Informatica Data Quality

One of the root causes of poor quality information is defects in the data definition, specifically the “information product specifications.” Because information is a product of our business, manufacturing and service processes, the analogy of an “information product” is real, and the requirement for quality in “information product specifications” is a critical requirement for Information Quality.

This blog is the first of a series of three blogs on the critical quality characteristics (or measures) of information quality required to achieve Total Information Quality Management.

1. Information Product Specification Data Quality
2. Information Content Quality
3. Information Presentation Quality

What constitutes the “Information Product Specifications” data?

• Information standards
• Data names
• Data definitions
• Attribute valid value set or range of values
• Value format for structured attributes (VIN, SSN, Product Codes)
• Business rule specifications of constraints on data
• Information Steward accountable for data definition quality
Quality of Information Product Specifications data includes:

• Information standard quality: Standards are enterprise-focused, adopted by all information stakeholders and developers. Enterprise information standards are always used to ensure consistency of data names, definitions, business rule specifications in the same way that Financial standards (GL Chart of Accounts) are adopted by all in developing budgets.

• Data name quality: Data names are common and intuitive to all information stakeholders in labelling classifications of objects or events (entity types) and attributes (facts) that the enterprise needs to know about.

• Data definition quality: Definitions of business concepts (business terms), entity types and attributes completely, correctly and clearly define the real world object (entity type), fact (attribute) or business concept (business term) that the enterprise needs to know about, so there is no misinterpretation of data across business areas. Definitions must be of a single class of real world objects (entity type), characteristic (attribute) of a real world object or business concept that generally does not show up as an entity type or attribute, but may be used in an entity type or attribute definition.

• Attribute valid value set or range of value quality: Classifications of things, such as country code, gender, territory, industry code or medical procedure, require standardized codes or names to assure consistency and avoid value synonyms (different codes mean the same thing) or homonyms (same code means different things).

• Value format for structured attributes (VIN, SSN, Product Codes) quality: Value structures should be standardized at the most global level possible, and must be standardized at an enterprise level. Well-defined formats for certain types of information generally allow easy recognition and memory of values and their meaning. Such formats should be able to reduce errors in data capture or in information presentation.

• Quantitative attribute value UoM (measurement, currency amounts) quality: Quantitative attribute values have explicit and unambiguous Unit-of-Measure associated with the value. This prevents misinterpretation of numeric values.

• Business rule specification quality: The specifications for constraints on data should represent the inherent constraints on the real world objects and events and their characteristics and secondarily the enterprise’s business policies. Failing to define business rules that are inherent to the real world can lead to faulty information model relationships and database designs. Faulty information model relationships and database designs can lead to unstable designs and high modification costs—and at worst—inability to share information and high costs of redundant databases and data transformation and movement. The business rule specifications must be able to be implemented in both manual tests and within the database designs and software in a way that is easily verifiable and modifiable should they change.

• Business Information Steward accountable for data definition quality: Every data object (business term, entity type and attribute) should have both a business subject matter expert and an Information Resource specialist approve all information product specification data for the set of Information Resource Data they oversee for the well-being of the enterprise and its customers and stakeholders.

For more about Information Product Specification Quality, see Chapter 5, “Assessing Data Definition and Information Architecture Quality,” in Improving Data Warehouse and Information Quality. This contains a more comprehensive list of quality characteristics with examples. It also describes how to measure these quality characteristics, including how to measure data reuse.

What do you think? Share your thoughts about “Information Product Specification Data” and how to help bring in the Information Quality Revolution!!! The next blog will discuss data content quality characteristics.