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The data collection Codes of practice define a model of consensual governance for these relationships that aims to balance the needs of data collectors and higher education providers.</p> <p>Specifically, this model of consensual governance aims to:</p> <ol class="ol"> <li><strong>Enable better outcomes with data</strong>: this means getting the right data, at the right time, and at the right quality in support of well stated outcomes understood by all parties.</li> <li><strong>Improve operational efficiency</strong>: well governed data reduces confusion about how data needs to be produced and reduces time consuming manual intervention.</li> <li><strong>Minimise burden</strong>:<strong> </strong>in order to minimise burden, requirements must be clearly specified, supported by rigorous impact analysis and include different solution options. Evaluation of burden should consider both the cost and value of any change i.e. the net burden.</li> <li><strong>Rationalise and harmonise the landscape through best practice</strong>: the governance process is founded on shared processes, models, definitions, and guidance. It fairly addresses all party鈥檚 needs to ensure data collections are managed in the most efficient and simple way possible.</li> <li><strong>Visibly demonstrate adherence to sector-wide consensual governance</strong>: all data stakeholders will participate in a fair, transparent, and informed manner to ensure shared understanding of requirements and outcomes.</li> </ol> <h3>Principles</h3> <p>To support these goals, three principles should be consistently applied by all parties. While the individual Codes of practice may differ for demand and supply, the following principles inform and bind this best practice.</p> <h4><strong>Honesty</strong></h4> <p>To be clear on the providence, meaning, and use of data, the necessary quality expectations for that data, and the transparent production, processing, and consumption of that data.</p> <h4>Impartiality</h4> <p>To engage in the sector-wide data governance process without organisation, political, or personal interest in the production, processing, and consumption of that data.</p> <h4>Rigour</h4> <p>To demonstrate robust and repeatable processes in the production, processing, and consumption of that data so that it is auditable and defensible.</p> <h3>Codes of practice</h3> <p>Click through on the links below to access our Codes of practice for demand and supply, and the burden assessment methodology that has been developed to support the Codes of practice.</p> <p>The supply-side Code of practice applies to organisations that have to respond to requests for data. The demand-side Code of practice applies to organisations that make requests for data.</p> <p><a class="button | button-pointy | button-big" href="/web/20240406190334/https://www.hesa.ac.uk/innovation/data-landscape/Codes-of-practice/Supply-side">Supply-side Code of practice</a></p> <p><a class="button | button-pointy | button-big" href="/web/20240406190334/https://www.hesa.ac.uk/innovation/data-landscape/Codes-of-practice/Demand-side">Demand-side Code of practice</a></p> <p><a class="button | button-pointy | button-big" href="/web/20240406190334/https://www.hesa.ac.uk/innovation/data-landscape/Codes-of-practice/methodology">Burden assessment methodology</a></p> <p> </p> </div> <div class="content-aside | col-1/3 | col-reverse"> </div> </div> </div> </div> </div> <div class="cms-controls"> </div> </div> </main> <footer role="contentinfo" class="footer-site"> <div class="banner"> <div class="wrap"> <div class="grid | grid-spaced | 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