Unfortunately, generative AI alone is famously poor at crunching numbers, and it tends to hallucinate incorrect facts and figures, so it’s not a reliable foundation for data-driven decision-making. the weaknesses that each technology possesses individually. GenAI expands BI to provide text explanations for However, combining AI with BI can address data visualizations, answer natural language questions, and suggest wording for SQL queries, which can expand access. BI can provide generative AI with the data governance guardrails requir to provide reliable intelligence. And, with the right design, this AI+BI combination can be integrat into the day-to-day apps of every employee, from the factory floor to the executive suite.
Trust BI can serve this function more effectively if it leverages a semantic layer. By itself, GenAI that searches internal data repositories will invariably find some good data along with data that is incorrect, outdat, or mislabel. This is one core reason why GenAI models hallucinate: They struggle to differentiate between good and bad information.
Did the sales team change their territory
Chances are, there are some data sets that belgium whatsapp number data still reflect the previous sales territories. Did they realign territories the year before too? If the answer is yes, for a GenAI model, understanding what’s current versus what’s outdat or simply wrong is like the fairground game of three cups with a coin under one cup. To a data algorithm, each cup has slightly over a 33% likelihood of being right, so the algorithm picks one of the three at random to answer a data analytics question. For demanding enterprises, those odds are far too low for a winning business outcome.
This is where the beauty of a semantic
A semantic layer technology vett in production there have been other investigative by demanding enterprises and public sector agencies enables authoriz users with AI-power assistance to create reports, dashboards, and visualizations alb directory without neing to know the underlying data structures. The semantic layer provides a consistent view of data across the organization, ensuring that everyone is using the same definitions and calculations, without the multi-year painful saga of previous-generation master data management initiatives.