Organizations today have huge volumes of data across various cloud and on-premises systems which keep growing by the second. To derive value from this data, organizations must query the data regularly and share insights with relevant teams and departments. Automating this process using natural language processing (NLP) and artificial intelligence and machine learning (AI/ML) enables line-of-business personnel to query the data faster, generate reports themselves without depending on IT, and make quick decisions. Some organizations have started using NLP in self-service analytics to quickly identify patterns and simplify data visualization. Our Analytics and Data Benchmark Research finds that about 81% of organizations expect to use natural language search for analytics to make timely and informed decisions.
ThoughtSpot is a cloud data analytics company that offers natural language search, AI and business intelligence capabilities. It enables business personnel to conduct self-service data analysis by using natural language query (NLQ). Its AI/ML can analyze big data from various cloud data warehouses including Amazon Redshift, Snowflake, Google Big Query, Azure Synapse Analytics, Teradata, SAP HANA and Databricks.
ThoughtSpot recently raised $100 million in its Series F funding, raising the company valuation to $4.2 billion. The company aims to invest in its cloud analytics platform and to accelerate growth in the cloud ecosystem by strengthening its analytics capabilities. The company also announced integrations and partnerships with leading players like Snowflake, Databricks, Amazon Web Services, Microsoft Azure, Google BigQuery, DataRobot, dbt Labs, Dremio and Starburst, among others.
Organizations also need to ensure that processes, applications and data can be integrated across cloud and on-premises systems and are easily accessible. Our research shows that accessing and integrating data is still the most time-consuming part of the data preparation process.
Earlier this year, ThoughtSpot acquired Diyotta, a serverless data integration platform, to expand integrations with data platforms, AI/ML services and data applications built by web developers. It also announced the release of ThoughtSpot Data Workspace which enables analytics engineers, analysts and developers to connect to various cloud data platforms, model their data and run live search queries directly on these platforms.
ThoughtSpot SpotApps is its integrated scriptable application building functionality with pre-built templates to support third party ISVs including ServiceNow and Snowflake. It also allows personnel to migrate data models and content between different environments, such as development and production.
Organizations are moving their IT infrastructures to the cloud, adopting a cloud-native and AI-powered approach to cloud analytics to accelerate time to value, drive growth and enhance scalability. Cloud analytics can rapidly increase the ease, accessibility and capability of performing data analysis on big data sets. And NLP can simplify the data exploration, enabling personnel to find answers themselves.
ThoughtSpot SpotIQ is its AI-analytics engine that can generate insights on search results, visualizations or a data set. It claims it can query the data for billions of data points and identify trends, correlations and outliers. SpotIQ works on ThoughtSpot's built-in DataRank ML algorithm and reinforcement learning to find related data and get better with use. Line-of-business personnel can then enhance insights with feedback to ensure relevant machine-discovered insights.
ThoughtSpot Everywhere is its low-code embedded analytics platform that allows developers and product leaders to build interactive data applications and incorporate services, including search and AI-driven analytics, directly into applications, products and services. ThoughtSpot Everywhere also offers modeling flexibility via ThoughtSpot Modeling Language (TML).
While ThoughtSpot, through its application of NLP, has made it easier for line-of-business individuals to access data, there are some areas for potential improvement. The modeling process could be streamlined to make the setup process easier. In addition, the search constructs could provide more flexibility in the phrasing it supports.
I recommend that organizations looking to provide self-service analytics to a broader portion of their workforce should examine the capabilities of ThoughtSpot. Its intelligent query generation using AI and NLP enables non-technical personnel to search across complex schemas more easily. And it can integrate with various cloud platforms to analyze data directly from those systems.