About us

Performance

Data Quality

HFI Data Quality Statement

HFI recognises and is committed to providing information that has high data quality. High data quality allows both HFI and our stakeholders to have confidence in our figures; it allows robust and timely information for effective decision making. High quality data can back up and drive forward service improvements and allows us to meet our statutory responsibilities and business plan objectives. HFI recognises that improving data quality is everyone’s responsibility.

Data quality applies to both financial and performance data and includes:

• All data that is held on HFI IT systems including the housing management database, iWorld, and the financial system, CEDAR
• All data that is provided to management which is then used for decision making
• All publicly reported data, both performance and financial, regarding the performance of HFI
• Data supplied by HFI to, or received from, partner organisations

HFI seeks to maintain the highest standards of data quality and get all data “right first time”. To do this we must:

• Follow all data quality instructions within HFI systems and procedures
• Ensure that we meet the expectations contained within our job descriptions and in our Personal Development Reviews regarding data quality
• Report any data quality issues, and where possible make possible recommendations for improvements

HFI has identified seven key aspects in ensuring high data quality:

• Accuracy - Data should be sufficiently accurate for the intended use and be captured only once, although it may have multiple uses. Data should be captured at the point of activity
• Validity - Data should be consistent and comply with relevant rules and definitions, whether local or national
• Reliability - Data should reflect stable and consistent data collection processes across collection points and over time
• Timeliness - Data is captured as quickly as possible during or after the event or activity. Data should be available within a reasonable time period to support information needs and to support decision-making
• Relevance - Data captured should be relevant to the purposes for which it is to be used and the requirements should be reviewed periodically to reflect changing needs
• Completeness - All relevant data is included. Missing, incomplete, or invalid records are minimised