Overview

Data Warehousing started as a means for bringing enterprise data together at one place from fragmented legacy systems. Its original objective was to provide managers and analysts with periodic summary data for decision making. With the advent of E-Business its role changed to support online marketing via Web enabled systems. Then, analytical demands such as customer behavior pattern analysis grew rapidly.

In spite of its significance and growing demand the failure rate for Data Warehousing is very high. There are many technical, managerial cultural and organizational problems plaguing Data Warehousing. The lack of a sound business approach and a rush to deploy new technique or technology often causes failure.

This seminar starts with a business definition of Data Warehousing and discusses major problems and issues along with practical solutions. Since a data warehouse is different from traditional applications, its planning, requirements analysis and development require different method and skills. Simple and pragmatic methods for development are discussed.

Once a Data Warehouse is built, it is not an end but just a beginning of an implementation cycle. Data Warehousing in a way is like a retail store buying wholesale and selling retail. Unlike normal IT application systems, which usually have homogeneous and small user bases, Data Warehouses have vast user base with diverse culture, skills and needs. So it requires a different kind of implementation management.

The seminar brings PDC's 20 years of experience of management consulting experience and helping clients deliver value from Data Warehousing projects. The session will be interactive giving participants an opportunity do discuss their problems and issues.

Questions Answered

• What is the purpose of a data warehouse?
  
• Does my enterprise really need a data warehouse?
  
• Do I start with a data mart or a data warehouse?
  
• The role of data warehousing in E-Business.
  
• What kind of project team do I need for data warehouse projects? Should project members be full time?
  
• How do I train the project team?
  
• How do I define data warehouse requirements?
  
• What are major problems and risks and how to avoid them?
  
• How do I incorporate metrics in managing a data warehouse?
  
• How do I know that data warehousing is delivering value?
  
• How do I foster a partnership between IT and business users?
  
• How do I manage the compounding growth with limited resources?
  
• What skills are required to manage data warehouse and data marts?
  
• What are the critical success factors?

Who Should Attend

• Senior Management
  
• Operational management
  
• IT Management
  
• Data Warehouse Managers
  
• Data Warehouse Project Team
  
• E-Business Project Managers
  
• IT Architects
  
• DA/DBA staff
  
• Business Managers
  
• Project Managers
  
• Business Analysts
  
• Systems Analysts

Agenda

• Mission, objectives and goals
  
• Data transformation process
  
• Types of contents - strategic, tactical and operational
  
• Data, information and knowledge
  
• Data Warehousing and data marts
  
• The Data Warehouse architecture
  
• The role of Data Warehousing in E-Business

Planning and Scoping the Project phases

• Identify stakeholders
  
• Project team selection and training
  
• Management education
  
• Project (phase) selection
  
• Project deliverables definition
  
• Methodology customization
  
• Project initiation document
  
• Obtaining commitment from an executive sponsor

• Requirements Analysis
  
• Data modeling - how this is different from traditional modeling
  
• Database design - denormalization and performance issues
  
• Extract-transform-load (ETL)
  
• Technology selection
  
• Iterative Implementation

Critical Problems and Solutions

• New paradigms for data management
  
• Quality - a critical issue before data is loaded
  
• Meta data - an absolute necessity
  
• Incorporating quality management processes
  
• Partnering with data stewards for data quality
  
• Change Management
  
• Data mining - hyperbole or practical results

Post-Implementation Management

• Short-term and long-term planning
  
• Establishing metrics and service level agreements
  
• Promoting Data Warehousing usage
  
• Leveraging a Data Warehouse User Group for planning and standard practices
  
• Partnering with key users for departmental training and consulting
  
• Achieving continuous improvement using techniques such as Root Cause Analysis and Shared Learning
  
• Data Warehousing management functions
  
• Roles of different stakeholders

Practical Tips for Success

• The Data Warehouse Manager's skills requirements
  
• Critical success factors
  
• Words of wisdom