Capturing Meta Data on a Different Kind of Data Presented by: Mary McGrath and Mary Jo Roy Bank of...
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Transcript of Capturing Meta Data on a Different Kind of Data Presented by: Mary McGrath and Mary Jo Roy Bank of...
Capturing Meta Data on a Different Kind of Data
Presented by:Mary McGrath and Mary Jo Roy Bank of Canada
Bank of Canada Banque du Canada
Overview
• The Environment
• The Project
• The Challenges
• The Rewards
• The Lessons
• The Future
The Environment
• Departments
• IT Environment
• Statistical Data
Departments
Financial Markets
InternationalMonetary
And FinancialAnalysis
Research
|---------------The economics departments-------------|
Communica-tions
CorporateServices
Debt Administration
Office
Executive and LegalServices
Banking Operations
Financial Services
Audit
IT Environment
• Two Desktops– SUN Solaris
– Windows XP
• One Connector– Citrix Metaframe
• Two Browsers– Mozilla
– Internet Explorer
Statistical Data
• Time Series: a set of ordered observations on a phenomenon (e.g.: Population in Canada) over a given period of time and captured with a set frequency.
• Formula: a set of mathematical and conditional statements that produce time series values when executed.
• Scalar: a single instance of a variable such as a weight or indexing factor which is normally used in a formula.
• Case Series: a set of ordered observations on a phenomenon, without a time component.
The Project
• Why ?
• The Scope
• The People
• The Development
• The Repository
Why?
• Capture information on Statistical Data– Managed by Data Management Teams
– Generated by Economists, Analysts, and Research Assistants
The Scope
• Structured Meta Data for statistical Meta Data Repository
• Establish MEDR Meta Data Standards
• Address maintenance, shared ownership, accountability
The People
Project Sponsor
Business Project Leader IT Project Leader
Lead Developer
DevelopersContent Coordinators
Core Business Team
Steering Committee
The Development
Calendar Phases
Determine the solution (requirements, RFI, RFP, Evaluation)
Define meta data (standards, collection, translation)
Develop application(staffing, requirements, environment,building and testing)
Define business processes(produce, review, promote, support)
Implement application(information sessions, training)
Maintain (maintain meta data,improve FAME objects, involve otherbusiness units and departments)
2003 2004 2005
The Repository
• Input – Automated : FAME 8
– Manual : Oracle / OG
• Search : Verity– Quick
– Detail
• Administration
Maintain Meta Data Screen
Enter Data Screen
Search Overview
Home
Quick Search
Detailed Search
Quick Search
SelectResource
Types
SelectElementValues
ResultsSummary
ResultsDetail
Resource
Results Summary
The Challenges
• Resources
• Build / Buy
• Quality / Coverage
• Building bridges / Attaining balance
Quality
Return
Validate
Author Content Coordinator
Reviewer
Translation
Publish
Submit
Submit
Submit
Recall
Submit
OR
Coverage
2004 2005
Data Research
Management Assistants EconomistsDatasets Datasets Analytical projects
Enterprise applications Enterprise applications Datasets
External organizations External organizations
Statistical products Statistical products
Bridges and Balance
Content
Coord.
Data Mgt.
Authors
Client
Authors
Translation
Library
Archives
Knowledge
Mgt.
Records
Mgt.
IT Team
Core Bus.
Team
MEDR
The Rewards
• 6 million data objects located
• Enhanced relationships
• New partnerships
• Research data from analytical projects located
• Expanded role of data management
The Lessons
• Best solution not achieved in isolation
• Develop partnerships
• Strengthen relationships
• Utilize expertise of others
• Exploit commonalities
• No “One right solution”
• No “One size fits all”
The Future
• Business practices– Maintenance
– Shared ownership
– Accountability
– Buy-in
– Advertising
Questions
?
• Contact– For more information contact
• Acknowledgement– Gail Ritchie – Business Project Leader– Gilles Tessier – IT Project Leader– Winnie Spires – PowerPoint Presentation
Bank of Canada Banque du Canada