Data quality, analytics and reporting

Data cleaning is the documented process of detecting, investigating and resolving invalid, inconsistent, duplicate, incomplete or implausible records before analysis. Good cleaning preserves an audit trail and avoids deleting inconvenient data without a rule.

Data validation is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Data-editing rules is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Missing-data analysis is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Outlier detection is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Duplicate-response detection is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Fraud detection is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Logical-consistency checks is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Range checks is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Skip-logic verification is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Open-end coding is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Codeframe development is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Automated coding is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Human coding is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Inter-coder reliability is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Data tabulation is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Cross-tabulation is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Banner tables is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Significance testing is a research approach within data quality, analytics and reporting used to reduce uncertainty around is the data internally coherent? The design should connect the business question, target population, evidence source, analysis and decision rule.

Statistical significance asks whether an observed pattern is unlikely under a specified null model; business significance asks whether the size and consequence of that pattern matter for a decision. A result can satisfy one test and fail the other.

Effect size is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Confidence intervals is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Multiple-comparison correction is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Weighting data is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Derived variables is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Composite indices is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Scale construction is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Reliability testing is a research approach within data quality, analytics and reporting used to reduce uncertainty around is the data internally coherent? The design should connect the business question, target population, evidence source, analysis and decision rule.

Validity testing is a research approach within data quality, analytics and reporting used to reduce uncertainty around is the data internally coherent? The design should connect the business question, target population, evidence source, analysis and decision rule.

Norms and benchmarks is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Dashboard design is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Data visualisation is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Storytelling with data is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Executive-summary writing is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Insight versus observation is a decision comparison between Insight and observation. The useful question is not which is universally better, but which approach fits the objective, evidence, population, timing and consequence of error.

Implication versus recommendation is a decision comparison between Implication and recommendation. The useful question is not which is universally better, but which approach fits the objective, evidence, population, timing and consequence of error.

From data to decision is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

How to avoid overclaiming is a practical research workflow for moving from a decision question to a defensible design, evidence trail and action. The method should make assumptions, quality checks and interpretation rules explicit before data collection begins.

Causation versus correlation is a decision comparison between Causation and correlation. The useful question is not which is universally better, but which approach fits the objective, evidence, population, timing and consequence of error.

Research-report structure is a research approach within data quality, analytics and reporting used to reduce uncertainty around is the data internally coherent? The design should connect the business question, target population, evidence source, analysis and decision rule.

Reporting uncertainty is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Reporting limitations is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Verbatim selection is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

City-level analysis with small samples is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Segment-level analysis is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Heat maps is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Importance-performance maps is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Driver maps is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Perceptual maps is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Research scorecards is a research approach within data quality, analytics and reporting used to reduce uncertainty around is the data internally coherent? The design should connect the business question, target population, evidence source, analysis and decision rule.

Research repositories is a research approach within data quality, analytics and reporting used to reduce uncertainty around is the data internally coherent? The design should connect the business question, target population, evidence source, analysis and decision rule.

Knowledge-management systems is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Taxonomy design for insights is a concept within data quality, analytics and reporting that helps organise evidence around is the data internally coherent? It should be defined operationally so different teams measure and interpret it consistently.

Research-decision traceability is a research approach within data quality, analytics and reporting used to reduce uncertainty around is the data internally coherent? The design should connect the business question, target population, evidence source, analysis and decision rule.