Data integration brings records from different systems into a shared analytical view. Systems often use different identifiers, definitions, dates and levels of detail, so matching fields alone can create a polished but unreliable picture. Marketways reconciles the business meaning and tests whether the combined data can answer the intended question. This creates a dependable foundation for reporting, modelling and automation.
The decision this method supports
We use Data Integration & Analytical Readiness to help clients answer: Can information from different systems be combined without changing its meaning or creating false comparisons?
How the method works
Analytical readiness means that data from different sources can be combined without losing its business meaning. The work reconciles identifiers, definitions, dates, levels of detail and ownership. Integration is therefore a semantic task as well as a technical one.
A business example
A property company may combine maintenance records by asset with financial records by building and supplier invoices by contract. The datasets cannot be compared responsibly until the relationships between assets, buildings, suppliers and time periods are made explicit.
How the client uses the result
Integrated data allows a business to examine a customer, asset or process across systems instead of making decisions from disconnected fragments. The benefit is a more complete view and a dependable foundation for analysis, reporting and automation.
What we deliver
We produce a documented dataset, quality assessment, measure dictionary, analytical-readiness decision or management view. A manager should be able to see what the information represents, which limitations remain and what action the resulting view can support.
Limits and complementary methods
Standardisation enables comparison, but careless standardisation can remove meaningful differences between systems, locations or periods.
Selected methods and techniques
We select from these established methods according to the decision, evidence and operating conditions.
- API and integration analysis: Analyse how technical systems exchange data and services through APIs, messaging, files or integration platforms, including data contracts, volumes, latency, error handling, security and ownership. It is used where technical systems form part of the business system. It does not replace detailed software engineering.
- Availability / performance / quality decomposition: Separate lost effective production into availability, performance and quality components using stated and internally consistent bases. Define planned production time, running time, ideal cycle time or rate, total units, good units, exclusions and the asset and period covered; then calculate the three factors and reconcile their combined effect without counting the same loss twice. The method performs the decomposition, the Overall Equipment Effectiveness framework supplies the common metric structure, and the resulting Availability / Performance / Quality Breakdown records the populated calculation. The factors locate types of loss but do not explain their operational causes.
- Confidence provenance analysis: Determine what produced a stated confidence level, including the initial model score or prior, later evidence, repeated or dependent signals, questions, tool results and update rules. Separate final confidence from evidence newly earned during the process and from empirical calibration. The analysis can expose decorative reasoning or confidence inflation; provenance alone does not say whether the conclusion is correct.
- Critical-to-quality analysis: Translate what customers or stakeholders value into specific, measurable characteristics the system must deliver, with targets and tolerances. It links needs to measurable performance requirements, with targets and tolerances validated against operational capability as well as stakeholder preference. The characteristics are only as good as the evidence about what stakeholders actually value.
- Data-flow exposure analysis: Trace where data, especially sensitive or personal data, travels through a system and identify the points where it could be exposed, copied, retained unnecessarily or reach parties without a need to see it. It specialises data-flow modelling for protection. It depends on data flows being mapped completely.
- Data-flow modelling: Model how data moves through a system: its sources, the processes that transform it, where it is stored and where it goes. It exposes duplication, gaps, delays and privacy exposure in information handling. It models data movement, not the physical databases used.
- Decision provenance analysis: Trace material parts of a decision, such as its frame, assumptions, evidence, alternatives and recommendations, to their documented origins and subsequent changes. Use timestamps, version history and independent pre-exposure records where available, while marking mixed or unknown origins. The method establishes lineage and sequence; a claim that a source influenced judgement requires additional evidence.
- Multi-source evidence integration: Combine evidence about the same person or team from several sources, such as ratings, interviews, exercises, observation and performance records, using a stated procedure. Where sources disagree, examine and report the disagreement rather than averaging it away. Integration improves confidence when sources are relevant, sufficiently distinct and interpreted with their dependence and limitations visible.
- Performance-data integration: Combine performance evidence from different sources after aligning the person, role, team, period, event, metric, denominator and definition used in each source. Document lineage, access rules, unmatched records, missing data and changes in measurement, and protect personal or confidential information. Integration creates a comparable evidence base; joining records does not correct biased measures, make missing evidence complete or prove why performance differs.
- SERVQUAL-style service-quality assessment: Assess customers' perceived service quality across defined dimensions using either the full SERVQUAL instrument or a clearly labelled adaptation. Specify the service context, population, sample, item wording, response scales, dimensions and scoring. If expectations and perceptions are both measured, explain when and how each is collected and how gaps are calculated; test whether adapted items and dimensions remain valid for the context. Using familiar SERVQUAL dimensions without the original instrument is SERVQUAL-style work, not automatically a full SERVQUAL study. Perceived quality is distinct from satisfaction and from observed compliance with service standards, and a gap score does not by itself identify its operational cause.
Parent method family
Data Foundations & Business Intelligence explains how this method connects to adjacent methods and relevant services.
Related service families
These service families contain business questions supported by this method. Service pages link to the wider method family so readers can understand the complete analytical approach.