Libraries, Leadership & Data Management€¦ · Libraries, Leadership & Data Management. ARTHUR...
Transcript of Libraries, Leadership & Data Management€¦ · Libraries, Leadership & Data Management. ARTHUR...
INGRID REICHE & RENEE REAUME, THE UNIVERSITY OF CALGARY
Libraries, Leadership & Data Management
ARTHUR DOWNING, BARUCH COLLEGE/CUNY
RENEE REAUME,UNIVERSITY OF CALGARY
Qualifying the Quantitative: Research Data as Community Development
INGRID REICHE,UNIVERSITY OF CALGARY
Training and Development Librarian and Anthropology Liaison, University of Calgary
Renee Reaume
Metadata Librarian, University of Calgary
Ingrid Reiche
Metadata Services in The
Andrew W. Mellon Foundation’s Grant Academic Research and
University Libraries: A New Model for Collaboration
12 Projects Funded
• Multidisciplinary• Substantial work with the library (research
platform)• Research Themes:
Smart Cities, Cultural Discourse, Arctic Studies
12 Projects Funded• Multidisciplinary• Substantial work with the library (research platform)• Research Themes:
Smart Cities, Cultural Discourse, Arctic Studies
Analytics &Visualization
Collaborative Spaces
Data Curation & Sharing
Digitization Metadata Services
Rights & Dissemination
Web Development
Virtual Reality
Platform
Mapping Victorian Literary Sociability
Visualizing a Canadian Author Archive: Alice Munro
Soper’s World
Metadata Services
o controlled vocabularieso subject creationo data standardso templateso instructiono procedures
o quality assuranceo data transformation
Digitally Preserving Alberta’s Diverse Cultural Heritage
Soper’s World: A Journey into the Canadian Arctic Through Art
• Leveraged existing metadata templates
• Increased expertise with Dublin Core
• Quality assurance for subject creation
• Used Online tools to improve existing workflows
Enterprise Metadata Services Model: Workflows for Supporting Research and Metadata Services
Workflows for Supporting Research: Digitally Preserving Alberta’s Diverse Cultural Heritage
Consultation with research teamQuotes for services
Templates for resourcesProcedures for data entry
Instructional session for resource descriptionLibrary and Research Team
Keywords for subject
Resources descriptionResearch
Team
Quality assurance for data entry
Subject creationConsultation with
ResearchersLibrary
Data DepositResearch
Team
Mapping Victorian Literary Sociability
• Continued consultation• Template creation• Data transformation and crosswalks • Deepening understanding of data standards (TEI) • Selection of controlled vocabulary • Linked data principles using VIAF’s authority file• Data creation using software to harvest data with
OpenRefine• Collaborative teaching opportunity• Contributions to scholarship• Continued Collaboration
Embedded Metadata Services Model:Improving Expertise in Data Transformation
0 1 2 3 4 5 6
Support Staff Involved
Data Creation Support
Metadata Templates
Controlled Vocabularies
Quality Assurance
Total Consultations
Metadata Services throughout Mellon
Round 2 Round 1
Resource Allocation and Reshaping Services
Round 2: 2018 – 2019
• Data harvesting
• Data transformation and crosswalks
• Templates for text encoding
Round 1: 2017 – 2018
• Drop-in hours for researchers
• Templates for non-bibliographic materials and data
• Support for data deposits
Common Resources and Service• Templates for data creation
• New metadata schemas
• Expertise data standards, controlled vocabularies, subject creation, and quality assurance
• Metadata education/instruction
• Data creation support
• Opportunities for continued collaboration
Thank youRenee ReaumeUNIVERSITY OF CALGARY
Ingrid ReicheUNIVERSITY OF CALGARY
ARTHUR DOWNINGBARUCH COLLEGE, CITY UNIVERSITY OF NEW YORK
Positioning the Library for Leadership in Institutional Data Governance
Vice President for Information Services and Dean of the Library
Arthur Downing
Data Governance for Enterprise Data Management
Interest in Enterprise DM• Enterprise Data Management to support analytics/BI• Higher Ed Drivers: Enrollment (~Revenue) and Student
Success (Retention, Graduation)• 2018 study by NASPA, Educause and AIR:
– 89% of higher ed institutions investing in predictive analytics for student outcomes
– “this requires high levels of coordination between the many units that collect and analyze student data.”
