Data quality issues management

Identifying data quality issues is only the first step in solving them. In this chapter, Jack Olson explains the need for a data quality assurance management system -- one that asesses the impact and causes of poor data quality.

The following excerpt from Data Quality: The Accuracy Dimension is printed with permission from Morgan Kaufmann,...

a division of Elsevier. Copyright 2003. Click here to read the complete Chapter 5.

Data quality investigations are all designed to surface problems with the data. This is true whether the problems come from stand-alone assessments or through data profiling services to projects. It also does not matter whether assessments reveal problems from an inside-out or an outside-in method. The output of all these efforts is a collection of facts that get consolidated into issues. An issue is a problem with the database that calls for action. In the context of data quality assurance, it is derived from a collection of information that defines a problem that has a single root cause or can be grouped to describe a single course of action.

That is clearly not the end of the data quality effort. Just identifying issues does nothing to improve things. The issues need to drive changes that will improve the quality of the data for the eventual users.

It is important to have a formal process for moving issues from information to action. It is also important to track the progress of issues as they go through this process. The disposition of issues and the results obtained from implementing changes as a result of those issues are the true documentation of the work done and value of the data quality assurance department.

Figure 5.1 shows the phases for managing issues after they are created. It does not matter who performs these phases. The data quality assurance department may own the entire process. However, much of the work lies outside this department. It may be a good idea to form a committee to meet regularly and discuss progress of issue activity. The leader of the committee should probably be from the data quality assurance department. At any rate, the department has a vested interest in getting issues turned into actions and in results being measured. They should not be passive in pursuing issue resolution. This is the fruit of their work.

An issue management system should be used to formally document and track issue activity. There are a number of good project management systems available for tracking problems through a work flow process. 

The collection of issues and the management process can differ if the issues surface from a "services to project" activity. The project may have an issues management system in place to handle all issues related to the project. They certainly should. In this case, the data quality issues may be mixed with other issues, such as extraction, transformation, target database design, and packaged application modification issues. It is helpful if data quality issues are kept in a separate tracking database or are separately identified within a central project management system, so that they can be tracked as such. If "project services" data profiling surfaces the need to upgrade the source applications to generate less bad data, this should be broken out into a separate project or subproject and managed independently.

Turning facts into issues

Data quality investigations turn up facts. The primary job of the investigations is to identify inaccurate data. The data profiling process will produce inaccuracy facts that in some cases identify specific instances of wrong values. Other cases identify where wrong values exist but identification of which value is wrong is not known, and in yet other cases identify facts that raise suspicions about the presence of wrong values.

Facts are individually granular. This means that each rule has a list of violations. You can build a report that lists rules, the number of violations, and the percentage of tests performed (rows, objects, groups tested) that violated the rule. The violations can be itemized and aggregated.


There is a strong temptation for quality groups to generate metrics about the facts and to "grade" a data source accordingly. Sometimes this is useful; sometimes not. Examples of metrics that can be gathered are

  • number of rows containing at least one wrong value
  • graph of errors found by data element
  • number of key violations (nonredundant primary keys, primary/foreign key orphans)
  • graph of data rules executed and number of violations returned
  • breakdown of errors based on data entry locations
  • breakdown of errors based on data creation date

The data profiling process can yield an interesting database of errors derived from a large variety of rules. A creative analyst can turn this into volumes of graphs and reports. You can invent an aggregation value that grades the entire data source. This can be a computed value that weights each rule based on its importance and the number of violations. You could say, for example, that this database has a quality rating of 7 on a scale of 10.


Dig Deeper on MDM best practices