5 Reasons your business needs an OmniAI Layer today
Why invest in an OmniAI Layer?
The idea of a semantic layer has been around since the 1990s. However, the rise of self-service analytics in the 2000s led to fragmented business definitions spread across various data consumers. To address this issue, some vendors have introduced the concept of an OmniAI Layer.
An OmniAI Layer is an independent yet interoperable component of the modern data stack that sits between data sources and consumers. It ensures that all data endpoints—whether they are business intelligence (BI) tools, embedded analytics, AI agents, or chatbots—work with consistent semantics and underlying data, leading to reliable insights and more informed business decisions. Although it can't solve all business challenges, it facilitates the use of complete, consistent data insights to navigate uncertainties. Having large volumes of data doesn’t inherently create value; it’s the consistency and reliability of data that enable informed decision-making. Furthermore, an OmniAI Layer can function as an AI data analyst, providing advanced insights and enabling users to chat with their company data more effectively.
Here are five ways an OmniAI Layer can address issues related to data organization, management, and accessibility:
1. Unify fragmented business logic across the modern data stack
The democratization of data has led to duplicated metric definitions and fragmented business logic across organizations. An OmniAI Layer consolidates business logic into a single source of truth, providing consistent and accurate data across all data experiences—BI platforms, embedded analytics, AI agents, and chatbots.
2. Connect data across BI tools and enterprise software systems
According to forrester research, 79% of data teams struggle with real-time, insight-driven actions due to siloed data. An OmniAI Layer bridges these silos, integrating data from various tools and sources to power data applications, regardless of the tools used for visualization or data storage.
3. Centralize and enforce fine-grained data access security controls for easier governance
By centralizing data access controls, an OmniAI Layer simplifies data governance for internal and external users. It automatically rewrites queries and incorporates the appropriate security context, ensuring that only authorized users can access specific data.
4. Optimize query performance and control cloud data warehouse costs
Through performance insights provided by the OmniAI Layer, organizations can identify redundant queries and opportunities for caching or pre-aggregating query results. This optimization helps reduce data warehouse compute requirements, leading to significant cost savings.
5. Enhance AI capabilities with contextual data
As AI applications become increasingly prevalent, they require contextual data to deliver accurate results. An OmniAI Layer provides the necessary business logic to large language models (LLMs), preventing inaccuracies and enabling AI chatbots to effectively answer questions based on a company’s data. This capability is crucial for AI assistants for BI, which rely on accurate, contextual data to provide valuable insights and facilitate data-driven decision-making.
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A final word
For organizations to make actionable decisions, they need uniform and accurate data. An OmniAI Layer acts as the connective tissue that links crucial knowledge, information, and data assets. It facilitates collaboration through shared data definitions, ensures data governance and security, integrates fragmented data silos, optimizes cloud expenses, and leverages AI effectively. In essence, an OmniAI Layer translates into a significant competitive advantage—an indispensable asset for any forward-thinking organization.
