Is it worth the time experimenting with a new skill in order to make a library’s task more efficient?
VALA2020 CC3 TABLE 3
Wednesday 12 February 2020, 10.50-12.30
- Victoria University
- Victoria University
Please tag your comments, tweets, and blog posts about this session: #vala2020 #cc3
Taking inspiration from an xkcd cartoon, this Critical Conversation will examine the question – is it worth the time spent learning a new skill in order to make a library work task more efficient?
Although there are large scale library automation projects in libraries (e.g. automatic indexing, inter-linking library data) this presentation focusses on the many small scale tasks that librarians regularly undertake that are repetitive and/or time consuming in nature. Pressure on library budgets coupled with organisational restructuring mean that many libraries operate with fewer staff than previously. Potential automation of routine library tasks or using existing software more effectively holds the promise of workplace efficiency and improved productivity. Time consuming and repetitive tasks are good candidates for programming and can present opportunities to experiment with software features or try out new tools.
The problem however is the learning curve. It takes time to learn a new skill before it becomes useful, then there is the actual time it will take to write and test a program or to apply the new software. Library professionals are reluctant, or are unable, to find the time to explore and experiment with programming or other data techniques. It is hard to justify time spent learning how to use and record a macro for example when it might only take 5 to 10 minutes every month to copy and paste in an Excel spreadsheet.
We will look at a range of real life library tasks that were subsequently automated or improved by using a variety of coding and software tools. The examples are drawn from the functional library work areas of repository management, research data support, cataloguing and acquisitions. Techniques or tools utilised include XSLT, Open Refine, Python, Regular Expressions, and APIs.
We will attempt to quantify the trade-off between time spent making a task more efficient versus the time saved. However, this simple time trade off doesn’t take into account other potential benefits of automation. These other benefits such as professional development and elimination of human errors will also be explored.
When all is considered – is it worth your time?
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