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COURSE Title:
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INFORMATION QUALITY IN THE DATA WAREHOUSE |
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Why This Course?
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A manufacturing firm wasted $1 million on its data warehouse before it recognized the need for quality data architecture and for data quality control in its data warehousing processes. A major bank scrapped a $29 million data warehouse to start over from scratch. The reason? Failing to understand and avoid the pitfalls described here. This tutorial details the essential ingredients of an effective information quality environment for the data warehouse.
Mr. English describes how to assess information quality (IQ) at the data sources and in the warehouse. You learn processes for correcting defective data and for preventing recurrence of data defects. Mr. English defines how to control data movement from source to warehouse. You learn both the technical and management requirements for a sustainable information quality environment for data warehousing.
"This course has been the top-rated course in two [Data Warehousing Institute] Implementation Conferences." - Data Warehousing Institute Leadership Conference brochure
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Learning Outcomes:
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- Define information quality from the data warehouse customers' perspective
- Assess data warehouse data definition and data architecture quality
- Assess information quality
- Implement a data cleansing and data reengineering process
- Audit and control the ECTL (Extract, Correct, Transform and Load) processes
- Describe strengths of IQ tools, their limitations and how to overcome them
- Improve information processes to eliminate the causes of defective data
- Implement an information quality environment for data warehousing, business intelligence (BI) and customer relationship management (CRM)
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Audience:
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Data Warehouse Managers and staff, Information Quality Managers and staff, Data Administrators, Data Analysts, Database Administrators, Systems Analysts and Business Analysts, Quality Assurance Personnel, Information Stewards, Internal Auditors, Quality Program staff and business management who need quality information
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Format:
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Lecture with discussion to reinforce the concepts
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Duration:
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1 Day
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Pre-requisites:
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Basic data management concepts
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Abstract:
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ABSTRACT: A manufacturing firm wasted $1 million on its data warehouse before it recognized the need for quality data architecture and for data quality control in its data warehousing processes. A major bank scrapped a $29 million data warehouse to start over from scratch. The reason? Failing to understand and avoid the pitfalls described here. This tutorial details the essential ingredients of an effective information quality environment for the data warehouse.
Mr. English describes how to assess information quality (IQ) at the data sources and in the warehouse. You learn processes for correcting defective data and for preventing recurrence of data defects. Mr. English defines how to control data movement from source to warehouse. You learn both the technical and management requirements for a sustainable information quality environment for data warehousing.
"This course has been the top-rated course in two [Data Warehousing Institute] Implementation Conferences." - Data Warehousing Institute Leadership Conference brochure
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Business Intelligence and Information Quality
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- The high?and hidden?costs of low quality information
- Why information quality is a critical success factor for data warehousing and BI
- Defining information quality
- Three components of information quality in the data warehouse
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Applying Quality Principles to the Data Warehouse
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- TQM principles and information quality
- Information as a product
- Information ?customers? and ?suppliers?
- Total Information Quality Management (TIQM®) process for data warehousing
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Information Quality Tools and Processes
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- Categories of IQ tools
- IQ tools in the TIQM® process
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Assessing Data Definition (Metadata) Quality
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- Guidelines for a quality data warehouse data model
- Data standards quality characteristics
- Data definition quality characteristics
- Metrics for data definition quality
- Assuring data definition quality
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Assessing Information Quality
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- Information quality characteristics
- Metrics for information quality
- How to take a random sample
- Automated assessments for business rule conformance
- Assessment Tools: examples, strengths, limitations, and guidelines
- Physical assessments for accuracy
- Information quality interpretation and reporting techniques
- Assuring consistency of source and warehouse data
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Improving Information Product Quality: Data Reengineering and Cleansing
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- Legacy data problems
- Defining a data correction (clean-up) strategy and process
- Identifying data sources
- ?Discovering? business rules in legacy data
- Mapping and reengineering legacy data to data warehouse architecture
- Cleansing Tools: examples, strengths, limitations, and guidelines
- Transformation Tools: examples, strengths, limitations, and guidelines
- Auditing and Controlling the ECTL (Extract, Correct, Transform, and Load) Processes
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Improving Information Process Quality: Data Defection Prevention
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- The Plan-Do-Check-Act cycle for information quality improvement
- Root cause analysis: solving the right problems
- Planning and implementing process improvements for sustainable information quality
- Best practices in information quality for data warehouse and beyond
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Implementing an Information Quality Environment for the Data Warehouse
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- Steps to start an information quality environment
- Steps to sustain an information quality environment
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