Data Science
The extraction of valid insight from data: statistics, measurement theory, analytical methods, machine learning and — most relevantly for workplace evidence — the disciplines of validity, bias, uncertainty and inference that separate finding from artefact.
Workplace strategy now runs on quantitative evidence streams, and every one of them can mislead: samples skew, sensors miscount, averages hide, correlations masquerade as causes. Data science supplies the immune system — the methodological discipline that keeps triangulation honest, flags false precision, and distinguishes a demand signal from a measurement artefact. Without it, more data means more confident error.
- Is this finding real, or an artefact of how it was measured?
- What uncertainty does this number carry, and does the decision respect it?
- When is team-level analysis valid, and when does small-cohort noise masquerade as signal?
- What can and cannot be inferred from observational workplace data?
- Statistical and measurement methodology
- The workplace's own data with its documented limitations (every stream has them)
- Validation studies: sensor accuracy, survey reliability, reported-versus-observed calibration
- Causal inference literature — the discipline of what observational data cannot say
Confidence levels attached to every quantitative input (the Calibration discipline); triangulation as standard practice, single-stream conclusions as flagged exceptions; aggregation thresholds respected analytically, not just legally; AI-supported analysis treated as acceleration of interpretation, never replacement of it.
Calibration
The systematic exposure and governance of every assumption the strategy stands on — growth, attendance, sharing, overlap, standards, finance, policy and technology.
Occupancy Assessment
Occupancy validates, challenges and enriches workplace demand — measuring how space is actually used, not how its use is reported.
Dynamics Assessment
A structured workforce assessment capturing reported People and Process demand: work patterns, activities, locations, preferences, experience and reported occupancy.
Targeted Insights
Focused mixed-method investigations that explain specific contradictions the broad evidence has surfaced but cannot resolve.
Precision theatre: dashboards reporting utilisation to decimal places from sensors with ±10% error, correlations presented as causes, and 'data-driven' invoked as authority rather than method. The most dangerous workplace analytics are the confident ones.
Measurement validity in workplace sensing — what each technology actually measures versus what it is sold as measuring — deserves far more independent study than it receives.