Data Warehousing at STC MSIS 2007 Geneva, May 8-10, 2007 Karen Doherty Director General Informatics Branch Statistics Canada.

Slides:



Advertisements
Similar presentations
April, 2004 Lars Thygesen International Trade Expert meeting Whats going on at OECD: statistical information management.
Advertisements

Guidelines on Integrated Economic Statistics United Nations Statistics Division Regional Seminar on Developing a Programme for the Implementation Programme.
DIGIDOC A web based tool to Manage Documents. System Overview DigiDoc is a web-based customizable, integrated solution for Business Process Management.
Input Data Warehousing Canada’s Experience with Establishment Level Information Presentation to the Third International Conference on Establishment Statistics.
PRIME MINISTRY REPUBLIC OF TURKEY TURKISH STATISTICAL INSTITUTE TurkStat NATIONAL ACCOUNTS IN TURKEY 1 TurkStat.
Sharing Enterprise Data Data administration Data administration Data downloading Data downloading Data warehousing Data warehousing.
Chapter 3 Database Management
Data Sources Data Warehouse Analysis Results Data visualisation Analytical tools OLAP Data Mining Overview of Business Intelligence Data visualisation.
By George Squillace New Horizons Great Lakes George SquillaceGeorge Squillace Husband, Dad, Coach, MCT, MCSE, MCDBA MCITP – Database Administration MCITP.
Business Intelligence System September 2013 BI.
Introduction to Building a BI Solution 권오주 OLAPForum
Business Intelligence components Introduction. Microsoft® SQL Server™ 2005 is a complete business intelligence (BI) platform that provides the features,
Data Warehouse Components
Center of Excellence for IT at Bellevue College. IT-enabled business decision making based on simple to complex data analysis processes  Database development.
Data Warehousing: Defined and Its Applications Pete Johnson April 2002.
Microsoft Business Intelligence Gustavo Santade Business Intelligence Project Manager Improving Business Insight Building a cube using Analysis Services.
MDC Open Information Model West Virginia University CS486 Presentation Feb 18, 2000 Lijian Liu (OIM:
Setting up a National Warehouse of Official Statistics in India P C Mohanan Deputy Director general National Statistical Organisation Ministry of Statistics.
Metadata: Integral Part of Statistics Canada Quality Framework International Conference on Agriculture Statistics October 22-24, 2007 Marcelle Dion Director.
5.1 © 2007 by Prentice Hall 5 Chapter Foundations of Business Intelligence: Databases and Information Management.
Intro to MIS – MGS351 Databases and Data Warehouses Chapter 3.
SharePoint 2010 Business Intelligence Module 2: Business Intelligence.
Database Systems – Data Warehousing
PROJECT NAME: DHS Watch List Integration (WLI) Information Sharing Environment (ISE) MANAGER: Michael Borden PHONE: (703) extension 105.
ECO Statistical Network Statistical Center of Iran.
PO320: Reporting with the EPM Solution Keshav Puttaswamy Program Manager Lead Project Business Unit Microsoft Corporation.
DW-1: Introduction to Data Warehousing. Overview What is Database What Is Data Warehousing Data Marts and Data Warehouses The Data Warehousing Process.
OLAP Theory-English version On-Line Analytical processing (Business Intelligence) [Ing.J.Skorkovský,CSc.] Department of corporate economy.
Business Intelligence Zamaneh Jahed. What is Business Intelligence? Business Intelligence (BI) is a broad category of applications and technologies for.
Case 2: Emerson and Sanofi Data stewards seek data conformity
Enterprise Reporting Solution
1 Data Warehouses BUAD/American University Data Warehouses.
Metadata Architecture at StatCan MSIS 2008 Luxembourg, April 7-9, 2008 Karen Doherty Director General Informatics Branch Statistics Canada.
1 XML Based Networking Method for Connecting Distributed Anthropometric Databases 24 October 2006 Huaining Cheng Dr. Kathleen M. Robinette Human Effectiveness.
Ihr Logo Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization Turban, Aronson, and Liang.
Datawarehouse A sneak preview. 2 Data Warehouse Approach An old idea with a new interest: Cheap Computing Power Special Purpose Hardware New Data Structures.
MANAGING DATA RESOURCES ~ pertemuan 7 ~ Oleh: Ir. Abdul Hayat, MTI.
Joint UNECE/EUROSTAT/OECD Meeting on National Accounts Comments on papers submitted by: o EUROSTAT o CISSTAT o Statistics Netherlands Michel Girard Statistics.
OLAP in DWH Ján Genči PDT. 2 Outline OLAP Definitions and Rules The term OLAP was introduced in a paper entitled “Providing On-Line Analytical.
Unified information system of statistical data collection, processing, storage and dissemination (Rosstat UIS ) Overview FEDERAL STATE STATISTICS SERVICE.
© 2003 Prentice Hall, Inc.3-1 Chapter 3 Database Management Information Systems Today Leonard Jessup and Joseph Valacich.
Advanced Database Concepts
Library Online Resource Analysis (LORA) System Introduction Electronic information resources and databases have become an essential part of library collections.
China ’ s Input-Output Survey and Its Tabulation Method QI Shuchang Dept. of National Accounts, NBS.
Central Warehousing Work Session of Friends of the Chair Group Bern, Switzerland 6 – 8 June 2007 Marie Brodeur/Michel Girard Statistics Canada.
1 Copyright © Oracle Corporation, All rights reserved. Business Intelligence and Data Warehousing.
Simon Compton Methodology Directorate Office for National Statistics
2 Copyright © 2006, Oracle. All rights reserved. Defining Data Warehouse Concepts and Terminology.
BUSINESS INTELLIGENCE. The new technology for understanding the past & predicting the future … BI is broad category of technologies that allows for gathering,
Managing Data Resources File Organization and databases for business information systems.
OLAP Theory-English version On-Line Analytical processing (Buisness Intelligence) Ing.Skorkovský,CSc Department of Corporate Economy Faculty of Economics.
Data Mining and Data Warehousing: Concepts and Techniques What is a Data Warehouse? Data Warehouse vs. other systems, OLTP vs. OLAP Conceptual Modeling.
Business Intelligence Overview
Intro to MIS – MGS351 Databases and Data Warehouses
Reporting and Analysis With Microsoft Office
Business Intelligence & Data Warehousing
Data Warehouse.
Overview of LDB Technology and Tools
Guidelines on Integrated Economic Statistics
Canadian Culture Satellite Account, 2010
Business Intelligence
MANAGING DATA RESOURCES
Data Warehouse Overview September 28, 2012 presented by Terry Bilskie
Metadata in the modernization of statistical production at Statistics Canada Carmen Greenough June 2, 2014.
Guidelines on Integrated Economic Statistics
Evaluation & Experiences ‘YTY-System’ Statistics Finland
OLAP in DWH Ján Genči PDT.
30 January – February 1,2013 Kingston, Jamaica
Guidelines on Integrated Economic Statistics
Data Warehousing Concepts
Presentation transcript:

