Data Warehousing and Business Intelligence for e-Commerce

Chapter 9: Data Quality and Integrity Issues

Overview

The areas of quality and data warehousing have had an uneasy relationship since the modern data warehousing era began in the early 1990s. Hardly anyone would argue with the need for high-quality, correct data as a key underpinning of business intelligence and decision support. And nearly every data warehousing architecture diagram that has ever been produced illustrates how the extraction/transformation/loading (ETL) process is infused with quality assurance processes, often labeled as "data cleansing" functions.

Yet when actually deployed, many arguably most data warehousing environments are burdened with quality problems of one form or another. Decisions made on the basis of data warehousing content are questioned; two or three data marts with overlapping data content provide different results to the exact same query; and so on.

And now, in the context of e-commerce, the data quality problem worsens. Why? As we'll discuss in this chapter, there are several reasons, including an increase in cross-enterprise data sharing for B2B scenarios and the need to incorporate consumer-provided data into B2C data warehouses. We'll explore the problems and present tactics to deal with the issues and to help ensure that e-commerce data warehouses contain accurate, correct data for business intelligence purposes.

A Brief Overview of Data Quality and Data Warehousing

Since the mid-1970s, students studying computer science or business information systems have had the importance of data quality impressed upon them. "Garbage in, garbage out" abbreviated as "GIGO" has long been a watchword. In the realm of transactional applications, initially enforcing data quality...

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