“Global Trends in Knowledge and Information Management from the iSchool at Toronto” Presentation by Seamus Ross Dean and Professor May 21, 2014

ischool.utoronto.ca

Who are we and what is our legacy

TIMELINE CHART

  University of Toronto Library School Founded First ALA Accreditation 1937 1928 1970 71  Move beyond Library Science into Information Systems, Archives, Records Management, etc.

70s 90s 1994  Master of Museum Studies joins.

2006 2008 2014   MLIS Founded (1970) PhD Founded (1971)  Renamed MLIS to MISt in recognition of changes in and evolution of the discipline  New Name: Faculty of Information

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People, Places, and Programs

• Programs offered: – Master of Information (MI) – Master of Museum Studies (MMSt) – Doctoral Studies (PhD) • People – – 28 faculty, 20 Staff and Librarians 405 MI, 76 MMSt, and 52 PhD students • Places – Bissell Bldg – McLuhan Coach House – Semaphore Labs in Robarts Library

• • • •

Foundations

Information penetrates all aspects of our digitally mediated society Information professionals need to understand the political, technological, and epistemological consequences of changing information practices Education of information professionals must therefore address issues of leadership and critical thinking , and engage students with fundamental concepts, theories, and practices Explorations of Information Use, Users, Technology and Information Using Cultures are essential

Key program characteristics (MI)

• • • Broad-based and inclusive, with information focused fields from different disciplinary and professional viewpoints Flexible and customizable curriculum – – – General Program Seven concentration options Thesis Option Increasing emphasis on Experiential and Experimental learning

Concentrations

• • • • • • • Archives and Records Management Critical Information Policy Studies Culture and Technology

Information Systems and Design Knowledge Management and Information Management

Knowledge Media Design Library and Information Science

Information Systems and Design

• • • • Focuses on the practices organizations. complex interplay between information and communication technologies (ICTs) and information , in the context of the needs and different objectives of classes of information users in society and Core themes of stewardship, organization, and accessibility of information artifacts explored in relation to the design and implementation of digital information systems and media. Attention to personal, social, organizational, and societal implications .

Individuals, communities, and organizations who sponsor or use these systems to create, communicate, collaborate, and record information are, willingly or not, engaged in an ongoing transformation of their information environments.

Knowledge Management and Information Management

• • • • • • • concepts, tools, and practices that work Topics include: enable the systematic, imaginative, and responsible management of information in an organization or community to promote social learning and innovation , and to provide groups and individuals with the information they need to perform their Theoretical frameworks that integrate the creation, sharing, and utilization of information and knowledge Effective use of information to support decision-making Knowledge access management, including metadata-enabled search and resource discovery Strategic modeling of goals and dependencies for knowledge management Design of information platforms for creating and sharing knowledge Health informatics as an elective specialization

• • • • • •

Recent IS&D Graduate Destinations

Knowledge Management Operations Specialist, PwC Business Analyst, Wunderman Business Analyst - Oracle ERP, Deloitte Business Systems Analyst, Regina Police Service Sr. Systems Analyst, Application Analyst (Enterprise Architecture), Rogers Senior Manager, Data Platform, Globo.com

Recent KMIM Graduate Destinations

• • • • • • Business Analyst, Telefilm Canada Information Specialist, Bell Canada Business Analyst, Ontario government Knowledge Manager - Financial Services & Innovation, PwC Information Specialist, George Brown College Intelligence Analyst, Bennett Jones LLP

Professor Periklis Andritsos

Research focuses on the analysis of large repositories and, more specifically, the structure of discovery in order to facilitate design and query optimisation .

Among his achievements: clustering algorithm for categorical data on discovering alternative schemas in , which has also formed the basis of his work databases with inconsistencies and errors.

His patented techniques have been adopted.

Prof Chun Wei Choo

Conducts learning research on knowledge and information management, information seeking, organizational , and management of information technology.

Recent externally funded research focuses on: “Information seeking and use in early warning systems”

Prof Fiorella Foscarini

Research explores the issues of diplomatics, genre theory, and structuration within records management and archival principles and methods. What is the relationship between record keeping and organizational cultures?

Prof Andrew Clement

Research explores the social and public policy implications of information. He focuses on identity, privacy, and surveillance issues, as well as related public education initiatives. Coordinates the Information Policy Research Program (IPRP).

Prof Eric Yu

Research focuses on the analysis and design of information systems and services in social contexts .

