Shared data and the future of libraries

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Shared data and the future of libraries

  1. 1. May 2, 2013Shared Data:What it Means for the Future ofLibraries
  2. 2. May 2, 2013Peter Murray,LYRASIS Digital Technology ServicesRobin Fay,Head, DBM/CatalogingUniversity of Georgia Libraries
  3. 3. Using this software
  4. 4. Agenda• Overview of big data• What is big data? What is shared data?• Implications and challenges• Discussion
  5. 5. How did our data get big?• Technology that has unforeseen consequences• Technology changes• We leave digital trails wherever we go• Think> internet browsing history, email, medicalrecords, bank transactions, buying history atshopping sites, Amazon reviews, Facebookphotos, comments on websites, and much more
  6. 6. How did our data get big?• “Collectively the datathat we leave behindis Big DataBig DataBig DataBig Data”• and of course.. Thereis the data that others(people andmachines) createabout us• Big Data is about usand has far reachingconsequences
  7. 7. What is Big Data?• It is a not a technology –it is a shift in how weview and use information• Taking large amounts ofinformation spreadacross many differentresources in differentformats making themexplore• It doesn’t have to be“that big just bigger thanwhat you can go throughby hand”
  8. 8. 3 attributes of Big Data• Large• Fast (manualtime needed)• andunstructured(formats differ)=3 Vs of Big Data
  9. 9. Big Data• Relational (relationships) database - our ILS systems are oftenrelational databases• Mathematical database – computations• Big Data is the intersection of two• Health– analyzing health records to identify allergies, sickness, etc• Philanthropy (datakind) – analyze behavior of farmers andknowledge workers to evaluate the impact (ROI) of philanthropicwork• Think about potential for library use: we have patron data,bibliographic data and more!
  10. 10. Concerns: Big Data• Privacy – erodes privacy potentially leaking privateinformation• Justify stereotypes (data can be misused or used in anegative) and polarize social groups• Facebook open graph search – pulling together informationfrom diverse information to get lists of seemingly innocentways such as movie watching habits or music can be used innegative ways to reinforce stereotypes or drawn conclusionsabout people• “Personalization can look like prejudice”• We live in grey areas• Computers do not understand that
  11. 11. Which side of the fence?• Big Data is going to change our lives!• Are you• a semantic idealist?a semantic idealist?a semantic idealist?a semantic idealist? if we can “taxonomize” andorganize it, we can make sense of it– Wolfram Alpha – we can ask it and it will reason(mathematical)• A chaotic nihilistA chaotic nihilistA chaotic nihilistA chaotic nihilist? Algorithms will handle it – correctdata will bubble up given enough information– Watson – doesn’t know answers but will analyze tointerpret answer
  12. 12. So, how would you file a cup of coffee?So, how would you file a cup of coffee?So, how would you file a cup of coffee?So, how would you file a cup of coffee?• Depends upon how you will use theinformation!• Understandings do not takeadvantage of digital informationwhich slows semantic idealism –much information not organized sowe have to rely algorithms (for now)but it is vulnerable.• Tagging is often done by machines– even in libraries we batch load,harvest, update data globally.
  13. 13. Humans and technologyHumans and technologyHumans and technologyHumans and technology• Our reasoning can be flawed - we make decisionsevolutionary – we look at simple correlations andpatterns (false positives)• If comments after a post are highly negative,responders are more likely to take polarizingviewpoints• Even when math is good, data can be wrong
  14. 14. Shared dataShared dataShared dataShared data• We are a mosaic of data from other resources• Unified digital history – record of all of our data and couldaggregate health information and share with doctors – justone example• Veracity (can verify) and Value (how we can make sense ofour data)• Shared data : connecting networks will collect data;algorithms will tag and assign metadata but it will be up tohumans to add value - this can then be shared in ways thatare useful
  15. 15. Linked data makes it possibleLinked data makes it possibleLinked data makes it possibleLinked data makes it possible• Linked data keeps us from having to re-enter orcopy informationIt makes data:• reusable• easy to correct (correct one record instead ofmultiples)• efficient• and potentially useful to others
  16. 16. Linked data makes it possibleLinked data makes it possibleLinked data makes it possibleLinked data makes it possible• It can build relationships in different ways -allowing us to create temporary collections (a usercould organize their search results in a way thatmakes sense to them) or more permanent(collocating ALL works by a particular author moreeasily; pulling together photographs more easily)• It can help make sense of Big Data and facilitatesharing data
  17. 17. Linked data makes it possibleLinked data makes it possibleLinked data makes it possibleLinked data makes it possible• Linked data keeps us from having to re-enter orcopy informationIt makes data:• reusable• easy to correct (correct one record instead ofmultiples)• efficient• and potentially useful to others
  18. 18. Thinking of data in the library environmentThinking of data in the library environmentThinking of data in the library environmentThinking of data in the library environment• Automation and new technologies• The web has changed• Large scale bibliographic databases• User expectations and needs• Patron data• Cooperative cataloging• Greater variety of media in library collections (electronic!)• FRBR is our data model – semantic web friendly!
  19. 19. Discussion points• Obviously, WorldCat is a shared data resource wehave all been using for years. What are some otherexamples of big data, shared data, or linked datathat libraries use now?• What are some examples of data that librariescould share that we arent sharing already?• What are some of the pitfalls of data sharing on amassive scale?
  20. 20. Thank you!• Our speakers• You!• Questions?• russell.palmer@lyrasis.org

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