Semantic Layer — One Layer to Serve Them All
Bridging the Gap between Technology and Business
Last update: 2024–08–29: Knowledge Graph and Semantic Layer
- The semantic layer serves as a bridge between complex data structures and business terms, offering a unified view of data, simplifying access, and ensuring consistency in organizational decision-making.
- Leveraging semantic layers improves data governance and facilitates AI integration, ensuring reliable, transparent analysis and informed decisions while optimizing organizational processes for efficiency and adaptability.
Table of Contents
· Introduction
· Why Do We Need a Semantic Layer?
· What is a Semantic Layer
· How The Semantic Layer Supports Business Needs
· Implementing a Semantic Layer
· Semantic Layer in Data Analysis
· The Impact of Semantic Layer on Data Governance
· Empowering AI with a Semantic Layer
· Future Directions and Emerging Trends
· Conclusion
· References
Introduction
The semantic layer is increasingly becoming an essential factor in modern data management. The lack of such a layer brings with it significant challenges, ranging from limited data availability, inconsistencies in reporting and poor decision making to an increased burden on IT resources. The struggle to democratize data is exacerbated by the proliferation of disparate BI tools and data sources, which often leads to inconsistent analytics results and governance issues.
The introduction of a semantic layer helps to solve these challenges by serving as a consistent representation of business data. By translating complicated data structures into familiar business terms, a unified view of data is created across the organization, simplifying access and ensuring consistency. Experts emphasize the importance of bridging the gap between data engineering and business analysis.
This article looks at the definition and functions of semantic layers and explains their central role in organizing and abstracting enterprise data to facilitate decision-making processes. It explores the multiple benefits of implementing semantic layers, including improved data consistency, governance and agility. In addressing the complexity of modern enterprise data and AI management, the semantic layer emerges as a cornerstone that promises improved operational efficiency and more informed decision-making capabilities.
Why Do We Need a Semantic Layer?
Micah Horner (TimeXtender) puts it in a nutshell. He argues, that without a semantic layer, organizations face challenges that hinder the effective use of data:
- Limited Data Access and Usability: Data silos and complexity make it difficult for non-technical users to access data and hinder informed decisions and data-driven initiatives without a semantic layer.
- Lack of a Unified Data Language: Different terminologies accross departments lead to confusion and misinterpretation and, without a semantic level, make it difficult to coordinate business objectives.
- Inconsistency in Reporting and Analytics: Contradictory findings and unreliable decisions arise from inconsistent data definitions and calculations that undermine trust and lead to costly errors.
- Increased IT Burden: Without a semantic layer, IT teams spend too much time on data access requests and issues, diverting resources away from strategic initiatives.
- Limited Agility and Scalability: Manual data integration processes hinder the ability to adapt to business changes and scale operations, limiting responsiveness to market changes.
- Data Governance and Compliance Risks: Inconsistent data management poses risks to corporate governance and compliance, which can have legal and financial consequences.
- Lost Insights and Competitive Advantage: Inaccessible data and a lack of shared understanding lead to missed insights and reduced competitive advantage in the market.
Kieran O’Driscoll (AtScale), Kyle Hale and Soham Bhatt (Databricks) explain in their blog article, that most enterprises are still struggling with data democratization.
Making data available to decision-makers is challenging, especially for large organizations. Over half of enterprises use three or more BI tools, with data scientists and application developers having their own preferences.
Different tools and query languages create conflicting analytics outputs. Multiple business units using different data copies or OLAP solutions like Tableau Hyper Extracts, Power BI Premium Imports, or SSAS intensify this issue.
Storing data in various marts, warehouses, and reporting tools prevents a single version of truth. This increases data movement, ETL, security, and complexity, creating a data governance nightmare and relying on potentially stale data.
AtScale uses an example to illustrate very clearly why companies need a semantic layer:
A marketing team, for example, may refer to a business as a “prospect” by managing the leads in Salesforce. The sales team might call that same business a “client” as orders and deliveries are managed in SAP ERP, and the finance team calls the same business entity a “counter party” as the invoicing process is managed in Oracle EBS. In this complex environment, how do you get a report that aligns all three data elements to one? In the current siloed data landscape, it is not possible to get a single “Lead to Cash” report due to different data definitions originating from multiple source systems.
The solution lies in having one standard and consistent definition for this business entity where “prospect,” “client,” and “counterpart” are mapped to one data entity. With the semantic layer, different data definitions from different sources can be quickly mapped for a unified and single view of data.
