Craig Milne – Manager, Scholarly Resource & Library Campus Services
Kelly Johnson – Team Leader, Scholarly Resource Services
Surfing the data waves:
driving change and
exploring the
possibilities
Surfing the data waves: driving
change and exploring the possibilities
Craig Milne – Manager, Scholarly Resource & Library Campus Services
Kelly Johnson – Team Leader, Scholarly Resource Services
Griffith at a glance
 5 campuses (3 in Brisbane, Logan,
Gold Coast)
 50,000 current students
 8,500 international students
 Trimesters implemented 2017
 Multiple time periods across the
year
 3 Trimesters
 7 Study Periods/Sessions - OUA
 6 Teaching Periods - Griffith Online
Talis at Griffith
 Managed by centralised team – Scholarly Resource
Services (SRS)
 Supported by Library Technology Services and
Frontline Services
SRS
 Rollover lists each Trimester at Week 5 of previous
Trimester
 Rollover all lists, not just active courses
 Create new lists
 Manage electronic/copyright/newer editions referrals
 Use reports instead of Reviews for housekeeping
Academics
 Must re-request digitisations
 Add bookmarks and modify list structure
 Own their lists and responsible to update
Take off - catching the wave in
2016/17
Using all items report to try and push towards our 95% required and
recommended readings online KPI
 Targeted conversion p to e
 Informing discussion / negotiations with publishers
 Using data for bulk ordering / ISBN matching services
 Using data in deselection processes (950 tags)
 Using data to identify potential issues if resources were
cancelled or removed from aggregated collections
 Quickly assessing publisher offers comparing offer against lists
 Basic pivot tables for reporting number of courses, items, items
by type, etc.
In the whitewater
Caught the wave but there were issues still to be solved:
 KPI reporting – 95% Required and Recommended readings online (no
online flag)
 Managing the volume of data – we want to drop in at anytime on any
set of data without re-running reports
 Cleaning / improving the data to make it more usable
 Making data readily accessible for others to query
Wanted to be able to use our data more effectively to:
 Become smarter in our p to e strategies (more targeted)
 Influence a cultural shift in reading list creation and reading selection at
an academic level (using data to support policy and get more buy-in )
Duck diving into the data
 Data consultancy webinars
 Initial brief and 2 separate discussions (2 x 2hrs)
 Discussed our data options with TALIS
 Useful in understanding limitations of data
 Helpful to talk through the problems and solutions
 Understand the impact of our processes on the data
 Established data limitations (usage data / clicks)
 Established how to determine % online
 Identified datasets we needed to combine from TALIS systems
 Identified what data we needed that wasn’t in TALIS
 Worked out how to derive the conditional fields we required
Boards and manoeuvres
 Excel 2016 “Get and Transform”
 Tableau
 Query – Load and format data types
 Transform – Split columns, calculated columns, power query
 Merge Query – SQL type joins (No more VLOOKUP!)
 Conditional Columns - Online flag field populated based on
DOI, Web address and request data columns
 Custom columns – manipulating / transforming the data with
power query
 Split columns / Power Query - useful for stripping data and
building new columns for data matching (URL, Doc ID, etc.)
Creating the ideal set
Carving the data waves
 We are now discovering new insights as we can
readily explore the data
 Report on KPI’s
 Create a story for Learning &Teaching
Committee, Schools and Academics that is
visual and doesn’t rely on pivot tables and
numbers alone.
Number of Reading Lists
% Importance by time period
Count of importance by time period
Available online by time period
Available online by time period OUA
Required online by time period
Recommended online by time period
Not Online by Importance / Type
Chapters and Books not online by School
Surf’s up
Cleanup projects for 2018
 Digitise previously blocked items (Australian
copyright law changed!)
 Non-requested digitisations – investigate and
target communication at school level
 Missing Importance tags - investigate and
target communication at school level
 Mistakes in resource types/ metadata - e.g.
websites with no URLs
Future directions
 Set up dashboard / access to data so staff can quickly pull info and reports
 Make the data accessible to the broader university and explore other uses
 Academic has already asked for data across all courses with a program
to work on standardising texts / readings
 Investigations into use of data for Bookshop
 Further analysis of the data
 Drill down into the data at School / Program level
 Use analysis to help drive cultural shift in resource selection
 Bring in usage data to tell the story of the physical and electronic resource
 Still want to see the dashboard data from Talis available as either additional
fields in all items report OR as a separate report with clear definitions
 Training staff in Excel “Get and Transform”
Thank You

Talis Insight Asia-Pacific 2018 - Craig Milne and Kelly Johson, Griffith University

  • 1.
    Craig Milne –Manager, Scholarly Resource & Library Campus Services Kelly Johnson – Team Leader, Scholarly Resource Services Surfing the data waves: driving change and exploring the possibilities
  • 2.
    Surfing the datawaves: driving change and exploring the possibilities Craig Milne – Manager, Scholarly Resource & Library Campus Services Kelly Johnson – Team Leader, Scholarly Resource Services
  • 3.
