• Access and facilities (i.e # Average number of libraries per 1000
• Collection (i.e # Average number of volumes in public libraries per
1000 literate inhabitants)
• Library use and users (i.e # Registered users in higher education
libraries as a percentage of number of students)
• Library staff (i.e # Average number of employees in public libraries)
• Expenditure (i.e $ Expenditure on literature and information per
inhabitant in public libraries)
• Ellis, S., Heaney, M., Meunier, P., Poll. R. (2009), “Global Library
Statistics”, IFLA Journal, Vol. 35 No. 2, pp. 123-130
– Via http://www.smartkpis.com/blog/2010/03/29/performance-
• But really via Google + MY search terms..
Blah blah blah blah, blah blah blah blah, blah,
blah blah, blah blah, blah blah, blah.
Blah blah blah, blah, blah blah blah, blah, blah,
blah, and up by blah, and down by bleurghh,
and blah blah, blah blah, blah blah, bah!
What data do you have?
• Collection data
• Usage data
• User (geo)demographics
• Occupancy/usage of physical space (and how
is the space used?)
• What journals are being photocopied?
• What books are referred to but not borrowed?
• What requests/searches aren’t being fulfilled?
(desired output for given input)
• Borrowed this, borrowed that
• Borrowed this, studied that
• Study this so might borrow that
• Know these people who all borrowed that
• Are in this group of people, who tend to
borrow the same thing at around the same
time, or just before (or after) another group