1.1 The purpose of this document is to provide a clear statement of St George’s, University of London (SGUL) intent in the management of the quality of its data.
1.2 The policy is part of the Information Governance Framework.
2 Data Quality (DQ)
2.1 The integrity, quality and cost of SGUL operations (research, education and corporate), depend on the decisions that people make. Poor quality information can lead to poor decisions or inappropriate conclusions that can affect our operations and so impact on staff, students and external partners.
2.2 As the size and complexity of an organisation increases, so does its dependency on systems, processes and technology to manage its information. The person who is responsible for recording a particular item of data may be several steps removed from the person who is responsible for using that data. This increases the risk that the data is inaccurate, inconsistent, out of date, incomplete, missing or simply misinterpreted. Therefore, good quality data which is collected, captured and stored in the right way, should then be turned into fit-for-purpose information which is well analysed, relevant and right for context and used to inform good quality decisions.
2.3 The requirement to maintain good quality data held by SGUL is covered by legislation and funding bodies, for example:
- Data Protection Act 2018
- Higher Education Statistics Agency
- Office for Students
- Research England
- Research Excellence Framework
- UK Research and Innovation.
2.4 Data quality can be seen as the aspect of information management that focuses on information's integrity and fitness for purpose. With increasing use of joined up information systems, information sharing (digital or paper), public accountability and transparency, data quality is an essential aspect of Information Governance.
3 Roles and responsibilities
3.1 Ownership of the DQ Policy is with the Information Governance Steering Group (IGSG) who provide high level oversight in the determination of the Data Quality across SGUL.
3.2 The Chief Operating Officer is responsible for the effective delivery of professional services and for challenging progress made against corporate and operational data quality objectives.
3.3 Directors have overall responsibility for challenging data quality and are accountable for the accuracy and quality of data and information within their areas and need to ensure local data quality procedures are in place and that their staff are aware of them.
3.4 Directors, in their role of Information Asset Owners, who have responsibility for data systems are responsible for ensuring those data systems are used appropriately and that support and training is provided to staff to enable this.
3.5 Managers are responsible for the accuracy and quality of data and information, undertaking necessary checks and complying with the guidance as well as identifying and implementing improvement measures to improve the quality of data.
3.6 All staff need to be aware of the data quality policy and, where applicable, are appropriately trained on systems where their day to day activities contribute to collecting and entering data and taking reasonable steps to keep data accurate and up to date. It is also the responsibility of individual staff members to maintain the accuracy of their own data where self-service facilities exist.
3.7 This Policy applies to all data within SGUL, including data captured by SGUL; gathered from partners or external data sources; provided to the public, partners, Government or others. It should be read in conjunction with the SGUL Information Governance Framework policies and procedures, in particular the Research Data Management Policy.
4.1 This Policy uses the terms data, information and records. Data is unprocessed facts and figures and information is data that has been interpreted so that it has meaning to the user. Records (manual or digital, unstructured) are any recorded information created, received, used or maintained as evidence of or information about the conduct of SGUL activities. SGUL records are created by or on behalf of SGUL when undertaking normal duties and making decisions related to SGUL activities Which ever term is used the need for quality collection, entry and maintenance processes is the same.
5 Data Quality Characteristics
5.1 Detailed below are key characteristics of good quality data and will form the SGULs approach to data quality:
Data should be sufficiently accurate for its intended purpose, representing clearly and in enough 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, and time of activity as possible. Reported information that is based on accurate data provides a fair picture of performance and should enable informed decision making.
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 are made on accuracy, the resulting limitations of the data should be clear to their users. This must be a judgement determined by the local circumstances and is unlikely to be appropriate in the case of the data supporting published performance indicators.
||Data should be recorded in an agreed format and used in compliance with relevant requirements, including the correct application of any rules or definitions. When used to analyse trends, data must be consistent between periods. All performance indicators must have a clear owner who is responsible for confirming the validity of the data. In the absence of a data owner, whoever is producing or publishing the data must assume responsibility for ensuring its validity.
||Data should reflect stable and consistent data collection and recording processes across collection points and over time, whether using manual or computer based systems, or a combination. Managers and stakeholders should be confident that progress towards performance targets reflects real change rather than variations in the methods or approaches in data collection. Wherever possible, changes in definition must be avoided in order to be able to provide consistent trend data overtime.
||Data must be captured as quickly as possible after the event or activity it records and must be made available for the intended use within a reasonable time period deemed appropriate for its use. Data must be available quickly and frequently enough to support information needs and to influence service or management decisions.
6.1 The purpose of this Policy is to set out SGUL's requirements for ensuring that the data collected is of an appropriate quality for the uses to which it is put, or might be put in the future. The objectives of the Policy are:
- 6.1.1 To instil confidence that all data and information that is used from operational information to strategic decision-making information, including that which is shared with other organisations or made public.
- 6.1.2 Enhanced efficiency and effectiveness arising from the reduction of errors and improved accuracy and reliability resulting from a more structured approach to improving data quality.
- 6.1.3 To underpin the better use of information to support SGUL operations and improve accessibility and transparency for the wider community and partners.
7 Risk Management
7.1 The approach is, as for Information Governance generally, squarely centred on risk assessment.
7.2 The key risks arising from relying on information which is not fit-for-purpose are significant and may include:
- Failure to improve research and training outcomes
- Reputational damage and potential loss of future research funding
- Mistakes and delays in providing a service,
- Unnecessary costs, both at operational level and strategic level
- Failure to spot and address performance concerns
- Breaches of information security
- Published information which is misleading
- Poor use of SGUL resources
- Poor policy decisions
- Not recognising and rewarding good performance
8 Data Quality and Records Management
8.1 There is a close relationship between data quality and records management, the former being focused on data collection and the latter on collections of records. Sometimes these overlap where data input or recording is less structured.
8.2 For example, data collection in structured ICT based systems widespread use is made of 'Free Text' or 'Comments' fields, which do not lend themselves to data validation.
8.3 Staff recording in these fields will need additional guidance on what is appropriate to record as it is sometimes overlooked that any information recorded may be required to be disclosed under FOI or DPA legislation. Guidance on how to record may be given in local recording practice procedures.
8.4 The Data Quality Policy and Records Management Policy should be read together because they are complementary and form a full management approach to the 'information life cycle' for SGUL.
9.1 Responsibility for staff compliance with the SGUL IM Policy is with SGUL Executive Board.
9.2 The Senior Information Risk Owner (SIRO) has overall corporate responsibility for the Information Governance Framework including Data Quality Management and reports to the Executive Board accordingly.
9.3 Staff will be made aware of this policy upon publication and on a regular basis afterwards through SGUL internal communications channels, including the Intranet, Staff Update and team briefings.
9.4 New staff will be informed of the policy through the induction process.
10.1 An annual review of internal control systems over SGUL DQ arrangements will be managed by regular audits (to be determined) and reported to the IGSG quarterly and the Executive Board annually.
10.2 This Policy will be reviewed every 3 years, or as and when changes occur legislatively or within SGUL.