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 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.