Data Warehousing at STC MSIS 2007 Geneva, May 8-10, 2007 Karen Doherty Director General Informatics Branch Statistics Canada

2 Table of Contents Canadian Systems of National Accounts SNA Warehouse Data Warehouse Framework Lessons Learned

3 System of National Accounts The SNA provides a conceptually integrated framework of statistics and analysis for studying the state and behaviour of the Canadian economy The accounts are centered on the measurement of activities associated with production of goods and services, the sales of goods and services in final markets, the supporting financial transactions, and the resulting wealth positions

4 System of National Accounts Input-Output Division (now the Industry Accounts Division) –produces the output, input, and final demand tables for each provincial and territorial jurisdiction the tables are linked through an interprovincial flows table that shows imports and exports between jurisdictions –covers all economic activities (persons, businesses, government and non-profit organizations, and external entities that generate imports or exports (interprovincially or internationally) The I-O tables represent the most detailed accounting of the Canadian economy available and thus serve as benchmarks to the Canadian System of National Accounts

5 I-O Re-engineering Project Impetus –aging production systems and work processes –no tools for data verification and table balancing imposed a heavy burden on staff –lack of integration and standardization of processes and procedures impeded the division’s ability to handle the growing amount of input data Project Objectives –maximize knowledge retention and reuse through the introduction of software to specify derivation and balancing methodologies –maximize operational integration potential of the various divisions of the SNA through the introduction of a data management system to integrate data and meta-data –facilitate data reconciliation between I-O and other SNA divisions –maximize analytical potential through the introduction of main stream analytical tools to detect problems in the I-O Tables and source data

6 Solution A data warehouse with three components: –user-supplied micro and aggregate data to facilitate data confrontation –aggregate (macro) data to support data reconciliation with the GDP outputs from system divisions –tools for analysts standard statistical functions tools to calculate industry and commodity specific ratios

7 System Capabilities Analysts can: –compare data from different sources, ensuring consistency of estimates by making dissimilar classifications comparable –reconcile information across divisions in the SNA and make effective decisions during the annual production cycle –compare statistics in terms of ratios, proportions, growth rates, by region and in chronological series –review the metadata and information on how the data was established, concepts and definitions, classifications and concordances and best practices with respect to processing or analytical procedures –create reports which are automatically updated whenever they are opened –perform graphics-based analysis

8 Results Phase 1 – I-O Division –standardization of the analytical process –enhanced data coherency –standardized and normalized analytical procedures which allows the division to operate with less experience staff –more transparent, repeatable and efficient analysis of data Phase 2 – SNA –the success in I-O led to an SNA-wide warehouse

9 I-O Data Warehouse Architecture

10 Data Warehouse Framework

11 Technology Microsoft Data Warehouse Framework for SQLServer 2000 –currently working on the SQL Server 2005 version –includes the MS Enterprise Manager, Data Transformation Services (DTS) and Analysis Services –fully integrated with Microsoft Excel XP

12 Technology Cubes –based on the OLAP standard Data Transformations –any Extract, Load, Transform (ETL) product can be used but the team has standardized on the Microsoft product API –XML for Analysis standard –uses Microsoft’s MDX query language

13 Technology Reporting and End-user Tools –EzWeb OLAP Report Browser and EzWeb OLAP Report Designer developed by the Data Warehouse Web team at STC provides a web like interface that conforms to the Government of Canada’s Look and Feel Standard Microsoft Office Web Components (OWC) Pivot Table provides OLAP functionality can navigate from one OLAP report to another Data Marts provide users with a customized subset of data and reports –Excel XP –Business Intelligence tools, Data Mining tools, etc. STCWiki (in pilot mode) –implemented using MediaWiki (product used by Wikipedia) –two-way communications with STC’s Integrated Metadata database

14 Lessons Learned Technical –standardized framework greatly reduces development costs –loosely connected customized data marts are more effective: partitions the effort involved in harmonization and the management of security and access rights allows users to customize their personal portal to list only those sources which are of business interest to them –appropriate for any type of data (operational, data processing, analysis of published data, etc.)

15 Lessons Learned Business –the real challenge lies with how data should be processed, analysed and classified (data harmonization) –value gained by harmonization usually results in modified working procedures –start by having good analytical tools to allow business units to detect problems and improve and adapt the methods used to ensure that the data is of high quality