Overcoming data inconsistency with a universal semantic layer [Video]

Disparate BI, analytics, and data science tools result in discrepancies in data interpretation, business logic, and definitions among user groups. A universal semantic layer resolves those discrepancies.

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According to Gartner, bad data costs organizations $12.9 million a year. As a result, data leaders for decades have been searching for a single source of truth for their business intelligence (BI) and analytics to ensure that everyone bases business decisions on the same data and definitions.

To bring consistency to data, BI providers introduced the concept of a semantic layer — an abstraction layer between the raw data described in rows, columns, and field names that only data experts can understand and that informed insights for business users. A semantic layer hides the complexity of the data and maps it to business definitions, logic, and relationships. It allows business users to conduct self-serve analytics using standard terms like revenue and profit.

Semantic layers proliferate

Semantic layers …