Aggregation Functions with DAX and Power BI

Advanced Data Analytics in Business Intelligence
1. Auflage 2027
ISBN: 979-8-8688-3248-2
Verlag: APRESS L.P.

Buch, Englisch, Format (B × H): 155 mm x 235 mm

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.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Campos Bracho, Carlos

Pedrycz, Witold

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Microsoft Programmierung

Inhaltsverzeichnis

Chapter 1: DAX Aggregation Functions.- Chapter 2: Leveraging (DAX) MIN and MAX DAX Aggregation Functions in Conjunctive Fuzzy Logic for BI Applications.- Chapter 3: Leveraging (DAX) MIN and MAX DAX Aggregation Functions in Disjunctive Fuzzy Logic for BI Applications.- Chapter 4: Discrete Additive Integrals in BI: A Continuous View of Discrete Data with DAX Aggregation Functions.- Chapter 5: Discrete Non-Additive Integrals in BI: A Continuous View of Discrete Data with DAX Aggregation Functions.- Chapter 6: Markov Chains and Martingales: Stochastic Processes in BI.

Über Autor(innen)

Dr. Witold Pedrycz is a Professor in the Department of Electrical and Computer Engineering at the University of Alberta and a member of the Polish Academy of Sciences. He is also an author of 14 research monographs covering various aspects of computational intelligence and software engineering. Additionally, he is an accomplished Editor-in-Chief, having organized journals like Information Sciences and IEEE Transactions on Systems, Man, and Cybernetics. In 2007 he received the prestigious Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Council. He is a recipient of the IEEE Canada Computer Engineering Medal in 2008. In 2009 he received a Cajastur Prize for Soft Computing from the European Centre for Soft Computing for "pioneering and multifaceted contributions to Granular Computing."

Carlos Campos Bracho is a Business Intelligence consultant with over 15 years in the IT industry specializing in business intelligence, data analytics, and computational intelligence. His experience spans multiple industries including banks, oil and gas, pulp and paper, mining, and energy. Early in his career, he worked as a Data Strategic Analyst for the City of Edmonton in Canada, designing data architecture for transit analytics and dashboards for executive presentation. He also researched real-time business intelligence at the University of Alberta.