    Griffith at aglance  5 campuses (3 in Brisbane, Logan, Gold Coast)  50,000 current students  8,500 international students  Trimesters implemented 2017  Multiple time periods across the year  3 Trimesters  7 Study Periods/Sessions - OUA  6 Teaching Periods - Griffith Online
  • 4.
    Talis at Griffith Managed by centralised team – Scholarly Resource Services (SRS)  Supported by Library Technology Services and Frontline Services SRS  Rollover lists each Trimester at Week 5 of previous Trimester  Rollover all lists, not just active courses  Create new lists  Manage electronic/copyright/newer editions referrals  Use reports instead of Reviews for housekeeping Academics  Must re-request digitisations  Add bookmarks and modify list structure  Own their lists and responsible to update
  • 5.
    Take off -catching the wave in 2016/17 Using all items report to try and push towards our 95% required and recommended readings online KPI  Targeted conversion p to e  Informing discussion / negotiations with publishers  Using data for bulk ordering / ISBN matching services  Using data in deselection processes (950 tags)  Using data to identify potential issues if resources were cancelled or removed from aggregated collections  Quickly assessing publisher offers comparing offer against lists  Basic pivot tables for reporting number of courses, items, items by type, etc.
  • 6.
    In the whitewater Caughtthe wave but there were issues still to be solved:  KPI reporting – 95% Required and Recommended readings online (no online flag)  Managing the volume of data – we want to drop in at anytime on any set of data without re-running reports  Cleaning / improving the data to make it more usable  Making data readily accessible for others to query Wanted to be able to use our data more effectively to:  Become smarter in our p to e strategies (more targeted)  Influence a cultural shift in reading list creation and reading selection at an academic level (using data to support policy and get more buy-in )
  • 7.
    Duck diving intothe data  Data consultancy webinars  Initial brief and 2 separate discussions (2 x 2hrs)  Discussed our data options with TALIS  Useful in understanding limitations of data  Helpful to talk through the problems and solutions  Understand the impact of our processes on the data  Established data limitations (usage data / clicks)  Established how to determine % online  Identified datasets we needed to combine from TALIS systems  Identified what data we needed that wasn’t in TALIS  Worked out how to derive the conditional fields we required
  • 8.
    Boards and manoeuvres Excel 2016 “Get and Transform”  Tableau  Query – Load and format data types  Transform – Split columns, calculated columns, power query  Merge Query – SQL type joins (No more VLOOKUP!)  Conditional Columns - Online flag field populated based on DOI, Web address and request data columns  Custom columns – manipulating / transforming the data with power query  Split columns / Power Query - useful for stripping data and building new columns for data matching (URL, Doc ID, etc.)
  • 9.
  • 10.
    Carving the datawaves  We are now discovering new insights as we can readily explore the data  Report on KPI’s  Create a story for Learning &Teaching Committee, Schools and Academics that is visual and doesn’t rely on pivot tables and numbers alone.
  • 11.
  • 12.
    % Importance bytime period
  • 13.
    Count of importanceby time period
  • 14.
  • 15.
    Available online bytime period OUA
  • 16.
    Required online bytime period
  • 17.
  • 18.
    Not Online byImportance / Type
  • 19.
    Chapters and Booksnot online by School
  • 25.
    Surf’s up Cleanup projectsfor 2018  Digitise previously blocked items (Australian copyright law changed!)  Non-requested digitisations – investigate and target communication at school level  Missing Importance tags - investigate and target communication at school level  Mistakes in resource types/ metadata - e.g. websites with no URLs
  • 26.
    Future directions  Setup dashboard / access to data so staff can quickly pull info and reports  Make the data accessible to the broader university and explore other uses  Academic has already asked for data across all courses with a program to work on standardising texts / readings  Investigations into use of data for Bookshop  Further analysis of the data  Drill down into the data at School / Program level  Use analysis to help drive cultural shift in resource selection  Bring in usage data to tell the story of the physical and electronic resource  Still want to see the dashboard data from Talis available as either additional fields in all items report OR as a separate report with clear definitions  Training staff in Excel “Get and Transform”
  • 27.

