Copyright 2007, Information Builders. Slide 1 The Relevance of Data Governance in Higher Education...

Copyright 2007, Information Builders. Slide 1

The Relevance of Data Governance in Higher Education

Tim Beckett

Higher Education Solutions

November 9, 2011

Agenda

Welcome & introduction – Tim BeckettPart I – Panel Discussion Part II – PresentationQ&A


Introduction

Getting serious about the mission

Questions are being asked By the government By the Ministry By the state By donors By executives By accreditation bodies

“It depends…”


Part I

Panel Discussion Data Governance at Three Higher Education Institutions Moderated by Dan Schultz, Director, iWay Software

Peter Webb, Institutional Research, University of the Fraser Valley (British Columbia)

Mark Horseman, Data Administration Analyst, University of Saskatchewan

Linda Pennington, Information Technology Services, San Jacinto College (Texas)


http://www.usask.ca/

http://www.ufv.ca/home.htm



Dan Schultz

Director, iWay Software

November 9, 2011

Data Governance in Higher Education

Fundamental Requirements for Success


Creating the Environment to SucceedMost fundamental requirements

Data Integration Maximize availability of data assets

Data Governance Create, implement, and maintain data policy

Data Quality Optimize information asset integrity

Data Synchronization Link and manage complex data relationships

Business Intelligence Real-time decision making based on the right information

Guiding Methodology Best practice approach


Technology & business process working together

Information Builders

Enterprise Information Management (EIM) Framework

Creating and Supporting a

Data Governance Initiative


Business Process

IB Enterprise Information Management Framework

Data Quality

Data Governance

Business Intelligence(Analytics/Operations)

System, Data, & Intellectual Fragmentation(Costly Business & Technical Problems)

Single View(MDM)

Strategy

Roadmap

Education

Implementation

Mentor

Advocacy

Best Practices

Experience

ProfileCleanseMatch

Remediate

Data Access Data Movement

Consulting

Data Policy

Standards

Business Rules

Roles & Responsibilities

Stewardship

Data Ownership Data Integration

Technology

Data Integration

Maximizing the Availability of Information Assets

A fundamental requirement to the success of a



Data Integration


Maximizing availability of Information Assets

Data Quality

Optimizing Information Asset Integrity




1. Profiling: Investigation into data - Analysis of data to provide insight into the quality of the data and aid in the identification of data quality issues.

2. Parsing, standardization, & Cleansing: Decomposition of fields into component parts and formatting of values into consistent layouts based on industry standards, local standards, user-defined business rules and knowledge bases of values and patterns. Modification of data values to meet domain restrictions, integrity constraints or other business rules that define sufficient data quality for the organization.

3. Enrichment - add contextual or complete the data. Geographical data, increase the data information for entities.

4. Monitoring: Deployment of controls to ensure ongoing conformance of data to business rules that define data quality for the organization.

Components of Data Quality Technology

Data Quality – Automated tools for optimizing information integrity

Data Governance

Creating, implementing, and maintaining data policy




Data Governance – Fundamental Concepts

Technology or business process?

It requires an element of both to be successful.

Business process allows the institution to coordinate, establish, and oversee institutional

data policies and practices.

Technology allows for the implementation and automation of data policies established through

the data governance business process.

Business Process

Data Governance within the IB EIM Framework

Data Quality

Data Governance

Business Intelligence(Analytics/Operations)

System, Data, & Intellectual Fragmentation(Costly Business & Technical Problems)

Single View(MDM)

Strategy

Roadmap

Education

Implementation

Mentor

Advocacy

Best Practices

Experience

ProfileCleanseMatch

Remediate

Data Access Data Movement

Consulting

Data Policy

Standards

Business Rules

Roles & Responsibilities

Stewardship

Data Ownership Data Integration

Technology

• DG enabled technology• Business rule based• Work flow enabled

Single View of the Citizen


Data remediation work flow for the data exceptions

Single View of the Citizen

Data Synchronization

Link and manage complex data relationships




Convert to a “Single View” Based Culture


Master Data are the facts describing your core business entities: Students, faculty, classes, facilities, chart of accounts, location and employees. It is the high value information an organization uses repeatedly across many business processes.

Single View technology decouples master information from individual applications and ensures consistent master information across transactional and analytical systems

Single View solutions are software products that: Support the global identification, linking and

synchronization of customer information across heterogeneous data sources

Create and manage a central repository or a database-based system of record

Enable the delivery of a single view for all stakeholders

An Single View program potentially encompasses the management of student, asset, person or party, supplier and financial masters.

Questions?

Next Step Opportunities

Speak to Information Builders about Institutional Intelligence

and/or Enterprise Information Management

Please give us your feedback (evaluation form)Tim Beckett

Information Builders

Higher Ed / K-12 Solutions Manager

Direct: 832-251-5702 [email protected]


mailto:[email protected]

Copyright 2007, Information Builders. Slide 1 The Relevance of Data Governance in Higher Education...

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