Big data was one of my target areas for this conference, as it’s a topic about I should (and want) to know more. This panel seemed like one of the more entry-level sessions about it, and featured some interesting speakers too. What follows is a condensed version of my notes, but the short version is that I found it accessible, engaging and thought-provoking. Speaking were Amy Affelt, Daniel Lee, Kim Silk, and moderator Jane Dysart.
What’s not to love about a presentation which begins with a book recommendation?! In this case we were directed to The Human Face of Big Data, also the website humanfaceofbigdata.com
Amy Affelt began by demystifying the whole concept of big data – at the end of the day it’s just data, which is not news to information professionals. Currently big data is not seen as a librarian thing, but we need to change that. Ways in which we can do so include keeping our eyes open for potential big data projects, getting involved, offering assistance. N.B. Amy’s book The Accidental Data Scientist will be out in 2015!
Daniel Lee saw issues around digital privacy and data security as a potential growth area – helping people understand the implications of these can be a real opportunity for info pros. We can see the connections between all the dots!
Kim Silk added copyright, data policy work, privacy and digital preservation to the list of potential job opportunities. Data management and planning is becoming a big deal, particularly as US funding bodies are now asking for data management plans to be submitted with grant applications – librarians could and should be working with researchers on these.
Kim was encouraged to describe her work on the data behind an infographic produced as part of the reporting on research into the economic impact of Toronto’s public library services (scroll down the page). They calculated the market values of everything offered by libraries; not just the books and other physical and digital resources, but things like training courses, meeting space, the staff, facilities costs, even the toilet paper in the library bathrooms! The result is powerful, with a direct message.
There are lots of free tools to use, both for reformatting data and for repurposing it (eg. into infographics) BUT you need to learn how to read data first (cf. the Six Big Data tools anyone can use article on gigaom). Visualizations are a great way of communicating the contents of big unwieldy datasets, and help telling the stories which engage other people. Coding is a useful skill to have, but other skills like adding metadata and applying a controlled vocabulary are already familiar to most librarians.
Kim had a great piece of advice, which was to regard your datasets as just another collection – you don’t have to read all the books in your library but you should gain a good overview of them as a collection, and it’s exactly the same for data.
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