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How a Semantic Layer Brings Data Mesh to Life

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The Buyer’s Guide to the Best Semantic Layer Tools for Data and Analytics

As a savvy data and analytics leader, you may be looking to use a semantic layer to help your organization make smarter data-driven decisions at scale. But it can be hard to know what to choose and how to implement it well.

The Buyer’s Guide to a Semantic Layer for data and analytics

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The reality is, companies must define a semantic layer, no matter what. If you don’t have your data and analytics colleagues do it, all your end users will do it for themselves in Tableau, Qlik, Excel or whichever front end tool they are using.

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“Data and analytics leaders must adopt a semantic layer approach to their enterprise data assets or face losing the battle for competitive advantage.”

Guido De Simoni, Senior Director, Analyst at Gartner

This guide looks at several technical approaches to implementing a semantic layer for your data and analytics stack. Included is an implementation checklist, technology scorecard, and chart of pros and cons with several example scenarios.

As a data and analytics leader, either on the business or tech side, reading this guide will help you adopt a semantic layer approach for your data assets. This guide explains where a semantic layer fits into modernizing your data and analytics infrastructure. It will help you:

  • Drive consistency
  • Reduce compute costs
  • And improve ease of use for a wide variety of consumption types and use cases
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Spoiler Alert!

Enterprises that implement a semantic layer powered by data virtualization get instant access to their structured and unstructured data from relational data warehouses, data lakes, and enterprise SaaS applications to create logical data warehouses, accessible with SQL, MDX, DAX, Python and REST. This provides access to data from a broader set of distributed sources and storage formats – without requiring users to know where the data resides or how it’s formatted.

The Top Signs You Need a Semantic Layer

Watch out for multiple analytics tools, complaints about data access, and inconsistent reports – along with other indicators.

  1. Business units or groups have strong preferences for different analytics tools
  2. Business analysts and/or data scientists complain about a lack of data access
  3. The slow pace of data integration drives the business to build their own Solutions
  4. Reports from different BI tools use similar terms but show different results
  5. Business executives express doubts about their confidence in the numbers
  6. And improve ease of use for a wide variety of consumption types and use cases

Key Considerations When Implementing a Semantic Layer

To start, you’ll want to cover all of your use cases, leverage data virtualization, and future-proof your technology choices.

  1. Business units or groups have strong preferences for different analytics tools
    Your semantic layer must work across a variety of BI and ML consumers. It should be decoupled from a single consumption style.
  2. Offers Tabular and Multidimensional Views
    Your semantic layer must offer both tabular and multidimensional views to cover the widest range of use cases.
  3. Supports Data Platform Virtualization
    Your semantic layer must leverage data virtualization capabilities to abstract away data platform differences and minimize platform lock-in.
  4. Easy Model Development and Sharing
    Your semantic layer should provide a multi-user design environment and markup language to promote re-use and enforce standardization.
  5. Ability to Express Business Concepts and Functions
    Your semantic layer must support business constructs and core analytics requirements around time intelligence and hierarchical rollups.
  6. Query Performance & Caching
    Your semantic layer should include a comprehensive performance management system that goes beyond simple caching techniques.
  7. Support for Business Intelligence and Data Science Workloads
    Your semantic layer needs to support a variety of workloads including business intelligence and data science.
  8. Security & Governance
    Your semantic layer should integrate with your single sign-on (SSO) standards and support column-level security, row-level security and impersonation.