Data Quality Strategy
Hidden costs of poor information quality can cost companies 20-30% of sales resulting in loss of customers, profits and shareholder value. We are experts in developing data quality strategies to improve the management of mission-critical company information assets.
The benefits of a data quality strategy
- Reduction of data de-duplication and data cleansing efforts
- Realization of single source of truth across the enterprise
- Compliance with regulatory requirements
- Improved and faster business decision making ability
- Information and data become strategic enterprise assets
For most organizations, the goals we pursue include:
- Establish a consistent means of measuring data quality
- Develop a policy statement outlining the obligations of the various organizational departments
- Develop a generalized process that can be used to ensure adequate enterprise data quality
- Develop a set of tools, guidelines and other resources for PMO or functional projects to embed data quality into business processes
- Ensure data quality management processes are broadly applicable, identifying process areas, tools or other attributes as required by the specific needs of the organization
Deliverables we will help our clients build include:
Data Quality Document
Description / purpose
|Overview of the company policy regarding adherence to acceptable data quality standards and processes.
|Metrics associated with the Data Quality Policy Summary, outlining the specific quality goals set by executive leadership.
|Standards Deviation Authorization
|Used to authorize deviations from the acceptable data quality standards as required by exceptional circumstances.
|Documents the data quality current state, the gap between the current state and the projected future state, and the lessons learned during the assessment effort.
|Management Process Description
|The process that is used to rollout projects to ensure adequate data quality in their delivery and to provide administrators with the means to maintain data quality.
|Management Process: PM artifacts
|Work breakdown structure with generic staffing and levels of effort for a common data quality effort.
|Management Process: tools and guidelines
|Data quality assessment tools and guidelines, which may include:
|A description of data-quality-related activities anticipated during an 18 to 24 month timeframe.