Provides a systematic approach to measure and evaluate data quality according to processes, techniques, and against data quality rules.
Monthly Archives: noviembre 2017
Introductory Notes
Data profiling supports an e ective data quality program and is an important first step for many information technology initiatives. Data profiling should be conducted both periodically for critical data stores, and event-driven as an important activity in evalu- ating data quality for a specific purpose. For example, organizations may profile data prior to finalizing […]
Introductory Notes
Data quality assessments support the development and attainment of predefined quality expectations and measure data quality for the most important business data attributes, organized by subject area. Initiation of assessments is driven by business priorities, often focused on highly shared data required by multiple business areas, data required for accurate financial statements, and data sets […]
Goals
1. A standard set of methods, tools, and processes for data profiling is established and followed. 2. Produce recommendations for improving the data quality improvements to data assets. 3. Physical data representation is factual, understandable, and enhances business understanding of the set of data under management.
Related Process Area
Business Glossary covers the standardization and approval of business terms that should be referenced in data profiling efforts. Metadata Management addresses classifications and detailed information about attributes and data elements referenced in data profiling efforts. Architectural Standards contains best practices that support the design and data representation standards referenced in data profiling e orts.
Goals
1. Establish and sustain a business-driven function to evaluate and improve the quality of data assets. 2. Standardize data quality assessment objectives, targets, and thresholds according to industry accepted techniques and processes. 3. Adopt standard data quality dimensions across domains for development of thresholds, targets, and metrics. 4. Establish an empirical method for statistical evaluation […]
Core Questions
1. Does the organization have a standard method for profiling data? 2. Has the organization trained or acquired sta resources with expertise in data profiling tools and techniques? 3. Does the organization apply statistical models to analyze data profiling reports? 4. Do policies and processes specify the criteria for a data store to undergo profiling? […]
Core Questions
1. Are standard data quality assessment techniques and methods documented and followed? 2. How are data quality assessments conducted, and are they scheduled or event-driven? 3. Are standard data quality rules developed for core data attributes? 4. Are data quality rules engines or assessment tools employed? 5. Are the business, technical, and cost impacts of […]
Related Process Area
Data Quality Strategy contains additional information related to data quality dimen- sions, including accuracy, timeliness, uniqueness, etc. Data Profiling contains additional information related to practices associated with determination of data quality used for data quality assessment. Metadata Management contains additional information related to metadata management information and expectations.
Functional Practice
[tabby title=»Performed»] Level 1: Performed 1.1 Basic profiling is performed for a data store(s). Basic profiling includes such things as analyzing the types or number of distinct values in a column, number or percent of zero, blank or null values, string length, date ranges, patterns, as opposed to the more advanced analysis such as cardinality, […]