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

medr@bankofcanada.ca

• Acknowledgement– Gail Ritchie – Business Project Leader– Gilles Tessier – IT Project Leader– Winnie Spires – PowerPoint Presentation

Bank of Canada Banque du Canada