Insightsoftware Unifies Semantic Layer and Governance to Aid AI
Insightsoftware has launched Simba Intelligence, a feature set designed to enable customers to develop AI applications using trusted data that includes a semantic layer unified with automatically applied governance and security policies.
Understanding Semantic Models
Semantic models are sets of common definitions of data characteristics. When applied across an enterprise’s data, they make data consistent so it can be searched, discovered, and integrated to inform models and applications, including AI chatbots and agents.
Although the concept of semantic modeling dates back to the 1970s, its popularity has increased significantly in recent years as more enterprises invest in AI development. AI tools require enormous volumes of high-quality data to be accurate and fresh, real-time data to remain current.
The Growing Necessity of Semantic Modeling
According to William McKnight, president of McKnight Consulting, AI is making semantic modeling more essential. He notes that large language models require governed, consistent data when querying massive data environments; otherwise, they risk producing inaccurate results, or “hallucinations.” He emphasized, “Without that control layer, scale will amplify any inconsistency.”
Features of Simba Intelligence
Launched on March 3, Simba Intelligence features a semantic layer to ensure data consistency and discoverability. By pairing semantic modeling with governance and security policies, Insightsoftware’s new feature set makes AI outputs transparent and trustworthy.
McKnight describes Simba Intelligence as a significant advancement for Insightsoftware, stating, “They are making it possible to conduct agentic AI on the imperfect data that every company has.”
Industry Context and Response
Insightsoftware, based in Raleigh, N.C., has evolved from an ERP specialist to a provider of data management and analytics through a series of acquisitions, including the purchase of Logi Analytics.
Many other vendors, such as DBT Labs, Cube, Google’s Looker, and ThoughtSpot, are also integrating semantic modeling capabilities into their offerings. Notably, a group of vendors including Snowflake and Salesforce formed the Open Semantic Interchange in September 2025 to develop an open-source standard for semantic data modeling.
Challenges in AI Development
Despite increased investments in AI since the launch of ChatGPT by OpenAI in November 2022, many AI initiatives do not progress past the pilot stage. Common challenges include disorganized data that complicates relevant data discovery and poor data quality that undermines AI outputs.
Innovative Responses from Data Management Vendors
In response to these challenges, several data management vendors have introduced new capabilities aimed at facilitating AI tool development. For instance:
- Databricks released Instructed Retriever, an alternative to standard retrieval-augmented generation.
- MongoDB launched a series of vector embedding and ranking models to enhance data retrieval for AI accuracy.
- Domo, Teradata, and Graphwise also introduced new features to support AI project development.
Future Plans and Market Opportunities
As it plans its data management and analytics roadmap, Insightsoftware aims to enable customers to develop and deploy AI tools in on-premises environments, which are often preferred by enterprises in highly regulated industries.
Matt Belkin, president of the Data + Analytics business unit, acknowledged the underserved market for on-premises AI development, stating, “We are investing in giving organizations full control over data residency without giving up AI capability.”
Looking ahead, Insightsoftware plans to expand feature sets like Simba Intelligence to better facilitate AI project production. Analysts suggest that expanding into areas such as data governance, data quality, and data pipeline development could attract new customers.
Conclusion
With Simba Intelligence, Insightsoftware is positioning itself as a leader in integrating semantic modeling with AI capabilities, ensuring that customers have access to trusted, business-ready data. As AI continues to evolve, the importance of a robust semantic layer becomes increasingly clear, enabling enterprises to leverage their data more effectively.
