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portada Data for Business Performance: The Goal-Question-Metric (Gqm) Model to Transform Business Data Into an Enterprise Asset
Type
Physical Book
Year
2017
Language
English
Pages
318
Format
Paperback
Dimensions
23.4 x 18.8 x 2.0 cm
Weight
0.86 kg.
ISBN13
9781634621847

Data for Business Performance: The Goal-Question-Metric (Gqm) Model to Transform Business Data Into an Enterprise Asset

Prashanth Southekal (Author) · Technics Publications · Paperback

Data for Business Performance: The Goal-Question-Metric (Gqm) Model to Transform Business Data Into an Enterprise Asset - Southekal, Prashanth

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£ 39.35

  • Condition: New
Origin: U.S.A. (Import costs included in the price)
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Synopsis "Data for Business Performance: The Goal-Question-Metric (Gqm) Model to Transform Business Data Into an Enterprise Asset"

Master how to leverage your data to improve business performance. Today, digitization is dramatically changing the business landscape, and many progressive organizations have started to treat data as a valuable business asset. While many enterprises are investing in improved data management, only a few have leveraged data to truly impact business performance. To address this problem, Data for Business Performance provides readers with practical guidance and proven techniques to derive value from data in today's business environment. Specifically, the book has five key elements that make it unique: 1. The book is holistic, as it looks at deriving value for all three key purposes of data: decision making, compliance, and customer service. 2. The book is for practitioners, with practical guidance and proven techniques supported by real world examples. 3. The book is relevant for the current business and IT landscape. 4. The book is novel, with the adoption of the Goal-Question-Metric (GQM) framework as the core mechanism to monetize data in the organization, based on business goals, key questions, and key performance indicators (KPIs). 5. The book is technology-agnostic, as concepts are used for unlocking the value of data without any reference to proprietary technologies.

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