V Skill HF-D7.2-004 cross-sector 9 skills
Assessing data quality
Checks data sets for completeness, accuracy, duplicates and timeliness, describes the defects found and determines whether the data is usable.
How hrmforce measures this
- Assessment method
- Work sample test · rho 0.33 (SD 0.09)
- hrmforce instrument
- Work sample test, Knowledge test (client-specific)
- Competency (50-framework)
- Accuracy
- Trainability
- high
- Demand outlook 2026 to 2030
- rising
The candidate performs a representative work sample under standardised conditions.
Behavioural anchors
| Level | Behaviour at this level |
|---|---|
| N1 Guided | Works through a checklist on a supplied data set and reports missing or duplicate records to the person responsible. works under supervision and follows instruction · routine, one variable at a time · own task |
| N3 Proficient | Defines validation rules for a data source, quantifies the defects and advises whether the data is fit for use. sets own approach and seeks input proactively · several variables, some ambiguity · own team or process |
| N5 Leading | Establishes the data quality framework for the entire organisation and decides which sources count as reliable. sets the standard and the policy · strategic, under high uncertainty · organisation, value chain or profession |
N2 and N4 are deliberately not anchored. Raters place them between the anchors, following the O*NET convention.
Underlying skills
These skills inherit the assessment route and the behavioural anchors of this construct.
| T | Skill | Definition | Demand outlook 2026 to 2030 |
|---|---|---|---|
| V | Checking completeness of a data set Completeness check | Checks whether all expected records, periods and mandatory fields are present and reports what is missing. | rising |
| V | Detecting and merging duplicates Deduplication | Finds duplicate records with exact and fuzzy matching and merges them without losing information. | stable |
| V | Handling missing values Missing data · Imputation | Investigates why values are missing and chooses in a justified way between exclusion, imputation or separate reporting. | rising |
| V | Performing data profiling Data profiling | Maps range, distribution, unique values and fill rate per field before an analysis starts. | rising |
| V | Validating dates and formats Format validation | Checks date, currency and code fields for format, time zone and impossible values and corrects deviations. | declining |
| V | Checking referential integrity Referential integrity | Checks whether references between tables hold and detects orphan records without a matching parent. | stable |
| V | Defining data quality rules Data quality rules | Records testable rules per field for accuracy, range and obligation and has them checked automatically on a schedule. | rising |
| V | Reconciling data sources Data reconciliation | Compares counts and totals between two sources, explains the differences and documents the reconciliation. | stable |
| V | Recognising measurement errors in registration Measurement error | Recognises systematic entry and registration errors in source systems and discusses correction with the process owner. | rising |