by Angela Guess
SmartData Collective continued their series on root causes of poor data quality with two more problems. The first is corporate evolution: “An organizations undergoes business process change to improve itself. Good, right? Prime examples include: company expansion into new markets; new partnership deals; new regulatory reporting laws; financial reporting to a parent company; downsizing. If data quality is defined as “fitness for purpose,” what happens when the purpose changes? It’s these new data uses that bring about changes in perceived level of data quality even though underlying data is the same. It’s natural for data to change. As it does, the data quality rules, business rules and data integration layers must also change.”
Another root problem is “secret code.” The article explains, “Databases rarely start begin their life empty. The starting point is typically a data conversion from some previously existing data source. The problem is that while the data may work perfectly well in the source application, it may fail in the target. It’s difficult to see all the custom code and special processes that happen beneath the data unless you profile.”
Read about how to address these problems here.
photo credit: psyberartist
