
Unstructured Data Analytics
Description
Alles über E-Books | Antworten auf Fragen rund um E-Books, Kopierschutz und Dateiformate finden Sie in unserem Info- & Hilfebereich.
Unstructured Data Analytics provides an accessible, non-technical introduction to the analysis of unstructured data. Written by global experts in the analytics space, this book presents unstructured data analysis (UDA) concepts in a practical way, highlighting the broad scope of applications across industries, companies, and business functions. The discussion covers key aspects of UDA implementation, beginning with an explanation of the data and the information it provides, then moving into a holistic framework for implementation. Case studies show how real-world companies are leveraging UDA in security and customer management, and provide clear examples of both traditional business applications and newer, more innovative practices.
Roughly 80 percent of today's data is unstructured in the form of emails, chats, social media, audio, and video. These data assets contain a wealth of valuable information that can be used to great advantage, but accessing that data in a meaningful way remains a challenge for many companies. This book provides the baseline knowledge and the practical understanding companies need to put this data to work.
Supported by research with several industry leaders and packed with frontline stories from leading organizations such as Google, Amazon, Spotify, LinkedIn, Pfizer Manulife, AXA, Monster Worldwide, Under Armour, the Houston Rockets, DELL, IBM, and SAS Institute, this book provide a framework for building and implementing a successful UDA center of excellence.
You will learn:
* How to increase Customer Acquisition and Customer Retention with UDA
* The Power of UDA for Fraud Detection and Prevention
* The Power of UDA in Human Capital Management & Human Resource
* The Power of UDA in Health Care and Medical Research
* The Power of UDA in National Security
* The Power of UDA in Legal Services
* The Power of UDA for product development
* The Power of UDA in Sports
* The future of UDA
From small businesses to large multinational organizations, unstructured data provides the opportunity to gain consumer information straight from the source. Data is only as valuable as it is useful, and a robust, effective UDA strategy is the first step toward gaining the full advantage. Unstructured Data Analytics lays this space open for examination, and provides a solid framework for beginning meaningful analysis.
More details
Other editions
Additional editions

Content
- Cover
- Title Page
- Copyright
- Contents
- Foreword
- Preface
- Acknowledgments
- Chapter 1: The Age of Advanced Business Analytics
- Introduction
- Why the Analytics Hype Today?
- 1. Costs to Store and Process Information Have Reduced
- 2. Interactive Devices and Censors Have Increased
- 3. Data Analytics Infrastructures and Software Have Increased
- 4. User-Friendly and Invisible Data Analytics Tools Have Emerged
- 5. Data Analytics Is Becoming Mainstream, and It Means a Lot to Our Economy and World
- 6. Major Leading Tech Companies Have Pioneered the Data Economy
- 7. Big Data Analytics Has Become a Big Market Opportunity
- 8. The Number of Data Science University Programs and MOOCs Has Intensified
- A Short History of Data Analytics
- Early Adopters: Insurance and Finance
- What is the Analytics Age?
- Interview with Wayne Thompson, Chief Data Scientist at SAS Institute
- Key Takeaways
- Notes
- Further Reading
- Chapter 2: Unstructured Data Analytics: The Next Frontier of Analytics Innovation
- Introduction
- What Is UDA?
- Why UDA Today?
- Big Data as a Catalyst
- Artificial Intelligence (AI)
- Machine Learning
- Deep Learning
- Representation Learning or Feature Learning
- Natural Language Processing
- Cognitive Computing/Analytics
- Neural Network
- The UDA Industry
- Uses of UDA
- How UDA Works
- Why UDA Is the Next Analytical Frontier?
- Interview with Seth Grimes on Analytics as the Next Business Frontier
- UDA Success Stories
- Amazon.com
- Spotify
- ITA Software
- Internet Search Engines: Bing.com, Google.com, and the Like
- Monster Worldwide
- The Golden Age of UDA
- Key Takeaways
- Notes
- Further Reading
- Chapter 3: The Framework to Put UDA to Work
- Introduction
- Why Have a Framework to Analyze Unstructured Data?
- The IMPACT Cycle Applied to Unstructured Data
- Focusing on the IMPACT
- Identify Business Questions
- Master the Data
- Text Parsing Example
- The T3
- Technique
- Tools
- Interview with Cindy Forbes, Chief Analytics Officer and Executive Vice President at Manulife Financial
- Case Study
- Key Takeaways
- Notes
- Further Reading
- Chapter 4: How to Increase Customer Acquisition and Retention with UDA
- The Voice of the Customer: A Goldmine for Understanding Customers
- Why Should You Care about UDA for Customer Acquisition and Retention?
