Rachel Bruce - The three stages of data
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Presentation from Rachel Bruce, Innovation Director, Digital Infrastructure, JISC, to the SCONUL Conference 20-21 June 2013, Dublin
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Rachel Bruce - The three stages of data
- 1. 24/06/2013 Venue Name: Go to 'View' menu > 'Header and Footer' to change slide 1 The 3 Stages of Data … Rachel Bruce Innovation Director, Jisc
- 2. 24/06/2013 Venue Name: Go to 'View' menu > 'Header and Footer' to change slide 2 Personalisation
- 3. 24/06/2013 SCONUL Conference June 2013 slide 3 •Relevance •Competition with the Web •Push & Pull •Amazon •Usage data
- 4. 24/06/2013 slide 4 $1m prize!!! – Recommender system Open University - Recommendations Improve the Search Experience – addressing inadequate library search & the user experience
- 5. 24/06/2013 Venue Name: Go to 'View' menu > 'Header and Footer' to change slide 5 SALT – Manchester/COPAC – Surfacing the Academic Longtail
- 6. 24/06/2013 Venue Name: Go to 'View' menu > 'Header and Footer' to change slide 6 Decision Making
- 7. 24/06/2013 Venue Name: Go to 'View' menu > 'Header and Footer' to change slide 7 …metro doc at Penn …
- 8. 24/06/2013 slide 8 The vision of the library analytics and metrics project is to put data at the fingertips of librarians to improve student attainment and satisfaction and achieve new efficiencies and economies through innovative services and tailored support. •Gate data •Usage •Circulation •Registry •Student services •Assessment Use by staff, students, discipline, dem ographics Collection Management Benchmarking Student attainment
- 9. 24/06/2013 Venue Name: Go to 'View' menu > 'Header and Footer' to change slide 9 New Services
- 10. 24/06/2013 slide 10 Lemon Tree at Huddersfield University – gamification The Learning Registry – paradata layer … Library Cloud & Shelf Life – DPLA – Harvard Library Innovation Lab Course signals – Purdue – in time personalized learner support
- 11. 24/06/2013 slide 11 Personlisation – individual Decision making – across the institution and the library New Services – create experiences
- 12. 24/06/2013 slide 12 "Every day I wake up and ask, 'how can I flow data better, manage data better, analyse data better?" Rollin Ford, the CIO of Wal-Mart
- 13. 24/06/2013 Venue Name: Go to 'View' menu > 'Header and Footer' to change slide 13
Notes de l'éditeur
- But each aspect is about recognising that there is lots of data that lies inert within our library systems & that it can be used in different ways for positive effect.
- Key issue is relevance – how to make – for the user and for the library -Web 2.0 – library in Push & Pull Amazon user preferences – converged The Netflix Prize was an open competition for the best collaborative filteringalgorithm to predict user ratings for films, based on previous ratings without any other information about the users or films, i.e. without the users or the films being identified except by numbers assigned for the contest.The competition was held by Netflix, an online DVD-rental service, and was open to anyone not connected with Netflix (current and former employees, agents, close relatives of Netflix employees, etc.) or a resident of Cuba, Iran, Syria, North Korea, Myanmar, or Sudan. On 21 September 2009, the grand prize of US$1,000,000 was given to the BellKor's Pragmatic Chaos team which bested Netflix's own algorithm for predicting ratings by 10.06%.[1]
- NetFlix – open competition for an algorithm to predict user ratings based on previous user ratings – they estimate that 75% of their business is driven by the recommendation system. RISE set out to test two thingsCan you use search data to make recommendationsAre recommendations useful for these new systems. SAMS single sign on. The EZProxy system from OCLC which allows students to access our resources as if they were locally within the library We are using SFX from ExLibris as our resources knowledge base and as the OpenURL link resolver and then finally the Ebsco Discovery Solution in place of an older federated search systemEZProxy log files – data pushed through it from web, VLE, library system etc – match it with bibliographic data from lib system & cross ref. People on course A viewed resource B – people who looked at resource C also looked at resource D. Recommendations, rate recommendations, search interface capture search terms means they can also say people who used that search term looked at these resources. Integrate in to mobile too. To extract loan transactions from Talis Alto. For each transaction (the borrowing of a physical item but not its renewal or return) data showing the item id, basic bibliographic details, borrow id and transaction date are retrieved.
- Simply use and don’t library decision - Correlations between different sorts –Library impact data use of the library and student performanceRecognise data as central …. “Higher Education institutions, for the most part, are collecting more data than ever before . . most of these data are used to satisfy credentialing or reporting requirements rather than to address strategic questions, and much of the data collected are not used at all”
- LAMP – from HEIs, Jusp , IRUS , KB+ Copac visualise, coherent stories about usage, student behaviour , Gate data UsageCirculation Registry Student servicesAssessment Our Library Analytics and Metrics Project (LAMP) plans to use data to support the development of new services, for example the personalisation of resources or student support, and allow libraries to understand their impact across the university. This includes how they support new research patterns or student attainment.The project will develop a prototype service for libraries to try, probably a dashboard view of different data sources. Participants who helped us test and validated use cases, in the areas of demographics, collection management, disciplinary and student support, recently flagged the sort of data that could be relevant to the dashboard. Some examples are data from UCAS and the national student survey.
- http://wiki.creativecommons.org/images/d/d6/The_Learning_Registry_-_Sharing_Federal_Resources.pdfDevelopments such as Purdue's Course Signals and the work of Virginia Tech in Mathematics have illustrated the value of real time data in focusing 'in time' personalized learner support at the point of need, with the added benefit of signaling opportunities for longer term course improvement