Introduction

AI has attracted significant attention in recent years, prompting me to explore enterprise AI architectures through a multi-part blog series this year. Part 1 of this series introduced the key technical components required for implementing an enterprise AI architecture. Part 2 discussed our typical approaches and experiences in structuring unstructured content with a semantic layer. In the third installment, we will focus on leveraging structured data to power enterprise AI use cases.

Today, many organizations have developed the technical ability to capture enormous amounts of data to power improved business operations or compliance with regulatory bodies. For large organizations, this data collection process is typically decentralized so that organizations can move quickly in the face of competition and regulations. Over time, such decentralization results in increased complexities with data management, such as inconsistent data formats across various data platforms and multiple definitions for the same data concept. A common example in EK’s engagements includes reviewing customer data from different sources with variations in spelling and abbreviations (such as “Bob Smith” vs. “Robert Smith” or “123 Main St” vs. “123 Main Street”), or seeing the same business concept (such as customer or supplier) being referred to differently across various departments in an organization.  Obviously, with such extensive data quality and inconsistency issues, it is often impossible to integrate and harmonize data from the diverse underlying systems for a 360-degree view of the enterprise and enable cross-functional analysis and reporting. This is exactly the problem a semantic layer solves.  

A semantic layer is a business representation of data that offers a unified and consolidated view of data across an organization. It establishes common data definitions, metadata, categories and relationships, thereby enabling data mapping and interpretation across all organizational data assets. A semantic layer injects intelligence into structured data assets in an organization by providing standardized meaning and context to the data in a machine-readable format, which can be readily leveraged by Artificial Intelligence (AI) systems. We call this process of embedding business context into organizational data assets for effective use by AI systems knowledge intelligence (KI).  Providing a common understanding of structured data using a semantic layer will be the focus of this blog. 

How a Semantic Layer Provides Context for Structured Data 

A semantic layer provides AI with a programmatic framework to make organizational context and domain knowledge machine readable. It does so by using one or more components such as metadata, business glossary, taxonomy, ontology and knowledge graph. Specifically, it helps enterprise AI systems:

• Leverage metadata to power understanding of the operational context;
• Improve shared understanding of organizational nomenclature using business glossaries;
• Provide a mechanism to categorize and organize the same data through taxonomies and controlled vocabularies;
• Encode domain-specific business logic and rules in ontologies; and
• Enable a normalized view of siloed datasets via knowledge graphs 

Embedding Business Context into Structured Data: An Architectural Perspective

The figure below illustrates how the semantic layer components work together to enable Enterprise AI. This shows the key integration patterns via which structured data sources can be connected using a knowledge graph in the KI layer,including batch and incremental data pull using declarative and custom data mappings, as well as data virtualization.

Enterprise AI Architecture: Injecting Business Content into Structured Data using a Semantic Layer

AI models can reason and infer based on explicit knowledge encoded in the graph. This is achieved when both the knowledge or data schema (e.g. ontology) and its instantiation are represented in the knowledge graph. This representation is made possible through a custom service that allows the ontology to be synchronized with the graph (labeled as Ontology Sync with Graph in the figure) and graph construction pipelines described above.

Enterprise AI can derive additional context on linked data when taxonomies are ingested into the same graph via a custom service that allows the taxonomy to be synchronized with the graph (labeled as Taxonomy Sync with Graph in the figure). This is because taxonomies can be used to consistently organize this data and provide clear relationships between different data points. Finally, technical metadata collected from structured data sources can be connected with other semantic assets in the knowledge graph through a custom service that allows this metadata to be loaded into the graph (labeled as Metadata Load into Graph in the figure). This brings in additional context regarding data sourcing, ownership, versioning, access levels, entitlements, consuming systems and applications into a single location.

As is evident from the figure above ,a semantic layer enables data from different sources to be quickly mapped and connected using a variety of mapping techniques, thus enabling a unified, consistent, and single view of data for use in advacned analytics. In addition, by injecting business context into this unified view via semantic assets such as taxonomies, ontologies and glossaries, organizations can power AI applications ranging from semantic recommenders and knowledge panels to traditional machine learning (ML) model training and LLM-powered AI agents.

Case Studies & Enterprise Applications

In many engagements, EK has used semantic layers with structured data to power various use cases, from enterprise 360 to AI enablement. As part of enterprise AI engagements, a common issue we’ve seen is a lack of business context surrounding data. AI engineers continue to struggle to locate relevant data and ensure its suitability for specific tasks, hindering model selection and leading to suboptimal results and abandoned AI initiatives. These experiences show that raw data lacks inherent value; it becomes valuable only when contextualized for its users. Semantic layers provide this context to both AI models and AI teams, driving successful Enterprise AI endeavors.

Last year, a global retailer partnered with EK to overcome delays in retrieving store performance metrics and creating executive dashboards. Their centralized data lakehouse lacked sufficient metadata, hindering engineers from locating and understanding crucial metrics. By standardizing metadata, aligning business glossaries, and establishing taxonomy, we empowered their data visualization engineers to perform self-service analytics and rapidly create dashboards. This streamlined their insight generation without relying on source data system owners and IT teams. You can read more about how we helped this organization democratize their AI efforts using a semantic layer here.

In a separate case, EK facilitated the rapid development of AI models for a multinational financial institution by integrating business context into the company’s structured risk data through a semantic layer. The semantic layer expedited data exploration, connection, and feature extraction for the AI team, leading to the efficient implementation of enterprise AI systems like intelligent search engines, recommendation engines, and anomaly detection applications. EK also integrated AI model outputs into the risk management graph, enabling the development of proactive alerts for critical changes or potential risks, which, in turn, improved the productivity and decision-making of the risk assessment team.

Finally, the significant role a semantic layer plays in reducing data cleaning efforts and streamlining data management. Research consistently shows AI teams spend more time cleaning data than modeling it to produce valuable insights. By connecting previously siloed data using an identity graph, EK helped a large digital marketing firm gain a deeper understanding of its customer base through behavior and trend analytics. This solution resolved the discrepancy between 2 billion distinct records in their relational databases and the actual user base of 240 million.

Closing

Semantic layers effectively represent complex relationships between data objects, unlike traditional applications built for structured data. This allows them to support highly interconnected use cases like analyzing supply chains and recommendation systems. To adopt this framework, organizations must shift from an application-centric to a data-centric enterprise architecture. A semantic layer ensures that data retains its meaning and context when extracted from a relational database. In the AI era, this metadata-first framework is crucial for staying competitive. Organizations need to provide their AI systems with a consolidated, context-rich view of all transactional data for more accurate predictions. 

This article completes our discussion about the technical integration between semantic layers and enterprise AI, introduced here. In the next segment of this KI architecture blog series, we will move onto the second KI component and discuss the technical approaches for encoding expert knowledge into enterprise AI systems.

To get started with leveraging structured data, building a semantic layer, and the KI journey at your organization, contact EK!