Defines an integrated plan to improve and maintain the level of data quality needed to accomplish business goals and objectives.
Data quality planning is the process of defining the business goals, objectives, specfic initiatives, and sustained activities to improve data integrity, accuracy, and trustworthiness. The data quality plan, providing an approved organization-wide approach becomes a unifying force to foster shared responsibility for quality.
Organizations typically approach data quality improvement on a project by project basis, data store by data store. However, without an overall organization-wide focus on the business criticality of high quality data, positive results are frequently isolated in disconnected pockets. If processes, methods, and procedures are developed independently for each effort, the organization risks: lack of awareness on the part of business staff about quality needs across the data lifecycle; undue effort and duplicative costs; and uncoordinated inefficient implementations (for example, repeatedly cleansing data in a downstream data store while not improving data quality at the source).
The data quality plan has a simple purpose – support focused, strategic thinking. By engaging in organization-wide collaborative considerations of basic questions, issues and objectives will be surfaced and agreement can be forged. The organization should engage stakeholders from all relevant business areas to pose these questions about patient demographic data:
In a large organization, this planning effort may extend over a few weeks. In a small organization, it may be completed in a few brief meetings. Once the approach and corresponding specific initiatives are approved (See Data Governance) the data quality plan can be finalized. It should address:
Although technologies, methods, and specific techniques can be required, high quality data is the result of continued attention by all relevant stakeholders, communicated and shared across the organization. Creating and following the data quality plan is a positive cultural shift, demonstrating the organization’s executive commitment, and an intention to educate, engage, and sustain effective and direct attention focused on patient demographic data. It is essential to conduct training for everyone who enters, updates, or modifies data (e.g., during a records merge). Training topics should include: process workflow, current system capabilities, privacy and security/HIPAA, and how feedback should be submitted for suggested improvements.
The data quality plan establishes and is implemented by the key quality processes. (See Data Profiling, Data Quality Assessment, and Data Cleansing and Improvement.) Following the data quality plan will assist the organization to realize the following:
The data quality plan should align with organizational goals (e.g., improve patient safety). Consequently, the plan’s objectives should support organizational goals (e.g., patient identity integrity) and the scope should address the entire lifecycle for patient demographic data. Relevant stakeholders should select the data attributes critical to important processes performed along the lifecycle.
It is essential that all relevant stakeholders define and approve the quality objectives and criteria that will ensure that the data is ultimately fit for purpose. This provides guidance on the definition of thresholds and targets to be developed through data quality assessments (See Data Quality Assessment). In addition, it is important to understand that data quality is a learning process. Accordingly, objectives and success criteria should be updated periodically to reflect refined data quality requirements resulting from successive data cleansing, feedback, and improvement efforts.
Sustainable improvements to the quality of data require the implementation of quality rules, because data defects often result from a lack of thorough requirements analysis. Data quality rules can be implemented in the form of procedures to be followed for data entry personnel or as enhancements to data capture systems. It is best to strengthen data capture with automated quality rules whenever possible.
Automation of data quality rules can range from partial to nearly complete automation. For example, full automation can include exception reports that still must be manually reviewed. Nearly fully automated quality checks are typically applied to data stores, which integrate bulk data from multiple sources. Alternatively, partial automation of data quality rules is often implemented at the point of data capture, which is usually accomplished through manual entry. Here quality rules enforce the use of standards by restricting data entry to a predefined range of inputs that are stored in a consistent format (e.g., state code, phone number, ZIP code, etc.), and accommodate a diverse patient population with unique identifiers.
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Data quality policies, plans, and processes define standards and guidelines for performing the activities that will fulfill the organization’s quality objectives. In particular, the policies and processes should provide specificity for implementing activities across the patient demographic data lifecycle as well as satisfy the goals of the data quality plan.
Adoption of data quality principles, policies, and processes is key to the success of the data quality plan. Correspondingly, it is important for data governance to be comprised of stakeholders who represent the critical steps along the entire lifecycle of patient demographic data.
Data profiling, assessment, and cleansing activities are often performed sequentially and iteratively. The entire process is often repeated, because anomalies may surface at any point and may require analysis. Once defects have been determined, all affected steps must be refined to ensure defect rates adhere to the tolerances specified in the data quality plan. Therefore, it is important to regularly monitor the implementation of the data quality plan and make adjustments when needed.
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Sustaining data quality over time depends on aligning the efforts of stakeholders who impact shared data at different points along the lifecycle of patient demographic data. Turnover of personnel is one of many risks to data quality that can be mitigated through effective governance. The organization should assign clearly defined roles, responsibilities, and accountabilities that are aimed to ensure a consistent stewardship of shared data assets from their creation to destruction.
Assigning accountability is necessary for ensuring that the organization maintains sustainable data quality policies, repeatable processes, and effective methods. Therefore, it is recommended that the data management function carry out those responsibilities for the data quality program and serve as the assigned steward for overall data quality metrics.
Example Work Products
1.1 Are all relevant stakeholders involved in determining data quality objectives and success criteria for patient demographic data?
1.2 Does the organization have plans to improve data quality through the application of quality rules?
2.1 Has the organization established policies, processes, and guidelines to implement the data quality plan?
2.2 Is the data quality plan regularly monitored to evaluate progress?
3.1 Has the organization defined roles, responsibilities, and accountability for data quality governance and management of data quality processes?