Data Quality

One-Day Course

Certificate Course

Prerequisite: This course assumes basic knowledge of data warehousing fundamentals

You Will Learn:

   • The components of a data quality plan
   • Rules for data integrity and data correctness
   • The roles of defect detection, correction, and prevention
   • To make informed choices between source data cleansing and target data cleansing
   • To customize a data quality plan to your needs and environment

Geared To:   Data warehouse designers and developers; data warehouse program and project managers; data warehouse administrators.

   Delivering high-quality, trustworthy data is essential to business intelligence and data warehousing success. This course is designed for those who need to develop a plan for data quality in the data warehouse. It identifies the components of a data quality plan and describes techniques and skills to develop and implement a plan tailored to your specific needs. Key topics include techniques to identify rules for data integrity and data correctness, to detect data quality defects, and to choose among actions for defect correction Data Quality:

Module One

   Data Quality Fundamentals

    • Introduction to Data Quality

        •Processes and Quality
        •Quality Economics
        •Data Quality in Data Warehousing

   • Data Quality and Business Rules
        •The Role of Business Rules
        •Kinds of Business Rules

   • Data Quality Planning
        •Planning Activities
        •Rule Based Data Cleansing
        •Components of a Plan

Module Two

   Data Correctness

   • Data Correctness Concepts

   • Overview of Data Correctness Rules

   • Accuracy
        • Description and Techniques
        •Testing and Measurement
        • Completeness
        • Description and Techniques
        •Testing and Measurement

   • Balancing
        • Description and Techniques
        •Testing and Measurement
        •Description and Techniques
        •Testing and Measurement

   • Precision

        •Description and Techniques
        •Testing and Measurement

   • Granularity
        •Description and Techniques
        •Testing and Measurement

   • Currency
        •Description and Techniques
        •Testing and Measurement

   • Duration
        •Description and Techniques
        •Testing and Measurement

   • Retention
        •Description and Techniques
        •Testing and Measurement


        •Description and Techniques
        •Testing and Measurement

   • Precedence

        •Description and Techniques
        •Testing and Measurement
        •Data Correctness Summary

Module Three

   Data Integrity

   • Data Integrity Concepts

   • Data Integrity Rules Overview

  • Identity
        •Description and Techniques
        •Testing and Measurement

   • Reference
        •Description and Techniques
        •Testing and Measurement

   • Cardinality
        •Description and Techniques
        •Testing and Measurement

   • Value Set

        •Description and Techniques
        •Testing and Measurement

   • Inheritance
        •Description and Techniques
        •Testing and Measurement

   • Relationship Dependency

        •State Dependent Relationships
        •Mutually Dependent and Mutually Exclusive Relationships
        •Testing and Measurement

   • Attribute Dependency

        •State Dependent Attributes
        •Mutually Dependent and Mutually Exclusive Attributes
        •Mutually Constrained Attributes
        •Testing and Measurement

   • Integrity Rules and Data Models

        •Explicit and Implicit Rules
        •The Number of Possible Rules

   • Data Integrity Summary

Module Four

   Data Profiling and Transformation

   • Profiling and Transformations Concepts
        • Understand First … (profiling)
        • Then Change … (transformation)

   • Data Profiling
        • Understanding the Data
        • Examples

   • Data Transformation
        • Changing the Data

   • Data Cleansing Procedures
        • Definition and Concepts
        • Data Cleansing Actions
        • Cleansing throughout the Warehousing Process

   • Auditing Data Quality
        • Source Data Audits
        • Target Data Audits
        • Implementing Audits

   • Filtering Data Quality Defects
        • Source Data Filtering
        • Target Data Filtering
        • Implementing Filters

   • Correcting Data Quality Defects
        • Source Data Correction
        • Target Data Correction
        • Implementing Correction

   • Preventing Data Quality Defects
        • Defect Prevention at the Source
        • Implementing Prevention

   • Developing a Data Cleansing Strategy
        • Choosing Cleansing Techniques
        • Data Cleansing Economics
        • Packaging Cleansing Procedures
        • Consistent Rules / Evolving Actions
        • An Example Problem
        • An Example Solution

Module Five

   Building and Executing a Data Quality Plan

• Data Quality Planning
        • Setting the Scope
        • Assessing the Current State
        • Goals and Measures
        • Gap Analysis
        • Quality Improvement Actions

• Executing the Plan
        • Quality Improvement Actions
        • Measuring and Monitoring
        • Continuous Quality Improvement

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