Description
Book SynopsisAn integrated, strategic approach to higher-value analytics Leaders and Innovators: How Data-Driven Organizations Are Winning with Analytics shows how businesses leverage enterprise analytics to gain strategic insights for profitability and growth. The key factor is integrated, end-to-end capabilities that encompass data management and analytics from a business and IT perspective; with analytics running inside a database where the data reside, everyday analytical processes become streamlined and more efficient. This book shows you what analytics is, what it can do, and how you can integrate old and new technologies to get more out of your data. Case studies and examples illustrate real-world scenarios in which an optimized analytics system revolutionized an organization''s business. Using in-database and in-memory analytics along with Hadoop, you''ll be equipped to improve performance while reducing processing time from days or weeks to hours or minutes. This more stra
Trade Review
“Data management and analytics to beat your competitors; full of examples and case studies of big
data wins” The Magpi, issue 60, Aug 2017
Table of ContentsForeword xi
Acknowledgments xv
About the Author xvii
Introduction xix
Chapter 1 The Analytical Data Life Cycle 1
Stage 1: Data Exploration 2
Stage 2: Data Preparation 3
Stage 3: Model Development 4
Stage 4: Model Deployment 6
End-to-End Process 8
Chapter 2 In-Database Processing 11
Background 12
Traditional Approach 13
In-Database Approach 15
The Need for In-Database Analytics 16
Success Stories and Use Cases 18
In-Database Data Quality 35
Investment for In-Database Processing 44
Endnotes 47
Chapter 3 In-Memory Analytics 49
Background 50
Traditional Approach 51
In-Memory Analytics Approach 53
The Need for In-Memory Analytics 56
Success Stories and Use Cases 65
Investment for In-Memory Analytics 80
Chapter 4 Hadoop 83
Background 84
Hadoop in the Big Data Environment 86
Use Cases for Hadoop 87
Hadoop Architecture 89
Best Practices 92
Benefits of Hadoop 95
Use Cases and Success Stories 97
A Collection of Use Cases 103
Endnote 105
Chapter 5 Bringing It All Together 107
Background 108
Collaborative Data Architecture 109
Scenarios for the Collaborative Data Architecture 113
How In-Database, In-Memory, and Hadoop Are
Complementary in a Collaborative Data Architecture 119
Use Cases and Customer Success Stories 122
Investment and Costs 150
Endnotes 151
Chapter 6 Final Thoughts and Conclusion 153
Five Focus Areas 154
Cloud Computing 157
Security: Cyber, Data Breach 168
Automating Prescriptive Analytics: IoT, Events, and Data Streams 179
Cognitive Analytics 188
Anything as a Service (XaaS) 197
Conclusion 204
Afterword 208
Index 210
