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Home UK Quality Code UK Quality Code for Higher Education 2024 Advice and Guidance 2024 Quality Code: Advice and Guidance - Principle 4
Advice and Guidance 2024

Principle 4 - Using data to inform and evaluate quality

This principle emphasises the importance of evaluating the impact of data. It encourages providers to not only collect data and implement change but also reflect on those changes and the difference they have made. 


Evaluating impact means considering what has improved and why, what has remained the same, and what may need to change again. This should inform better decision making and lead to improved outcomes over time.

 

It does not require formal evaluation reports or complex processes. Instead, it can be straightforward and embedded in everyday practice. This helps colleagues continually develop their practice and enhance student learning and the wider experience. 


How this works in practice


When thinking about the collection of data to inform and evaluate quality, providers might consider: 

 


  • factoring in evaluation at the start of any new action or project 
  • building monitoring and evaluation into regular review and planning processes, rather than treating them as separate add-ons 
  • being clear about what success would look like before implementing a change 
  • whether the data collected or used is sufficient to complete the task or answer specific questions 
  • how decisions about data collection influence what can be meaningfully evaluated, including any limitations on the interpretation of impact 
  • their own context, recognising that approaches that work for a small specialist provider may differ from those that are effective in a larger provider. 

Key Practices

A consistent, coherent and evidence-informed strategic approach to the collection, storage and management of data is employed across the provider.
 
The provider makes explicit the type and level of data utilised (such as departmental, programme, module level) and the policies and processes that underpin its use in the maintenance of academic standards and the assurance and enhancement of quality.
What this means 

Strategic data management informs a provider-wide understanding of quality and supports continuous enhancement. This is embedded as a shared strategic priority, with clear leadership, governance and accountability structures to ensure data is effectively owned, managed and used across the organisation.   

In practice

Implementing a data strategy effectively requires ongoing commitment and regular review. This involves:  
 
  • making data accessible 
  • ensuring staff understand how to access and engage with it 
  • defining and embedding clear responsibilities to support confident and consistent use across the institution 
  • regular review to help keep the strategy effective, aligned with institutional priorities and responsive to evolving needs.  

Why this matters

Governance mechanisms can support data trust by agreeing common definitions, reviewing data quality concerns, and ensuring data is interpreted consistently and proportionately.   

Providers might consider establishing governance frameworks that include:  

 

  • provider-wide standards for measuring key metrics   
  • common data definitions and measurements 
  • standardised data collection methods to ensure data consistency across the provider.   

While these capabilities will not be fully developed by every provider, they represent best practice that should be worked towards.   




Staff and students are aware of the types of data gathered and how it is stored and used in the management of quality and standards. 


What this means

Staff and students should have a shared and proportionate understanding of what data is collected and why, how it is used to inform decisions, and what safeguards are in place to protect individuals.  

This should enable staff and students to engage confidently with data, ask informed questions, and understand how data contributes to quality assurance, enhancement and maintaining standards. 


In practice 

Providers may find it helpful to set clear, role-appropriate expectations. For example: 

  • Staff need to understand what data they can use locally, how it aligns with institutional datasets, and where to seek advice when handling or interpreting data. 
  • Staff with leadership or governance responsibilities should understand how data is aggregated, assured and used to inform decision making at provider level. 
  • Students and student representative bodies should understand what data is collected about the student experience, how it informs enhancement activity, and how to access and interpret relevant outputs.
 
Why this matters 

Anyone responsible for managing data is advised to guard against the risks of data collection that might not align to institutional approaches. For example, by:  

  • building trust so staff and students feel comfortable to ask questions about data, raise concerns about interpretation, and challenge assumptions 
  • encouraging open dialogue about uncertainty, limitations and context 
  • establishing a central point of contact that staff, student representative bodies and students can access for advice and guidance about the collection, management or manipulation of data.  


When designing and operating monitoring and evaluation arrangements, staff and students adhere to ethical and data protection requirements relating to gathering and submitting data for national datasets, regulatory purposes, and internal monitoring and evaluation.
What this means

Data should be used with a clear purpose. Careful consideration should be given to how it is collected, analysed and applied.  Data protection officers can offer advice and guidance on the ethical considerations and data protection requirements of using data. This supports a coherent and integrated approach that protects individuals and strengthens confidence in how universities manage and use data. 


In practice

This means providers should implement safeguards to protect data throughout its lifecycle from collection to disposal. These include:  

  • only collecting the data needed  
  • using aggregated data wherever possible
  • protecting individuals in small datasets
  • anonymising or pseudonymising data 
  • ensuring secure storage and transfer of data 
  • using trusted third-party services 
  • maintaining clear retention and deletion policies 
  • enabling individuals to exercise their data rights. 

