Loan-related data entry survey – interim results (19 September 2023)

This page summarises interim results from the Documentation Standards Working Group’s survey of the time taken by museum staff to transfer object data between systems by hand when borrowing and lending objects. It covers returns received until 23:59 GMT / UTC on 19 February 2023.

The sample

We had 59 usable results (although not everyone filled in all the questions).

7. In what country is your museum?

Map showing the countries of museums which have responded to the survey
Respondents by country (results of the CIDOC DSWG museum loan data-entry survey, as of 19 September 2023)

Because of the channels used to circulate the survey, there’s a strong geographical bias to the sample:

Australia 1
Austria 1
Canada 2
Finland 1
Germany 10
Israel 1
Netherlands 2
Sweden 4
Switzerland 3
United Kingdom 21
United States 11
  • 36% of responses came from the UK
  • Next were the United States with 21% and Germany with 17%
  • The remainder were distributed between a further 8 countries

6. What is your museum’s annual operating budget?

Chart showing the relative numbers of museums of different sizes which have responded to the survey
Respondents by museum size (results of the CIDOC DSWG museum loan data-entry survey, as of 19 September 2023)

One respondent noted: ‘Don’t know about our operating budget. Just guessing.’

  • As we might expect, a third of our responses came from the largest museums – indicated by an annual operating budget of more than 10 million Euros or US dollars
  • Just under half were from museums with a budget of 5 million EUR / USD
  • Nearly 80% of responses came from museums with budgets greater than 1 million Euros or dollars
  • Only 20% of responses came from museums with budgets less than 1 million Euros or dollars,
  • And just 7% from museums operating on less than 100,000 Euros or dollars

5. Which collections management system do you use?

Chart showing the collections management systems used by museums which have responded to the survey
Collections management systems used by respondents (results of the CIDOC DSWG museum loan data-entry survey, as of 19 September 2023)

Again, due to the channels initially available to us, there’s a strong bias towards users of the TMS collections management system in the sample: they make up 31%. Next are Mimsy XG and MuseumPlus users, closely followed by imdas pro users, then users of EMu and (perhaps worryingly?) bespoke systems. It’s worth noting that if we lump all Axiell users together they account for 21% of responses.

Data tidying and presentation

Responses necessarily varied in quality. Although asked to provide single figures, respondents often produced ranges; for the purposes of this report, the average figure has been used. Figures reported as ‘less than’ or ‘greater than’ have simply used the number reported, as there was no obvious way of discovering the other boundary.

One museum reported that they spent 5,760 minutes to enter the data for a single loan in; based on their other responses, this has been taken as a data-entry error for 57-60 minutes which, although high, is not atypical.

In order to visualise data in a relatively useful form, individual figures have been assigned to bands, arranged in a roughly (but not strictly) logarithmic scale; these have been plotted against the number of museums reporting a figure within the relevant band. Figures for loans out (generally less time-consuming) are in green; those for loans in are in red.

The orthography of free-text responses has been tidied up; any expansions or interpolations are included in square brackets.

Number of loans out and in, in a year

1. Roughly how many objects do you lend in a year (i.e. loans out)?

3. Roughly how many objects do you borrow  in a year (i.e. loans in)?

Respondents were asked to include short and long-term loans. For all loans, if a loan is for more than a year, it should only be counted when the initial loan is made, or it has to be renewed and new agreements etc. are issued.

Graph plotting the number of museum objects lent and borrowed in a year, against the number of museums reporting the relevant number
Number of object loans out and loans in per year (results of the CIDOC DSWG museum loan data-entry survey, as of 19 September 2023)
Loans out
minimum 2
median 50
mean 159
maximum 3000
Loans in
minimum 0
median 100
mean 201
maximum 1000

Museums with small volumes of loans tend to lend more objects than they borrow, but those with higher volumes of loans tend to borrow more than they lend. Generally, for both loans out and in, the majority of museums lend or borrow fewer objects, whilst a few museums lend or borrow significantly more – so the medians for both types of loan are significantly less than the means: a median of 50 objects lent and 100 borrowed, compared to a mean of 159 lent and 200 borrowed. But do bear in mind the maximums: 3,000 objects lent and 1,000 borrowed.

Time taken to enter data

2. Roughly how many minutes does it take you to transfer a single object’s identification and requirement data from your collections management system into loan agreements and other paperwork to send to the lender, for loans out?

4. Roughly how many minutes does it take you to transfer the lender’s object identification and requirement data into your collections management system, for a single object, for loans in?

Graph plotting minutes taken to enter data per museum object for loans out and in, against the number of museums reporting the relevant time
Time taken to enter data for loans out and loans, in minutes per object (results of the CIDOC DSWG museum loan data-entry survey, as of 19 September 2023)
Loans out
minimum 0
median 15
mean 25
maximum 180
Loans in
minimum 0
median 17.5
mean 29
maximum 240

When it comes to the time spent on data-entry, the differences are significantly reduced, with medians of 15 and 17½ minutes per object for loans out and in, compared to means of 25 and 29 minutes for loans out and in. (We shouldn’t be surprised to find that loans out generally take a little less time, as lenders can often export the data from their collections management system directly into reports.) We do have two quite worrying maximums, of 3 hours per object for loans out, and 4 hours per object for loans in. In short, the burden generally falls on the borrowing institution.

