Semantic Agents: Bridging the Gap between Data Silos and Actionable Insights

This post was written by Dr. Arun Marar, PhD, MS, Dr. Prashanth Southekal, PhD, MBA, and Tobias Zwingmann, MS. It was originally published on the website D2A2.

Most business enterprises have data silos due to a combination of organizational, technical, and cultural factors. Data silos typically result when different business units or teams store their data separately in various IT applications without sharing it across the organization. This data fragmentation ultimately hinders the organization’s ability to derive actionable insights for measuring and improving business performance.

Addressing data silos typically requires a cultural shift, leadership buy-in, and investments in technology for cross-departmental data sharing and collaboration. From a technical perspective, breaking down data silos involves implementing unified data platforms with consistent data management and data governance standards and procedures across the company. In this regard, one proven solution to address the data silos problem from an analytics perspective is implementing the semantic layer. A semantic layer addresses the problem of data silos by providing a unified “one source of truth” model and business-friendly abstraction over disparate data sources.

Semantic Layer

The semantic layer serves as a bridge that connects diverse and often siloed data sources, translating them into a unified “one source of truth” model in business friendly terms. This makes it easier to generate dashboards and reports for the business users to conduct analysis, derive insights, and make informed decisions to measure and improve business performance.

For example, if a retail company has customer data in a CRM, sales data in an ERP, and website analytics in Google Analytics, the semantic layer allows business users to query “Customer Lifetime Value” (CLV) across all sources without needing to access multiple databases separately. The semantic layer, which usually maps various data sources in the canonical system like the data warehouse (DWH) or data lake in a typical IT architecture is as shown below.

However, achieving this unified “one source of truth” model is not without its challenges. It typically involves a mix of data modeling, transformation logic, and mapping metadata definitions. Often, the process of mapping fields from different databases and IT systems to a single, consistent metric or field in the semantic layer is done manually. However, this manual mapping often leads to inconsistencies, errors, and inefficiencies, apart from being very time consuming.

Research by D2A2, found that the average implementation time of a semantic layer in a business enterprise is about 1200 hours. In addition, if appropriate access controls are not implemented in the semantic layer, there is also a risk of exposing sensitive data to unauthorized users. Overall, implementing the semantic layer can be expensive and risky.

What are Semantic Agents?

What’s the solution to implement the semantic layer quickly with minimized risks? Today, enterprises are increasingly considering AI agents that can operate autonomously to perform various tasks.

An AI agent is a software entity that perceives its environment, makes decisions, and takes actions to achieve specific goals, using a combination of machine learning, natural language processing, and automation features. According to Gartner, “agentic AI” describes these agents as “digital workers” empowered to plan and act independently on an organization’s behalf. Unlike traditional hard-coded bots, AI agents can perceive context, reason over information, and adapt their behavior dynamically to achieve their goals (source: Gartner).

In this backdrop, Semantic Agents combine semantic layers with AI agents to allow organizations unlock context-aware intelligence that neither could achieve alone and streamline the deployment of the semantic layer.

A Semantic Agent is an intelligent system that leverages semantic understanding and reasoning to interpret the context and meaning behind data. For example, if an enterprise has 17 different ways to refer to a “customer” across various databases, an AI agent can be able to identify these references and suggest updates to the semantic layer for harmonizing the concepts.

How do Semantic Agents work?

The building blocks of Semantic Agents combine AI-driven technologies and semantic principles to create a cohesive and an intelligent data ecosystem. Concretely, there are 4 key building blocks that allow organisations to build robust, scalable Semantic Agents:

