Evaluating the Impact of Advanced Time Intelligence Models on Strategic Financial Decision-Making Using Power BI

Abstract:

Introduction: Strategic financial decision-making is, at its core, an exercise in comparison across time: actual against budget, current quarter against the same quarter of the previous year, and the recent trend against the longer-run average. This study evaluates whether advanced time-intelligence models, implemented as governed DAX measures in Microsoft Power BI, measurably improve the quality, timeliness and confidence of such decisions in the finance function of a mid-sized enterprise.

Methods: A descriptive-cum-evaluative, matched-task pre/post design was adopted for the financial years 2020 to 2024 (twenty quarters). Two reporting environments were compared: a conventional spreadsheet baseline with manually prepared comparatives, and a Power BI semantic model built on a dedicated fiscal calendar table with advanced DAX time-intelligence measures (SAMEPERIODLASTYEAR, DATEADD, PARALLELPERIOD, TOTALYTD, DATESINPERIOD, DATESBETWEEN and rolling four-quarter baselines). Twenty-four finance professionals completed identical forecasting, variance-analysis and working-capital tasks in both environments.

Results: The advanced time-intelligence model reduced the mean absolute percentage error of quarterly re-forecasts from 11.8, 15.6 and 21.3 per cent to 6.4, 8.9 and 12.7 per cent at one-, two- and four-quarter horizons respectively, a relative improvement of 40 to 46 per cent. Variance-report preparation time fell from 6.5 days to half a day, budget-revision lead time from 34 to 12 days and the strategic decision cycle from 21 to 9 days. Mean decision confidence rose from 3.2 to 4.3 on a five-point scale, and reported working-capital days declined from 68 to 54 over the evaluation window.