Overcoming Semantic Layer Limitations for AI-driven Analytics

Semantic layers are important components in data architectures, serving as middle tiers between complex database management systems and simple data access and retrieval mechanisms. The semantic layer has evolved from a simple metadata layer to a complex and powerful tool for managing and analyzing data. In the 1990s, semantic layers were used to abstract queries, and now they are core components of modern BI tools. However, with the emergence of generative AI, what role does the semantic layer now play?

This article examines the fundamental concepts underpinning semantic layers. In particular, it addresses the semantic layer's limitations for generative AI and introduces the Context Layer as a necessary extension of the semantic layer.

Summary of semantic layer key concepts

What were semantic layers designed for?

Many companies faced challenges in data handling, including the following:

• Segregated data sources are common, where data is stored in spreadsheets,  multiple databases, or even cloud applications, which leads to data segregation. 
• Inconsistent data definitions occur when the definitions and terminology used to describe the data are poorly defined. 
• Complicated access procedures develop because data is stored in various systems, making it difficult to access. 

These problems led to data prisons that prevented the formation of a holistic view and made it very challenging to obtain a single source of truth. One solution to this challenge is the use of a semantic layer. It links the underlying technical data and the business vocabulary, providing a common platform for data understanding and extraction because everyone relies on the same facts. 

A semantic layer is a type of data abstraction that turns raw technical data into words businesses can understand. It gives a uniform view of all data sources so business users can work with simple data models instead of complicated schemas. This standardization is necessary to turn data into valuable insights.

Semantic layers help bridge the gap between raw data and end-user inquiries by standardizing data interpretation. By aligning queries with a consistent data model, they eliminate mistakes, improve query efficiency, and ensure that business intelligence tools and users receive unified, accurate results. The layer streamlines data access, allowing for more efficient and precise querying across systems, particularly in large businesses with diverse data needs.

Nevertheless, the conventional semantic layer has substantial constraints despite its practicality. It is primarily intended for structured query use cases, and it cannot effectively address contemporary AI-driven requirements, including natural language interpretation, dynamic schema changes, and large-scale data systems. As businesses become more dependent on generative AI for insights, these constraints become obstacles to implementing effective analytics.

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Core components of a semantic layer

This section reviews the core components of a semantic layer. It will be used to explain how the semantic layer needs to evolve for generative AI-based data analytics.

The core components are metadata repositories and schema mapping. The semantic layer integrates data from various sources into a cohesive framework and business logic setup. It ensures uniform calculations and rules throughout queries and query engines, which enhance and carry out queries efficiently. The integration of these components allows the semantic layer to provide a structured, reliable, and performance-oriented environment, enabling users to make data-driven decisions confidently.

Metadata repositories and schema mapping

The most effective technique to make datasets easier to organize, comprehend, and manage is to include rich and descriptive data, often known as metadata. Metadata is important in a semantic layer because it provides information and context for the underlying data. This includes developing a unified approach to providing the organization with information about data sources, standardized data, data element relationships, security and access controls, versioning, lineage, data quality and governance measures, and other relevant details to drive efficient labeling and categorization.

• Metadata must support natural language descriptions and definitions to facilitate the accurate interpretation of user queries in generative AI applications.

Taxonomy/ontology management

Business taxonomies enable you to characterize, coordinate, and express organizational terminology in a structured manner, supplementing metadata by adding another degree of organization. Taxonomy is important in a semantic layer because it ensures consistency in name conventions and classification standards, reduces ambiguity, and promotes a shared understanding of business concepts. For many businesses, the primary use case is to build cross-functional taxonomies that can be used across departments and business units, facilitating data discovery and exploration of shared data via faceting.

• Generative AI systems require dynamic, user-expandable taxonomies and ontologies that can integrate input from natural language interactions.

Graph data storage

A graph database is essential for creating a semantic layer that represents and manages complex relationships among data elements. It enables companies to store data with semantics, context, and relationships and to use a flexible schema for use cases that necessitate the comprehension and analysis of data relationships

• By incorporating a knowledge graph into the semantic layer, it is possible to dynamically represent intricate relationships. In the context of generative AI, this entails contextualizing user inquiries and guaranteeing that the system comprehends the business relevance of data relationships and their data relationships.

