One of the common traps I see data quality analysts falling into is measuring data quality in a uniform way across the entire data landscape. For example, you may have a transactional dataset that has hundreds of records with missing values or badly entered formats. In contrast, you may have

If you are looking for a way to fund your data quality objectives, consider looking in the closets of the organization.  For example, look for issues that cost the company money that could have been avoided by better availability of data, better quality of the data or reliability of the

Once in a while, people run into an issue with the data that doesn't really need to be fixed right to ensure success of a specific project.  So, the data issues are put into production and forgotten.  Everyone always says, “We will go back and correct this later.”  But that

There are companies that have no data quality initiative, and truly do believe that if they see no data problem. In effect, they say that if it does not interfere with day-to-day business, then there is no data quality problem.  From what I have seen in my consulting experience, it usually

Over my last two posts, I suggested that our expectations for data quality morph over the duration of business processes, and it is only at a point that the process has completed that we can demand that all statically-applied data quality rules be observed. However, over the duration of the

One of the significant problems data quality leaders face is changing people's perception of data quality. For example, one common misconception is that data quality represents just another data processing activity. If you have a data warehouse, you will almost certainly have some form of data processing in the form