Why this matters

Providers are responsible for meeting the data requirements of their national regulators within the relevant legal frameworks, ensuring the accuracy of data. Providers should also ensure the data they submit has been internally verified, to avoid additional resource being used to make corrections.

 

Resources


Staff who are required to collect, manipulate and analyse data for reporting, quality assurance and enhancement purposes receive training that enables them to undertake these activities effectively, ethically and securely. Policies cover any third-party use of data, including applications utilising Generative Artificial Intelligence. 
What this means 

Providers are advised to assess the capability of individual staff involved in data handling before deciding what training to offer them. This could be incorporated into the institution's overall approach to data strategy, governance and processes.    

In practice 

Providers should assess individuals' data literacy  before expecting them to engage with data or draw conclusions from it. They should bear in mind that: 

  • literacy and experience will differ across and within stakeholder groups 
  • responsibility for handling data does not guarantee a consistent level of knowledge or approach 
  • this needs to be considered when developing and communicating institutional strategies for data collection, management and storage 
  • establishing a clear, shared definition of data literacy will help providers to assess it and to identify development training needs across the organisation 
  • a range of support should be offered to create clear pathways for building data knowledge and skills. 

Why this matters 

Training employees on the collection, manipulation and use of data and policies establishes a common baseline of data literacy and ensures staff are aware of effective, ethical and secure data collection handling and analysis. 

Training in the use of data collection and manipulation also reinforces the organisational approaches to data handling and communicates relevant policies, procedures, and legal and regulatory requirements. 

Training can also:   

  • explain how data handling aligns with other institutional policies, including generative AI 
  • demonstrate how data analysis informs risk-based decision making  
  • clarify the purpose and value of stakeholder involvement in data activities.   

 

Resources


Providers in partnership arrangements (including the student representative body, where applicable) ensure data sharing agreements and reporting requirements are clearly stated, understood and reviewed periodically. 
What this means 

Providers should establish clear systems for the collection and use of consistent information. This is particularly important in internal and external partnership arrangements. The governance framework should be comprehensive and transparent to allow the strategic approach to be implemented across all academic provision, including partnerships.   

In practice 

Effective data governance is critical for all arrangements involving the sharing of data, whether with external partners (commercial, research, regulatory bodies and partner providers) or between internal departments. A robust policy for data sharing should clearly articulate:  

  • the scope of data to be shared 
  • the purpose for sharing 
  • the specific reporting requirements for all parties, including those of the partners and student representative body (if applicable).  

The policy should enable all stakeholders to understand their obligations and will include a protocol for regular review and updates to maintain relevance and compliance. 

Why this matters 

Providers should adhere to clearly defined rules governing the sharing and reporting of data. These arrangements should be mutually agreed, formally documented and regularly reviewed to ensure data subjects are protected, legal obligations are met, and ethical standards are consistently upheld.

 

Resources


Data is collected and analysed in ways that enable providers to understand and respond to the needs of their student populations, promoting equality, diversity and inclusion, and environmental sustainability. 
What this means 

Providers are encouraged to take a purposeful approach to the collection and analysis of data. This means it can be used as a powerful enhancement tool, aligned to the varied needs of different student populations and the institution's strategic approach to equality, diversity and inclusion, and environmental sustainability.  

In practice 

This involves identifying:  

  • leading indicators (for example, student engagement with digital platforms, early assessment performance, or participation in support services), which can signal both operational issues that can be immediately addressed and opportunities for improvement that might be implemented for the next cycle 
  • lagging indicators (such as progression, attainment or graduate outcomes), which enable evaluation of long-term trends and strategic impact. By combining both perspectives, providers can ensure that data analysis informs decisive action at the right level, helping staff and students focus on solutions and continuous improvement. 
 
Why this matters 

A core element of this practice is consistent and systematic use of disaggregated data to understand the outcomes for different student groups. 

Providers can analyse quantitative data by protected characteristics, such as gender or disability status, to identify any disparities, attainment gaps and emerging risks to inclusion across the student lifecycle. 

This supports a more anticipatory approach to inclusive practice, enabling providers to address potential barriers in learning design, assessment and support before this results in differential outcomes. 

Such analysis should be undertaken in line with institutional policies on ethical and inclusive data use, supported by staff guidance and proportionate safeguards, to avoid the identification of individual students.  

 

Resources


Chloe Hastings, Quality Enhancement Manager, Ulster University

 

 

This resource will be genuinely useful for colleagues across the sector. It is particularly valuable in Northern Ireland, where we do not have OfS data requirements to benchmark against in the same way as our counterparts in England. We will use it to review our own data strategy, making sure our approach to collecting, using and reporting on data reflects sector-wide expectations and aligns with practice across other institutions. It also provides a useful reference point when discussing why certain data practices matter, rather than relying on institutional judgement alone.

Chloe Hastings, Quality Enhancement Manager, Ulster University
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