DIGITAL PRESERVATION ISSUES: BUDGETS, COSTS, STAFF AND SKILLS
Saving
digital heritage is now an absolute necessity in contemporary information
sciences. However, reaching sustainable digital curation requires one to pass
through multiple serious difficulties that can become real obstacles even for
wealthy organisations. While the technical side of saving information can be
complicated, the greatest and the most urgent problems have an organisational
and financial nature (Beagrie, Lavoie, & Wollard, 2010). The post discusses
key challenges related to budgeting, staffing, and skill set in digital
curation and emphasises the importance of collaboration in solving these
problems.
Budget
and Costs
The most concrete and often mentioned challenge to digital curation is insufficient funding. The expenses in this case can include the cost of storage systems, software, and the amount of energy needed to keep them. When developing a business case for organisations, one of the major stumbling blocks is leadership’s reluctance to embrace data and information as capital. In this regard, information departments are always underfunded, leaving data curation efforts in limbo.
In response to financial limitations, organisations have increasingly been forced to adopt a more rigorous framework for decision making. As an illustration, one recent undertaking provided a new points driven interpretation of the Data Rescue Framework put forward by Hoffman et al., designed to enable organisations to prioritise datasets that need to be rescued, plan their workloads, identify any potential challenges, and estimate costs involved amid severe budget reductions (Hoffman et al., 2026, p. 5).These kinds of priority models, however, presuppose the presence of institutional readiness which might not exist within developing countries. According to Chawinga and Zinn (2020), "inappropriate data infrastructure" and "lack of institutional research data management policies" made it necessary for researchers to use "high risk data storage mechanisms such as personal computers, flash disks, and external hard drives" (p. 476). This example indicates that inadequate budgets do not only imply lack of funds but also the lack of basic infrastructure which can be used to effectively conserve the data collected.
Staffing
One of the issues that is strongly associated with the issue of budget is staffing. For digital curation, one needs a workforce that has specialised knowledge, which tends to be costly to acquire. According to Beagrie et al., "it is relatively easy for an organisation to allocate money for activities but much harder to allocate money for skills" (2010, p. 45). Inability to do so results in under-staffing or the use of unskilled personnel, thus making it unsustainable.The situation is further exacerbated because very few organisations are able to employ all the necessary expertise within their organisation.Johnston et al. (2018) explain that it is impractical for organisations to “hire and sustain all of the data curation expertise locally,” since a collective effort will help supplement at peak times and even “stabilise service levels to account for local staff transition” (p. 128). The creation of the DCN is a great example of the necessity to work collectively in order to share the cost of employing and training specialists in this area.
Skills
Gap
Once staff members have been hired, one difficulty remains, and that is the constant evolution of skill requirements. As identified by the Council on Library and Information Resources, it was necessary to "create advanced courses, advanced modules, and topic-specific material that could be used to supplement basic curriculum and offer ongoing learning to curators" (Council on Library and Information Resources, 2016, p. 23). To bridge the skills gap, curators have adopted a two-step approach. This involves specialising and continuous upskilling. With regard to specialising, most organisations are trying to adopt the cohort model.
References
Beagrie,
N., Lavoie, B., & Wollard, M. (2010). Keeping Research Data Safe 2.
JISC.
Chawinga,
W. D., & Zinn, S. (2020). Research data management at a public university
in Malawi: the role of "three
hands". Library Management, 41(6/7), 467– 485. https://doi.org/10.1108/LM-03-2020-0042
Council
on Library and Information Resources. (2016). Preparing the Workforce
for Digital Curation.
CLIR.
Ferguson,
S. J., Cape, J. N., Crossley, A., Harvey, F., Fowler, D., Leaver, D., &
Braban, C. F. (2025). Application and
evaluation of the Hoffman et al. (2020) data rescue framework using an historic Scottish cloud and rain
chemistry dataset exemplar. International Journal of Digital Curation, 19(1),
19. https://doi.org/10.2218/ijdc.v19i1.1027
Johnston,
L. R., Carlson, J., Hudson-Vitale, C., et al. (2018). Data Curation Network: A
Cross- Institutional Staffing
Model for Curating Research Data. International Journal of Digital Curation, 13(1), 125-140. https://doi.org/10.2218/ijdc.v13i1.616
Research
Information Network. (2008). JISC Data Sharing Final Report. JISC.
Rusbridge,
C. (2006). Tomorrow, and tomorrow, and tomorrow: poor players on the
digital curation stage. University of
Edinburgh. https://era.ed.ac.uk/handle/1842/2150
Good one
ReplyDeleteWell done
ReplyDeleteWell done
ReplyDeleteIndeed continuous professional development is key. The point has reminded me of the work I did during my undergrad studies 'Professional development and training'
ReplyDeleteThis is a very good write up and the flow is just on point.
ReplyDeleteGood write up
ReplyDeleteGreat work
ReplyDeleteI think institutions whose top management underestimate the power of data in today's economy will soon find it hard to compete aggressively in their industry. It is just a matter of time... a ticking boom.
ReplyDelete