Data inconsistency is a significant challenge for organizations, with Gartner estimating that poor data costs businesses a staggering $12.9 million annually. For decades, data leaders have searched for a unified solution—a “single source of truth”—that can align all business intelligence (BI) and analytics efforts. The goal is to ensure that everyone within an organization makes decisions based on the same data definitions and metrics, promoting consistency and clarity across departments.
To address these inconsistencies, BI providers introduced the concept of a semantic layer. This abstraction layer acts as a bridge between raw data, which is typically stored in complex tables with cryptic field names, and the understandable business logic required for decision-making. By mapping the raw data to familiar terms and definitions like “revenue” or “profit,” the semantic layer allows business users to perform self-service analytics without needing deep technical expertise in data structures. This, in turn, helps organizations maintain consistent data interpretation across various departments.
However, as BI tools and their semantic layers have proliferated across organizations, the ideal of a single source of truth has become increasingly elusive. Tools like SAP BusinessObjects introduced semantic layers in the 1990s, but as BI tools such as Tableau, Power BI, and Looker became more user-friendly, they replaced monolithic solutions that were harder to navigate. Today, organizations face a landscape where various BI, analytics, and data science tools are used across different departments, each with its own semantic layer and set of definitions. This has led to discrepancies in how data is understood and used within the same organization.
The result is a growing mistrust of the data and intelligence derived from these reports. Different parts of the business may use varying definitions for the same metrics, leading to confusion and inconsistencies in decision-making. For example, how should the organization define an “active customer”? Is it someone with an ongoing subscription, someone who logged in within the past week, or someone using a free trial? Inconsistent definitions like these can cause problems across departments—finance might face billing issues, operations may struggle with reporting, and customer success teams could have trouble identifying key metrics for renewals. As organizations continue to scale, overcoming these inconsistencies requires adopting a more universal and unified semantic layer to ensure consistent, accurate decision-making across all business units.