[AtScale: What is a Semantic Layer?]
Donald Farmer (AtScale) highlights the profound challenges between data engineers and business analysts and explains why semantic layers are helpful in bridging this gap.
Many organizations struggle with data-driven decisions due to a disconnect between data engineers, who prefer code-based environments, and business analysts, who lean towards non-code interfaces. This misalignment causes inefficiencies, inconsistent data definitions, and poor decision-making. A robust semantic layer can bridge this gap by offering APIs for engineers and intuitive interfaces for analysts, optimizing organizational skills and techniques.
Semantic layers provide a unified platform accommodating diverse working styles, enhancing collaboration, data governance, and innovation. They offer robust APIs for engineers to programmatically access and manipulate data, integrating seamlessly into workflows. Simultaneously, they provide intuitive interfaces for analysts to explore and visualize data without extensive technical expertise. This unified foundation ensures consistent definitions and metrics, fostering better communication and alignment. Features like shared workspaces, version control, and annotation capabilities promote collaboration and knowledge sharing, enabling organizations to harness data effectively and drive business value.
What is a Semantic Layer
Wikipedia defines the semantic layer as follows:
A semantic layer is a business representation of corporate data that helps end users access data autonomously using common business terms managed through Business semantics management. A semantic layer maps complex data into familiar business terms such as product, customer, or revenue to offer a unified, consolidated view of data across the organization.
By using common business terms, rather than data language, to access, manipulate, and organize information, a semantic layer simplifies the complexity of business data. Business terms are stored as objects in a semantic layer, which are accessed through business views.
The semantic layer enables business users to have a common “look and feel” when accessing and analyzing data stored in relational databases and OLAP cubes.
…
[Semantic Layer]
AtScale gives following definition for a semantic layer:
A semantic layer is a business representation of data and offers a unified and consolidated view of data across an organization. With a semantic layer, different data definitions from different data sources can be quickly mapped for a unified, consistent, and single view of data for analytics and other business purposes.
[What is a Semantic Layer?]
and further explanation …
A semantic layer maps business data into familiar business terms to offer a unified, consolidated view of data across the organization and meet the growing analytics needs of an enterprise. …
The semantic layer is a metadata and abstraction layer built on the source data (eg.. data warehouse, data lake, or data mart). The metadata is defined so that the data model gets enriched and becomes simple enough for the business user to understand.
[What is a Semantic Layer?]
Lulit Tesfaye (Enterprise Knowledge) defines the Semantic Layer as follows:
A semantic layer is a standardized framework that organizes and abstracts organizational data (structured, unstructured, semi-structured) and serves as a data connector for data and knowledge. Larger than a data fabric, that is more focused on structured data, a semantic layer connects all organizational knowledge assets including content items, files, videos, media, etc. via a well defined and standardized semantic framework. It allows organizations to represent organizational knowledge and domain meaning to systems and applications, defining the relationship between content and data.
[What is a Semantic Layer? (Components and Enterprise Applications)]
and further …
… a semantic layer that incorporates content makes it possible to organize and enrich content with semantic meaning, empowering consuming systems and end users with advanced content management, discovery, and analytical capabilities.
[Adding Context to Content in the Semantic Layer]
Summarizing these definitions, the semantic layer can be defined as follows:
A semantic layer translates complex enterprise data into familiar business terms and provides a unified view by mapping different data sources and managing data relationships. It simplifies data models for users and includes structured, unstructured and semi-structured data. By acting as a metadata layer, it improves the management and analysis of content. It serves as an abstraction layer between databases and end users, provides consistent data views and facilitates intuitive queries without SQL knowledge. It also supports data management through access controls, data quality assurance and policy enforcement.
How The Semantic Layer Supports Business Needs
A semantic layer offers a unified view of data, enabling consistent access and queries. It enhances user experience, organizational efficiency, and provides a standardized approach for enterprise-wide analytics, resulting in numerous benefits:
- Single source of truth: Data is made available in a standardized format, regardless of its source, so that users can analyze it with different tools and techniques. Companies that use the semantic layer can carry out cross-departmental analyses without being restricted to a single data source.
- Simplified Data Access: Semantic layers provide a simplified and unified view of complex data structures and make it easier for users to access and understand data without the need for in-depth technical knowledge, thus democratizing data access in companies.
