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Curating Library Data,
Big Data, Data Sets
(Using Big Data for Library Advocacy)
Erin Bartolo Jill Hurst-Wahl
@ErinBartolo @Jill_HW
Sept. 27, 2013
THEPOTENTIALREALIZED
ANEXAMPLE
“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
THE FULL DATA LIFE CYCLE
The 3 V‟s
 Volume (how much)
 Velocity (how fast)
 Variety (how complex)
 Data complexity
State of the data
HOW IS BIG DATA DIFFERENT
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
TYPES OF DATA
Challenges & opportunities
Personalization in consumer experience
Predictive analytics in defense
Supply chain management
Retail or emergency planning
ACROSS INDUSTRIES
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
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/
DATA SCIENCE & LIS
Collect
Organize
Store
Curate
Manage
LIBRAR* & DATA SKILLS
http://tweetping.net/
 Analyze
 Report
 Visualize
 Secure
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/
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
“Storytelling”
Visualize usage patterns
Determine influences
Propose policy changes
Improve services planning
Drive data-based decisions
Make content discoverable
Assess current services
Discern the correct questions
OUR DATA: POTENTIAL USES
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
PRIVACY &
THE USA PATRIOT ACT
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/
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

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Curating Library Data, Big Data, Data Sets

  • 1. Curating Library Data, Big Data, Data Sets (Using Big Data for Library Advocacy) Erin Bartolo Jill Hurst-Wahl @ErinBartolo @Jill_HW Sept. 27, 2013
  • 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
  • 4. THE FULL DATA LIFE CYCLE
  • 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
  • 8. Challenges & opportunities Personalization in consumer experience Predictive analytics in defense Supply chain management Retail or emergency planning ACROSS INDUSTRIES
  • 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/
  • 12. Collect Organize Store Curate Manage LIBRAR* & DATA SKILLS http://tweetping.net/  Analyze  Report  Visualize  Secure
  • 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
  • 15. “Storytelling” Visualize usage patterns Determine influences Propose policy changes Improve services planning Drive data-based decisions Make content discoverable Assess current services Discern the correct questions OUR DATA: POTENTIAL USES
  • 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
  • 17. PRIVACY & THE USA PATRIOT ACT
  • 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

Notas do Editor

  1. 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
  2. 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.
  3. Not everything that can be counted counts, and not everything that counts can be counted. - Albert Einstein
  4. 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