Why a semantic layer is critical to modern data and analytics architecture

This whitepaper explains what a semantic layer is, why it is so valuable, and where it fits into a data and analytics architecture.

Companies will have to define a semantic layer, no matter what. If you don’t assign experts on your data and analytics team to do this, then end users will do it for themselves in Tableau, PowerBI, Qlik, DataRobot, or whichever business intelligence (BI) or machine learning (ML) front end they are using.

On the business side, besides being a colossal waste of time, allowing BI users and data scientists to each create their own metrics and business terms creates chaos and inconsistency.

On the technical side, requiring data engineering to support numerous extracts and ETL pipelines to feed BI/ML tools is a maintenance headache and adds complexity for governance professionals.

For data and analytics leaders on both the business and technical sides, this whitepaper will explain how a semantic layer:

  • Will make life easier on both business and technical fronts
  • Provides simple and secure access to clean, understandable data for BI users and data scientists alike
  • Saves time on data wrangling
  • Frees up data engineering resources to work on more rewarding and valuable projects

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Three Reasons to Implement a Semantic Layer

  1. Simplify your data & analytics stack
    When a semantic layer is added to the architecture, the stack is simplified, not further complicated. You can retire multiple proprietary and conflicting semantic layers that are tough to maintain and impossible to keep in sync.
  2. Future-proof your technology choices
    By investing in a semantic layer, you free the business from vendors’ proprietary chains and create the flexibility you’ll need as new cloud data platforms and BI/ML tools inevitably continue to proliferate and evolve.
  3. Create a shared data literacy
    Best of all, a semantic layer gets everyone speaking the same language, playing by the same rules of data access, and relying on the same insights for smarter decision-making.