- Democratization of Analytics and AI: As data analytics expands, depending on a single BI or ML platform for all needs becomes impractical. A semantic layer platform connects diverse data platforms, protocols, and tools, decoupling data from consumption and democratizing analytics and ML.
- Improved Data Consistency: Semantic layers enhance data quality by ensuring consistency across enterprise data assets. By aligning technical data with business concepts, they establish a single source of truth, minimizing the risk of discrepancies and errors.
- Reduced Data Cleaning Effort: The semantic layer reduces data cleaning efforts by ensuring consistent data definitions, streamlining access, integration, and feature creation. Pre-built controls enhance reliability, while supporting a logical schema with views, procedures, and functions for enhanced data management
- Better Data Integration: Semantic layers eliminate the need for data integration processes by creating reusable code. This code can then be used by multiple applications to enable consistent responses to similar queries and provide a way for the applications to share the same data.
- Improved Data Governance: A semantic layer establishes a common language for data description, enhancing data governance by controlling access to meet organizational and regulatory standards. Positioned between data platforms and analytics tools, it enforces security via authentication and role-based access control.
- Improved Query Performance: Semantic layers enhance query performance by caching frequently used data, leveraging cloud scalability, and employing comprehensive performance management systems. This optimization reduces computing costs and enhances efficiency beyond simple caching techniques.
- Greater Flexibility: With a well-designed semantic layer, it can be much easier to accommodate complex data sources and make changes to the system over time.
- Improved Collaboration: Semantic layers facilitate collaboration between technical and business teams by bridging the gap in understanding and enabling effective communication through a common data language.
- Reduced IT Dependency: Business users can self-serve and access the data they need through semantic layers, reducing the burden on IT departments for ad-hoc data requests.
Implementing a Semantic Layer
Businesses require a tool capable of abstracting data from various sources, contextualizing it, and extracting actionable insights consistently, enabling data literacy for all users. A modern “universal” semantic layer platform enhances the original strengths of the semantic layer by centralizing governance and promoting a business-oriented view of data.
Building Blocks of Semantic Layers
Micah Horner (TimeXtender) explains the building blocks of a semantic layers that seamlessly translate complex data structures into intuitive user experiences:
1. Data ingestion
- Ingesting Data from Multiple Sources: Gather data from databases, spreadsheets, APIs, and more to centralize relevant information for further processing, initiating the creation of a semantic layer.
2. Data Preparation
- Transforming and Prepping Data: Clean, validate, and transform ingested data to ensure accuracy and usability for analytics, creating a reliable dataset.
- Dimensional Modeling of Data: Structure data into dimensions and facts to simplify complex relationships, essential for building a semantic layer that provides meaningful insights.
3. Data Delivery
- Semantic Layer: Create a semantic model translating technical data into business-friendly terms, making data comprehensible and relevant to all users.
- Data Products: Develop department-specific models (data products) to provide tailored data access, ensuring each team gets the data they need without being overwhelmed.
David P. Mariani (AtScale), argues for the implementing of a universal semantic layer that leverages transformation services in the metrics layer of the A16Z data stack, data modeling, workflow management, and permissions and security. This layer offers significant benefits when properly coordinated, such as:
- Creating a single source of truth for enterprise metrics and hierarchical dimensions, accessible from any analytics tool
- Providing the agility to easily update or define new metrics, design domain-specific views of data, and incorporate new raw data assets
- Optimize analytics performance while monitoring and optimizing cloud resource consumption
- Enforce governance policies related to access control, definitions, performance, and resource consumption
Success depends on taking advantage of a centrally managed semantic layer that allows users to innovate freely without semantic sprawl.
The Metrics Layer: The metrics layer serves as the single source of truth for enterprise metrics, accessible by various analytics tools. It provides a metrics store for BI tools, applications, reverse ETL, and data science tools, with design and change management integral to its function. Effective metrics layers require curation, change management, discoverability, and serving capabilities for consistency, efficiency and a seamless user experience.
Data Modeling: Data modeling involves creating logical data concepts mapped to physical structures in a warehouse or lakehouse. It can use visual frameworks or code-based languages. Key activities include making data “analytics-ready,” defining conformed dimensions, and designing metrics. This embeds business semantics into the data model, promoting consistency, governance, and innovation through a composable analytics approach.
