Enterprise Analytics Optimize Performance, Process, and Decisions Through Big Data

ISBN-10: 0133039439

ISBN-13: 9780133039436

Edition: 2013 (Revised)

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Description: Organizations are capturing exponentially larger amounts of data than ever, and now they have to figure out what to do with it. Using analytics, you can harness this data, discover hidden patterns, and use this knowledge to act meaningfully for competitive advantage. Suddenly, you can go beyond understanding “how, when, and where” events have occurred, to understandwhy– and use this knowledge to reshape the future. Now, analytics pioneer Tom Davenport and the world-renowned experts at the International Institute for Analytics (IIA) have brought together the latest techniques, best practices, and research on analytics in a single primer for maximizing the value of enterprise data.Enterprise Analyticsis today’s definitive guide to analytics strategy, planning, organization, implementation, and usage. It covers everything from building better analytics organizations to gathering data; implementing predictive analytics to linking analysis with organizational performance. The authors offer specific insights for optimizing supply chains, online services, marketing, fraud detection, and many other business functions. They support their powerful techniques with many real-world examples, including chapter-length case studies from healthcare, retail, and financial services.Enterprise Analyticswill be an invaluable resource for every business and technical professional who wants to make better data-driven decisions: operations, supply chain, and product managers; product, financial, and marketing analysts; CIOs and other IT leaders; data, web, and data warehouse specialists, and many others.

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Book details

List price: $49.99
Copyright year: 2013
Publisher: FT Press
Publication date: 9/13/2012
Binding: Hardcover
Pages: 304
Size: 6.50" wide x 9.50" long x 1.00" tall
Weight: 1.430
Language: English

Foreword and Acknowledgments
About the Authors
Introduction: The New World of Enterprise Analytics
Overview of Analytics and Their Value
What Do We Talk About When We Talk About Analytics?
Why We Needed a New Term: Issues with Traditional Business Intelligence
Three Types of Analytics
Where Does Data Mining Fit In?
Business Analytics Versus Other Types
Web Analytics
Big-Data Analytics
The Return on Investments in Analytics
Traditional ROI Analysis
The Teradata Method for Evaluating Analytics Investments
An Example of Calculating the Value
Analytics ROI at Freescale Semiconductor
Application of Analytics
Leveraging Proprietary Data for Analytical Advantage
Issues with Managing Proprietary Data and Analytics
Lessons Learned from Payments Data
Analytics on Web Data: The Original Big Data
Web Data Overview
What Web Data Reveals
Web Data in Action
The Analytics of Online Engagement
The Definition of Engagement
A Model to Measure Online Engagement
The Value of Engagement Scores
Engagement Analytics at PBS
Engagement Analytics at Philly.com
The Path to "Next Best Offers" for Retail Customers
Analytics and the Path to Effective Next Best Offers
Offer Strategy Design
Know Your Customer
Know Your Offers
Know the Purchase Context
Analytics and Execution: Deciding on and Making die Offer
Learning from and Adapting NBOs
Technologies for Analytics
Applying Analytics at Production Scale
Decisions Involve Actions
Time to Business Impact
Business Decisions in Operation
Compliance Issues
Data Considerations
Example of Analytics at Production Scale: YouSee
Lessons Learned from Other Successful Companies
Predictive Analytics in the Cloud
Business Solutions Focus
Five Key Opportunities
The State of the Market
Pros and Cons
Adopting Cloud-Based Predictive Analytics
Analytical Technology and the Business User
Separate but Unequal
Staged Data
Generally Complex
Premises- and Product-Based
Exclusively Quantitative
Business Unit-Driven
Specialized Vendors
Problems with the Current Model
Changes Emerging in Analytical Technology
Creating the Analytical Apps of the Future
Linking Decisions and Analytics for Organizational Performance
A Study of Decisions and Analytics
Linking Decisions and Analytics
A Process for Connecting Decisions and Information
Looking Ahead in Decision Management
The Human Side of Analytics
Organizing Analysts
Why Organization Matters
General Goals of Organizational Structure
Goals of a Particular Analytics Organization
Basic Models for Organizing Analysts
Coordination Approaches
What Model Fits Your Business?
How Bold Can You Be?
Triangulating on Your Model and Coordination Mechanisms
Analytical Leadership and the Chief Analytics Officer
To Where Should Analytical Functions Report?
Building an Analytical Ecosystem
Developing the Analytical Organization Over Time
The Bottom Line
Engaging Analytical Talent
Four Breeds of Analytical Talent
Engaging Analysts
Arm Analysts with Critical Information About the Business
Define Roles and Expectations
Feed Analysts' Love of New Techniques, Tools, and Technologies
Employ More Centralized Analytical Organization Structures
Governance for Analytics
Guiding Principles
Elements of Governance
You Know You're Succeeding When
Building a Global Analytical Capability
Widespread Geographic Variation
Central Coordination, Centralized Organization
A Strong Center of Excellence
A Coordinated "Division of Labor" Approach
Other Global Analytics Trends
Case Studies in the Use of Analytics
Partners HealthCare System
Centralized Data and Systems at Partners
Managing Clinical Informatics and Knowledge at Partners
High-Performance Medicine at Partners
New Analytical Challenges for Partners
Centralized Business Analytics at Partners
Hospital-Specific Analytical Activities: Massachusetts General Hospital
Hospital-Specific Analytical Activities: Brigham & Women's Hospital
Analytics in the HR Function at Sears Holdings Corporation
What We Do
Who Make Good HR Analysts
Our Recipe for Maximum Value
Key Lessons Learned
Commercial Analytics Culture and Relationships at Merck
Decision-Maker Partnerships
Reasons for the Group's Success
Embedding Analyses into Tools
Future Directions for Commercial Analytics and Decision Sciences
Descriptive Analytics for the Supply Chain at Bernard Chaus, Inc
The Need for Supply Chain Visibility
Analytics Strengthened Alignment Between Chaus's IT and Business Units
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