Graph plotting time spent per year to enter data for museum loans out and in, against the number of museums reporting the relevant time
Time taken to enter data for loans out and loans, in hours per year (results of the CIDOC DSWG museum loan data-entry survey, as of 19 September 2023)
Loans out
minimum 0.00
median 8.75
mean 26.42
maximum 375.00
Loans in
minimum 0.00
median 35.33
mean 88.93
maximum 525.00

When we look at the time spent per year on loan–related data-entry, shown here in hours, then that difference becomes quite obvious: the median for loans out is 8 hours 45 minutes per year, for loans in it is 35 hours 20 minutes. Once again, a few institutions report much longer times, which gives us means of nearly 26 hours 30 minutes per year for loans out, and nearly 89 hours for loans in – and maximums of 375 hours for loans out and 525 for loans in.

The results become more striking if we focus on the figures for loans in, and convert them into days spent on data entry over a 10-year period. We assume that staff spend a full 7 hours a day on data-entry. This gives a median time spent on data entry for loans over a ten year period of 50 days, and a mean of 127 days – very nearly 7 months, so well over half a working year. And in the case of one institution, loan-in data entry would take 750 days: nearly 3½ working years. There are too many variables to convert these figures into salary costs, but there would seem to be a significant potential to make long-term savings if we can automate loan-related data entry.

Additional information

Respondents were also asked:

8. Is there anything else you think we should know about the answers you have given, or loan-related data-entry in general?

This section combines responses to that question, with explanations added to responses to other questions.

Loans out

  • Up to 1 minute, the CMS outputs the data in the CMS into a loan agreement for us.
  • Pertaining to question 2: we use crystal reports to attach object data to our agreements. So it doesn’t take nearly as much time as “copy and pasting” data from objects on loan. It requires verifying the current data (which is done during/as part of the loan request approval process), building a group and running the report.
  • Outgoing loans managed through CMS.
  • As we have low IT skills and variable data we rarely do this.
  • As lenders we issue our loan agreements in a standard form (which draws the object info directly from the CMS record) so the 5 minutes is mainly checking and tidying up data.

Loans in

  • [Time is] per record (new object), many are renewals so data already exists and is updated.
  • We only record long-term loans in our collection man[agement] sys[tem]; short-term loans only in excel object lists.
  • We don’t.
  • Currently the module for this isn’t developed on the CMS so this takes considerably longer, probably up to 1hr all told.
  • Pertaining to question 3: The museum has 3 large managed loan collections which do require yearly work regarding records updates, updated agreements, etc. We also facilitate loans on behalf of those collections to other institutions.
  • N/a – Incoming loans not managed through CMS.
  • As we have low IT skills and variable data we rarely do this.
  • Incoming loans: 45 to perm collection; 500 to temp exhibitions.
  • For inward loans the records need creating on the database so this can often be more time consuming.
  • For incoming loans info is provided in a number of ways from various types of lenders, institutions, private lenders, artists etc so it takes more time to enter into our CMS.
  • Don’t use our management system for loans in.
  • [Use] excels for loans in.
  • We had a tab in EMu designed to take minimal information for a loan, prior to that it was over 30 minutes per object as we had to create full catalogue records.
  • We have to enter basic object info for incoming loans before first contact with lender/ in order to contact lender.
  • Multiple-format migration, then formatting


  • Not included is time spent setting up organisational record and loan record which may involve setting up new authority records. Bespoke system means that there are some fields required by system which are not always relevant. Private lenders often aren’t able to provide required information so this has to wait until arrival adding to the time involved in data entry.
  • We write our own Oracle based software and it would be great for our loans to link to our CMS, but sadly no resources at the moment. We are a small part of a larger data based institution.
  • We have built custom Crystal Reports to generate outgoing loan paperwork such a loan agreements, condition reports, and shipping receipts. This has minimized the time spent copying and pasting into other documents, though it hasn’t eliminated it. Not all staff has TMS access and many internal cross-departmental workflows employ Google Suite, so that is where most of the copying and pasting happens. Incoming loans require significantly more copying and pasting on the front end as we receive information piecemeal and in many different formats from curators, lenders, gallerists, etc.
  • The answer is given for a group of five municipal museums.
  • Questions often difficult to answer. With special exhibitions [there] are more external object to work on, without exhibition, of course, not. Several systems in use at the same time, no reconciliation between the systems, still a lot of manual work for loan contracts, permanent loan contracts, these are managed with Word and Excel.
  • This assumes everyone thinks about working this way – there will be a significant number that just don’t think like this. Who is allowed access to CMS or wants ‘control’ are important factors as well as how easy is it to do.
  • LOTS of issues with information sharing around exhibitions.
  • For larger loans the object schedule can be produced through a reporting program. Information is also copied for transport handlers; curators; conservators etc.
  • We are in a process of procuring a new collections management system.
  • My replies are only short term loans and out (long term loans are not included).
  • Museum is in a Scheduled Ancient Monument Norman motte & bailey castle so does not operate in a standard structure building. Entails detailed information on UK Registrars Forms for Loans-in. The responses required on UK Registrar Forms don’t always have a fit with such a unique building which often means additional information / work.
  • Really glad you’re looking into this important issue and unnecessary drain on resources!
  • Being able to generate loan agreements directly from EMu with existing collections data is a wonderful thing, but if borrowers are wanting to enter our items into their systems, they would still need a format which would allow import. Loan agreements are PDF only so not great there!
  • I don’t have answer for most of the questions due to my current job duties, but have worked with records entered via multiple methods over the decades. The concern for me would be figuring out which fields are going to be the must-haves, and how to make sure that data is marked clearly and accurately.

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