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

rreaume@ucalgary.ca

Ingrid ReicheUNIVERSITY OF CALGARY

ingrid.reiche@ucalgary.ca

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

arthur.downing@baruch.cuny.edu