if ThoughtSpot had an integration tool for migration purposes. For example, when moving from Oracle-based analytics to ThoughtSpot or from Power BI to ThoughtSpot, a tool that could pull in all connections, understand the design in Power BI, and replicate it in ThoughtSpot would be valuable. This would eliminate most migration challenges, make the process smoother, and require less manpower.

The initial setup of ThoughtSpot was straightforward.

I handled the deployment myself and purchased ThoughtSpot from the vendor.

Compared to Power BI, ThoughtSpot is more expensive. This is why we haven't implemented it for most projects currently. Based on our implementation in one project, we are trying to raise more funding to expand its use.

The main difference is the heavy reliance on developers for visualization development and data modeling in other technologies compared to ThoughtSpot. Additionally, the in-memory computation power is significantly lower in competitor products. Oracle DV has recently gained attention due to its predictive model capability and flexible design options.

ThoughtSpot is quite intuitive. We haven't used it for workflows yet as most workflows and pipelines are developed in Snowflake  using Snowpipe and PL/SQLs. The main benefit is the reduced dependency for generating reports.

Two major benefits are the reduced reliance on developers to get accurate data and the impressive data processing capabilities.

The organization-level setup is straightforward, and the integration with AD server is seamless. The SSO  configurations work smoothly.

It would be beneficial to have more variety of visuals included in ThoughtSpot, though the current offerings perform adequately.

The in-memory computing and NLQ capabilities are significant advantages compared to other technologies in the market.

On a scale of 1-10, I rate ThoughtSpot an 8.