Editor's Notes

  • #3 Photo credit: Les Tours de Surfers Paradise [Image]. ( 2012). Retrieved from https://www.flickr.com/photos/aloys_dharambure/7200528388/in/photolist-dSUaxT-cJKZbC-cJKYY1-bYhAeb-bT5odR-bpPF4V-9zGMWt-cdNxMm-9DNXVD-TQYALk-i3GEf2-qipJCc-iihW1k-bvQhm7-9DhSrL-nmtfkJ-a6VmYX-bvQgFU-bnWXpK-75vLQS-cgeQrd-2uT6aE-a1NYb5-6aMaHf-HYYD7T-qV49fb-b3HH9c-9MU84v-9DRaNC-37SNME-qiooA2-6o7fbh-iijvn6-73nmmp-9MUPnM-bYhzxs-a2A5B4-bys6Uy-JUSuWz-qipMTk-iiicku-bbtoY2-9ahMSF-b 2nZEx-9zJX9Q-e8iygA-9DRNGN-9ejEpm-iiiqNQ-qV2Kcm/ CC BY-NC-ND 2.0
  • #4 Photo: Griffith marketing
  • #5 Photo: Griffith marketing
  • #6 Photo credit: Waves [Image]. (2016). Retrieved from https://www.flickr.com/photos/massimo_riserbo/33993227276/in/photolist-TMSbas-2Re47S-nWte3h-oELzRz-8PF1dL-6adRty-5rAgYX-6FcfMG-57bGSa-jNQd8K-jG1C3b-jwmdkS-TbB9jJ-qQ3ok-5jpMFU-7egKAq-5Bt2E6-oEMh22-bh6Z4D-6JaDZk-3Cw1cR-8aakgK-X8CAgh-7tvhrr-edoijs-6PHDXz-oXbDV6-mgmQZY-f14jVp-41nHxP-41mzRa-Cu18yc-pC32tZ-5uPgn4-CY7hev-dxUFJJ-41ics3-V4qAYG-9BwEEv-nsGXHz-9o12t1-nKhvVB-6JeJUJ-6kbhEL-6bQiTW-m6hvpp-4kH3fx-DNVWYg-stkrBz-6QUGYe CC BY-NC 2.0
  • #7 Photo credit: Big Wave Day [Image]. (2010). Retrieved from https://www.flickr.com/photos/tjt195/4377130458/ CC BY 2.0
  • #8 Photo credit: IMG_3687-1 [Image]. (2007). Retrieved from https://flic.kr/p/5KLriJ CC BY 2.0
  • #9 Photo credit: Surfing Burleigh 31 310311 [Image]. (2011). Retrieved from https://www.flickr.com/photos/tk_five_0/5632664208/in/photolist-9zJSQ9-qV6emu-97JwEC-bJK6R8-bYhG6J-Seqrtm-qfBPjY-7JivJh-8fW6ZQ-asFaNg-a5HQ6C-9P3uhT-bnMEoK-pCQsoQ-ct7z8h-6DfW6r-6Xp4CD-a6Y9Ps-bKSCQz-fwAokv-bbteKx-7Jg6oz-9QNNmG-cgexuW-a6W3DX-9zGK8V-a6ZL7J-a5HQGo-9fs2QZ-rctLjT-qih8pJ-bJK2cX-a6Y3b1-67qoYM-a6XZsg-rcDYLv-b2uZxB-a6Zy73-cJKZtJ-JDRh8z-ab2ooD-cJL1qq-22gMHz-rcCzMM-7C2Z37-9DPuku-b4Ese6-a6XH3N-22fW72-anWaCh CC BY-NC 2.0
  • #11 Photo credit: Big Wave Surfing Teahupoo Tahiti [Image]. (2007). Retrieved from https://www.flickr.com/photos/thelastminute/1973927918/in/photolist-41qTZY-Uz3aJ4-iwqVuM-VB9GcV-Uw6YP9-7SQEE7-VdCMPb-qZk9aM-6qLAvR-r3BLMJ-TMSbas-2Re47S-nWte3h-oELzRz-8PF1dL-6adRty-5rAgYX-6FcfMG-57bGSa-jNQd8K-jG1C3b-jwmdkS-TbB9jJ-qQ3ok-5jpMFU-7egKAq-5Bt2E6-oEMh22-bh6Z4D-6JaDZk-3Cw1cR-8aakgK-X8CAgh-7tvhrr-edoijs-6PHDXz-oXbDV6-mgmQZY-f14jVp-41nHxP-41mzRa-Cu18yc-pC32tZ-5uPgn4-CY7hev-dxUFJJ-41ics3-V4qAYG-9BwEEv-nsGXHz CC BY-NC 2.0
  • #20 Just to illustrate how else we can look at the data we can drill into detail at a school level this shows all (Req / Rec / Null) Chapters and Books not online by School Probably no surprises in with QCA (purple), GFS Red, QCGU brown, Greeny brown Humanities, Languages and Social Sciences
  • #26 Photo credit: SDC09028 [Image]. (2015). Retrieved from https://www.flickr.com/photos/chillmimi/17366656148/ CC BY 2.0
  • #27 Photo credit: Burleigh Heads Sunrise_18_200711 [Image]. (2011). Retrieved from https://www.flickr.com/photos/tk_five_0/5959822583/in/photolist-a5DDBM-byeEbp-T22MBq-qfQxbt-8nJarG-9zJSQ9-qV6emu-97JwEC-bJK6R8-bYhG6J-Seqrtm-qfBPjY-7JivJh-8fW6ZQ-asFaNg-a5HQ6C-9P3uhT-bnMEoK-pCQsoQ-ct7z8h-6DfW6r-6Xp4CD-a6Y9Ps-bKSCQz-fwAokv-bbteKx-7Jg6oz-9QNNmG-cgexuW-a6W3DX-9zGK8V-a6ZL7J-a5HQGo-9fs2QZ-rctLjT-qih8pJ-bJK2cX-a6Y3b1-67qoYM-a6XZsg-rcDYLv-b2uZxB-a6Zy73-cJKZtJ-JDRh8z-ab2ooD-cJL1qq-22gMHz-rcCzMM-7C2Z37 CC BY-NC 2.0