Full Description
Business Intelligence (BI) has entered a new era—one where organizations no longer compete on creating the most astonishing dashboards, but on the mathematical sophistication embedded within their KPI metrics. As data volumes grow, data distributions become skewed, data uncertainty becomes structural, and decision‑making requires more nuance than simple averages or totals can provide.
Most DAX-based BI systems rely on a limited set of aggregation functions such as SUM, AVERAGE, MIN, MAX, COUNT. These functions are easy to use, but they fail in scenarios involving skewed data distributions, multi‑criteria business decision‑making, non‑linear interactions between business criteria, data uncertainty, vagueness, and time‑dependent data.
In practice, when this set of DAX aggregation functions are applied to the above data scenarios, the outcomes could bring distorted KPIs, misrepresent risk, or oversimplify complex business scenarios.
This book presents how to transcend these limitations using mathematically grounded methods implemented directly in DAX. It is written to address that gap: to show how Power BI and DAX aggregation functions, when combined with advanced mathematical frameworks, can produce metrics that are not only more accurate, but more intelligent with higher business value.
What You Will Learn:
Build smarter KPIs with higher business value
Model uncertainty and human judgment applied in BI scenarios
Handle skewed, irregular, or incomplete data for BI applications
Implement nonlinear and non‑additive aggregation for BI multi-criteria decision making
Create predictive, state‑aware BI systems
Use DAX as a mathematical language, not just a plain calculation tool
Who This Book is For:
This book is suitable for mid-to-senior level BI professionals, data analytics managers, and data scientists. Users should be comfortable with DAX, Power BI, and semantic modeling.
