Develop a custom, agile data warehousing and business intelligence architecture
Empower your users and drive better decision making across your enterprise with detailed instructions and best practices from an expert developer and trainer. "The Data Warehouse Mentor: Practical Data Warehouse and Business Intelligence Insights" shows how to plan, design, construct, and administer an integrated end-to-end DW/BI solution. Learn how to choose appropriate components, build an enterprise data model, configure data marts and data warehouses, establish data flow, and mitigate risk. Change management, data governance, and security are also covered in this comprehensive guide.
Understand the components of BI and data warehouse systems Establish project goals and implement an effective deployment plan Build accurate logical and physical enterprise data models Gain insight into your company's transactions with data mining Input, cleanse, and normalize data using ETL (Extract, Transform, and Load) techniques Use structured input files to define data requirements Employ top-down, bottom-up, and hybrid design methodologies Handle security and optimize performance using data governance tools
Robert Laberge is the founder of several Internet ventures and a principle consultant for the IBM Industry Models and Assets Lab, which has a focus on data warehousing and business intelligence solutions.
Robert Laberge is a lead principle consultant for the IBM Industry Models and Assets Lab in Dublin which focuses on data warehousing and business intelligence solutions. He has helped more than 50 large organizations in 20 countries by mentoring, training, and demonstrating data warehouse and business intelligence solutions, including Target, Qwest, ING, Mayo Clinic, Canadian Tire, Shoppers Drug Mart, BMW, Korea Telecom, Scotiabank, Capital One, Reliance Infocomm, and Tata Group. Robert has a Masters of Business Administration from the University of Durham (UK) and has nearly 30 years of experience in IT.
Part I: Preparation Chapter 1: Data Warehouse and Business Intelligence Overview Chapter 2: Data in the Organization Chapter 3: Reasons for Building Chapter 4: Business Intelligence and Data Warehouse Strategy Chapter 5: Project Resources: Roles and Insights Chapter 6: Write-It-Up Overview Part II: Components Chapter 7: Business Intelligence: Data Marts and Usage Chapter 8: Enterprise Data Models Chapter 9: Data Warehouse Architecture: Components Chapter 10: ETL and Data Quality Chapter 11: Project Planning and Methodology Part III: Let's Build Chapter 12: Working Scenarios Chapter 13: Data Governance Chapter 14: Post-Project Review Index
McGraw-Hill Education - Europe
Practical Data Warehouse and Business Intelligence Insights
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DATA WAREHOUSE MENTOR