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The Future of AI Workflows: Integrating Semantic Layers for Smarter Data Insights

By Mr. Pratik Jain, Senior Technical Architect, Kyvos Insights

How Semantic Models Improve AI Workflows?

AI innovation keeps on evolving in the context of enhancing the business processes for different industry verticals. In this regard, many organizations are contemplating the use of conversational business intelligence (BI) and AI to streamline business processes and increase efficiency . 

This technology allows users to combine various contexts and other forms of specifications to get the desired output. Also, users can input queries in their own words using natural language processing (NLP), which makes queries much quicker and easier. 

The opportunities of conversational BI are real, but the uptake of this technology has been poor because of two main issues: accuracy and speed. 

Obstacles in Using Conversational Business Intelligence 

Accuracy: There is a great possibility that conversational BI will misinterpret user input and subsequent queries because it has no actual context, which is why companies are reluctant to use it on a larger scale. 

Speed: There are instances where conversational BI fails to respond in a timely manner, making decision-making a tedious process. 

Semantic Models in Conversational BI 

Semantic layer provides a unified business view of the data where information is integrated across different departments.  It helps map data definitions from different data sources in a consistent, context-aware model that's understood easily by both humans and machines. Here are some benefits of semantic models that resolve the speed and accuracy challenges of conversational BI.  

Clean Metadata for Context 

Semantic models define, organize and enrich metadata taken from data assets such as tables and columns, providing context and meanings to large language models (LLMs). This process lets the LLMs produce accurate and meaningful SQL queries, making them suitable for retrieval augmented generation (RAG) systems. RAG is an architecture for improving the performance of an artificial intelligence (AI) model by linking it to external knowledge stores. The system empowers LLMs to generate more relevant and high-quality responses. Without well-organized metadata, AI models cannot yield intelligible results.

Simplifying Complex Data Modelling 

Data modeling is a fundamentally complex process, particularly when organizations have to manage hundreds of interconnected tables in modern data warehouses. Semantic models clearly define terms and rules, which, in turn, facilitates LLMs to infer relationships and apply the right joins between tables, charts and other datasets. This reduces uncertainty, increases speed and fine-tunes results.  

Accuracy of KPI Calculations 

Business organizations need key performance indicator (KPI) calculations and parameters constantly, but when these calculations are carried out at the database or data warehouse level, it affects the speed and authenticity of results. A universal semantic model, however, acts as a single source of truth presenting consistent definitions of KPIs, such as monthly sales or customer retention rates. LLMs can also use these definitions as a base for answering BI queries more accurately. 

Improved Security and Governance 

The need for data security and governance has made semantic layers more indispensable than ever before. The layer enriches an AI-enabled BI tool with knowledge related to regulations or security protocols. This ensures that only authorized users get access to specific data sources. Organizations can centralize governance and security policies within the semantic model and enable role-based access controls at all levels of their data architecture.   

Explainable AI and Compliances 

Explainable AI (XAI) assists in meeting compliance requirements, and organizations need it to stay ahead. However, it needs a semantic layer to ensure transparency in query execution. But how? Semantic models abstract away complex datasets and structures them into domain-specific terminology that stakeholders can understand. With clear and enriched metadata, it ensures traceability as it joins back to the fundamental data and logic.  

Using this same capability, AI systems can trace the lineage of query, explaining which dataset, filter or join was used to generate the response.   

Reliable Mapping of Query Filters 

One of the most challenging aspects of NLQ processing is matching values in natural language queries to the right database columns for filtering. But a semantic layer abstracts technical terms into meaningful, context-enriched and user-friendly language.  

For example, a natural language query is "Provide sales details of Google". Here ‘Google' is one of the values in one of the columns. But, this query lacks the context about the said column, whether it is about a ‘product'-the search engine-or Google as the ‘company'. To map or accurately answer this, the system needs metadata mapping where it knows that the X set of values belongs to which column. Since a semantic layer is a centralized layer, it can hold that information or that additional metadata layer where value to column mapping exists.  

Conclusion 

Conversational BI with semantic layers can revolutionize data interpretation and analytics. Through methodical implementation, it has the potential to unlock exceptional levels of productivity and efficiency in business decision-making. The key is to position this technology intelligently for maximum benefit.  

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ABOUT THE AUTHOR

Pratik Jain 

Pratik Jain is the Senior Technical Architect at Kyvos Insights, a data analytics and business intelligence company. He has been part of the company since its inception and has overall 18 years of experience in building highly secured and scalable distributed Business Intelligence Products. He is also responsible for the ongoing development and improvement of the platform, managing the engineering team, and strategizing on the product roadmap. He is also head of the user experience aspects of the product and owns the frontend part of multiple applications of Kyvos Insights. His passion for technology and his commitment to excellence have been instrumental in Kyvos Insights’ success in the analytics industry.

Published Tuesday, February 04, 2025 7:30 AM by David Marshall
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