The Data Governance Group is responsible for approving the procedures related to the University's Data Governance Policy and ensuring data processes are used in all of the University's data creating use and dissemination.
View the Terms of Reference for the Data Governance Group (Word).
View the Data Governance Policy (Word).
Data quality
Data is fundamental to effective, evidence-based decision-making. It underpins everything from major policy decisions to routine operational process. Often, however, our data is of unknown for questionable quality. This presents huge challenges. Poor or unknown quality data weakens evidence, undermines trust, and ultimately leads to poor outcomes. It makes organisations less efficient, and impedes effective decision-making. To make better decisions, we need better quality data.
Data quality is about fitness for purpose and is an output of better data management. Data quality requirements are defined as accuracy, completeness, currency, precision, privacy, reasonableness, integrity, timeliness, uniqueness and validity. These metrics will be core for us at St Mary’s University as we work towards developing a strong data culture.
Data may be used by several different users and each for different purposes. Communicating the quality of data to users gives them a fuller picture of your data and its journey and mitigates against people using the data for the wrong purposes.
Data quality principles have been defined to support organisations to create a data quality culture and Data quality dimensions which should be used to make assessments of data quality and to identify data quality issues.
There are 6 core data quality dimensions:
- Accuracy: the data recorded is correct and free of errors.
- Completeness: the data should have all the relavnt information to meet business goals and no missing information.
- Consistency: everyone is collecting data in the same way.
- Timeliness: data should be available at teh tiem it is required.
- Validity: informatio is in a specific format and follows business rules.
- Uniqueness: data is not duplicated and only one instance in which the infromation appears in the database.
View our data quality principles and data quality dimensions (Word).
View our key dates for statutory returns (Excel)
Having clear Standard Operating Procedures (SOP) with clear responsibilites assigned to roles and teams is part of creating a strong data quality culture.
See our SOP template (Word).
FAQs
Why is data quality important for us at St Marys?
Why should data reported externally be of the highest possible quality?
What are the implications of poor data quality?
How can we boost our data quality culture?
What are the key roles in ensuring data quality?
Who is responsible for data quality?
What should I do, if I come across a data quality Issue?
What should I do, if I am aware of any data quality breach or non-compliance?
What is St Mary’s as an institution aiming to do in order to improve quality of data and raise awareness?
St Mary’s University needs complete, accurate, and reliable information in order to manage its business, including:
- Delivering an efficient service to staff, students, and stakeholders.
- Providing informative and reliable management information and reporting.
- Demonstrating public accountability.
- Presenting a responsible and true public face.
- Meeting statutory and regulatory obligations.
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- The external representation of the University has assumed greater significance in the context of increased tuition fees. It is imperative that we provide students and potential students with clear, accurate data that can help inform their decision-making.
- To ensure accurate funding allocations for both teaching and research (data supplied by the University to the Office for Students [OfS] and the Higher Education Statistics agency [HESA] affects the funding the University receives).
- Good quality data and external returns reflect a well-run institution and this is likely to increase the University’s external credibility.
- The University’s Audit Committee is required to give, as part of its annual opinion, assurance over management and quality assurance of data submitted to HESA, the OfS and other external agencies.
- The University’s senior management are required to provide representations in the annual audited financial statements that internal controls are effective, including assurance over data quality.
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- reputational damage
- financial loss
- poor decision making
- inaccurate reporting/analysis
- unreliable data leading to mistrust.
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- Be curious about quality.
- Find out who is responsible for quality.
- Understanding your data and the source of origin and how it gets to you.
- Challenge quality.
- Champion data quality in your organisation.
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- Data or institutional data: general term used to refer to the University’s information resources and administrative records.
- Data quality: refers to the validity, relevancy, accuracy and currency of data.
- Data trustee: accountable for the strategic co-ordination of data management and reporting.
- Data owner: accountable for the fitness or purpose of a defined data area and primarily performed by the leads of those functions.
- Data steward: responsible for the definition and quality of defined dataset(s) within a data area, wherever the data is retained.
- Data custodian: responsible for the safe custody, storage and retention of data.
- Data user: members of staff who uses data in the normal course of business.
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St Mary’s University, rather than any individual or organisational unit, is the owner of all institutional data. As such, the University will require a culture that demonstrates a high commitment to data quality at a senior level. All members of SMT have a strategic responsibility for maintaining good data quality.
The table provides a high-level overview of the levels of data ownership.
|
Dataset
|
Data Trustee
|
Data Owner
|
Data Steward
|
Data Custodian
|
|
Staff Data
|
COO
|
Director of HR
|
Head of HR Operations
|
CIO
|
|
Student Data (Enrolments)
|
COO
|
Director of Student Operations
|
Head of Registry
|
CIO
|
|
Student Data (Applications)
|
Provost
|
Interim Director of Global Engagement
|
Head of Admissions
|
CIO
|
|
Academic Programme Data
|
Provost
|
Dean of Learning and Teaching
|
Head of Quality and Academic Partnerships
|
CIO
|
|
Research data/publications
|
Provost
|
Provost
|
Head of Research Services/ Head of Library and Digital Support
|
CIO
|
|
Finance Data
|
CFO
|
Financial Controller
|
Head of Accounting Services
|
CIO
|
|
Estates Data
|
COO
|
Director of Estates and Campus Services
|
Estates and Campus Services Administrator
|
CIO
|
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Any issues should be referred to the Data Steward and the Data Quality Manager who will log the issue and aim to find a resolution by contacting relevant stakeholders.
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Any issues in the first instance should be referred to the Data steward and the Data Quality Manager. The Data Quality Manager will maintain a log of all data issues, the proposed plan for resolution, and the timeline for doing so. If a data quality issue is not satisfactorily resolved then this should be escalated to the Data Owner and ultimately to the Data Trustee. If the issue is still not satisfactorily resolved then this should be reported to the Data Governance Group and ultimately to the Audit Committee if concerns are not adequately addressed.
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- Updated data governance policy to highlight importance of data and data quality principles and outlining roles and responsibilities.
- We have identified data quality issues and compiled a log and prioritised them and working through them to find possible resolutions.
- We are working on reports for some of the prioritised areas to identify these issues in order to resolve them in a timely manner.
- We are also working with business support areas to document their Standard Operating Procedures.
- We are in the process of producing resources/documentation to highlight the importance of a strong data quality culture.
- Dedicated resource to support data quality improvements.
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