Page 21 - Data and Digital Technology Strategy 2024-2026

DATA AND DIGITAL TECHNOLOGY STRATEGY 2024-2026
METRICS (metric numbering references each strategy and action number)
 2.1. Proportion of relevant data
classifi ed over time
 2.2. Percent of RVC systems
contributing to the RVC data
platform
 2.3. Percentage reduction in
number of technologies and
applications in use
 2.4. Implementation of new
architectural modelling platform committees actively using
 2.5. Establishment of digital
and physical asset inventory
and percentage assets
classifi ed and managed
 2.6. Proportion of compliance
training completed
 3.1. Percentage of RVC data future investment required for  6.4. Evidence of external
catalogued and classifi ed maintaining or replacing end certifi cation and audit
 3.2. Simplifi cation of reporting of life or redundant technology performance
for statutory reasons (reduction based on ecosystem roadmaps
 7.1 Utilisation of metrics
of staff time spent preparing)  4.4. Completed 3-5 year
from TOPdesk and external
 3.3. Timely and accurate roadmaps for all major areas of technology providers to
production of an auditable RVC activity
measure service provision and
inventory
 5.1. Improved power effi ciency effectiveness
 3.4. Percentage of the RVC
of data centre operations  7.2. Recruitment and retention
 5.2. Improved fi nancial return of IT and related teams
dashboards to inform strategic on device management and  7.3. Percentage of RVC digital
and operational decisions
disposal and technology projects
 4.1. Implementation of new  5.3. Internal audit assessment managed under the same
costing methodology for data methodology
 6.1. Gartner measures of
and technology projects  7.4. Suite of regular report on
institutional engagement and
 4.2. Implementation of revised entirety of RVC project digital
derived benefi ts
procurement policies with RVC technology portfolio
Finance and Procurement  6.2. HESA data maturity  7.5. Ability to report on project
assessment measures
 4.3. Implementation of fi ve-year lifecycle against budget
forecast model to predict the  6.3. Evidence of completion of
security awareness training
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