Semantic Agents – Key Building Blocks

1. Knowledge Representation
   Semantic agents are designed to focus on the meaning, context, and relationships between concepts, allowing them to interact with users across diverse systems using techniques like semantic parsing, reasoning, and contextual embeddings for meaningful and relevant data. This includes:
   • Ontologies that define concepts and relationships in a specific domain of interest.
   • Semantic Networks or Knowledge Graphs that represent concepts and edges to represent the relationships between entities.
   • Frames that organize knowledge into predefined categories, describing the various aspects of an entity, including its properties, relationships, and actions – like a “template” that makes reasoning about the world easier and more structured.
2. Semantic Modeling Language
   Underpinning the workings of a semantic agent is the universal modeling language the Semantic Modeling Language (SML). SML defines data relationships, thereby improving searchability, and supporting intuitive queries. Examples of SML include OWL, RDF, and SPARQL.
3. Automated Semantic Mapping
   Reduces the need for manual data mapping by intelligently aligning data across sources using AI/ML techniques. ASM uses schema matching, ontology alignment, NLP, and AI to detect patterns, resolve ambiguities, and ensure consistency, allowing better integration, cross-source querying, and improved analytics across diverse datasets.
4. Scalability and Performance Optimization
   Semantic agents leverage distributed processing to handle vast datasets, use incremental reasoning to update knowledge dynamically, and implement efficient indexing and caching for faster retrieval. Parallel query execution and load balancing prevent bottlenecks, while adaptive learning continuously refines mappings and ontologies. By integrating these techniques, semantic agents minimize computational overhead, enhance performance, and enable seamless data processing across complex, high-volume environments.

Once the Semantic Agent is built, the Natural Language Queries (NLQ) enhance the semantic layer by enabling users to interact with data using plain language instead of complex query languages like SQL. It improves user accessibility, allowing non-technical users to retrieve insights effortlessly. NLQ leverages semantic understanding to map queries to structured data, ensuring context awareness by recognizing intent, synonyms, and relationships. It automates query translation, optimizing database queries for performance. With caching and indexing, it enhances response times and supports faster decision-making.

Adopting the Semantic Layer With the Help of Semantic Agents in a Retail Chain

Data, analytics, and AI play a crucial role in enhancing customer experience, optimizing operations, and increasing profitability in the retail industry. With the rise of e-commerce, omnichannel shopping, and AI-driven insights, retailers are leveraging data more than ever to stay competitive. However most retail firms store data separately in various IT systems, preventing seamless data sharing and integration.

Let us take an example where a retail chain is struggling with data silos across CRM, ERP, and data warehouse systems, leading to substantial business problems:

• Store managers would be using different definitions of “best-selling products”
• Marketing teams would struggle to accurately determine which campaigns drive in-store purchases
• Finance and operations might have conflicting inventory valuation reports
• Executive dashboards could show inconsistent numbers depending on the data source

How can these issues be resolved using a Semantic Layer and Semantic Agents in particular?

Rather than attempting to automate the entire semantic layer creation, the retail company would implement a Semantic Agent solution with clearly defined roles between humans and AI:

Initial Discovery Phase:

• The Semantic Agent would analyze data structures across all systems and could identify patterns such as multiple product identifiers, customer definition variations, and different revenue calculation methods.
• Data specialists would review these findings, determining which variations represent legitimate business distinctions and which are redundancies.
• Business stakeholders would provide essential context about why certain departments need different metrics.

Collaborative Mapping:

• The Semantic Agent would suggest initial mappings between similar fields across systems.
• Data engineers would validate each mapping, potentially rejecting 25-30% of the agent’s suggestions that don’t align with business requirements.
• The team would create standardized definitions for key metrics like “active customer,” “product margin,” and “inventory turnover.”

Iterative Refinement:

• The agent would learn from corrections and improve its suggestions over time.
• Data specialists would maintain control over all final implementations, using the agent as an assistant rather than an autonomous system.
• Business users would be involved in validation workshops to ensure the semantic model matches their needs.

Governance Implementation:

• Human experts would establish data access policies and controls.
• The Semantic Agent would flag potential sensitive data exposures, but the security team would make all final decisions.
• Regular audits and updates would be scheduled with both automated scanning and human review.