Workflow Management: Workflow management orchestrates physical transformations for the semantic layer, optimizing cost and performance. Users demand minimal query latency, requiring data aggregate materialization due to cloud-scale data. Performance management automates materializations, adapting dynamically while considering cloud and labor costs. Leveraging semantic layer data, workflow management optimizes performance and costs.
Entitlements and Security: Entitlements and security in the semantic layer enforce data governance policies dynamically at query time, ensuring users access the correct data. Managing various entitlements and consistent definitions maintains trust and integrity. Performance optimization considers user entitlements and use case priorities. Real-time policy enforcement is crucial, while broader security services extend beyond the semantic layer.
Integrating the Semantic Layer within the Modern Data Stack
David P. Mariani also points out that the layers of the modern data stack must be seamlessly integrated with the surrounding layers.
The semantic layer requires deep integration with the neighboring data layers, including the data platform, analytics and output layer, as well as the metadata and services layer.
A universal semantic layer on a cloud data platform centralizes data in a warehouse or lakehouse. Hybrid/multi-cloud setups need data virtualization for cross-platform queries. Effective workflow management requires tight integration with various data platform architectures:
Query Engine Orchestration: Dynamically translates queries from consumers to platform-specific SQL, optimizing for platform idiosyncrasies and reflecting logical-to-physical mapping in the semantic model.
Transformation Orchestration: Manages materialization of views into physical tables, optimizing for performance and cost within the data platform.
Writeback Orchestration: Handles creation of new data or metadata within the data platform, based on user or AI/ML interaction.
User Defined Function (UDF): Utilizes libraries of functions in cloud data platforms for analysis and output, enhancing semantic layer capabilities.
Metadata & Support Services: The semantic layer collaborates with various tools in the data fabric ecosystem by supporting integrations with metadata and support services:
- A semantic layer must share its metadata and lineage with enterprise data cataloging tools for metric and data model discovery.
- A semantic layer must be capable of importing metadata from other tools to automate and standardize semantic data models.
- A semantic layer must have monitoring endpoints for managing user access, uptime, and system performance.
Building a Semantic Lakehouse With AtScale and Databricks
The collaboration between AtScale and Databricks aims to create a Semantic Lakehouse, offering an abstraction layer on physical tables. This simplifies data consumption by defining entities, attributes, and joins, providing a business-friendly view for analysts and end users.
The AtScale semantic layer sits between analytics tools and the Databricks Lakehouse, abstracting data to make it easily consumable. It connects via Hive SQL, SSAS cube, or web service, pushing queries to Databricks for optimized SQL execution, ensuring performance and scalability.
AtScale’s Universal Semantic Layer uses autonomous performance optimization to identify query patterns and automatically manage aggregates. This eliminates manual effort, creating “Diamond Layer” aggregates in Delta Lake, enhancing BI report performance and simplifying analytics data pipelines and engineering.
Creating a tool-agnostic semantic lakehouse
The Databricks Lakehouse Platform unifies data, analytics, and AI workloads. AtScale’s Semantic Lakehouse extends this by supporting BI and AI/ML through a tool-agnostic Semantic Layer, enabling consistent use across Tableau, Power BI, Excel, and Looker.
AtScale’s Universal Semantic Layer unifies BI and AI/ML teams, providing consistent access to enterprise data. It ensures business users in Excel and data scientists using Notebooks can leverage Databricks Lakehouse’s full power.
Using a Knowledge Graph to Power a Semantic Layer
Knowledge Graphs play a significant role in structuring and relating data, commonly seen in search engines and social media platforms. In an enterprise setting, implementing a semantic layer using Knowledge Graphs helps organize and derive insights from large datasets, making information more accessible and actionable. This approach aids in enhancing applications like chatbots, recommendation systems, and business intelligence tools by transforming data into meaningful insights.
Knowledge graphs increase productivity and insight by organizing data into meaningful, entity-centric views. They enable more efficient data management and retrieval by establishing and deriving relationships between data points. This helps users understand and analyze complex information in different contexts, improving decision making and personalization.
The integration of Databricks and Stardog provides a practical solution for implementing this semantic layer. Databricks offers a platform for data storage, analytics, and AI, while Stardog’s knowledge graph capabilities enable the modeling of complex relationships within the data. By combining these technologies, organizations can facilitate complex cross-domain queries and improve data integration across different departments.
