Unstructured Data Analytics : (Record no. 166)
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| fixed length control field | 11100nam a22004693i 4500 |
| 001 - CONTROL NUMBER | |
| control field | EBC5317473 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | MiAaPQ |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240724120058.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS | |
| fixed length control field | m o d | |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr cnu|||||||| |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240724s2018 xx o ||||0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781119325505 |
| Qualifying information | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 9781119129752 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (MiAaPQ)EBC5317473 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (Au-PeEL)EBL5317473 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (CaPaEBR)ebr11540545 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (OCoLC)1028022728 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | MiAaPQ |
| Language of cataloging | eng |
| Description conventions | rda |
| -- | pn |
| Transcribing agency | MiAaPQ |
| Modifying agency | MiAaPQ |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Isson, Jean-Paul. |
| 245 10 - TITLE STATEMENT | |
| Title | Unstructured Data Analytics : |
| Remainder of title | How to Improve Customer Acquisition, Customer Retention, and Fraud Detection and Prevention. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Newark : |
| Name of producer, publisher, distributor, manufacturer | John Wiley & Sons, Incorporated, |
| Date of production, publication, distribution, manufacture, or copyright notice | 2018. |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | ©2017. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource (435 pages) |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | computer |
| Media type code | c |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Carrier type code | cr |
| Source | rdacarrier |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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 -- Facebook -- 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. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Data Governance and Data Security Will Remain the Number-One Risk and Threat. |
| 588 ## - SOURCE OF DESCRIPTION NOTE | |
| Source of description note | Description based on publisher supplied metadata and other sources. |
| 590 ## - LOCAL NOTE (RLIN) | |
| Local note | Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Industrial management-Statistical methods. |
| 655 #4 - INDEX TERM--GENRE/FORM | |
| Genre/form data or focus term | Electronic books. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Print version: |
| Main entry heading | Isson, Jean-Paul |
| Title | Unstructured Data Analytics |
| Place, publisher, and date of publication | Newark : John Wiley & Sons, Incorporated,c2018 |
| International Standard Book Number | 9781119129752 |
| 797 2# - LOCAL ADDED ENTRY--CORPORATE NAME (RLIN) | |
| Corporate name or jurisdiction name as entry element | ProQuest (Firm) |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://ebookcentral.proquest.com/lib/orpp/detail.action?docID=5317473">https://ebookcentral.proquest.com/lib/orpp/detail.action?docID=5317473</a> |
| Public note | Click to View |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Proquest Ebooks |
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