Heuristics in analytics : a practical perspective of what influences our analytical world /
Employ heuristic adjustments for truly accurate analysis. Heuristics in Analytics presents an approach to analysis that accounts for the randomness of business and the competitive marketplace, creating a model that more accurately reflects the scenario at hand. With an emphasis on the importance of...
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| Main Authors: | , |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hoboken, New Jersey :
Wiley,
[2014]
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| Series: | Wiley & SAS business series
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| Subjects: | |
| Online Access: | CONNECT |
MARC
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| 100 | 1 | |a Reis Pinheiro, Carlos Andre, |d 1940- |e author. | |
| 245 | 1 | 0 | |a Heuristics in analytics : |b a practical perspective of what influences our analytical world / |c Carlos Andre Reis Pinheiro, Fiona McNeill. |
| 264 | 1 | |a Hoboken, New Jersey : |b Wiley, |c [2014] | |
| 264 | 4 | |c ©2014 | |
| 300 | |a 1 online resource (xxiv, 225 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 504 | |a Includes bibliographical references (pages 209-216) and index. | ||
| 505 | 0 | |a Unplanned events, heuristics, and the randomness of the world -- The heuristic approach and why we use it -- The analytical approach -- Knowledge applications that solve business problems -- The graph analysis approach -- Graph analysis case studies -- Text analytics. | |
| 505 | 0 | |a Heuristics in Analytics: A Practical Perspective of What Influences Our Analytical World; Copyright; Contents; Preface; Acknowledgments; About the Authors; Chapter 1: Introduction; The Monty Hall Problem; Evolving Analytics; The Business Relevance of Analytics; The Role of Analytics in Innovation; Innovation in a Changing World; Summary; Chapter 2: Unplanned Events, Heuristics, and the Randomness in Our World; Heuristics Concepts; Heuristics in Operations; The Butterfly Effect; Random Walks; The Drunkard's Walk; Probability and Chance; Summary. | |
| 505 | 8 | |a Chapter 3: The Heuristic Approach and Why We Use It Heuristics in Computing; Heuristic Problem-Solving Methods; Genetic Algorithms: A Formal Heuristic Approach; Foundation of Genetic Algorithms; Initialization; Selection; Reproduction; Termination; Pseudo-Code Algorithm; Benefits of Genetic Algorithms; Influences in Competitive Industries; Genetic Algorithms Solving Business Problems; Summary; Chapter 4: The Analytical Approach; Introduction to Analytical Modeling; The Competitive-Intelligence Cycle; Data; Information; Knowledge; Intelligence; Experience; Summary. | |
| 505 | 8 | |a Chapter 5: Knowledge Applications That Solve Business Problems customer Behavior Segmentation; Collection Models; Insolvency Segmentation; Collection Notice Recovery; Anticipating Revenue from Collection Actions; Insolvency Prevention; Bad-Debt Classification; Avoiding Taxes; Fraud-Propensity Models; New Fraud Detection; Classifying Fraudulent Usage Behavior; Summary; Chapter 6: The Graph Analysis Approach; Introduction to Graph Analysis; Graphs Structures, Network Metrics, and Analyses Approaches; Network Metrics; Types of Subgraphs; Summary; Chapter 7: Graph Analysis Case Studies. | |
| 505 | 8 | |a Case Study: Identifying Influencers in Telecommunications background in Churn and Sales; Internal Networks; Customer Influence; Customer Influence and Business Event Correlation; Possible Business Applications and Final Figures in Churn and Sales; Case Study: Claim Validity Detection in Motor Insurance; Background in Insurance and Claims; Network Definition; Participant Networks; Group Analysis; Identifying Outliers; Final Figures in Claims; Visualizing for More Insight; Final Figures in Insurance Exaggeration; Case Study: Fraud Identification in Mobile Operations. | |
| 505 | 8 | |a Background in Telecommunications Fraud Social Networks and Fraud; Community Detection; Finding the Outliers within Communities; Rules and Thresholds for Community Outliers; Fraudsters Visualization; Final Figures in Fraud; Summary; Chapter 8: Text Analytics; Text Analytics in the Competitive-Intelligence Cycle; Information Revisited; Knowledge Revisited; Linguistic Models; Text-Mining Models; Intelligence Revisited; Experience Revisited; Summary; Bibliography; Index. | |
| 520 | |a Employ heuristic adjustments for truly accurate analysis. Heuristics in Analytics presents an approach to analysis that accounts for the randomness of business and the competitive marketplace, creating a model that more accurately reflects the scenario at hand. With an emphasis on the importance of proper analytical tools, the book describes the analytical process from exploratory analysis through model developments, to deployments and possible outcomes. Beginning with an introduction to heuristic concepts, readers will find heuristics applied to statistics and probability, mathematics, stochastic, and artificial intelligence models, ending with the knowledge applications that solve business problems. Case studies illustrate the everyday application and implication of the techniques presented, while the heuristic approach is integrated into analytical modeling, graph analysis, text analytics, and more. Robust analytics has become crucial in the corporate environment, and randomness plays an enormous role in business and the competitive marketplace. Failing to account for randomness can steer a model in an entirely wrong direction, negatively affecting the final outcome and potentially devastating the bottom line. Heuristics in Analytics describes how the heuristic characteristics of analysis can be overcome with problem design, math and statistics, helping readers to: Realize just how random the world is, and how unplanned events can affect analysis; Integrate heuristic and analytical approaches to modeling and problem solving; Discover how graph analysis is applied in real-world scenarios around the globe; Apply analytical knowledge to customer behavior, insolvency prevention, fraud detection, and more; Understand how text analytics can be applied to increase the business knowledge. Every single factor, no matter how large or how small, must be taken into account when modeling a scenario or event--even the unknowns. The presence or absence of even a single detail can dramatically alter eventual outcomes. From raw data to final report, Heuristics in Analytics contains the information analysts need to improve accuracy, and ultimately, predictive, and descriptive power.-- |c Publisher description. | ||
| 590 | |a O'Reilly Online Learning Platform: Academic Edition (SAML SSO Access) | ||
| 650 | 0 | |a Management |x Statistical methods. | |
| 650 | 0 | |a Decision making |x Statistical methods. | |
| 650 | 0 | |a Business planning |x Statistical methods. | |
| 650 | 0 | |a Heuristic algorithms. | |
| 650 | 0 | |a System analysis. | |
| 700 | 1 | |a McNeill, Fiona, |e author. | |
| 730 | 0 | |a WORLDSHARE SUB RECORDS | |
| 776 | 0 | 8 | |i Print version: |a Reis Pinheiro, Carlos Andre, 1940- |t Heuristics in analytics. |d Hoboken, New Jersey : Wiley, [2014] |z 9781118347607 |w (DLC) 2013039734 |w (OCoLC)795164251 |
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