Today's businesses are driven by data. Unlock the potential of your structured and unstructured data, anticipate market changes, and drive decision making with this comprehensive guide to SAP Predictive Analysis tools-SAP Predictive Analysis module, the PAL Library, R Integration, and SAP HANA. Filled with simple examples, customer case studies, and explanations of the business benefits, this book helps you navigate the complex predictive analysis process. From cluster analysis to text analysis, transform your raw data into improved business process. 1. Predictive Analysis Overview Learn what predictive analysis is, the practical business value that it provides, and the tools in SAP that support it. 2. Algorithm Selection Choose the right algorithm for your needs and understand the strengths and weaknesses of each algorithm and method of predictive analysis. 3. Predictive Analysis Applied Simplify the complex predictive analysis process and learn how to apply predictive analysis with practical examples, case studies, and business explanations. 4. Data Visualized Learn how to investigate large amounts of data via useful and comprehensive visualizations, and share the analysis with ease! 5. Jumpstart Your Analysis Full code listings are provided to help facilitate your using the SAP HANA Predictive Analysis Library (PAL), including data sets and parameter settings required for the analysis. Highlights include: Predictive Analysis Library (PAL) in SAP HANA The R Integration for SAP HANA SAP Predictive Analysis (PA) Data and text mining Outlier analysis Association analysis Cluster analysis Classification analysis Regression analysis Decision tree analysis Timeseries analysis Text analysis and text mining Galileo Press heißt jetzt Rheinwerk Verlag.

InhaltsangabeIntroduction. 17Acknowledgments. 21PART I: Predictive Analysis Overview. 231. An Introduction to Predictive Analysis. 25 1.1. Definitions of Predictive Analysis. 25 1.2. The Value of Predictive Analysis. 28 1.3. User Personas. 31 1.4. Applications of Predictive Analysis. 33 1.5. Classes of Applications. 37 1.6. Algorithms for Predictive Analysis. 39 1.7. The Predictive Analysis Process. 41 1.8. Hot Topics and Trends. 44 1.9. Challenges and Criteria for Success. 45 1.10. Summary. 47 2. An Overview of the Predictive Analysis Products in SAP. 49 2.1. The Predictive Analysis Library in SAP HANA. 53 2.2. The R Integration for SAP HANA. 59 2.3. SAP Predictive Analysis. 63 2.4. SAP Business Solutions with Predictive Analysis. 73 2.5. Summary. 77 PART II: Predictive Analysis Applied. 793. Initial Data Exploration. 81 3.1. Data Types. 83 3.2. Data Visualization for Data Exploration. 86 3.3. Sampling. 92 3.4. Scaling. 97 3.5. Binning. 101 3.6. Outliers. 104 3.7. Summary. 105 4. Which Algorithm When?. 107 4.1. The Main Factors When Selecting an Algorithm. 107 4.2. Classes of Applications and Algorithms. 109 4.3. Matrix of Application Tasks, Variable Types and Output. 113 4.4. Which Algorithm Is the Best?. 115 4.5. A Set of Rules for Which Algorithm When. 116 4.6. Summary. 118 5. When Mining, Beware of Mines. 119 5.1. Data Mining Heaven and Hell. 119 5.2. Five Myths. 121 5.3. Five Pitfalls. 124 5.4. Further Challenges and Resolution. 126 5.5. Key Factors for Success. 137 5.6. Summary. 138 6. Applications in SAP. 139 6.1. SAP Smart Meter Analytics. 139 6.2. SAP Customer Engagement Intelligence. 142 6.3. SAP Enterprise Inventory & Service-Level Optimization. 149 6.4. SAP Precision Gaming. 158 6.5. SAP Affinity Insight. 161 6.6. SAP Demand Signal Management. 166 6.7. SAP OnShelf Availability. 172 6.8. SAP Product Recommendation Intelligence. 177 6.9. SAP Credit Insight. 182 6.10. SAP Convergent Pricing Simulation. 184 6.11. Summary. 187 7. SAP Predictive Analysis. 189 7.1. Getting Started in PA. 189 7.2. Accessing and Viewing the Data Source. 195 7.3. Preparing Data for Analysis. 199 7.4. Applying Algorithms to Analyze the Data. 202 7.5. Running the Model and Viewing the Results. 209 7.6. Deploying the Model in a Business Application. 213 7.7. Summary. 219 PART III: Predictive Analysis Categories. 2218. Outlier Analysis. 223 8.1. Introduction to Outlier Analysis. 223 8.2. Applications of Outlier Analysis. 225 8.3. The Inter-Quartile Range Test. 227 8.4. The Variance Test. 232 8.5. K Nearest Neighbor Outlier. 235 8.6. Anomaly Detection using Cluster Analysis. 238 8.7. The Business Case for Outlier Analysis. 243 8.8. Strengths and Weaknesses of Outlier Analysis. 244 8.9. Summary. 245 9. Association Analysis. 247 9.1. Applications of Association Analysis. 248 9.2. Apriori Association Analysis. 250 9.3. Apriori Association Analysis in the PAL. 255 9.4. An Example of Apriori Association Analysis in the PAL. 257 9.5. An Example of Apriori in SAP Predictive Analysis. 260 9.6. Apriori Lite Association An