Today, companies are collecting more data than ever before, but the real competitive advantage will be gained by those who can interpret it quickly and accurately. This requires a semantic layer that simplifies complex data structures and presents them to decision makers in a way that they can understand. We show how Strategy’s semantic graph opens new dimensions in data-driven operations.

What is semantic layer?

The semantic layer helps non-technical users understand database language by translating it into simpler terms. This allows users to interact with and understand data without being overwhelmed by its complexity.

The semantic layer acts as a sophisticated translator, translating the complex language of databases into business-friendly terms that enable non-technical users to interact with and use data effectively.

Why does your organization need it?

Some benefits of a well implemented semantic layer:

Consistent data understanding: Promotes a culture of data governance. This ensures that everyone in the organization shares a common understanding of metrics and definitions.

Adaptability and scalability: Easily accommodate new data sources and evolve business needs, keeping your analytics infrastructure agile and responsive to change.

Enhanced user empowerment: Empower non-technical users to perform sophisticated analytics without relying on IT, driving a more data-centric culture.

Exploring the semantic layer landscape

There are three primary approaches to semantic layers, each with unique benefits and limitations:

Hyperscaler-specific semantic layers: These are designed for organizations that heavily use specific cloud platforms. This can limit flexibility and lock you into a vendor.

Universal semantic layers: Serve as a presentation layer for data models. While they excel at operational reporting, they often lack advanced computational capabilities, requiring additional tools for complex analytics.

BI semantic layers: These layers combine schema modeling with analysis and visualization, providing a robust computational engine for advanced analysis. Strategy’s semantic graph exemplifies this approach.

What makes Strategy’s semantic graph different?

Integrated engine framework: The semantic graph is more than just data mapping; it actively transforms data into multidimensional representations optimized for in-depth analysis. Its integrated engines, such as Dynamic SQL and Federated Multi-Source, provide a cohesive and scalable solution.

Seamless AI and BI integration: Strategy AI enhances the user experience with features such as Auto Answers and AI visualizations. The semantic graph maintains data integrity and governance, enabling accurate interpretation and computation.

Flexibility and portability: Easily move between data sources or cloud platforms without rebuilding your data models. This helps you avoid vendor lock-in.

Open architecture: The semantic graph’s flexible architecture supports a wide range of analytics tools, enhancing BI capabilities and empowering data scientists.

Summary

The position of the semantic layer within the data stack is critical. Universal semantic layers work well for operational reporting with fixed queries. In contrast, Strategy’s semantic graph works in the analytics layer. This enables real-time data interaction and supports complex analytics that users can perform on the fly.

Applying AI directly to raw data can lead to inaccurate results and governance issues. The semantic graph reduces risk by understanding user requests. It breaks them down into understandable pieces. It then performs the necessary calculations to deliver reliable results.

The Strategy semantic graph is a powerful and flexible tool that unlocks the full potential of your data. By connecting complex data structures with actionable business insights, it enables organizations to make informed decisions and drive success.

(source: https://www.strategysoftware.com/blog/unlocking-the-power-of-the-microstrategy-semantic-graph)