Museums Risk, Experimentation, and Contemporary Topics — Blog Schedule

The Beginning

Not to long ago, I was embroiled in a serious of disparate conversations on Twitter. The topics varied from social media to salary. But, in each, there seemed some essential kernels that stuck. With a field as large as museums (bigger than solar), it felt as if there are some big differences in perception depending on where you stand. In other words, if you are in some fields or roles, you seem to think we should experiment, for example; while in others, the lack of experimenting is suffocating. This hypothesis that role/ position results in differences in perception is not groundbreaking, but in chatting with friends, we couldn’t find the data out there (either to confirm or deny). So, we built a survey (not the prettiest one, but neither am I).

First, and foremost, thank you. Thank you to AAM, AAM EdComm, AAM Media & Tech, NAEA Museum Ed,  MCN and Museums and the Web for passing on my link through your channels. Thank you to all my friends who stepped up to complete and share. Thank you to everyone person, all 115 of you, who took time to add your ideas.  I have been reading your thoughts, mulling over your ideas as I walk the dog,  considering possible links between ideas as I wait in traffic, and coding them daily at my desk.

The data is amazing. And, firstly, it is not just mine. I am very happy to share anonymized  data. Drop me a line at and I will send it to you.  But, it is insanely rich.  There is so much there. So, I will be spending the next few weeks digging in and doing some interviews. It feels imperative to honor all the respondents by treating their ideas right.

The respondents and a grain of salt

There was a recent article on Hyperallergic about how museum salaries are going up, up, up across the board. While such a Camelot would be amazing, the headline is equally mythical. The article was drawing from art museums, and even then it didn’t highlight that salaries were rising in certain sectors at a higher rate than others. Why this aside? Well, the author fell into a trap. They make a leap from a pool of data without realizing it doesn’t hold water. As a weak swimmer, I will not make such leaps with this data.  Instead, I want to bring up where my data pool matches as well as deviates from the field. The results will over insights, but will not be the end all. Data is a good but it is not that good.

Most notably, my data pulls more heavily on art museums than other museums & museums with big budgets. I worked at such a museum for 17 years. I suspect my own personal networks monkeyed with my sample size. In actuality, art museums are a really small slice of the pie. (Want to learn more about number of museums nationally, by state, and type? Here is my look at the ginormous IMLS data set. 

accredited and all museums by field

What’s Next?

First, I will clean up the data for anyone who wants it. It will take me a quick minute to do that right. I want to clear anything that could point back to a specific respondent.  Once that I ready, I can email it to folks.

Second, I will do interviews and use that qualitative information, along with the quantitative data. Anyone else who chooses to work on it will be welcome to add any insights they have.

Finally, I will start put out posts.  I will be posting the first on experimentation in the middle of August with three subsequent posts after that.  Keep an eye out for them.

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