Data Quality

There are a number of theoretical frameworks for understanding data quality. A systems-theoretical approach influenced by American pragmatism expands the definition of data quality to include information quality, and emphasizes the inclusiveness of the fundamental dimensions of accuracy and precision on the basis of the theory of science (Ivanov, 1972). One framework seeks to integrate the product perspective (conformance to specifications) and the service perspective (meeting consumers' expectations) (Kahn et al. 2002). Another framework is based in semiotics to evaluate the quality of the form, meaning and use of the data (Price and Shanks, 2004). One highly theoretical approach analyzes the ontological nature of information systems to define data quality rigorously (Wand and Wang, 1996).

A considerable amount of data quality research involves investigating and describing various categories of desirable attributes (or dimensions) of data. These lists commonly include accuracy, correctness, currency, completeness and relevance. Nearly 200 such terms have been identified and there is little agreement in their nature (are these concepts, goals or criteria?), their definitions or measures (Wang et al., 1993). Software engineers may recognise this as a similar problem to "ilities".

MIT has a Total Data Quality Management program, led by Professor Richard Wang, which produces a large number of publications and hosts a significant international conference in this field (International Conference on Information Quality, ICIQ).

In practice, data quality is a concern for professionals involved with a wide range of information systems, ranging from datawarehousing and business intelligence to customer relationship management and supply chain management. One industry study estimated the total cost to the US economy of data quality problems at over US$600 billion per annum (Eckerson, 2002). In fact, the problem is such a concern that companies are beginning to set up a data governance team whose sole role in the corporation is to be responsible for data quality. In some organisations, this data governance function has been established as part of a larger Regulatory Compliance function - a recognition of the importance of Data/Information Quality to organisations

Problems with data quality don't only arise from incorrect data. Inconsistent data is a problem as well. Eliminating data shadow systems and centralizing data in a warehouse is one of the inititatives a company can take to ensure data consistency.

The market is going some way to providing data quality assurance. A number of vendors make tools for analysing and repairing poor quality data in situ, service providers can clean the data on a contract basis and consultants can advise on fixing processes or systems to avoid data quality problems in the first place. Most data quality tools offer a series of tools for improving data, which may include some or all of the following:

1. Data profiling - initially assessing the data to understand its quality challenges
2. Data standardization - a business rules engine that ensures that data conforms to quality rules
3. Geocoding - for name and address data. Corrects data to US and Worldwide postal standards
4. Matching or Linking - a way to compare data so that similar, but slightly different records can be aligned. Matching may use "fuzzy logic" to find duplicates in the data. It often recognizes that 'Bob' and 'Robert' may be the same individual. It might be able to manage 'householding', or finding links between husband and wife at the same address, for example. Finally, it often can build a 'best of breed' record, taking the best components from multiple data sources and building a single super-record.
5. Monitoring - keeping track of data quality over time and reporting variations in the quality of data. Software can also auto-correct the variations based on pre-defined business rules.
6. Batch and Real time - Once the data is initially cleansed (batch), companies often want to build the processes into enterprise applications to keep it clean.

There are several well-known authors and self-styled experts, with Larry English perhaps the most popular guru. In addition, the International Association for Information and Data Quality (IAIDQ) was established in 2004 to provide a focal point for professionals and researchers in this field.

ISO 8000 is the international standard for data quality.