Semantic Layers: When, Why and How to Use Them

As business leaders look to advance their analytics and AI journey, a structured approach to managing data and the diverse nature of language and meaning is crucial for business success. That’s where business semantic layers come in, leveraging the value of information and driving innovation.

In the ever-evolving landscape of knowledge management, the concept of a semantic layer has garnered significant attention. Semantic layers come into play, as businesses are seeking ways to contextualize and connect their enterprise information in meaningful ways. Sixty-nine percent of business leaders consider having a single source of truth for enterprise data critical for running an enterprise. As we delve into this subject, we'll explore when a business semantic layer is leveraged in an enterprise and how it can be effectively implemented to deliver optimal success.

What Is a Semantic Layer and Why Should Your Business Care?

A business semantic layer serves as an intermediary that translates complex technical data into accessible, business-friendly information, bridging the gap between raw data and actionable insights. Ultimately, it provides a consistent and business-friendly view of data, serving as a connector between organizational data and knowledge.

With a business semantic layer:

• Business users can gain easier and more intuitive access to data, enabling advanced search, cross-asset discovery and more.
• Data analytics can spend less time transforming and preparing data, focusing instead on analysis and insights.
• IT and data stewards can reduce the support demands by maintaining a single, consistent data model.
• Executives and decision-makers can receive more accurate and timely insights to power decision-making.

When Businesses Need a Semantic Layer

Attempts to use existing tools to systematically collect and connect data and transform it into insightful information have not been fruitful. IDC research indicates that enterprise information is unnecessarily replicated or never re-used as organizations simply don’t know where to find it or how to extract value from it. This is because most organizations tackle data projects as if they were IT projects, observing four main hurdles when it comes to delivering business value: lack of domain expertise, inadequate skill base and incomplete vision and failure to understand the value of the project. This eventually leads to expensive and tedious manual data manipulation tasks that are consuming funds by requiring IT resources to understand current data instead of providing insights to decision-makers.

Taking a metadata-centric approach to complex data problems yields better business outcomes. In this sense, the semantic layer can be a game-changing solution to connect data silos, discover knowledge gaps and drive actionable insights. IDC predicts that by 2026, “40% of enterprises will double investments in hyperconnected digital spaces to increase productivity, improve collaboration and boost energy efficiency.”

Based on previous work with semantic layer implementations, discussions with customers and partners and our experience deploying technology in some of the world’s largest, most complex enterprise architectures, we’ve identified several business scenarios where a semantic layer can be beneficial.

Information Silos and Inconsistent Reporting

• Sign: Different departments use their own reporting tools and databases, leading to inconsistent and fragmented data insights.
• Solution: A semantic layer unifies disparate data sources, providing a single version of the truth and driving consistency across the enterprise.

Complex Data Environments

• Sign: Your organization deals with a vast amount of data from various sources, leading to information silos and inconsistencies that impede your ability to drive actionable intelligence.
• Solution: By implementing a semantic layer, these diverse data sources can be integrated and standardized, making data more accessible and comprehensible for business users.

Rapid Decision-Making Requirements

• Sign: There is a need for real-time or near-real-time data insights to support agile decision-making processes.
• Solution: A semantic layer facilitates faster data retrieval and analysis, empowering decision-makers with timely and accurate information.

Regulatory and Compliance Demands

• Sign: Your enterprise must adhere to stringent regulatory requirements and data governance standards.
• Solution: A semantic layer facilitates data accuracy, lineage and auditability, helping your organization meet compliance obligations efficiently.

Advanced Analytics and BI Initiatives

• Sign: Your organization is embarking on advanced analytics projects, such as AI and machine learning, which require high-quality, consistent data.
• Solution: A semantic layer provides a foundation of well-defined, trustworthy data, enhancing the effectiveness of analytics and business intelligence initiatives.

As the business landscape evolves, embracing innovative approaches like the development of a semantic layer can help you make sense of your data and create a comprehensively connected data environment.

7 Steps for Implementing a Semantic Layer for Success

By implementing a semantic layer, enterprises can significantly enhance their data strategy leading to better decision-making, increased efficiency and a competitive advantage. With that in mind, here are some things that have worked for our enterprise customers that might help you in the planning and execution of your semantic layer initiatives and implementations.

Step 1. Assess and define objectives: Begin by identifying the specific business needs and objectives that the semantic business layer aims to address. Engage stakeholders from various departments to promote a comprehensive understanding of the requirements.

Step 2. Start with high-value items: Conduct an inventory of information sources within the defined high-value objective. Don’t boil the ocean. Map out how information flows and identify any gaps or inconsistencies that need to be addressed.

Step 3. Design the semantic layer: Design the semantic layer with a focus on simplicity, scalability and flexibility. Verify that it can accommodate future data sources and evolving business needs. Use industry-standard models, existing department terminology and frameworks where applicable.

Step 4. Choose the right tools and technologies: Select tools and technologies that align with your organization's existing infrastructure and capabilities. Consider factors such as integration capabilities, user-friendliness and support for security and robust governance.

Step 5. Data governance and quality management: Implement comprehensive data governance policies to maintain data quality, security and compliance. Establish clear roles and responsibilities for data stewardship and enable continuous monitoring and improvement. The right tool will make this step simpler to start and easier to maintain.

Step 6. Training and change management: Provide comprehensive training to business users and IT staff so they understand how to use and maintain the semantic layer. Foster a culture of data-driven decision-making and encourage collaboration across departments.

Step 7. Continuous improvement and feedback loop: Establish a feedback loop to gather insights from users and continuously improve the semantic layer. Regularly review and update the semantic layer to reflect changes in business processes and data sources.

Together, these tips help to provide a solid starting point for your semantic layer project. To build a comprehensive semantic layer, you need a modern semantic platform that can semantically link disparate data sources and enhance your data with contextual and meaningful metadata about the data. This is where Progress Semaphore comes in. This product provides a foundation for your semantic layer that supports new business initiatives and AI endeavors.

A Semantic Layer Example with Progress Semaphore

Consider this example of a Big Four Accounting firm that delivers automated digital labor and supports regulatory compliance with Progress Semaphore. The semantic AI platform is at the heart of the digital labor solution. The platform brings together a set of best-of-breed enterprise-grade technologies that streamline reporting, improve accuracy and reduce the cost of regulatory compliance for their clients. It harmonizes various data types from disparate sources to create logical data warehouses with a true semantic layer.

Read the full success story.

Building a Future-Ready Semantic Layer for Enterprise Success

Semantic layers hold great promise for the future of business, revolutionizing how we interact and work with data. Implementing a business semantic layer is not just a technical endeavor but a strategic initiative that can transform how your enterprise leverages data and information. By recognizing the signs that a semantic layer is needed and following best practices for its implementation, you can achieve a unified, transparent and actionable view of your enterprise information. This, in turn, empowers your organization to make informed decisions, drive innovation and sustain a competitive edge in the marketplace.

Incorporating a business semantic layer is a journey that requires careful planning, collaboration and ongoing commitment. But with the right approach (hint, start small and expand), a semantic layer can unlock tremendous value and pave the way for a data-driven future.

To learn more about how to unleash the power your enterprise data with semantic technologies, download our whitepaper. Check out our on-demand webinar “The Role of Taxonomy and Ontology in Semantic Layers,” where we discuss how to develop your semantic layer for actionable insights and business growth.

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