Data Quality Rules provide the foundation for operational management of Data Quality. Rules can be integrated into application services or Data services that supplement the Data lifecycle, either through Commercial-Off-The-Shelf …
Data Quality – Improvement (Part 1)
In order to improve the Data Quality, DQ Analysts must have the clear picture about Current State of Data Quality as well as Organizational Readiness for Data Quality. Getting a …
Data Quality Assessment
DQ Analysts will need to sort out and prioritize findings. The goal of an initial Data Quality Assessment is to learn about the Data in order to define an Actionable …
RDM – Reference Data Management
Reference Data is any data used to characterize or classify other data, or to relate data to information.Reference Data Management entails control and maintenance of defined domain values, definitions, and …
Data Management – What is Master Data?
Different types of Data play different roles within an organization. They also have different management requirements. Malcolm Chisholm has proposed a six-layer Taxonomy of Data that includes Metadata, Reference Data, …
Reference and Master Data Management – Guiding Principles
Shared Data: Reference and Master Data must be managed so that they are shareable across the organization. Ownership: Reference and Master Data belong to the organization, not to a particular …
Data Quality – Parsing and Transformation
Data Parsing and Formatting Data Parsing is the process of analyzing data using pre-determined rules to define its content or value. Data parsing enables the data analyst to define sets …
Data Quality – Cleansing and Enrichment
Data Cleansing / Scrubbing Data Cleansing or Scrubbing transforms data to make it conform to data standards and domain rules. Cleansing includes detecting and correcting data errors to bring the …
Data Quality – Data Profiling
Data Profiling is a form of data analysis used to inspect data and assess quality. Data profiling uses statistical techniques to discover the true structure, content, and quality of a …
Data Quality – Causes of Data Quality Issues – Part-03
Issues Caused by System Design Failure to Enforce Referential Integrity: Referential integrity is necessary to ensure high quality data at an application or system level. If referential integrity is not …