Metadata facilitates data integration by providing information about data structures, relationships, and formats, enabling efficient data merging and transformation.
Metadata-driven Data Quality
Metadata ensures data quality by enforcing standards, validating data, and providing a record of data changes, enabling data cleanliness, accuracy, and consistency.
Metadata-driven Data Profiling
Metadata enables data profiling by providing information about data characteristics, relationships, sources, meaning, frequency, statistics, and visualization, facilitating data analysis and quality assessment.
Data Characteristics
Data characteristics are properties or attributes that describe the nature, behavior, and relationships of data, including quantitative, qualitative, temporal, spatial, and relationship characteristics.
5 Categories of Data Characteristics
Data characteristics can be categorized into five categories: Quantitative, Qualitative, Temporal, Spatial, and Relationship characteristics.
Quantitative Characteristics Examples
Examples of quantitative characteristics include mean, median, mode, variance, standard deviation, length, precision, count, sum, and range.
Purpose of Standard Deviation
Standard deviation measures variability, identifies outliers, measures uncertainty, allows dataset comparison, and is used in statistical analysis and data modeling.
SD in Quality Control
Standard Deviation is used in quality control to set control limits, detect shifts, monitor process stability, determine acceptance criteria, determine lot size, and calculate process capability.
SD in Control Limits
Standard Deviation is used in control limits to calculate Upper and Lower Control Limits, detect Out-of-Control (OOC) conditions, specify tolerance limits, and adjust control limits.