JOURNAL OF BASIC SCIENCE AND ENGINEERING

Dynamic Calendar Intelligence and Advanced DAX for Accurate Forecasting in Power BI

DOI: https://doi.org/10.10399/JBSE.2837419260

Abstract:

Executive reporting is inherently multi-dimensional: a single board-level question typically ranges across a temporal grain, an organisational hierarchy, a comparative basis, a scenario assumption and a data-freshness expectation simultaneously. Business Intelligence platforms model the first four of these as separable concerns, but each of them silently depends on the fifth concern that is rarely modelled at all - the calendar against which every one of the other dimensions is resolved. This paper develops a multi-dimensional BI framework for executive reporting in Microsoft Power BI in which five reporting dimensions are specified explicitly and bound together by a calendar intelligence spine, and in which the maturity of that spine is measured on a six-axis Calendar Intelligence Maturity Index (CIMI). The framework is advanced as a design artefact and evaluated empirically.

A mixed-methods, multi-case design was adopted. The framework was implemented in four organisations of differing sector and calendar convention - a discrete-manufacturing group, a retail bank, a healthcare provider network and a higher-education institution - and evaluated through platform telemetry, documentary analysis of governance records, and a pre- and post-deployment survey of 142 executives and senior managers (72.4 % response). CIMI scores ranged from 2.67 to 4.67 across the four cases and ordered the outcome indicators without exception: executive report cycle time fell to 14.2 hours in the highest-maturity case against 44.1 hours in the lowest, time-to-insight for an ad-hoc executive query to 6.4 against 28.4 minutes, and reported-figure disputes to three against twenty-one per quarter. The overall executive reporting construct index rose from 2.77 to 4.25 on a five-point scale, and a respondent-level regression associated each additional CIMI point with a 0.46-point gain in that index (p < 0.001). Five design principles and a four-stage implementation roadmap are formalised.

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