Query engines  

Query engines serve as tools that allow turning user requests into SQL queries without writing complex queries. Query optimization engines enhance performance by effectively managing various elements such as data indexing, caching, and distribution, ensuring rapid and efficient data retrieval. A robust query engine can manage extensive datasets and intricate queries, allowing users to extract information efficiently and dependably, even from expansive, distributed data ecosystems. 

• In order to effectively process ambiguous natural language queries, query engines must support contextual enhancements. This involves utilizing the context layer to furnish LLMs with supplementary business logic and relationships during query execution.

Abstracted integrations and data flow

As an abstraction framework, a semantic layer uses data integration and transformation tools to link, unify, and transform data from several sources into a structured and semantically rich format. These include extract, transform, and load (ETL), data virtualization and integration platforms, and API management. 

• For generative AI, data integration tools within the semantic layer must transform data structurally and contextually, ensuring that it receives semantically rich responses.

Security layer 

A security layer is required to protect data confidentiality, integrity, and availability within the semantic layer. Security measures applied within a semantic layer should adhere to organizational protocols for entitlement and provisioning management to regulate access to distinct data elements based on user roles and permissions. 

• The security layer must extend beyond traditional access controls to manage context-aware data access. This ensures that generative AI systems can only retrieve relevant data based on the user's role and intent.

Addressing limitations of the semantic layer 

Integrating Generative AI with a Semantic Layer enhances the contextual understanding of large language models (LLMs) by enabling them to grasp business terminology, definitions, and the relationships among entities, attributes, and metrics. The Semantic Layer enhances data by incorporating annotations and labels, facilitating GenAI models' more effective understanding of the data and the production of precise insights. It helps reduce bias within generative AI models by defining business terminology and metrics, ensuring data consistency, and decreasing the probability of hallucinations. However, it does not provide the dynamic adaptability LLMs require to understand and process queries with contextual details. 

The semantic layer provides a comprehensive and clear view of the data. In contrast, the metrics layer ensures that all-important business metrics are well-defined and that all dependencies and calculations are preserved. The interplay between these layers is essential: the metrics layer ensures the completeness and validity of the metrics used, while the semantic layer translates these metrics into easily understood, business-value-oriented information that end users can easily understand and act upon.

The metrics layer defines and controls key business metrics, such as revenue, customer acquisition cost, churn rate, etc, in one place. This layer is a sound base for the computation and aggregation of metrics, most of the time supervising the primary calculations and their relationships. 

The semantic and metrics layers are indispensable components of modern data architecture with different purposes. The semantic layer is intended to map complicated data into clear and easily understandable business terms and ideas so that users can work with the data without needing a significant technical background. The solution provides a unified data vision by aligning definitions and business logic across multiple systems so that users across different departments will have a uniform understanding of data and its analysis. This layer provides a simple and easy-to-understand layout to interact with data logically, which makes it easier for a non-technical user to use language and measurements relevant to the business.

Semantic layers depend on established schemas and rules, resulting in a lack of flexibility when accommodating schema modifications, integrating new data sources, or adapting to changing business needs. The design of semantic layers for structuring data in BI dashboards limits their ability to interpret natural language queries. This is particularly evident when such queries depend on domain-specific context or specialized business terminology. Handling extensive databases characterized by intricate relationships poses significant challenges for conventional semantic layers, which frequently experience slowdowns or struggle to provide accurate real-time insights.  To overcome these limitations, WisdomAI introduces the Context Layer, a dynamic enhancement of the conventional semantic layer specifically tailored for generative AI applications. 

Applications of Context Layers in generative AI-based analytics

Context layers arrange data so that it becomes easier to understand the relationships among data elements, and, therefore, the results obtained from AI-driven questions are more specific. This is because, using context layers, such tools can build queries based on how companies discuss their data. This approach solves problems such as data ambiguity and generally improves the appearance of the user interface.

Context layer in text-to-SQL

Context layers ensure that SQL queries achieve higher accuracy, consistency, and efficiency in data confidentiality, integrity, and availability. Implementing metadata, schema mappings, and business rules within the semantic layer streamlines intricate query processes, enabling users to minimize errors and enhance clarity in data interpretation.

