What this is
Data Management is the process that turns collected or received data into a structured, documented, and analysis ready dataset.
It covers the work between the original data and the analytical file. This includes checking completeness, missingness, ranges, duplicates, coding, labels, skip patterns, inconsistencies, variable structures, and other issues that can affect analysis.
Good data management also means knowing what happened to the data. The original dataset should remain preserved, while cleaning and transformation take place in clearly identified working files. Decisions should be documented so that the path from the original data to the final analytical dataset can be understood and, where necessary, reproduced.
When you need it
You need Data Management when you already have data from AcadStat, another research team, an organisation, a previous study, a survey platform, a monitoring programme, or another source, and that data needs to be prepared for analysis.
You may also need it when an analytical dataset already exists but the preparation process is unclear, undocumented, inconsistent, or difficult to reproduce.
What AcadStat delivers
AcadStat preserves the original dataset as received and performs data preparation through controlled working files.
Depending on the project, this can include data cleaning, coding, recoding, variable construction, consistency checks, missing data assessment, duplicate review, data restructuring, merging of datasets, documentation, and preparation of the final analytical file.
You receive an analysis ready dataset together with the relevant codebook and documentation of important data preparation decisions.
The result is not simply cleaner data. It is data whose structure and preparation can be understood.
