Data management is not just the final cleaning stage. It begins with how information is structured and continues through validation, documentation and preparation for analysis. My experience includes organising large datasets, identifying discrepancies, cleaning and validating records, triangulating information and using Excel and statistical software to support data-quality checks.
How I approach the work
A practical workflow starts by understanding the questionnaire, data structure and intended analysis. From there, checks can be designed around required fields, valid ranges, internal consistency, duplicate records and relationships between variables. Where an issue is identified, it should be investigated against the source information or project protocol rather than corrected by assumption. Clear notes on changes help preserve an audit trail.
I also work with research teams to make quality checks useful in practice. Findings need to be communicated in a way that allows the appropriate team member to follow up, resolve an issue and understand whether it affects other records. Before analysis, the dataset is organised, variables labelled consistently where appropriate, and supporting documentation kept alongside the data.
What this looks like in practice
My CV records experience in data management, data cleaning and validation, Excel-based triangulation, data security and compliance, and the use of Stata and SPSS. The exact checks and deliverables depend on the study design, data-collection platform and client protocol.
Relevant areas of work
- Data cleaning and validation
- Consistency and discrepancy checks
- Dataset organisation and preparation
- Excel-based data triangulation
- Documentation and quality-control processes
- Data security and compliance