Using a Knowledge Graph as a semantic layer can increase productivity by reducing manual data processing and better integrating external data with internal sources. It enables the derivation of new relationships and simplifies complex cross-domain queries, improving insights and supporting enterprise-wide data-driven initiatives.
Semantic knowledge graphs can provide significant business value by transforming the way data is organized and used. They map entities and their relationships with rich context, enabling advanced data integration and insightful analytics. This structure supports complex queries and seamless data connection across disparate sources, improving decision making and providing actionable insights.
Semantic Layer in Data Analysis
The flexibility of data analysis is paramount for organizations that want to remain agile and make informed decisions quickly. A semantic layer plays a central role in enabling this flexibility by abstracting the data layer from the visualization layer. Thanks to this abstraction, analysts can easily create different views of the data, including charts, tables and graphs, and easily change analyzed fields and metrics. As a result, analysts can quickly gain new insights and enable companies to adapt their strategies to changing market conditions in a timely manner.
Richard Makara (Reconfigured) points out that companies with a semantic layer become more flexible in their decision-making as they can access updated data as soon as it becomes available. This real-time data access enables proactive responses to changes in the business environment and facilitates the identification of new opportunities and potential risks.
Leveraging this flexibility can be critical for organizations seeking competitive advantage and informed decision-making in today’s fast-paced business environment. It enables users to:
- Quickly and easily change how data is presented
- Adapt to changing business needs
- Easily access and analyze data from different sources.
- Identify trends, outliers, and other insights not readily apparent in existing reports
- Engage more easily in collaborative data analysis projects
Data analysis flexibility empowers organizations to swiftly adapt to changes in the business landscape while fostering innovation in data analysis and reporting. By providing versatile tools accommodating diverse data sources and analysis needs, organizations enhance their competitiveness and maximize data value.
The semantic layer empowers data analysis by providing a unified data source, regardless of its origin. This enables versatile analysis across departments, liberating organizations from single-source limitations. Adapting data models to changing business needs is seamless, allowing for flexible responses without disrupting users. The abstraction capability simplifies the creation of complex reports and visualizations, encouraging analytical creativity. Flexibility is further enhanced as technical constraints of data sources no longer hinder analysis. Users access and analyze data efficiently, enhancing decision-making. Integration of external sources and customizable data hierarchies facilitate multidimensional analysis.
Overall, the semantic layer empowers modern organizations by future-proofing data analysis, enhancing efficiency, and enabling informed decision-making.
The benefits of the semantic layer in data analysis
Sean Leslie (data.world) emphasizes that the semantic layer aids in data analysis for numerous reasons, including:
Simplifies data integration and abstraction: The semantic layer consolidates data from various sources, providing a unified view and simplifying data integration, allowing efficient combination and accessibility for data analysts and business users.
Enhances data understanding and accessibility: Semantic layers use a common business vocabulary, bridging technical data and business users, enabling self-service analytics and BI tools for easy, business-aligned data exploration and analysis.
Facilitates data governance and security: The semantic layer applies business rules for data consistency, maintains data integrity, and enforces access controls based on roles, ensuring secure and compliant data access.
The Impact of Semantic Layer on Data Governance
Richard Makara (Reconfigured) explains, that data governance refers to the process of managing data security, availability, usability, and integrity. It involves setting policies, procedures, and controls for managing data assets. The aim of data governance is to increase business value, reduce risk, and ensure compliance.
Data governance ensures proper data management through policies and controls. Its importance lies in maintaining data integrity, compliance, security, and facilitating effective decision-making:
- Compliance: Data governance ensures the organization complies with relevant laws and regulations, such as GDPR, HIPAA, and others.
- Reliable data: Data governance can help create policies for data standardization and metadata management, which can lead to more accurate data and better analysis.
- Better decision-making: Data governance can help produce trustworthy data that results in making better-informed business decisions.
- Mitigate risk: An organization can mitigate risk associated with data loss, data breaches, and other related issues when governance policies are in place.
- Data security: Data governance can include data security policies, which safeguard against unauthorized or malicious access.
- Enforce data policies: With strong data governance, an organization can ensure adherence to data policies across different functions and business units.
- Cost-saving: Effective data governance reduces costs associated with inaccurate data decisions, unreliable analysis, and duplication of data.
Data governance has a significant impact on data integrity and data management in organizations. Its importance is growing with the rise of big data and generative AI and requires adaptable data strategies to effectively handle the growing volumes of information.
