Category Archives: Data Quality

Focus on Quality

Data Warehouse Model Quality

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Data warehouses have a much different architecture and different business motivations than operational applications. For example, operational applications manage the day-to-day data needed to support the business. In contrast, data warehouse restructure operational data and place it in a format amenable to data mining and deep analysis. Operational applications rapidly read and write transactions with small amounts of data. In contrast, users only read data warehouses and can have extensive queries involving large data sets running for multiple minutes.

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Focus on Quality

Operational Model Quality

picture by xianrendujia via Flickr

Quality is an underappreciated aspect of data models. The purpose of a model is not just to capture the business requirements, but also to represent them well. A high quality model lessens the complexity of development, reduces the likelihood of bugs, and enhances the ability of a database to evolve. There are both qualitative and quantitative measures of quality.

This is the first of a two-part series. This blog discusses quality for day-to-day operational applications. Next month’s blog will discuss data warehouses.

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