Data Quality and Integrity Policy Template| Data Governance Framework

Effective data management with accurate and secure data enables organizations to produce informed decisions and establish effective policies and deliver superior services toward their customers.  The Data Quality and Integrity Policy exists to maintain data with specified characteristics of accuracy alongside validity, reliability, timeliness, relevance along with completeness and security. The organization must develop and maintain an environment which emphasizes high-quality data standards. 

Data Quality and Integrity Principles

• Accuracy

Data needs to preserve accuracy standards for its target application and also possess adequate information levels to fulfill its objectives. Data entry occurs once at its source while several users access it several times. The point of activity accuracy receives its optimal level when data gets recorded during its actual timeframe. Monitoring systems which track data accuracy need to exist to help make decisions. The pursuit of accurate data requires consideration of practical factors such as purposes of data application and expenses and workload involved in data gathering procedures. In certain situations researchers allow minor inaccuracies as long as the data becomes available quickly. The users of such data need to receive thorough explanations about accuracy restrictions when data precision suffers due to specific conditions. The measurement of strategic performance indicators relies on perfect data accuracy because inaccurate results for key performance indicators may not hold valid. It is essential to take necessary actions which will make KPI-related data more accurate.

• Relevance

All data collection efforts need to produce information that is suitable to achieve intended objectives.

• Validity

Agreed data formats as well as data requirements guidelines together with definition application rules must guide the recordkeeping process. Data analysis requires continuous consistency between different time periods. A performance indicator requires one designated owner who confirms its data validity. Data validity responsibility falls on the individual or team primarily responsible for data production whenever no one specifically designates the data owner.

• Reliability

Every data collection method must maintain stability in its processes which involve manual systems or computer-based systems or both of these systems. The organization together with its stakeholders and customers and regulatory bodies needs assurance that processed data maintains reliability for business goal achievement. Any modifications of data definitions need to be minimized because they create inconsistencies that affect time-based data consistency.

• Timeliness

The system needs to capture data right after activities occur while keeping the intended end-users supplied with results soon enough to suit the purpose. Data needs to become available regularly according to specific time requirements to satisfy operational needs and prompt decision-making in service management.

• Completeness

The data must represent all population elements while being completely presented without any organizational bias. A suitable collection of proper data details serves as essential for making significant outcomes. When data surveys measure the population the method must ensure unbiased results and the selected sample needs to provide a robust representation. The information collection must apply only to objectives and targets that belong to the organization. Data owners need to learn about dataset gaps which requires them to handle the necessary actions. Problems in both data quality and data recording processes can be detected through missing or incomplete or invalid records found in the datasets.

• Security

Users need access controls which protect stored data while maintaining proper security. Sensitive data needs to serve its designated collection purpose and remain available no longer than necessary for the research tasks. Information sharing with others needs to occur only after the council executes a proper system of controls and safeguards. Data accessibility together with usage needs to match the users' role requirements while following current legal mandates.

Key Performance Indicators (KPIs)

1. Every staff member needs clear understanding about the impact their regular work has on key performance indicator calculations. Such situations produce miscommunication between staff members which eventually disrupts reporting timelines. Such problems reduce the capacity to efficiently handle your business performance.

2. The organization should establish precise definitions together with standardized methods to determine vital performance indicators.

3. All staff members who handle KPI data need proper instruction in the correct method for its computation. 

4. Every key performance indicator requires both a reporting data custodian who collects and reports data while an owner verifies both the calculations and produced figures. The system needs to create a record tracing all modifications made to the program's definitional elements.

Data Requirements

Coordination of verification activities requires staff to perform following tasks:

• Data cleansing operations include the double-record removal process as well as the completion of missing data fields.
  
  
• Widespread checks confirm the data quality that third parties generate.
  
  
• Reconciliation of system-generated data with manual records.
  
  
• Tests should be performed on samples to stop particular errors from repeating.

Maintaining Quality of Data from Third Party Vendors

It is important to properly manage and supervise the validity and relevance of incoming and outgoing information. While forming strategic alliances, the partners should be held responsible for the accuracy of the information as defined in the documents of partnership or in the service level agreements (SLAs). In this case, responsibilities include the following actions:

• During the first stages of a contract, we will require information and data from the client.
  
  
• Assign ownership rights to the data through contractual agreements.
  
  
• Set organizational provider publishing data quality standards.
  
  
• Create uniform procedures for data collection and other relevant tasks from the providers.
  
  
• Legal bases for sharing personal data should have appropriate privacy regulations and specific data sharing agreements.

Maintenance of Systems

Every system should be reviewed on a regular basis while verifying that the systems work and are in good condition, paying particular attention to the following:

• Where relevant, system security for access is checked.
  
  
• The integrity of the data for the system is checked at intervals to ensure that the data and the computation done from it gives the expected results.
  
  
• The system is accompanied by constant upgrades.
  
  
• The amendments on the performance indicator definitions are changed whenever they need to be.
  
  
• Managers’ information needs are more than adequately provided for strong procedures for backing up data are put in place that provides for saving and restoring data.

Summary

Organization must implement all needed systems and skilled personnel alongside resources that enable fast delivery of quality data requirements alongside full compliance with regulatory standards. Maintaining data quality and integrity is essential for operating efficiently, proper decision-making by establishing clear standards in order to support long term business goals.