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
Policy Summary	Overview of the company policy regarding adherence to acceptable data quality standards and processes.
Policy Reference	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.
Assessment Report	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: Framework for data quality assessment Data sampling tools Economic impact assessment tool and guidelines Root cause analysis tool and guidelines
Enterprise Roadmap	A description of data-quality-related activities anticipated during an 18 to 24 month timeframe.