By offering a unified view of an organization’s data, the semantic layer simplifies the data in 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 like KPIs can be consistently and accurately defined and calculated. This ensures that metrics and dimensions, once established, are uniformly applied. For instance, any report or dashboard referencing “total revenue by month” will always use the same definition.
The semantic layer bridges the gap between raw data and business insights, ensuring the 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 analytics and decision-making will continue to grow. It will become a cornerstone of future analytical tools and indeed of the data landscape more broadly.
The rise of AI-driven analytics
Just as AI answers questions about code for developers, AI will be able to answer questions about reports for both data analysts and business users. Although data analysts will still join in at this stage if the technology can’t handle it, AI is poised to become even better in responding to questions. With time, AI will ingest more and more of a company’s data siloes—including data from CRM systems, support-ticket systems, and ERP systems. Data analytics platforms will also develop functionalities that allow company knowledge bases to be used, including information about its clients and metrics, along with information drawn from external sources (like stock exchange data, news feeds, and market analysis). Bolstered by amassing vast amounts of data, AI-powered data analytics platforms will further bridge the gap between data and business teams and allow them to collaborate much more efficiently.