Secondary Data Re-analysis & Advanced Analytics
Assess and re-analyse existing datasets to address focused research or analytical questions.
Data Science & Analytics
Turning existing and newly assembled data into transparent analysis, interpretable evidence and useful communication.
Start with the analytical need, not a catalogue of technologies
Support is organised around the question, dataset, decision context and reporting requirement.
Organisations and researchers may have underused datasets, unclear analytical pathways, models that need robust validation, or results that are difficult to interpret and communicate. The work can begin with data assessment and extend through preparation, modelling, validation, visualisation and reporting.
An engagement may focus on one defined analytical problem or a connected workflow; sophisticated tools are not a substitute for a clear question and fit-for-purpose data.
Analysis aligned with the question and intended use
Statistical Modelling
Use descriptive, inferential and multivariable methods suited to the design, measures and assumptions.
Machine Learning & Predictive Analytics
Develop and validate predictive approaches where the problem, data and intended use justify them.
Data Visualisation & Dashboards
Communicate patterns, uncertainty and performance through clear figures, reports and decision-focused dashboards.
Analytical Automation
Create reproducible analytical workflows and proportionate automation for repeatable preparation, analysis or reporting.
A reproducible path from question to communication
The sequence is adapted to the scope and may not require every stage.
Question
Data assessment
Preparation
Analysis / modelling
Validation
Interpretation
Communication
Analytical value depends on fit, validation and interpretation
Model complexity is useful only when it improves the answer to the defined problem.
Prediction and explanation are different aims, and association does not establish causation. Method selection therefore depends on what the analysis must support; machine learning is not automatically preferable to conventional statistical modelling or a simpler transparent method.
Training performance is not evidence of external or generalisation performance. Validation strategy, leakage, calibration, uncertainty and relevant comparison measures require attention. Poor-quality or unrepresentative data cannot be repaired merely by applying a more sophisticated algorithm.
Tools such as Python, R, SQL, Stata, SPSS and Power BI may support delivery where appropriate, within documented and reproducible analytical workflows.
Analytics across research and programme settings
- Secondary research and publication-oriented analysis
- Programme and monitoring datasets
- Public-health and socioeconomic evidence
- Recurring analytical and reporting needs
Deliverables shaped by data and use
- Cleaned and structured datasets
- Analytical datasets
- Statistical analyses
- Statistical models
- Model diagnostics
- Machine-learning analyses where appropriate
- Analytical figures
- Dashboards
- Automated analytical workflows
- Research tables
Connect analytics with research and programme evidence
Explore verified analytical contributions, selected projects or related service capabilities.
Have a dataset or analytical problem that requires deeper attention?
Share the question, available data and intended use to discuss a proportionate analytical approach.