Data quality

Data Quality refers to the quality of data. Data are of high quality "if they are fit for their intended uses in operations, decision making and planning" (J.M. Juran). Alternatively, the data are deemed of high quality if they correctly represent the real-world construct to which they refer. These two views can often be in disagreement, even about the same set of data used for the same purpose.

History

Most data technologies have grown out of early desire to accurately send mail. Before the rise of the inexpensive server, massive mainframe computers were used to maintain name and address data so that the mail could properly arrive at its destination. The mainframes used business rules to correct common misspellings and typos in name and address data, as well as to track customers who had moved, died, gone to prison, married, divorced, or experienced other life-changing events. Government agencies began to make postal data available to a few service companies to run customer against the national change of address registry (NCOA). This technology saved large companies millions of dollars compared to manually correcting customer data. Large companies saved on postage, as bills and direct marketing made its way to the intended customer more accurately. Initially sold as a service, data quality moved inside the walls of corporations, as low-cost and powerful server technology became available.

Although most companies think of name and address when they think of data quality, data quality is recognized today as the act of improving all types of data, such as supply chain data, ERP data, transactional data, and more. For example, making supply chain data conform to a certain standard has value to an organization by 1) avoiding overstocking of similar but slightly different stock; or 2) improving the understanding of vendor purchases to negotiate volume discounts; or 3) avoiding logistics costs in stocking and shipping parts across a large organization.

While name and address data has a clear standard as defined by local postal authorities, other types of data have few recognized standards. There is a movement in the industry today to standardize certain non-address data. The non-profit group GS1 is among those groups that are spearheading this movement.

Overview

There are a number of theoretical frameworks for understanding data quality. 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 the so-called 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.

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

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.
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.

References

• Eckerson, W. (2002) "Data Warehousing Special Report: Data quality and the bottom line", Article
• Kahn, B., Strong, D., Wang, R. (2002) "Information Quality Benchmarks: Product and Service Performance," Communications of the ACM, April 2002. pp. 184-192. Article
• Price, R. and Shanks, G. (2004) A Semiotic Information Quality Framework, Proc. IFIP International Conference on Decision Support Systems (DSS2004): Decision Support in an Uncertain and Complex World, Prato. Article
• Redman, T. C. (2004) Data: An Unfolding Quality Disaster Article
• Wand, Y. and Wang, R. (1996) “Anchoring Data Quality Dimensions in Ontological Foundations,” Communications of the ACM, November 1996. pp. 86-95. Article
• Wang, R., Kon, H. & Madnick, S. (1993), Data Quality Requirements Analysis and Modelling, Ninth International Conference of Data Engineering, Vienna, Austria. Article

See also

• Information quality
• Data profiling
• Data Cleansing
• Master Data Management
• Noisy text analytics

External links

• Gartner Magic Quandrant Report on Data Quality Tools (April 2006)
• MIT's Total Data Quality Management Program With academic and industry courses.
• International Conference on Information Quality Annual conference on Data Quality research.
• The International Association for Information and Data Quality (IAIDQ) Professional association for those interested in improving business effectiveness through quality data and information.