This session was moderated by Jane Dysart.
Book - The Human Face of Big Data
Promotional video for the book, http://m.youtube.com/watch?v=7K5d9ArRLJE
Data is the exhaust of our lives.
Amy Affelt (@aainfopro) - Librarians have always worked with data. Librarians have a role in working with data. We may not be the programmers.
We could have roles around describing data and helping with its use.
She is writing "The Accidental Data Scientist" (provisional title), which will be out next year. It can be preordered.
She mentioned the article on six big data tools that anyone can use. See http://gigaom.com/2013/01/31/data-for-dummies-5-data-analysis-tools-anyone-can-use/
Daniel Lee (@YankeeInCanada) - small data enthusiast and a big data wannabe. Need to learn how to scale from small data to big data.
How do you catalogue data at the question level?
We need the business acumen, a long with the data skills. Some librarians do have the technology skills that a data scientist needs.
We need to understand the privacy issues. This could be an area for information professionals. Professional associations could be providing education around privacy.
We could also get involved in helping organizations understand the security concerns.
Getting involved in data doesn't necessarily require a huge upfront cost. There are open source tools. He notes that our SLA vendor partners have data and data tools. He talked about using data created at an SLA conference through twitter and analyzing it.
Kim Silk (@KimberlySilk) - she is contributing to Amy's book. Her job title is data librarian. She supports the research team at her institution. There is more than one data librarian at the University of Toronto. One person works with the licensing of data. They help students with analysis.
It doesn't take a long time for data to get big. Data can get too big for Excel quickly. Then you need to use SAS, SPSS, or something else.
Data is just another media type. There will be a need for data policy librarians.
She mentioned "data ferret" as a tool for converting data sets.
She showed a graphic on "Toronto Public Library creates over $1 billion in total economic impact". The graphic is on page 1 of this report, http://martinprosperity.org/media/TPL%20Economic%20Impact_Dec2013_LR_FINAL.pdf. The tables used to create the graphic are in the appendices of the report. The graphic is something that could help the community understand the economic impact and would be something that the media could use. The table uses the market value for equivalent services that libraries provide.
Visualizing data makes big, hairy information understandable. Allows you to overlay data. She described a project in Toronto that surfaced and demonstrated transportation/transit deserts. A single map can tell you a million things.
Jane Dysart (@jdysart) - mentioned a library that hired a number of data visualization people.
In order to make decisions using data, we need to be able to understand it. Visualizations help.
Consider what data would impress your boss.
What skills are needed (from the panel):
- Coding - scripting languages
- Classification (coding, metadata)
- Data privacy
- Comfort with technology
- Ability to understand your data collection (and their subject areas)
- Sense of curiosity
- Can tell the story that the data is telling
Code4lib has job ads for data focused jobs.
Is there a need for backend computer skills or graphic design? - Silk has acquired more of her skills on the job. Some of the work in her organization (e.g., graphics) are done by other people. Lee asks why some information professionals are reticent to offer analysis. It is a hump that we need to get over.
Places to get additional training? - Lee is believes in training himself. He looks for free tools like Udacity, MIT, Code Academy, MOOCs. The problem is choosing the training, not finding it.