Data Governance Enables EDM• 2017 Study by NASPA, Educause and AIR:
“Institutions reported that prior to considering the use of predictive analytics, they created data governance committees to make decisions about how data would be accessed, collected, analyzed, and reported across departments and divisions.”
• Data Governance = The framework for making decisions about how to manage one’s data assets and “the exercise of decision-making authority for data-related matters” (Data Governance Institute).
Data Governance DefinedData Governance is a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe• Who can take what actions with data• When and under what circumstances• Using what methods
Source: Data Governance Institute
Data Governance Framework• Defined Scope• Principles
– “Collaboration – enterprise data is a shared resource and not owned by any specific business area.” (Ladley, 2012)
• Policies• Organization around Hierarchical Roles• Functions (write policies, assess data quality, etc.)• Metrics for Assessment
Roles and Responsibilities
Source: University of Georgia. Office of Institutional Research.
Libraries in Enterprise Data Governance
What Libraries Bring to the Process• Data Management Skills & Experience• Institution-Wide Orientation• Reputation for Collaboration• Data Literacy Instruction (see Koltay, 2016)• Use of Foundational Principles
– “All information resources that are provided directly or indirectly by the library, regardless of technology, format, or methods of delivery, should be readily, equally, and equitably accessible to all library users.” (ALA Core Values)
Efforts at Baruch College
Baruch College• Senior College with 18,700 students• CUNY system of 25 campuses• PeopleSoft ERP: HR, Finance & Student Information• Limited Data Governance
– Some business processes excluded (e.g., graduate admissions)– Campuses create own data warehouses & “shadow systems”
• Information Services = Library + IT + IR
Our Data Governance OrganizationPresident’s
Cabinet
DG Steering Committee
DG Governance
Council
DG Working Group A
DG Working Group B
DG Working Group C
DG Working Group …
Positioning the Library: 1st Steps• Include Librarians in Data Governance Charter• Designate Librarians in DG Roles (Stewards &
Custodians)• Demonstrate Librarians’ Relevant Data Skills
– Example Projects:• Metadata for Photographic Images• Review of Rankings
• Prepare Librarians to Participate
Sources - 1• American Library Association. (2006). Core values of librarianship.
http://www.ala.org/advocacy/intfreedom/corevalues (Accessed August 10, 2019)• Bhansali, Neera. (2014). Data governance: Creating value from information assets. Boca Raton, FL:
CRC.• Blair, D., et al. (2015). The compelling case for data governance, ECAR Working Group Paper.
Louisville, CO: Educause Center for Research and Analysis.• Burke, M., Parnell, A., Wesaw, A. & Kruger, K. (2017). Predictive analysis of student data.
Washington, DC: NASPA–Student Affairs Administrators in Higher Education, the Association for Institutional Research, and EDUCAUSE.
• Data Governance Institute. http://www.datagovernance.com. (Accessed September 19, 2019.)• Friedman, T., White, A. & Judah, S. (2016). Information governance requires a comprehensive and
interrelated range of policy types. Gartner Research, Document G00259783.• Judah, S. (2019). Hype cycle for aata and analytics governance and master data management,
Gartner Research, Document G00369901.
Sources - 2• Koltay, T. (2016). Data governance, data literacy and the management of data quality. IFLA Journal,
42(4), 303–312. https://doi.org/10.1177/0340035216672238.• Ladley, J. (2012). Data governance: How to design, deploy and sustain an effective data governance
program. Waltham, MA: Morgan Kaufmann.• Mandinach, E. & Gummer, E. (2013). A systemic view of implementing data literacy in educator
preparation. Educational Researcher, 42(1): 30–37.• Parnell, A., Jones, D., Wesaw, A., & Brooks, D. C. (2018). Institutions’ use of data and analytics for
student success: Results from a national landscape analysis. Washington, DC: NASPA–Student Affairs Administrators in Higher Education, the Association for Institutional Research, and EDUCAUSE.
• Sen, H. (2019). Data governance: Perspectives and practices. Basking Ridge, NJ: Technics Pubs.• Soares, S. (2014). The chief data officer handbook for data governance. Boise, ID: MC Press.• University of Georgia. Office of Institutional Research. Data roles and descriptions.
https://oir.uga.edu/governance/. Accessed September 16, 2019.
Thank youArthur DowningBARUCH COLLEGE