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
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Managing the quality of customer data has its challenges: It is typically collected from a wide range of sources and channels and very often those responsible for entering or capturing data have no incentive to do so accurately. Even if they do much of it, including contact data can go out-of-date rapidly. Despite this most customer data has one major advantage over many other types of data which is agreed and accepted standards and reference data. While these standards do vary from country to country, they are at least universally understood and have an enormous impact on the approach and the effort required for managing data quality.

Because of these global standards and references, there is general agreement on what a complete, valid and correctly formatted address should look like - likewise person or business name, telephone number, date of birth, email address etc. So this means that if I am sharing my customer data with my business partners at least we have a common view of what high quality data should look like and the checks we need to make to assess the quality levels.

Another huge benefit is that third party service providers and technology vendors also understand the requirements and standards by which to measure and improve customer data and they know that these requirements are largely the same for all vendors. As a result large numbers of service bureaux and technology vendors are able to offer well developed, generic, out-of-the-box products and services to tackle customer data quality issues. These can deliver a lot of value with minimal or no customization and the effort to acquire and implement these solutions is small.
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