How semantic layer improves data governance
A semantic layer simplifies complex data from various sources into business terms, ensuring a consistent understanding across the organization. It centralizes data definitions, simplifying modeling and enabling efficient management of changes. Acting as a single access point for reporting and analytics, it enhances data retrieval. This improves data governance by promoting consistency, standardization, and efficiency in managing data integrity, accuracy, security, and regulatory compliance:
Clear Definitions of Data: A semantic layer ensures consistent understanding of data, eliminating confusion and errors caused by inconsistent interpretations through clear definitions and context.
Improved Data Quality: The semantic layer ensures data consistency and accuracy, minimizing errors and enhancing data quality. Standardized definitions improve decision-making reliability, efficiency, and customer experiences by preventing errors and facilitating accurate data management and retrieval.
Flexibility in Data Modeling: The semantic layer simplifies data modeling, offering a unified model for multiple sources, ensuring consistency, adaptability to evolving business needs, and streamlining management and maintenance processes, thus enhancing overall data governance efficiency.
Controlled Access: Semantic layers enable precise data access control, allowing authorized users to manipulate data according to predefined rules, ensuring governance and data security.
Change Management: Semantic layers streamline change management by centralizing data definitions, eliminating manual updates across various sources, enhancing efficiency, and ensuring consistency.
Auditability: Semantic layers enhance auditability by regulating data access, enabling tracking of user activities, fostering accountability, and transparency in data usage and management.
Efficient Workflow: Semantic layers facilitate a unified environment for data analysis, sharing, and collaboration, optimizing workflow, minimizing duplication, and enhancing overall efficiency.
Data Consistency: Semantic layers ensure uniform data accessibility and integrity, governed by defined rules and standards, leading to more precise and reliable insights across the organization.
Improved Data Lineage: The semantic layer enables data lineage tracking, simplifying source-to-business concept mapping and transparently documenting transformations. This enhances governance by early error detection, ensuring data quality, regulatory compliance, and improved decision-making.
Simplified data auditing: Semantic layer provides the ability to track and audit data at a granular level for better data governance. This makes it easier to identify errors and inconsistencies in the data.
Simplified Data Governance: The semantic layer streamlines data governance by centralizing metadata and models, facilitating policy enforcement, automated quality checks, and maintaining consistent standards. This boosts data accuracy and efficiency while mitigating risks across all sources.
Enhanced data privacy and security: As more and more data is being processed and collected, the need for data security and privacy has also grown. Semantic layer helps to ensure that only authorized personnel have access to the data, driving better data security.
Governance & Auditing: Using a universal semantic layer leads to an auditable record of changes and clear ownership. It also makes it easier to stipulate who can and can’t define new metrics.
Improved regulatory compliance: With better data governance, semantic layer helps to ensure that all data is being processed according to regulatory requirements, reducing the risk of data breaches.
The implementation of a semantic layer offers substantial benefits to data governance. By providing a unified view of data, streamlining management processes, and enhancing data quality and security, it becomes an indispensable tool for effective data management and informed decision-making.
Empowering AI with a Semantic Layer
David P. Mariani (AtScale) points out, that leading data organizations prioritize augmented analytics with AI, integrating diagnostic, predictive, and prescriptive analytics, enhancing efficiency by integrating Data Science/Machine Learning platforms, reducing data movement.
Publishing Model-generated Insights: Production AI/ML models generate new data points (e.g. predictions, features) that need to be exposed to users in order to create value. A semantic layer can leverage existing analytics and output infrastructure to more easily disseminate augmented analytics.
Explainable AI / Trusted AI: The semantic layer can be leveraged to organize and disseminate information related to why an AI model is providing a particular answer. For instance, business users can gain value from knowing not only the prediction for sales next quarter, but also the key drivers for this prediction. Delivering better insight on the reasoning behind AI/ML model suggestions directly support explainability and enhance the level of trust in model-generated insights.
Imran Chaudhri (Progress) points out, that integrating Generative AI with enterprise data enhances reliability, transparency, and security while improving data quality and scalability. By leveraging semantic layers, enterprises reduce errors, ensure trustworthiness, and comply with governance standards, unlocking innovative cost-saving opportunities and paving the way for enhanced decision-making and operational efficiency. Two key benefits of semantic layers:
Reduced Hallucinations in the Generative AI’s Outputs: Generative AI models often produce incorrect answers, known as hallucinations, due to their lack of human reasoning and understanding. These errors, occurring 15–20% of the time, can be reduced by using semantic data platforms to contextualize and harmonize data. Proper data cleaning, curating, and modeling are essential for improving AI accuracy.
