AKALYSIS

Real-world data analysis

Real-world data analysis for observational, routine and linked datasets

For teams working with data that was not collected under ideal conditions but still has to support serious decisions.

Real-world data can be powerful, but it comes with structural messiness: inconsistent measurement, linkage issues, missingness, selection effects, exposure ambiguity, and operational constraints. AKALYSIS helps turn those datasets into credible evidence by combining statistical thinking, epidemiological reasoning, and practical data strategy.

Led by Dr. Andrew Kingston, statistician, epidemiologist, data scientist and educator. BSc (Hons) MSc PhD CStat SFHEA.

Area of Focus

Data quality and structure

Assessing what the data can and cannot support before the analysis starts drifting into false certainty.

Area of Focus

Observational interpretation

Distinguishing useful evidence from over-claiming, especially when confounding and selection risks are substantial.

Area of Focus

Linked and routine datasets

Working with administrative, service, cohort, or operational sources that evolve over time and need careful handling.

When this is a good fit

The dataset comes from routine systems, linked records, administrative data, or observational collection.

Measurement quality, missingness, and consistency are part of the analytical challenge.

Stakeholders need clear answers but the data has obvious limitations.

You need a more defensible route from raw data to interpreted evidence.

Typical analytical work

  • Linked data analysis strategy
  • Observational study interpretation
  • Bias, confounding, and data-quality review
  • Routine and administrative data workflows
  • Reporting limitations and strengths honestly without weakening the whole project

Why AKALYSIS

Statistical rigour with epidemiological and data science depth

AKALYSIS is designed for projects where the analysis needs to be methodologically coherent as well as useful. That means careful thinking about design, model choice, interpretation, bias, uncertainty, and how results will be challenged by collaborators, reviewers, decision-makers, or the real world.

Book a Free Consultation

Book a free initial call to discuss your data and what rigorous analysis would actually look like for your project. No obligation, no sales pitch.

Free initial consultation, no obligation.