Indatafelen

Indatafelen is a term used in data management and analytics to describe inaccuracies and inconsistencies that are introduced during the capture, entry, or transmission of data before it is stored or analyzed. The concept is linked to data quality and the reliability of subsequent analyses, models, and decisions.

Common sources of indatafel include manual data entry mistakes (typos, transpositions, miskeying), misinterpretation of field definitions, encoding or formatting errors, sensor or measurement device faults, transmission glitches, and timing or alignment issues across datasets. Examples include a date entered as 02/30/2023, a numeric value recorded with the wrong unit, or duplicate records that confuse aggregation.

The types of errors vary from simple typographical errors and missing values to more complex problems such as inconsistent categorizations, out-of-range values, incorrect data types, and timestamp misalignments. These faults can propagate through data pipelines, biasing statistics, degrading model performance, and undermining trust in analytics.

Mitigation focuses on preventing errors at the source and identifying them early. Practices include input validation and constrained fields, format and range checks, mandatory fields, check digits for critical identifiers, and standardized coding schemes. In data pipelines, ETL/ELT processes incorporate data cleansing, deduplication, normalization, and reconciliation against reference data. Regular data quality assessments measure accuracy, completeness, consistency, timeliness, and validity to monitor and improve indatafel handling over time.

Indatafelen is thus a central concern in data quality management, influencing the reliability of analyses and the integrity of data-driven decisions.