- The Voice of the Customer
- Predictive Models and Online Marketing
- Predictive Models
- UDA and Online Marketing: Optimizing Your Acquisition and Customer Response Models
- How Does UDA Applied to Customer Acquisition Work?
- The Power of UDA for E-mail Response and Ad Optimization
- How to Drive More Conversion and Engagement with UDA Applied to Content
- How UDA Applied to Customer Retention (Churn) Works
- What Is UDA Applied to Customer Acquisition?
- Consumer/Customer Decision Journey
- Lessons from McKinsey's Consumer Decision Journey
- What Is UDA Applied to Customer Retention (Churn)?
- The Power of UDA Powered by Virtual Agent
- Welcome to the AI Customer Service Assistant
- Benefits of a Virtual Agent or AI Assistant for Customer Experience
- Benefits and Case Studies
- Applying UDA to Your Social Media Presence and Native Ads to Increase Acquisitions
- Social Media Analytics
- Key Takeaways
- Notes
- Chapter 5: The Power of UDA to Improve Fraud Detection and Prevention
- Introduction
- Why Should You Care about UDA for Fraud Detection and Prevention?
- Unstructured Data Is a Goldmine of Evidence for Fraud Detection and Prevention
- Cost Savings, Productivity, and Performance Gains
- Help to Fully Leverage the Power of Predictive Analytics and Big Data
- Realize the Untapped Big Data Opportunity
- Address Weaknesses of Existing Fraud Detection Techniques
- Benefits of UDA
- Huge Costs If Left Unchecked/Huge Savings in Fraud Losses
- Banking and Finance
- E-commerce
- Healthcare
- Insurance
- What Is UDA for Fraud?
- How UDA Works in Fraud Detection and Prevention
- Sampling
- Benford's Law
- Recommendations
- UDA Framework for Fraud Detection and Prevention: Insurance
- Step 1: Claimant Report (Narrative)
- Step 2: Underwriter Report (Text-Heavy)
- Step 3: Fraud Management Tool (Detection and Prediction)
- Step 4: Scoring and Classification Outputs
- Step 5: Decisions and Actions
- Major Fraud Detection and Prevention Techniques
- Best Practices Using UDA for Fraud Detection and Prevention
- Assess Your Current Fraud Management System
- Interview with Vishwa Kolla, Assistant Vice President Advanced Analytics at John Hancock Financial Services
- Interview with Diane Deperrois, General Manager South-East and Overseas Region, AXA
- Key Takeaways
- Notes
- Further Reading
- Chapter 6: The Power of UDA in Human Capital Management
- Why Should You Care about UDA in Human Resources?
- What Is UDA in HR?
- What Is UDA in HR Really About?
- The Power of UDA in Online Recruitment: Supply and Demand Equation
- The Power of UDA in Talent Sourcing Analytics
- Assessment and Analysis of Culture Fit Score
- Social Job Ad and Twitter Job
- Employer Online Reputation: Social Media Feed and News Analysis
- Supply (Resume/Job Response) and Demand (Job Posting/Listing)
- UDA Applied to Candidate Resumes and Candidate Profile
- Candidate Video Resume
- Video Interview
- The Power of UDA in Talent Acquisition Analytics
- Artificial Intelligence as a Hiring Assistant
- The Power of UDA in Talent Retention
- The Power of UDA in Employee Wellness Analytics
- Interview with Arun Chidambaram, Director of Global workforce intelligence, Pfizer
- Employee Performance Appraisal Data Review Feedback
- How UDA Works
- Benefits of UDA in HR
- Case Studies
- The Container Store
- Interview with Stephani Kingsmill, Executive Vice President and Chief Human Resource Officer, Manulife
- Key Takeaways
- Further Reading
- Chapter 7: The Power of UDA in the Legal Industry
- Why Should You Care About UDA in Legal Services?
- What Is UDA Applied to Legal Services?
- How Does It Work?
- Benefits and Challenges
- Key Takeaways
- Notes
- Further Reading
- Chapter 8: The Power of UDA in Healthcare and Medical Research
- Why Should You Care about UDA in Healthcare?
- Untapped Potential of Healthcare Data Goldmine
- Ever-Increasing Volume of Patient Data from Internet of Things
- What's UDA in Healthcare?
- How UDA Works
- Data Complexity
- Aggregating Data
- Transforming Unstructured Data into Discrete Data
- IMPACT Cycle
- Benefits
- Interview with Mr. François Laviolette, Professor of Computer Science/Director of Big Data Research Centre at Laval University (QC) Canada
- Interview with Paul Zikopolous, Vice President Big Data Cognitive System at IBM
- Case Study
- Key Takeaways
- Notes
- Further Reading
- Chapter 9: The Power of UDA in Product and Service Development
- Why Should You Care about UDA for Product and Service Development?
