Approach to Data Quality Management for Creating Information Systems Based on DWH Technology

Submitted by admin on Sat, 01/03/2009 - 10:15

Author(s):

Kolomiets, Sergei

Author(s):

Translated by:

Bralgin, Igor

Note. If you use materials of the presentation "approach to managing data quality ...," please, reprint with reference to the authors..

Title slide of presentation "Approach to Data Quality Management for Creating Information Systems Based on DWH Technology"; Authors: Sergei Kolomiets, Vladimir Korenevich; August 29, 2007

Presentation Plan

Presentation plan: Data Quality Terminology; Features of Data Quality Control Procedures; Analysis of Variants of Data Quality Control Implementation; Decision Criteria for Selecting an Approach to Data Quality Control Implementation; Examples of Selecting an Approach to Data Quality Control Implementation

Architecture of Information Systems Based on DWH Technology

Architecture of DWH Information Systems: Data sources; ETL processes; Data Warehouse; Data Mart; Data Presentation Layer: OLAP, Reporting; Data Warehouse Development Methodology; Technical, Business, and Quality Metadata

Basic Concepts of Data Quality

Basic Concepts of Data Quality: Technical Quality of Source Data; Data Cleansing; Technical Data Quality Control; Business data quality; Procedures for verification the Data Quality; Data audit.

Common Scheme for Data Quality Control

Common Scheme for Data Quality Control: Data sources; ETL backroom; ETL frontroom; Data Warehouse; Data Audit; Data Mart; Data Quality Reports.

Features of Data Quality Control Procedures

Features of Data Quality Control Procedures: Technical Quality of the Source Data is stable defined by DWH model and ETL procedures. Business Data Quality is defined by business-users. Business Quality Requirements are constantly changing. The data that do not meet the Technical Quality Requirements cannot load into the DWH

Groups of Criteria for Technical Data Quality

Groups of Criteria for Technical Data Quality: Uniqueness of the key fields; Presence of data in mandatory fields; Referential integrity; Field Formats; Compliance with the acceptable values; Business logic of a record.

Variants of Data Quality Control Implementation

Variants of Data Quality Control Implementation: Using standard ETL tools without specialized data quality control tools and Using specially designed database of quality metadata. Using specialized data quality control tools

Implementation of Data Quality Control without Using Specialized Tools

SWOT: Strengths, Weaknesses, Opportunities, and Threats for Implementation of Data Quality Control without Using Specialized Tools

Implementation of Data Quality Control with Using Specialized Tools

SWOT: Strengths, Weaknesses, Opportunities, and Threats for Implementation of Data Quality Control with Using Specialized Tools

Decision Criteria for Selecting an Approach to Data Quality Control Implementation

Thank you for your attention. Questions?

Questions. The last slide of presentation Approach to Data Quality Management for Creating Information Systems Based on DWH Technology; Authors Sergei Kolomiets Vladimir Korenevich; August 29, 2007