Blissful DataAMACOM, 2004. gada 2. febr. - 224 lappuses Analyzing 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. |
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1.–5. rezultāts no 35.
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... DATA Wisdom and Strategies for Providing Meaningful , Useful , and Accessible Data for All Employees 62426 000 16795 7325 MARGARET Y. CHU 81175 871-25 452-30 404.75 ince the dawn of the computer era , com- Sinc. Front Cover.
... DATA Wisdom and Strategies for Providing Meaningful , Useful , and Accessible Data for All Employees 62426 000 16795 7325 MARGARET Y. CHU 81175 871-25 452-30 404.75 ince the dawn of the computer era , com- Sinc. Front Cover.
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... Employees MARGARET Y. CHU AMACOM American Management Association New York . Atlanta Brussels Chicago Mexico City . San Francisco Shanghai . Tokyo . Toronto . Washington , D. C. Special discounts on bulk quantities of AMACOM books are ...
... Employees MARGARET Y. CHU AMACOM American Management Association New York . Atlanta Brussels Chicago Mexico City . San Francisco Shanghai . Tokyo . Toronto . Washington , D. C. Special discounts on bulk quantities of AMACOM books are ...
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... employees / Margaret Y. Chu . P. cm . ISBN 0-8144-0780-3 1. Decision making - Data processing . 2. Management - Data processing . I. Title . HD30.23.C4739 2003 658.4'038 - dc21 © 2004 Margaret Y. Chu Illustrations © 2004 by Rita Asia ...
... employees / Margaret Y. Chu . P. cm . ISBN 0-8144-0780-3 1. Decision making - Data processing . 2. Management - Data processing . I. Title . HD30.23.C4739 2003 658.4'038 - dc21 © 2004 Margaret Y. Chu Illustrations © 2004 by Rita Asia ...
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Saturs
What Is a Data Warehouse? Why Should You Care? | 1 |
Data Warehouses Data Marts Whats in a Name? | 25 |
Myths and Misconceptions What Should You Eradicate? | 47 |
What Are Dirty Data? Where Do Dirty Data Come From? | 69 |
Politics Who Owns It Anyway? | 93 |
Politics Whos Going to Pay? | 116 |
Project Management I5 It the Silver Bullet? | 152 |
Data Modeling Why Model the Data? | 186 |
Case Studies Is There a Light at the End of the Tunnel? | 210 |
What Does That Mean Again? | 229 |
References | 237 |
239 | |
About the Author | |
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Blissful Data: Wisdom and Strategies for Providing Meaningful, Useful, and ... Margaret Y. Chu Priekšskatījums nav pieejams - 2004 |
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access tool advanced analysis Ain't Broke benefits blissful data business analysts business clients business intelligence business rules business units capabilities cause change agents Chapter codes contains the processes cost create data mining data model data quality data values data warehouse endeavor data warehouse sponsor data warehouse success data warehousing success databases decisions and taking defined deliverables dirty data employees environment Figure forecasting functional groups game plan goals implement increased independent data marts informational data integration investment involved knowledge areas look management contains MDDB multidimensional analysis objectives OLAP OLTP operational data operational systems organization organization's organizational culture performance problems process groups project management project quality queries requirements response right sponsors ROI analysis schedule scope share skills soft ROI solution source data stakeholders stovepipe subject areas successful data warehouse tion transformation ware