Behind the International Standard i*, which supports analysis and modeling of strategic actor relationships. His projects include designing for security and privacy, agile software development, and business modeling for business intelligence

3 Big Trends in Knowledge and Information Management:

•

Big Data

•

Open Data

•

Curation

Big Data opportunities and challenges

– –

Opportunities:

• Better-informed decisions • More effecting information targeting • Faster turnaround of new discoveries

Challenges:

• • • Data comes in diverse formats Data contains imprecision or inconsistency Existing techniques do not scale

• • •

Methods:

– – – Deploy them in parallel for speed-up Incorporate semantic information (ontologies) Combination of Data Management and Statistical methods

Systems:

– Turn hardware into commodities so it can be applied in different domains

Methods & System:

– Need the appropriate methods and data to test new methods and technological improvements

Trends in Open Data

• Trends in Open Data June 2013 G7 Open Data Charter – Massive projects across G7 to increase availability of machine-readable government data, from weather/climate data to small business suppliers of government services/contracts • McKinsey 2013 Report – Observed that making more data open could unleash an estimated $3 trillion in annual economic potential.

– – – Enhances the value of the “big data” initiatives Has the potential for massive benefit for regular consumers. (Big data has been largely beneficial to large private corporate actors, at least to date.) Can enhance productivity significantly

EU Open Data Initiative

• June 2013 – revision of data reuse directive gave all EU member states two years to revise national laws to comply with a directive with the principles of: – – – Charges to be made on the basis of actual cost and clearly published; No cross-subsidies or exclusive data arrangements; Requests must be processed within specified periods of time

US Open Data Initiative

• • Announced in 2013; in May 2014, the CIO for the USA announced some significant progress “Freely available data from the US government is an important national resource.” – strong recognition of the huge importance of large government datasets as economic driver.

Research Data Alliance

• • • • Aims to facilitate the construction of social and technical bridges that enable open sharing of data.

The RDA vision is researchers and innovators openly sharing data across technologies, disciplines, and countries to address the grand challenges of society.

Funded by Australia, EC, and USA

https://rd-alliance.org/

Trends in Digital and Data Curation

• Digital Curation an umbrella term – Digital curation material involves maintaining, preserving and adding value to digital throughout its lifecycle • That is it is Digital Curation applies more broadly than to purely research data DCC -- http://www.dcc.ac.uk/digital curation/what-digital-curation

• • • Discussions of digital longevity have been driven by perspective of memory institutions which have not traditionally been in the data business. Perspective has shaped the ways we educate folks in digital curation. We have focused on preparing them for curating data and not for creating value from it. -- We are moving focus to include both curating and value creation.

Stijn Viaene in an essay about the data science ecosystem as a process of “modeling, discovery,operationalizing, and cultivation got it right when he wrote “It will take a lot of conversation to make data science work. Data scientists can't do it on their own. Success in data science requires a multiskilled project team with data scientists and domain experts working closely together.” Data Scientists Aren't Domain Experts (IT Professional, Dec 2013)

Knowledge and Experiential requirements

• • • • • • • • • • • Curation – preservation sense Data Analytics Visualisation Statistics Machine Learning -- Automation Metadata & Annotation Information Architecture Data Quality Rights, Privacy, Security Interoperability Collection Development

Liz Lyon, Data Scientist and co-founder of DCC

• • • • “data engineer - focus on software development, coding, programming, tools data analyst – focus on business/scientific analytics and statistics e.g. R, SAS, Excel to support researchers and modellers, business data librarian – focus on advocacy, research data management / informatics in a university / institute data steward – focus on long term digital preservation, repositories, archives, data centres • data journalist – focus on telling stories and news” © liz-lyon-microsoft-escience-chicago-october- 2012-final.ppt (downloaded 17 Feb 2014)

Educational Directions for the iSchool

• • • • More experiential and experimental learning environments Data analytics Data curation Information behaviour • • More data scientists More digital media enablers

Challenges in Changing Direction

• • • Many of our students come from Humanities/Social Science background Lack of awareness of growth potential and variety among many recruits Perceived split between “traditional” and “new” information disciplines – does not actually exist, but perception is damaging

My Question for you

• • • • Are there opportunities for Collaborations?

What do we need to be doing in our programs?

What trends to you, as experts and employers, foresee?

How can we prepare our graduates for success in your industries?

Thank you [email protected]

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