Findings from a readiness assessment will help determine where to start and how quickly to proceed. Findings can also provide the basis for roadmapping program goals. If there is strong support for data quality improvement and the organization knows its own data, then it may be possible to launch a full strategic program. If the organization does not know the actual state of its data, then it may be necessary to focus on building that knowledge before developing a full strategy. Organizational readiness to adopt data quality practices can be assessed by considering the following characteristics:
- Management commitment to managing data as a strategic asset: As part of asking for support for a Data Quality program, it is import to determine how well senior management understands the role that data plays in the organization. To what degree does senior management recognize the value of data to strategic goals? What risks do they associate with poor quality data? How knowledgeable are they about the benefits of data governance? How optimistic about the ability to change culture to support quality improvement?
- The organization’s current understanding of the quality of its data: Before most organizations start their quality improvement journey, they generally understand the obstacles and pain points that signify poor quality data. Gaining knowledge of these is important. Through them, poor quality data can be directly associated with negative effects, including direct and indirect costs, on the organization. An understanding of pain points also helps identify and prioritize improvement projects.
- The actual state of the data: Finding an objective way to describe the condition of data that is causing pain points is the first step to improving the data. Data can be measured and described through profiling and analysis, as well as through quantification of known issues and pain points. If the DQ team does not know the actual state of the data, then it will be difficult to prioritize and act on opportunities for improvement.
- Risks associated with data creation, processing, or use: Identifying what can go wrong with data and the potential damage to an organization from poor quality data provides the basis for mitigating risks. If the organization does not recognize these risks, it may be challenging to get support for the Data Quality program.
- Cultural and technical readiness for scalable data quality monitoring: The quality of data can be negatively impacted by business and technical processes. Improving the quality of data depends on cooperation between business and IT teams. If the relationship between business and IT teams is not collaborative, then it will be difficult to make progress.