This collaborative approach leads to significantly improved results. While specific numbers will vary greatly depending on the nature of your business, here are some realistic estimates based on typical examples of retail businesses:

• Implementation Efficiency: The semantic layer might be completed in 400-500 hours instead of the industry average of 1200 hours, representing a 60% time savings while maintaining quality.
• Improved Decision Quality: Store managers could experience 25-30% faster decision-making with confident access to consistent data.
• Cost Savings: The company might realize 2-3 annual FTE-equivalent savings from reduced need for manual data reconciliation and report generation.
• Revenue Impact: A potential 5-10% increase in cross-sell/up-sell revenue due to better customer insights across channels.

The strong augmentation would in addition prevent the most typical pitfalls of fully automated approaches.

• Cultural adoption would be higher because teams would be involved in the process
• Business-critical nuances in data definitions would be preserved when needed
• Security and compliance requirements would be thoroughly addressed with human oversight
• Sustainable human-AI partnerships would allow the semantic layer to continue to evolve with emerging business needs.

By deploying Semantic Agents over time, the company would identify areas where higher levels of automation make sense and prioritize those areas as it scales its initiatives – iterating responsibly and profitably toward an even more effective approach to AI adoption.

Conclusion

By seamlessly integrating the semantic layer with AI, Semantic Agents and augmenting them with human expert skills can dramatically accelerate and simplify what has traditionally been a time-consuming, expensive process. Organizations can implement semantic layers in a fraction of the time, at significantly reduced cost, while maintaining high quality and business relevance.

Semantic Agents identify patterns, suggest mappings, and learn from feedback, making the entire process more accessible to enterprises of all sizes. With capabilities like automated discovery, assisted mapping, and intelligent governance support, Semantic Agents transform the way organizations add more context and relationships to their disparate data sources. The result is a faster path to unified, business-friendly data that empowers better decision-making and breaks down data silos, ultimately driving measurable business value through improved analytics capabilities.

Key Takeaways

1. Accelerated Deployment: Semantic Agents automate the tedious mapping of disparate data sources, cutting implementation time by up to 60% compared to manual efforts, and enabling a faster rollout of a unified semantic layer.
2. Human-AI Collaboration: By generating initial mapping suggestions that are iteratively refined through human oversight, Semantic Agents streamline the rollout process while ensuring that business-critical nuances are maintained.
3. Risk Mitigation: Integrating automated governance checks during deployment, Semantic Agents proactively flag potential security and compliance risks, allowing for quicker, safer semantic layer implementations.
4. Scalable and Adaptive Architecture: Leveraging distributed processing and incremental reasoning, Semantic Agents ensure that the semantic layer can be adapted in a scalable manner that continues to evolve with growing data volumes and business needs.

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Contributors: The Analyst Team

Arun Marar, Ph.D.
Arun Marar, Ph.D. is an expert at implementing AI/ML models utilizing a wide range of technologies such as supervised and unsupervised learning, text analytics and natural language processing and deep learning. Arun has consulted with clients all around the world across domains such as Banking and Financial Services, Retail, Healthcare and Transportation Logistics. Arun is also an expert in optimization, and this has helped him aid companies in moving up the value chain of analytics from predictive analytics to prescriptive analytics.

Prashanth Southekal, PhD, MBA
Prashanth Southekal, PhD, MBA, ICD.D is a Consultant, Author, and Educator. He has worked and consulted for over 80 organizations including P&G, GE, Shell, Apple, and SAP. He is the author of three books — “Data for Business Performance”, “Analytics Best Practices” and “Data Quality”. His second book, ANALYTICS BEST PRACTICES was ranked #1 analytics books of all time in May 2022 by BookAuthority. Apart from his consulting and advisory pursuits, he has trained over 4,500 professionals worldwide in Data and Analytics.

Tobias Zwingmann, MSc.
Tobias Zwingmann is a Consultant, Author, and Educator and is on a mission to help companies implement machine learning and AI faster while delivering meaningful business value. He is the author of 2 books “AI-Powered Business Intelligence” and “Augmented Analytics”. He brings more than 15 years of professional experience working in a corporate setting where his responsibilities included building data science use cases and developing an enterprise-wide data strategy.