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Managing Data Quality

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current customers, contact details exist and are correct in the customer relationship management database).

In summary, data quality dimensions prompt the analysis of data requirements. These dimensions are, however, ultimately superseded by the content of the resulting data specification, which becomes the formal basis on which to test the quality of each relevant data set.

Given these technical complexities that underpin data quality, organisations face a challenge to ensure a consistent, effective and efficient approach to data management across all relevant stakeholders. Facing this challenge is the role of data quality management.

What is data quality management?

The subject of this book is data quality management, so it is important that the meaning of this term is clear. ISO 8000-2 defines data quality management as:

Whilst definitions in ISO standards can sometimes require a little effort to understand, this definition is relatively clear. In essence, it describes an overall approach consisting of different activities to monitor, manage and control data quality with suitable oversight to direct and control these activities.

Data quality management is more than just managing data quality; it involves consideration of why data are incorrect in the first place. For example, if you are undertaking a data cleansing exercise without also addressing the underlying root cause of the data errors, then it is highly likely to result in the data cleansing having to be repeated on a regular basis.

Data quality management is also not about trying to achieve an idealistic, ‘perfect’ data set. As mentioned earlier, the costs, time and effort to achieve perfection will not be attractive to any organisation and would probably be impossible to achieve. Data quality management is, therefore, about balancing current data quality with required quality and the benefits that can be achieved by these improvements.

Summary

Data are a key element of any enterprise.

By treating data as an asset, the enterprise focuses on delivering value from data.

Data quality is conformance to requirements rather than abstract perfection.

The next chapter explores the challenge of managing the requirements to establish the foundation for conformance.

coordinated activities to direct and control an organization with regard to data quality.