Blissful DataAnalyzing information and acting accordingly is a key strategic goal of every business. But vast quantities of data are of little use if they are not structured and kept in such a way as to be readily accessible and applicable. Only optimally organized information can drive maximum productivity. Blissful Data is a reader-friendly book that reveals what it takes to achieve a state of perfect organization within the environment of a successful data warehouse. This timely book will help the reader: * understand how data evolves into information that drives better decision making * recognize the pitfalls, caused by people and politics, that lead to short-sighted solutions and long-term problems * manage data warehousing costs, performance, and expectations effectively * apply project management fundamentals to data warehouse endeavors. Blissful Data includes dozens of examples, as well as case studies illustrating successful, unsuccessful, and disastrous data warehouse strategies. |
From inside the book
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Page v
... Data Warehouses, Data Marts What's in a Name? 47 3 Myths and Misconceptions What Should You Eradicate? 4 69 What Are Dirty Data? Where Do Dirty Data Come From? 5 93 Politics Who Owns It Anyway? Politics Who's Going to Pay? 6 116 7 152 ...
... Data Warehouses, Data Marts What's in a Name? 47 3 Myths and Misconceptions What Should You Eradicate? 4 69 What Are Dirty Data? Where Do Dirty Data Come From? 5 93 Politics Who Owns It Anyway? Politics Who's Going to Pay? 6 116 7 152 ...
Page 19
... data in the warehouse contained errors; they could not trust the data. At the first opportunity, I asked EDM what problems were being encountered. How did this affect the data for the forecast project? EDM mentioned “politics,” “dirty data ...
... data in the warehouse contained errors; they could not trust the data. At the first opportunity, I asked EDM what problems were being encountered. How did this affect the data for the forecast project? EDM mentioned “politics,” “dirty data ...
Page 21
... data warehousing success. Chapter 3 distinguishes data warehouse facts from the fallacies to eradicate fears, break down barriers, and manage expectations. Chapter 4: What Are Dirty Data? – Where Do Dirty Data Come From? AXIOM 4 Dirty data ...
... data warehousing success. Chapter 3 distinguishes data warehouse facts from the fallacies to eradicate fears, break down barriers, and manage expectations. Chapter 4: What Are Dirty Data? – Where Do Dirty Data Come From? AXIOM 4 Dirty data ...
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Contents
Data Warehouses Data Marts | 25 |
Myths and Misconceptions | 47 |
What Are Dirty Data? Where Do Dirty Data Come From? | 69 |
Politics | 93 |
Politics | 116 |
Project Management | 152 |
Data Modeling | 186 |
Case Studies | 210 |
Glossary | 229 |
About the Author | 245 |
Other editions - View all
Blissful Data: Wisdom and Strategies for Providing Meaningful, Useful, and ... Margaret Y. Chu No preview available - 2004 |
Common terms and phrases
action additional already amounts analysis application areas become benefits better blissful data bring business clients business rules business units called cause Chapter clear codes communication completed contains cost create culture data mart data model data warehouse data warehousing databases decisions defined detailed dirty data effort employees endeavor example exist expectations field Figure functional game plan goals groups important improvement increased integration investment involved issues keep knowledge look measurement metadata method months objectives obtain once operational operational systems organization organizational culture performance person problems project management queries REMEMBER reports requirements response risk schedule scope share skills solution specific sponsor structure success tool types understand ware