Reducing SQL query errors and bias

The context layer helps reduce SQL query errors and data bias. It decreases the possibility of incorrect joins, filters, or operations by providing users with determined, exact data paths and consistent metrics. Defining standard terms and business logic guarantees consistent results regardless of the person running the query. 

For instance, inconsistent “total revenue” calculations across departments can misinterpret data. A semantic layer ensures that the calculation is consistently defined as SUM(sales_amount - discount_amount) across all queries, thereby standardizing the metric for all users.

Resolving ambiguities in natural language queries

Context layers provide clarity and standardization in responses to natural language queries. When users submit queries like “What are monthly sales in the last quarter,” the semantic layer processes them into the appropriate SQL format and returns the results to the user.

Improving the user experience with accurate data retrieval

Improve the user experience by optimizing data retrieval processes and ensuring the precision of query results. Data retrieval is enhanced through the abstraction of intricate joins, aggregations, and filtering mechanisms, becoming quicker and more user-friendly, particularly for those without a technical background. This layer functions to improve query performance and guarantee the delivery of precise and consistent results.

Consider a scenario where a user requires the total monthly sales for each product category within a retail database. The context layer streamlines the process by providing a predefined metric, enabling users to construct a simple query rather than dealing with a complex SQL statement.

SELECT category, monthly_sales
FROM sales_summary
WHERE date BETWEEN '2023-07-01' AND '2023-09-30';

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Behind the scenes, the semantic layer ensures that monthly_sales is calculated consistently as SUM(sales_amount - returns_amount), removing any guesswork for the user.

Context layer by WisdomAI

An exemplary instance of self-service capabilities within a semantic layer is WisdomAI’s Context Layer, which empowers non-technical users to perform data queries through natural language processing. WisdomAI’s Context Layer abstracts technical details and delivers context-aware responses, enabling users to effortlessly extract insights without requiring extensive database expertise.   In contrast to the traditional static methodology employed by semantic layers, the context layer:

• Integrating all enterprise knowledge into a perpetually updated knowledge graph dynamically delineates relationships among data entities, schemas, and business logic.
• The context layer is designed to adapt in real-time, leveraging user interactions to enhance and refine business terminology and user intent comprehension progressively.
• The integration of contextual metadata into user queries significantly improves query precision. This context layer enables generative AI systems, such as large language models (LLMs), to deliver accurate and semantically enriched results.
• Facilitates Generative AI workflows for text-to-SQL systems by dynamically supplying instructions and context to large language models.

The Context Layer significantly improves user experience by minimizing obstacles to data access, enabling users to engage with data more intuitively. The system functions as a mediator, translating requests and guaranteeing that the business logic is accurately implemented to yield pertinent outcomes. It combines cross-application semantics, granular governance, and contextual grounding with a unified search index.

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Last thoughts

The semantic layer is a business transformation tool for any organization that wants to leverage the vast amounts and types of data available. Through a unified approach to data, it enables educated decision-making and improves accessibility. It also improves data representation by modeling complicated relationships and offering a robust foundation. It enhances knowledge, content management capabilities, business intelligence, and analytics teams by enabling enhanced data analysis, discovery, modeling, and decision-making on connected data. 

Traditional semantic layers provide a strong basis for decision-making and data representation but frequently lack the adaptability for dynamic, real-time applications like text-to-SQL. To address this limitation, WisdomAI introduces the Context Layer, a dynamic extension of the semantic layer explicitly designed for generative AI applications.

Context layers are crucial in augmenting data accessibility, refining user experience, and enabling AI-driven insights within contemporary analytics frameworks. Establishing a cohesive data layer allows organizations to enhance self-service analytics, provide precise AI-driven insights, and support real-time query adjustments. This approach democratizes data across various teams, making advanced analytics more attainable.

Context layers facilitate self-service analytics by converting comprehensive data architectures into recognizable terminology and metrics, enhancing data accessibility. This methodology empowers employees to conduct ad hoc analyses and independently make informed, data-driven decisions, eliminating the need for data engineers or analysts to craft custom SQL queries.

To adopt context layers efficiently, enterprises should assess their current data infrastructure and define clear business objectives. Establishing common standards and choosing tools that work well with current systems requires cooperation between the business, analytics, and IT teams. To encourage data literacy, start with high-priority use cases, scale gradually, and offer training. Routinely review and improve the system to meet changing business requirements.