Data Quality issues can emerge at any point in the data lifecycle, from creation to disposal. When investigating root causes, analysts should look for potential culprits, like problems with data entry, data processing, system design, and manual intervention in automated processes.
These causes of issues also imply ways to prevent issues: through improvement to interface design, testing of data quality rules as part of processing, a focus on data quality within system design, and strict controls on manual intervention in automated processes.
Issues Caused by Lack of Leadership
Research indicates that many data quality problems are caused by a lack of organizational commitment to high quality data, which itself stems from a lack of leadership, in the form of both governance and management.
Within most organizations, data disparity (differences in data structure, format, and use of values) is a larger problem than just simple errors; it can be a major obstacle to the integration of data. One of the reasons data stewardship programs focus on defining terms and consolidating the language around data is because that is the starting point for getting to more consistent data.
A lack of recognition on the part of leadership means a lack of commitment within an organization to managing data as an asset, including managing its quality (Evans and Price, 2012).
Barriers to effective management of Data Quality include:
- Lack of Awareness on the part of Leadership and Staff
- Lack of Business Governance
- Lack of Leadership and Management
- Difficulty in Justification of Improvements
- Inappropriate or Ineffective Instruments to Measure Value
These barriers have negative effects on customer experience, productivity, morale, organizational effectiveness, revenue, and competitive advantage. They increase costs of running the organization and introduce risks as well.
