Implementation of a Business Data Analytics Model Using Power BI and DAX

Abstract

In the modern business environment, organizations generate large volumes of data from operational processes, financial transactions, customer interactions, and sales activities. Transforming these data into meaningful information is essential for improving organizational performance, identifying business trends, and supporting evidence-based decision-making. Business intelligence technologies provide effective solutions for integrating, processing, analyzing, and visualizing business data. This study explores the implementation of a business data analytics model using Microsoft Power BI and Data Analysis Expressions (DAX), focusing on data modeling, analytical calculations, key performance indicators (KPIs), interactive visualization, and business decision support.

The primary objective of the study is to develop an integrated analytical approach that transforms raw business data into structured, measurable, and visually interpretable information. The proposed approach incorporates several stages, including data collection, data preparation, data transformation, data modeling, the definition of analytical measures, and the development of interactive dashboards. Power BI is used as the principal platform for integrating data from different sources, establishing relationships between datasets, and presenting analytical results through interactive reports. DAX is applied to define calculated measures, implement business rules, and evaluate performance indicators according to selected analytical criteria.

Particular attention is given to the role of data modeling in ensuring consistency, accuracy, and analytical flexibility. An appropriately structured data model enables relationships between business entities to be represented effectively and supports reliable calculations across different analytical dimensions. DAX measures facilitate the calculation of key business indicators, including sales revenue, total costs, profit, profit margin, and performance comparisons across periods, products, and customer segments. These indicators provide a quantitative basis for evaluating operational and financial performance.

The study also examines the contribution of interactive visualization to business analytics. Dashboards incorporating charts, tables, filters, and slicers allow users to explore data dynamically, identify significant patterns, compare performance indicators, and investigate deviations from expected results. Integrating data modeling, DAX calculations, and interactive visualization creates a coherent analytical environment in which business information can be examined from multiple perspectives.

The proposed model demonstrates how Power BI and DAX can be combined to establish a practical framework for business data analytics. Its potential value lies in improving information accessibility, facilitating performance monitoring