Data Engineering

Data aggregation, normalization, cleaning, & deduplication

A coherent dataset from records that were never designed to work together.

We reconcile source formats before aligning their units and definitions. Time horizons are made comparable without disguising differences in granularity or authority. The resulting dataset is coherent but not artificially uniform. Genuine errors remain distinct from legitimate perspectives and from changes over time, with meaning and provenance preserved throughout reconciliation.

We profile distributions and missingness before normalising representation. Reference data is then aligned, with precedence and survivorship rules defined around the intended use. Duplicate detection begins with exact identifiers where they are trustworthy. Probabilistic similarity can extend coverage, while temporal evidence and domain-specific constraints prevent plausible but invalid matches. Uncertain merges remain reviewable rather than being forced. Transformations are reproducible and original values are retained where appropriate. Every correction or exclusion carries sufficient lineage, as does each consolidated record, so users can understand its derivation.

Entity resolution, and labelling

Evidence-based identity and labelling that remain consistent across records.

Evidence-Based Identity Resolution

We determine which records refer to the same real-world entity, then assign labels that remain consistent across sources. Evidence is selected for the entity and the decision context in question. The approach accommodates name changes and identifiers that are unreliable or reused. It also recognises legitimately shared attributes. Confidence thresholds and review effort remain proportionate to the consequences of a false match or label.

The method follows the entity type and available evidence. Authoritative identifiers provide a strong anchor. Deterministic rules establish relationships that can be stated exactly. Probabilistic comparison extends resolution when evidence is incomplete, while learned similarity can support larger or more varied collections. Graph context and temporal constraints keep a plausible match within its real-world setting. Resolution models represent splits and mergers, as well as succession or parent–subsidiary relationships, rather than assuming identity is static. Classification follows a similar principle. Expert annotation provides authoritative examples. Weak supervision can extend them, while active learning concentrates review on informative cases. Structured vocabularies keep the labels coherent. Every resulting link or label carries confidence and source provenance, together with the relevant model or rule version.

Applied statistics & data modelling

Data turned into defensible estimates that support explanation and decision.

We turn data into defensible estimates by clarifying the quantity involved and the decision it must support. Examining how the available data arose determines what can be explained. This distinction keeps a demand forecast separate from an estimate of an intervention’s effect, even when both use the same variables. Explicit assumptions show the basis of the result, while variation and uncertainty keep it within evidential limits.

Our work uses experimental design where intervention can be controlled and quasi-experimental design where it cannot. Sampling establishes what the evidence represents. Regression may explain relationships, while hierarchical and Bayesian models can share evidence without erasing meaningful variation. Time series address change over time. Causal inference tests intervention effects. Survival analysis handles time to event, classification separates defined groups and uncertainty quantification shows what remains unresolved. Method selection follows the data-generating process and sample structure, with the consequence of error setting the standard. Diagnostics test calibration and residual structure before examining sensitivity to alternative specifications. Distribution shift and subgroup behaviour show whether performance travels beyond the development sample. Practical decision thresholds connect the model to use. Analysis remains reproducible and states clearly what the model does not establish.

Advanced transformations & enrichment

Higher-value information with traceable derivation.

We derive higher-value analytical variables while preserving the conditions under which they remain valid. A temporal interval can place events in sequence, while spatial context shows where they occurred. Network measures expose structure between records. Classifications and embeddings add analytical representation. Composite indicators and derived relationships support broader comparison. Every variable retains its definition and lineage, together with its maintenance requirements. Enrichment remains reusable and auditable without hiding leakage or assumptions, and false precision can be managed rather than embedded.

We design transformation logic around the question being answered and implement it as reusable versioned operations over clearly defined inputs. External enrichment is first assessed against its legal terms. We then examine coverage and update cadence, followed by population bias. Geographic and temporal compatibility show whether the source can support the intended comparison. Reliable entity linkage determines whether it can be attached at all. Derived values remain connected to the source observations and algorithm version. Confidence flags or distributions preserve material uncertainty instead of flattening it into a single apparently exact field. A sparse internal record can, for example, be enriched with travel times and neighbourhood characteristics. Environmental exposure or market conditions may add further context, while network context can reveal connected effects. Analytical coverage increases without confusing what was observed with what was derived.

Data quality, integrity and completeness auditing

A clear view of when data is reliable and why its limits matter.

Use-Specific Data Quality

We audit data against the decisions and controls it supports. This shows where the evidence is fit for use and where a gap creates risk. The use-specific view distinguishes data suitable for aggregate planning from evidence reliable enough for an individual-level decision. It also exposes systematic missingness that a headline completion score can conceal.

We begin with validity and accuracy, then test whether records are unique and timely enough for the intended use. Referential integrity shows whether relationships hold. Lineage explains derivation, while distributional stability reveals changes that ordinary validation may miss. We also investigate the mechanisms behind missing or contradictory values. Comparisons with authoritative sources and business rules establish the expected state. Pipeline behaviour and manual interventions show how the actual state arose, including how a defect propagates into a report or model and then into automated action. Findings are prioritised by consequence. Each receives practical ownership and a route to remediation, followed by appropriate monitoring. Wherever possible, the control sits close to the point at which the error originates.