By providing a unified view of an organization’s data, the semantic layer simplifies data into common business terms. It acts as a translator between raw data and business applications, giving business context to the data. By modeling the organization’s data with clearly defined values and dimensions, higher-level concepts such as key performance indicators can be defined and calculated consistently and accurately. This ensures that measures and dimensions, once established, are applied consistently. For example, any report or dashboard that refers to “total revenue per month” will always use the same definition.
The semantic layer bridges the gap between raw data and business insights, ensuring consistent interpretation and reporting of data across an organization. As organizations increasingly rely on data-driven insights and metrics, the importance of the semantic layer in data analysis and decision-making will continue to grow. It will become the cornerstone of future analytics tools and, more broadly, the data landscape.
The Rise of AI-Based Analytics
Just as AI answers developers’ questions about code, it will be able to answer data analysts’ and business users’ questions about reports. While data analysts will continue to step in at this point if the technology doesn’t allow it, AI is poised to become even more adept at answering questions. Over time, AI will ingest more and more data from across a company’s silos, including data from CRM systems, support ticketing systems, and ERP systems. Data analytics platforms will also develop capabilities that leverage a company’s knowledge bases, including information about its customers and metrics, as well as insights from external sources (like stock data, news feeds, and market analysis). By accumulating vast amounts of data, AI-powered data analytics platforms will further bridge the gap between data and business teams, enabling them to collaborate much more effectively.
