Corporate Data Quality Policy

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1 Appendix A Corporate Data Quality Policy Right first time Author: Head of Policy Date: November 2008

2 Contents 1. INTRODUCTION STATEMENT OF MANAGEMENT INTENT POLICY AIM DEFINITION OF QUALITY DATA IMPORTANCE OF GOOD QUALITY DATA POLICY STATEMENT POLICY MANAGEMENT POLICY AWARENESS...4 APPENDIX A Definition of Quality Data...5 APPENDIX B Data Quality Sampling Framework...6

3 Broadland District Council, Data Quality Policy, April INTRODUCTION Good service delivery is reliant on the availability of relevant, accurate and up-to-date data. If data quality is poor it is impossible to gain an accurate picture of service standards and operational performance. 2. STATEMENT OF MANAGEMENT INTENT The Council is committed to the collection and use of high quality data which is right first time and can be relied upon for decision making and performance review. 3. POLICY AIM The Council collects and uses vast amounts of data from many sources, including customers, partners and government agencies. The aim of the policy is to assist the Council to develop improvements in data quality across all services, and to promote confidence in the confidentiality, integrity and availability of all data. This policy recognises and builds upon existing strategies and procedures which already provide assurance on data quality across various levels of the Council. These include: a) Significant financial controls and budget management b) Robust ICT security arrangements and standards c) Business continuity and risk management d) Broadland Business Plan and Performance Management Framework e) Staff development programme and Appraisals f) Service delivery legislation requirements and data verification practices g) Performance Indicator Control Sheets (PICS) h) Data protection policy i) Data retention policy This policy aims to drive continuous improvement and consistency across all Council Services, and to generate confidence in the use of all information derived from the Council s data. 4. DEFINITION OF QUALITY DATA There are six key characteristics of good data quality as defined in the Audit Commission document Improving information to support decision making: standards for better data quality. These characteristics are: 1. Accuracy 2. Validity 3. Reliability 4. Timeliness 5. Relevance 6. Completeness See Appendix B for detailed explanations. 3

4 Broadland District Council, Data Quality Policy, April IMPORTANCE OF GOOD QUALITY DATA Good quality data is essential for: a) Operational management and strategic planning b) Performance management c) Service improvement d) Customer care e) Efficient administration Please note: the source of the data you have used must be stated. 6. POLICY STATEMENT It is the policy of the Council to ensure: data is supplied once, appropriately verified and used as effectively as possible data capture is right first time reliable data is held to enable accurate and timely performance reporting all data is securely stored and is used in accordance with legal and regulatory requirements appropriate data quality monitoring systems and procedures are in place the Council appropriately trains staff in data quality issues measurable data quality improvement across the range of Council services Broadland District Council is a founding contributor to the Norfolk Data Sharing Policy and as such supports the principles and protocols of the Policy (due to be considered during 2009). 7. POLICY MANAGEMENT This policy will be reviewed by the Corporate Management Team at least annually. Internal Audit, in its generic role, reviews internal controls and systems procedures in operation within the council inline with the annual audit plan. 8. POLICY AWARENESS Managers and Responsible Officers must familiarise themselves with this policy and all Data Quality procedures associated with their Service Area, they must also ensure that their staff are aware of the policy and appropriately trained in the relevant Data Quality procedures, particular to their service area. 4

5 APPENDIX A Accuracy Validity Reliability Timeliness Relevance Completeness Data should be sufficiently accurate for its intended purposes, representing clearly and in sufficient detail the interaction provided at the point of activity. Data should be captured once only, although it may have multiple uses. Accuracy is most likely to be secured if data is captured as close to the point of activity as possible. Reported information that is based on accurate data provides a fair picture of performance and should enable informed decision making at all levels. The need for accuracy must be balanced with the importance of the uses for the data, and the costs and effort of collection. For example, it may be appropriate to accept some degree of inaccuracy where timeliness is important. Where compromises have to be made on accuracy, the resulting limitations of the data should be clear to its users. Data should be recorded and used in compliance with relevant requirements, including the correct application of any rules or definitions. This will ensure consistency between periods and with similar organisations. Where proxy data is used to compensate for an absence of actual data, organisations must consider how well this data is able to satisfy the intended purpose. Data should reflect stable and consistent data collection processes across collection points and over time, whether using manual or computerbased systems, or a combination. Managers and stakeholders should be confident that progress toward performance targets reflects real changes rather than variations in data collection approaches or methods. Data should be captured as quickly as possible after the event or activity and must be available for the intended use within a reasonable time period. Data must be available quickly and frequently enough to support information needs and to influence the appropriate level of service or management decisions. Data captured should be relevant to the purposes for which it is used. This entails periodic review of requirements to reflect changing needs. It may be necessary to capture data at the point of activity which is relevant only for other purposes, rather than for the current intervention. Quality assurance and feedback processes are needed to ensure the quality of such data. Data requirements should be clearly specified based on the information needs of the organisation and data collection processes matched to these requirements. Monitoring missing, incomplete, or invalid records can provide an indication of data quality and can also point to problems in the recording of certain data items. 5

6 APPENDIX B DATA QUALITY SAMPLING FRAMEWORK Introduction It is important for the Council not only to ensure that Data Quality is Right First Time but also that we manage this process. To ensure we achieve good service delivery, we are dependent on the availability of complete, accurate and up-to-date data. Therefore, it is important to have an oversight mechanism in place in order to sample the performance data. Rationale Why sample test? If data quality is poor it is impossible to gain an accurate picture of service standards and operational performance. Therefore sample testing will allow us to: Manage our quality in-house. Reduce the (internal/external) audit burden. Reduce the time taken by managers to report and manage performance. Ensure that errors are corrected internally and not through the official audit processes. Continually improve service quality. Improve the credibility of data provided. Ensure decisions made are based on accurate data and therefore justifiable. Sample Criteria Risk Based the criteria for sampling our Performance Indicators (for example) will be risk based. That is, what risks exist that may cause an error to be made? Here is the criteria we will use: New Performance Indicators. Amended Performance Indicators. New Responsible Officer. Poor Performance. Over Performance. Unexpected Variance (from target or over time). Methodology The methodology we will use for Data Quality Sampling will be: Identify PIs to be checked further based on the sample criteria. Investigate reduce the number of PIs to be checked by investigating reasons why PI is out of tolerance / variance etc. This will be based on the explanation given in the PICS. Quick desk-top audit of the remaining sample PIs and evidence provided. Refer to Internal Audit for checking. Recommendation to Data Quality Audit team. 6

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