Improved Reliability and Trustworthiness of the Generative AI’s Outputs: Generative AI models often produce irrelevant, inaccurate, or biased outputs, which can be problematic for critical business decisions. By integrating generative AI with enterprise semantic data systems, companies can enhance the accuracy and trustworthiness of AI outputs. Using private, semantically tagged data, generative AI gains deeper insights into an organization’s unique context. This integration ensures the AI system accesses real-time, updated data, addressing the “training data cut-off” issue and improving the overall quality of generated answers.
Cube emphasizes, that the semantic layer provides crucial context for large language models (LLMs) in AI-powered data experiences. By organizing data into meaningful business definitions and providing a query interface, it ensures LLMs understand data contextually, reducing errors and hallucinations while enabling innovative AI applications and simplifying querying processes.
LLMs, while revolutionary, encounter limitations, particularly in producing accurate outputs due to the “garbage in, garbage out” issue — LLMs hallucinate. Simply feeding them database schemas isn’t sufficient for generating correct SQL. They require a semantic layer to comprehend data contextually, including metrics, dimensions, and relationships. This layer organizes data into business definitions for querying, ensuring accuracy by enforcing LLMs to query through it. Thus, the semantic layer addresses the problem of LLM hallucination by providing necessary context and ensuring the correctness of queries and data outputs.
Combining LLMs with semantic layers unlocks new AI-driven data experiences. These layers provide essential context for AI agents, enabling accurate data querying and empowering organizations to build custom LLM applications. Positioned atop data warehouses, semantic layers facilitate seamless integration with AI technologies.
The semantic layer data model provides structure and definitions used as a context for LLMs to understand data and generate correct queries. By abstracting complex joins and calculations, The semantic layer provides a simplified interface based on business-level terminology, reducing errors and preventing hallucinations.
Artyom Keydunov (Cube) underlines that a universal semantic layer streamlines data consumption across various BI tools, eliminating re-work and fostering trust with a consistent data source. It defines metrics and metadata for all data experiences, ensuring data is accessible across diverse platforms, from BI software to AI tools. This versatility accommodates the evolving landscape of data delivery and supports next-gen data-driven applications.
No AI without consistent data: High-quality data is essential for AI, enabling reliable insights from vast datasets. The universal semantic layer becomes pivotal in this context, providing AI tools with business context and definitions to prevent errors. AI can enhance the semantic layer by suggesting improvements to definitions and code, facilitating data curation and democratization through natural language queries for business users.
The rise of applied AI: The proliferation of AI extends not only to large platforms but also to domain-specific applications, necessitating a universal semantic layer for consistent, accurate data. The semantic layer ensures that inputs are accurate, relevant and consistent. This is critical for accurate results and competitive advantage in AI-driven experiences.
The benefits of an AI-ready universal semantic layer: An AI-ready universal semantic layer is essential for connecting diverse data platforms, facilitating data democratization and powering customer-facing applications.
For example, an AI-ready universal semantic layer can inform business users in real time and in context. Imagine that you are a sales operations professional who spends all day in an application like Salesforce. There is no time to learn a BI tool or jump out of Salesforce to do deeper analysis across sales, support, and purchasing data. Instead, a universal semantic layer makes it possible to embed AI-assisted analytics into a tool like Salesforce, allowing for the analysis to be done within the domain-specific business context — and via a near real-time process that can be as easy as querying an AI chatbot.
[Real-time AI Experiences Can’t Advance Without a Universal Semantic Layer]
Patrik Liu Tran (Validio) emphasises that the semantic layer is attracting a lot of attention from companies that want to create innovative data experiences with AI and LLMs. An important goal is to enable natural language queries to LLMs to streamline data retrieval and free analysts from trivial tasks.
Integrating LLMs with semantic layers drastically improves accuracy, up to 300%, by pre-defining metrics and reducing the risk of erroneous assumptions. With semantic layers, LLMs adhere to agreed-upon business metrics, enhancing precision. As LLMs become more prevalent, the importance of semantic layers for data-driven organizations is becoming clearer. They promise more efficient and accurate data processing and analysis, which in turn enables better decision-making and operational efficiency.
