research students, bringing in £13.8m in 2015/16; this is derived from the HESA student return. HEFCE funding calculated from the HESES return directly now brings in £11.2m of funding. But these are small in relation to the University’s turnover of £1.4bn in 2014/15, with RDP amounting to less than one percent. The likelihood is not that we make a return with no PGR students (which would need to be the case to lose the entire £13.7m of RDP funding), more that a figure would be calculated. ‘wrong’ The gross risk is then calculated as a multiplication of the likelihood and impact scores on the grid below. This is known as the risk tolerance (or appetite). Likelihood 4 3 2 1 4 16 12 8 4 3 12 9 6 3 Impact 2 8 6 4 2 1 4 3 2 1 Risk High: 12-16 Medium: 6-11 Low: 1-5 The action required for each level of gross risk is discussed in the next section. In assessing risk there may be a number of dimensions which need to be taken into account. These may include (but are not limited to): Completeness of source data; Reliance on data from other departments or sources where the level of control over data quality is uncertain; Timeliness in the availability of source data; Complexity and timescale of the processes needed to complete the return; Availability and retention of knowledge, skills and experience needed to complete the return; IT infrastructure and appropriate technology; Culture among senior management and return compilers regarding the significance of the return, including post-collection usage such as league tables and institutional subscription rates; Quality of, and access to, relevant guidance on the requirements of the return and other uses to which the data is likely to be put; Compliance with regulatory requirements including data protection and data security. In using the above processes to assess risk, it may be necessary to work through the likelihood and impact for each dimension, with the overall assessment being the highest level of risk across the dimensions. A template is provided at Appendix 4 to aid the aggregation of these elements. The Appendix is available as a separate document for download and use.
research students, bringing in £13.8m in 2015/16; this is derived from the HESA student return. HEFCE funding calculated from the HESES return directly now brings in £11.2m of funding. But these are small in relation to the University’s turnover of £1.4bn in 2014/15, with RDP amounting to less than one percent. The likelihood is not that we make a return with no PGR students (which would need to be the case to lose the entire £13.7m of RDP funding), more that a ‘wrong’ figure would be calculated. The gross risk is then calculated as a multiplication of the likelihood and impact scores on the grid below. This is known as the risk tolerance (or appetite). Likelihood 4 3 2 1 Impact 4 16 12 8 4 3 12 9 6 3 2 8 6 4 2 1 4 3 2 1 Risk High: 12-16 Medium: 6-11 Low: 1-5 The action required for each level of gross risk is discussed in the next section. In assessing risk there may be a number of dimensions which need to be taken into account. These may include (but are not limited to): Completeness of source data; Reliance on data from other departments or sources where the level of control over data quality is uncertain; Timeliness in the availability of source data; Complexity and timescale of the processes needed to complete the return; Availability and retention of knowledge, skills and experience needed to complete the return; IT infrastructure and appropriate technology; Culture among senior management and return compilers regarding the significance of the return, including post-collection usage such as league tables and institutional subscription rates; Quality of, and access to, relevant guidance on the requirements of the return and other uses to which the data is likely to be put; Compliance with regulatory requirements including data protection and data security. In using the above processes to assess risk, it may be necessary to work through the likelihood and impact for each dimension, with the overall assessment being the highest level of risk across the dimensions. A template is provided at Appendix 4 to aid the aggregation of these elements. The Appendix is available as a separate document for download and use.
Data_Risk_Management_and_Assessment.docx