Meaning That Survives Transfer
We develop metadata architectures that preserve meaning as information moves across technical and organisational boundaries. A value remains connected to what it denotes and to the method by which it was produced. Its units and classifications stay explicit, as does its period of validity. Any restriction on use travels with it. This contextual layer complements shared exchange formats and gives people or applications a dependable basis for combining information.
We examine the source schema together with its identifiers and code lists. Lineage and business rules are considered in relation to the way information will be consumed. Shared concepts are separated from local representations, after which explicit mappings show how the two relate. Validation is designed into the exchange instead of being added once integration failures appear. The resulting framework may use a canonical model or an application profile. Registries can coordinate shared resources, while APIs and event contracts provide operational exchange. Linked-data conventions remain available when wider semantic coordination is required. The choice depends on how much coordination is needed and how much autonomy must remain, as well as the expected pace of change. Published contracts allow a new source or consumer to connect without another one-off conversion. Coverage can expand while repeated reconciliation falls. The same principle is especially valuable in research informatics: information from different instruments remains interpretable across institutions and disciplines, long after the project that produced it.
Statistical Schema Induction
Evidence-based structures for unfamiliar and inconsistent data.
We use statistical schema induction to infer likely structures from unfamiliar or inconsistently documented data. The same evidence reveals constraints and relationships that documentation may not contain. By treating the records themselves as evidence, we expose field drift and local variants. Operationally important attributes become visible, as do unwritten rules, while observed frequency remains distinct from semantic meaning.
From Patterns to Defensible Structure
Our analysis begins with type and distribution before examining cardinality. We profile missingness and value formats, then move into co-occurrence and hierarchy. Cross-field dependency analysis shows which structures travel together. Clustering helps distinguish recurring record families from noise, while probabilistic techniques quantify less certain patterns. Documentation and domain expertise establish whether an observation is intentional, accidental or dependent on context. The resulting model retains confidence and exceptions, including variation over time. It provides evidence for migration and validation, as well as better documentation, without imposing an artificially uniform schema. Migration and data-quality teams gain broad coverage of long-tail variants. Expert review can remain focused on patterns whose meaning is uncertain.
Schema Compositions, Alignment & Mapping
Connected data models that preserve important distinctions.
Alignment Without False Equivalence
We compose and map data models so information can travel between them without sacrificing the semantics used for analysis. Operational meaning remains intact, as does the evidence needed for audit. The work shows when concepts are genuinely shared and when one refines another. It also identifies apparent correspondences that would conceal a material difference. Scope and granularity may explain the divergence. Time and identity can also change what a field represents. The design accounts for measurement differences that similarly named fields alone leave unresolved.
We examine entities and their attributes before modelling relationships and constraints. Identifiers are checked against units and lifecycle assumptions so that a mapping does not outlive its valid context. Every correspondence is expressed at the appropriate strength. It may be exact, broader or narrower. In other cases it is conditional, derived or incompatible. Transformation rules remain traceable to the source evidence and the design decision behind them. The integration landscape determines the architecture. Some settings justify a common model, while others need a mediated mapping layer. A modular composition allows shared concepts and domain extensions to evolve independently.
Our experience
Problems our experts have solved
Metadata & Interoperable Data Systems
For a consortium receiving operational data from twelve partners, our experts resolved differences hidden behind superficially similar files. Field names did not carry the same meaning, and units varied. Granularity and update rules also differed. We established a common metadata profile supported by stable identifiers and unit conventions. Machine-readable mappings incorporated validation specific to each contributor. The partners retained their internal systems, while the consortium gained a repeatable ingestion process and a clear record of how every source related to the shared model.
Statistical Schema Induction
Our experts helped an organisation make sense of several years of inherited supplier records. There was no reliable data dictionary, despite hundreds of subtle variations behind a common file shape. We identified stable record families and inferred their field constraints. Anomalous exports could then be separated from legitimate supplier extensions. Migration planning proceeded against quantified patterns and known exceptions rather than assumptions drawn from a handful of sample files.
Schema Compositions, Alignment & Mapping
Following an acquisition, we resolved a core modelling conflict that prevented two businesses from consolidating customer and contract reporting. Each used “account” for a different thing: one meant a legal entity, while the other meant a commercial relationship. Our experts introduced separate concepts and defined the relationships permitted between them. Each source was then mapped accordingly. The consolidated view became reliable without embedding a false one-to-one equivalence in the new platform.