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
Results 1-5 of 87
Page ix
... data. Organizations didn't take long to realize that the best practice is to have ONE version of information, a centralized data warehouse of consolidated data for the entire organization to use. No more embarrassing, conflicting, and ...
... data. Organizations didn't take long to realize that the best practice is to have ONE version of information, a centralized data warehouse of consolidated data for the entire organization to use. No more embarrassing, conflicting, and ...
Page x
... data warehouse enabled them to keep ahead of the competition. The data warehouse then became their tool for survival. The increased rate of change in today's competitive market fuels a new generation of data warehouses. Predicting new ...
... data warehouse enabled them to keep ahead of the competition. The data warehouse then became their tool for survival. The increased rate of change in today's competitive market fuels a new generation of data warehouses. Predicting new ...
Page 4
Margaret Y. Chu. The method became known as data warehousing, and it utilized a storehouse of data called a data warehouse. It seemed that Peggy's dreams had been fulfilled—because the data within a data warehouse are BLISSFUL DATA. A data ...
Margaret Y. Chu. The method became known as data warehousing, and it utilized a storehouse of data called a data warehouse. It seemed that Peggy's dreams had been fulfilled—because the data within a data warehouse are BLISSFUL DATA. A data ...
Page 5
... data retrieved easily. This points to a database being a place to a store a bunch of data. But that's what a “data warehouse” breaks down to. What makes data warehouses different from databases? What's a Data Warehouse? 5.
... data retrieved easily. This points to a database being a place to a store a bunch of data. But that's what a “data warehouse” breaks down to. What makes data warehouses different from databases? What's a Data Warehouse? 5.
Page 6
Margaret Y. Chu. house” breaks down to. What makes data warehouses different from databases? How are data warehouses special? Aren't they just a large collection of databases? An easy way to realize the differences between ... Data Warehouse?
Margaret Y. Chu. house” breaks down to. What makes data warehouses different from databases? How are data warehouses special? Aren't they just a large collection of databases? An easy way to realize the differences between ... Data Warehouse?
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