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Win with advanced business analytics : creating business value from your data / Jean Paul Isson, Jesse Harriott
Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Wiley and SAS business series | Wiley and SAS Business SerPublisher: Hoboken, N.J : John Wiley & Sons, 2012, c2013Copyright date: ©2013Description: xviii, 398 pISBN:- 9781118370605
- 9781118420515
- 1283646129
- 9781283646123
- 658.4038
- 658.4/038 23
- HD30.28
Contents:
Summary: Includes bibliographical references and indexSummary: Plain English guidance for strategic business analytics and big data implementation In today's challenging economy, business analytics and big data have become more and more ubiquitous. While some businesses don't even know where to start, others are struggling to move from beyond basic reporting. In some instances management and executives do not see the value of analytics or have a clear understanding of business analytics vision mandate and benefits. Win with Advanced Analytics focuses on integrating multiple types of intelligence, such as web analytics, customer feedback, competitive intelPPN: PPN: 73835175XPackage identifier: Produktsigel: ZDB-38-EBR | ZDB-26-MYL | ZDB-30-PAD | ZDB-30-PBE | ZDB-30-PQE
Win with Advanced Business Analytics: Creating Business Value from Your Data; Copyright; Contents; Preface; Acknowledgments; Chapter 1: The Challenge of Business Analytics; The Challenge from Outside; The Challenge from Within; Notes; Chapter 2: Pillars of Business Analytics Success: The BASP Framework; Business Challenges Pillar; Data Foundation Pillar; Analytics Implementation Pillar; Insight Pillar; Execution and Measurement Pillar; Distributed Knowledge Pillar; Innovation Pillar; Conclusion; Note; Chapter 3: Aligning Key Business Challenges across the Enterprise; Mission Statement
Business ChallengeIdentifying Business Challenges as a Consultative Process; Identify and Prioritize Business Challenges; Analytics Solutions for Business Challenges; Notes; Chapter 4: Big and Little Data: Different Types of Intelligence; Big Data; Little Data; Laying the Data Foundation: Data Quality; Data Sources and Locations; Data Definition and Governance; Data Dictionary and Data Key Users; Sanity Check and Data Visualization; Customer Data Integration and Data Management; Data Privacy; Notes; Chapter 5: Who Cares about Data? How to Uncover Insights; The IMPACT Cycle
Curiosity Can Kill the CatMaster the Data; A Fact in Search of Meaning; Actions Speak Louder Than Data; ""Eat Like a Bird, Poop Like an Elephant""; Track Your Outcomes; The IMPACT Cycle in Action: The Monster Employment Index; Note; Chapter 6: Data Visualization: Presenting Information Clearly: The CONVINCE Framework; Convey Meaning; Objectivity: Be True to Your Data; Necessity: Don't Boil the Ocean; Visual Honesty: Size Matters; Imagine the Audience; Nimble: No Death by 1,000 Graphs; Context; Encourage Interaction; Conclusion; Further Reading; Notes
Chapter 7: Analytics Implementation: What Works and What Does NotAnalytics Implementation Model; Vision and Mandate; Strategy; Organizational Collaboration; Human Capital; Metrics and Measurement; Integrated Processes; Customer Experience; Technology and Tools; Change Management; Notes; Chapter 8: Voice-of-the-Customer Analytics and Insights; Customer Feedback Is Invaluable; The Makings of an Effective Voice-of-the-Customer Program; Listen, Monitor; Analyze, Interpret, Integrate; Recommend Actions; Actions Taken by Organization; Strategy and Elements of the VOC System
VOC Strategy by Organizational AreasVOC Strategy Based on Customer's Experience Stage; Common VOC Program Pitfalls; Further Reading; Notes; Chapter 9: Leveraging Digital Analytics Effectively; Strategic and Tactical Use of Digital Analytics; Understanding Digital Analytics Concepts; Digital Analytics Team: People Are Most Important for Analytical Success; Digital Analytics Tools; Advanced Digital Analytics; Digital Analytics and Voice of the Customer; Analytics of Site and Landing Page Optimization; Call to Action: Unify Traditional and Digital Analytics; Additional Resources; Notes
Chapter 10: Effective Predictive Analytics: What Works and What Does Not
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