- UDA and Big Data Analytics
- 1. Data Analytics: Data Products and Services
- 2. 365/24/7 Platform for Customers
- 3. Intersection between Analytics and Innovation
- 4. The Voice of the Customer (VoC)
- Interview with Fiona McNeill, Global Product Marketing Manager at SAS Institute
- What Is UDA Applied to Product Development?
- How Is UDA Applied to Product Development?
- How UDA Applied to Product Development Works
- Key Takeaways
- Notes
- Chapter 10: The Power of UDA in National Security
- National Security: Playground for UDA or Civil Liberty Threat?
- Edward J. Snowden, the NSA Whistle-Blower?
- What Is the NSA?
- What Is UDA for National Security?
- Data Sources of the NSA
- What Happened?
- What Is Happening Now, and Why?
- What Will Happen, and What Should We Do?
- Why UDA for National Security?
- September 11, 2001: Disparate Data and Intelligence Weakness
- How the CIA Uses Big Data to Predict Social Unrest
- Case Studies
- Business Challenge
- Solutions
- Benefit
- How UDA Works
- Key Takeaways
- Notes
- Further Reading
- Chapter 11: The Power of UDA in Sports
- The Short History of Sports Analytics: Moneyball
- Why Should You Care about UDA in Sports?
- UDA's Impact for Players
- UDA Impact for Coaches and Managers
- UDA Impact on Fans
- What Is UDA in Sports?
- Baseball and Football
- What Will Happen? And What Should We Do?
- How It Works
- Fan Data
- Player Data
- Team Data
- Interview with Winston Lin, Director of Strategy and Analytics for the Houston Rockets
- Key Takeaways
- Notes
- Further Reading
- Chapter 12: The Future of Analytics
- Harnessing These Evolving Technologies Will Generate Benefits
- Data Becomes Less Valuable and Analytics Becomes Mainstream
- Data Becomes Less Valuable
- Analytics Will Become Mainstream
- Predictive Analytics, AI, Machine Learning, and Deep Learning Become the New Standard
- People Analytics Becomes a Standard Department in Businesses
- UDA Becomes More Prevalent in Corporations and Businesses
- Cognitive Analytics Expansion
- The Internet of Things Evolves to the Analytics of Things
- MOOCs and Open Source Software and Applications Will Continue to Explode
- Blockchain and Analytics Will Solve Social Problems
- Human-Centered Computing Will Be Normalized
- Data Governance and Data Security Will Remain the Number-One Risk and Threat
- Key Takeaways
- Notes
- Further Reading
- Appendix A: Tech Corner Details
- Singular Value Decomposition (SVD) Algorithm and Applications
- Why SVD?
- What Is SVD?
- SVD Theorem
- How SVD Works
- SVD Interpretation
- Why Matrix Decomposition?
- Principal Component Analysis (PCA) and Applications
- Why PCA?
- What Is Principal Component Analysis?
- How PCA Works
- PCA Application to Facial Recognition: EigenFaces
- How it Works?
- How to classify a new face image?
- QR Factorization Algorithm and Applications
- Why QR Factorization?
- What Is QR Factorization?
- How QR Factorization Algorithm Works
- Gram-Schmidt Process Overview
- QR Factorization through an Example
- Note
- Further Reading
- About The Author
- Index
- EULA
System requirements
File format: PDF
Copy-Protection: Adobe-DRM (Digital Rights Management)
System requirements:
- Computer (Windows; MacOS X; Linux): Install the free reader Adobe Digital Editions prior to download (see eBook Help).
- Tablet/smartphone (Android; iOS): Install the free app Adobe Digital Editions or the app PocketBook before downloading (see eBook Help).
- E-reader: Bookeen, Kobo, Pocketbook, Sony, Tolino and many more (only limited: Kindle).
The file format PDF always displays a book page identically on any hardware. This makes PDF suitable for complex layouts such as those used in textbooks and reference books (images, tables, columns, footnotes). Unfortunately, on the small screens of e-readers or smartphones, PDFs are rather annoying, requiring too much scrolling.
This eBook uses Adobe-DRM, a „hard” copy protection. If the necessary requirements are not met, unfortunately you will not be able to open the eBook. You will therefore need to prepare your reading hardware before downloading.
Please note: We strongly recommend that you authorise using your personal Adobe ID after installation of any reading software.
For more information, see our eBook Help page.