Future Directions and Emerging Trends
Tomasz Tunguz (Theory Ventures) points out that semantic models will emerge as an important trend, unifying definitions across organizations to improve reusability and composability for simplified analysis that serves both human understanding and semantic synthesis of large language models.
The Semantic Model Becomes a Must-Have: Semantic models unify a single definition across an organization for a particular metric. Looker did this within the context of a BI system. But organizations need this layer across the stack. In addition to the reusability of definitions, composability — creating complex analysis with simple building blocks — will define this layer, both for humans who find it easier to understand and for large language models that synthesize semantics.
[Top 10 Trends for Data in 2024]
According to TimeXTender, semantic layer technology is constantly evolving and will influence data management strategies and open up new application possibilities in various industries.
Integration with AI and Machine Learning: Future semantic layers will deeply integrate AI and machine learning, enhancing data interpretation and predictive analytics. AI may automate data categorization, offer predictive insights, and recommend data sources based on usage and trends.
Enhanced Data Governance and Security Features:
Semantic layers will integrate stronger governance and security features in response to growing data privacy concerns, including advanced access controls and compliance tracking.Expansion into New Industries: Semantic layers offer potential for all industries that rely on data, including logistics, energy and agriculture. Their implementation could bring significant benefits by extracting insights from different data sources.
User Experience and Accessibility Improvements: Enhancing user experience and accessibility is prioritized, potentially through intuitive interfaces like search bars and LLM-powered chat, facilitating easier data interaction and insight extraction for non-technical users.
AtScale expects semantic layers to become increasingly important in the evolving data and AI landscape. They unify definitions across organizations to simplify analysis, promote reusability and enable both human understanding and semantic synthesis of large language models.
Two forces are expected to shape a new market around decision intelligence platforms: the combination of AI techniques such as natural language processing, semantic layers, and machine learning, and the convergence of technology clusters around composite AI, smart business processes, insight engines, decision management, and advanced personalization platforms.
[2024 Semantic Layer Innovations for Enterprise Analytics and Generative AI]
Responsible AI adoption, notably with GenAI, prioritizes ethical aspects like transparency and bias, aligned with regulatory standards. Emerging roles like AI stewards ensure ethical implementation. Organizations increasingly treat data as a product, crafting reusable data products for tailored business outcomes. Integration of Generative AI and Large Language Models (LLMs) transforms data exploration, with MDX Generation bolstering query flexibility and Natural Language Interfaces democratizing data access. Inbuilt AI functionalities such as forecasting and anomaly detection empower decision-making, aiming to broaden analytics accessibility and deepen insights, driving innovation forward.
Conclusion
The semantic layer becomes a central solution to the many challenges of modern data management. By providing a unified view of data and simplifying access, it addresses issues of limited data availability and inconsistent reporting. In addition, their integration promotes improved data governance and facilitates AI integration, increasing organizational efficiency and adaptability. The importance of semantic layers lies in their transformative potential to revolutionize data analytics and support AI applications to ultimately enable more informed decision-making processes. As organizations grapple with the complexity of an increasingly data-driven world, the semantic layer is a cornerstone for greater flexibility and effectiveness in managing and leveraging data resources.
References
Building a Semantic Lakehouse With AtScale and Databricks
The Power of a Universal Semantic Layer
Bringing Two Worlds Together: Uniting Data Engineers and Business Analysts
Where The Semantic Layer Fits In Your Data Strategy
The impact of semantic layer on data governance
Benefits of using semantic layer in data visualization
The Ultimate Guide to Semantic Layers
Rethinking Modern Business Intelligence with a Universal Semantic Layer
The Semantic Layer in the Modern Data Stack
What Is a Semantic Layer, and How Does It Turn Your Data into Knowledge?
The Top 3 Ways to Implement a Semantic Layer
What is a Semantic Layer? (Components and Enterprise Applications)
AtScale adds semantic layer support for AI, GenAI models
The Semantics of the Semantic Layer
Semantic Layer: The Backbone of AI-powered Data Experiences
Semantic Layers are the missing piece for AI-Enabled Analytics
Benefits of Generative AI for Enterprises and Semantic Data Integration
Real-time AI Experiences Can’t Advance Without a Universal Semantic Layer
Semantic layer 101: Why your data team should focus on metrics over data
2024 Semantic Layer Innovations for Enterprise Analytics and Generative AI
