NOTE: A resource list is in the notes section on the last slide. To view, please download the presentation.
Session description: In order to properly advocate for your library, you need to understand the impact of your services. As you assess the impact of your services, you have the opportunity to collect large amounts of complex data. This raises questions about what data is collected, how it is stored, and how it is used for advocacy.
This session will go over the basics of ”Big Data” and “Data Science” in order to discuss how those concepts can be used in libraries of all types.
Learning outcomes: Session participants can expect to learn the following:
General understanding of Big Data and Data Science
Relationship between these concepts and the work/skills of library staff
Application of these concepts to your organizations, including analyzing large amounts of complex data for advocacy
3. “Many people are presently comfortable with
sharing large amounts of personal information
online through social networks or online activity
tracking systems.”
People want to know the impact of their sharing
and be able to control how their information is
used.
INSIGHTS FROM THE IFLA TRENDS
REPORT
5. The 3 V‟s
Volume (how much)
Velocity (how fast)
Variety (how complex)
Data complexity
State of the data
HOW IS BIG DATA DIFFERENT
6. McKinsey Global Institute report
140,000-190,000 people lack analytical skills
1.5M managers lack ability to use big data
Big opportunities. Big challenges.
BIG DATA SKILLS GAP
https://www.facebook.com/note.php?note_id=469716398919
9. The Renaissance approach
Facts not hunches
Evidence based research
Data driven decision making
Breaking down silos
Adaptability
Data mashups across information sources
Data storytelling
SEPARATING OPPORTUNITY FROM HYPE
10. Grow talent from within
Easier to change course
SMALL ORGANIZATIONS BENEFIT, TOO
http://www.hightech-highway.com/virtualize/six-ways-to-use-virtualization-in-your-
small-business/
13. Use the tools and techniques
being used by government
and business
House data for researchers
Use data to advocate for
ourselves and our
communities
LIBRARIES CAN…
http://hint.fm/wind/
14. Circulation data
Transaction data
Gate counts
Data on event attendance
Data from your web site
Survey data
Collection data
Postal code
Gender
Language
Last activity
Circulation count
Branch use
Computer use
OUR DATA: WE HAVE
16. Data has your back
What are you advocating for?
How can you collect data to support that effort?
You can‟t collect at the last minute!
Plan…implement…analyze…use.
BIG DATA & ADVOCACY
18. Originally passed in 2001. Extended in 2011.
Four Year extension of three provision including
searches of business records.
“Section 215 orders for tangible things,
commonly referred to as the „library records‟
provision.” [Senator Leahy]
Two thoughts:
Privacy vs. better service
Info on individuals vs. aggregated info
THE USA PATRIOT ACT
www.flickr.com/photos/surreynews/6943531691/
19. Erin Bartolo
Data Science Program
Manager
ebartolo@syr.edu
(315) 443-3777
@ErinBartolo
Jill Hurst-Wahl
Director, LIS and School
Media Programs
jahurst@syr.edu
(315) 443-1070
@Jill_HW
Hinweis der Redaktion
Our original title was “Using Big Data for Library Advocacy.” Session description: In order to properly advocate for your library, you need to understand the impact of your services. As you assess the impact of your services, you have the opportunity to collect large amounts of complex data. This raises questions about what data is collected, how it is stored, and how it is used for advocacy. This session will go over the basics of ”Big Data” and “Data Science” in order to discuss how those concepts can be used in libraries of all types.Learning outcomes: Session participants can expect to learn the following:General understanding of Big Data and Data ScienceRelationship between these concepts and the work/skills of library staffApplication of these concepts to your organizations, including analyzing large amounts of complex data for advocacy
Many of us use services like Amazon that use data about us in order to provide better customer service. I, for one, like that Amazon will make recommendations and point me toward other options. If you like…others looked at…This occurs because we each have given up a piece of our privacy when we use Amazon. It keeps track of information for us and uses it to provide better service…and we like better service.
Not everything that can be counted counts, and not everything that counts can be counted. - Albert Einstein
Resources:Bartolo, Erin and Jill Hurst-Wahl (2013). Curating Library Data, Big Data, Data Sets. http://www.slideshare.net/EMC (February 27, 2012). Big ideas. How big is big data? http://www.youtube.com/watch?v=eEpxN0htRKIHurst-Wahl, Jill (2013). SLA 2013: Big Data, Mike Walsh, and Libraries, B/ITe, v.30, n.2, http://it.sla.org/bite/v30n2/bigdata/IFLA (2013). Riding the Waves or Caught in the Tide? Insights from the IFLA Trend Report, http://trends.ifla.org/insights-documentKoirala, Prashant (n.d.) What is Big Data and How Fast is it Growing? http://venturehire.co/blog/what-is-big-data-and-how-fast-is-it-growingMcHugh, Hazel (January 1, 2012). 12 big facts about big data. http://www.kurtosys.com/blog/12-big-facts-about-big-data/McKinsey Global Institute(2011). Big data: The next frontier for innovation, competition, and productivity. http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovationMozy.com (n.d.).How much is a petabyte infographic. http://www.infographicsshowcase.com/how-much-is-a-petabyte-infographic/Ng, Cynthia (June 7, 2013). Digital Odyssey 2013: Big Data, Small World Notes & Takeaways. http://cynng.wordpress.com/2013/06/07/digital-odyssey-2013/Press, G. (May 9, 2013). A Very Short History of Big Data. Forbes. http://www.forbes.com/sites/gilpress/2013/05/09/a-very-short-history-of-big-data/Stanton, J. (2013). Introduction to Data Science. Available through iTunes (free). Stanton, J. M., Palmer, C. L., Blake, C., & Allard, S. (2012). Interdisciplinary Data Science Education. In N. Xiao, N., & L. R. McEwen, Special Issues in Data Management (ACS Symposium Series, Volume 1110). Washington, DC: American Chemical Society.Syracuse University Library (2012). Data science subject guide. http://researchguides.library.syr.edu/datascience