Shared data and the future of libraries
- 1. May 2, 2013 Shared Data: What it Means for the Future of Libraries
- 2. May 2, 2013 Peter Murray, LYRASIS Digital Technology Services Robin Fay, Head, DBM/Cataloging University of Georgia Libraries
- 4. Agenda • Overview of big data • What is big data? What is shared data? • Implications and challenges • Discussion
- 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, medical records, bank transactions, buying history at shopping sites, Amazon reviews, Facebook photos, comments on websites, and much more
- 6. How did our data get big? • “Collectively the data that we leave behind is Big DataBig DataBig DataBig Data” • and of course.. There is the data that others (people and machines) create about us • Big Data is about us and has far reaching consequences
- 7. What is Big Data? • It is a not a technology – it is a shift in how we view and use information • Taking large amounts of information spread across many different resources in different formats making them explore • It doesn’t have to be “that big just bigger than what you can go through by hand”
- 8. 3 attributes of Big Data • Large • Fast (manual time needed) • and unstructured (formats differ) =3 Vs of Big Data
- 9. Big Data • Relational (relationships) database - our ILS systems are often relational 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 and knowledge workers to evaluate the impact (ROI) of philanthropic work • Think about potential for library use: we have patron data, bibliographic data and more!
- 10. Concerns: Big Data • Privacy – erodes privacy potentially leaking private information • Justify stereotypes (data can be misused or used in a negative) and polarize social groups • Facebook open graph search – pulling together information from diverse information to get lists of seemingly innocent ways such as movie watching habits or music can be used in negative ways to reinforce stereotypes or drawn conclusions about people • “Personalization can look like prejudice” • We live in grey areas • Computers do not understand that
- 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” and organize 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 – correct data will bubble up given enough information – Watson – doesn’t know answers but will analyze to interpret answer
- 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 the information! • Understandings do not take advantage of digital information which slows semantic idealism – much information not organized so we 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. Humans and technologyHumans and technologyHumans and technologyHumans and technology • Our reasoning can be flawed - we make decisions evolutionary – we look at simple correlations and patterns (false positives) • If comments after a post are highly negative, responders are more likely to take polarizing viewpoints • Even when math is good, data can be wrong
- 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 could aggregate health information and share with doctors – just one example • Veracity (can verify) and Value (how we can make sense of our data) • Shared data : connecting networks will collect data; algorithms will tag and assign metadata but it will be up to humans to add value - this can then be shared in ways that are useful
- 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 or copy information It makes data: • reusable • easy to correct (correct one record instead of multiples) • efficient • and potentially useful to others
- 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 user could organize their search results in a way that makes sense to them) or more permanent (collocating ALL works by a particular author more easily; pulling together photographs more easily) • It can help make sense of Big Data and facilitate sharing data
- 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 or copy information It makes data: • reusable • easy to correct (correct one record instead of multiples) • efficient • and potentially useful to others
- 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. Discussion points • Obviously, WorldCat is a shared data resource we have all been using for years. What are some other examples of big data, shared data, or linked data that libraries use now? • What are some examples of data that libraries could share that we aren't sharing already? • What are some of the pitfalls of data sharing on a massive scale?
- 20. Thank you! • Our speakers • You! • Questions? • russell.palmer@lyrasis.org