Data Modelling
Backtesting a forecast using earlier periods
Recreate historical forecast issue dates, prevent future-data leakage, compare errors at the right horizon and interpret the result’s limits.
Section
Data Modelling
Recreate historical forecast issue dates, prevent future-data leakage, compare errors at the right horizon and interpret the result’s limits.
Data Modelling
Define reporting grain, facts, dimensions, dates, measures and changing attributes in a BI model that produces explainable figures.
Data Modelling
Set consistent reporting dates, weeks, financial periods and date roles across BI reports, with practical checks for boundaries and incomplete data.
Data Modelling
Build a testable warehouse brief from report queries, refresh times, history, concurrent use and acceptance checks.
Data Modelling
Compare operational sites using aligned definitions, useful denominators and case-mix context while keeping raw results and uncertainty visible.
Data Modelling
Use meaning, reuse, access and change impact to decide which reports should share a data model.
Data Modelling
Define what each BI table row represents, identify valid keys and distinguish additive amounts from balances and ratios before building reports.
Data Modelling
Record currency pair, rate source, date, direction and missing-rate treatment so converted BI figures can be reproduced and reconciled.
Data Modelling
Choose when to overwrite customer details, retain dated versions or show a current grouping, and check how each choice affects historical BI reports.
Data Modelling
Make reusable datasets understandable with approved measures, visible limits, ownership and a practical first-user check.
Data Modelling
Check extraction, mappings, delivered fields and report figures after a source schema changes.