- #Business intelligence application developer drivers
- #Business intelligence application developer software
Six Sigma as a Performance Measurement System.īalanced Scorecard versus Six Sigma.Įffective Performance Measurement.Īpplication Case 3.6: ’s Customer Satisfaction Scorecard.Ĭhapter 4: Predictive Analytics I: Data Mining Process, Methods, and Algorithms.Ĥ.1. Business Performance Management.Īpplication Case 3.5: AARP Transforms Its BI Infrastructure and Achieves a 347% ROI in Three Years. Data Warehouse Administration, Security Issues, and Future Trends. Massive Data Warehouses and Scalability.Īpplication Case 3.4: EDW Helps Connect State Agencies in Michigan.ģ.8. Representation of Data in Data Warehouse.ģ.7. Data Warehouse Development.Īpplication Case 3.3: Use of Teradata Analytics for SAP Solutions Accelerates Big Data Delivery.ĭata Warehouse Development Approaches. Data Integration and the Extraction, Transformation, and Load (ETL) Processes.Īpplication Case 3.2: BP Lubricants Achieves BIGS Success.Įxtraction, Transformation, and Load.ģ.6. Data Warehousing Architectures.Īlternative Data Warehousing Architectures. Business Intelligence and Data Warehousing.Ī Historical Perspective to Data Warehousing.Ĭharacteristics of Data Warehousing.Īpplication Case 3.1: A Better Data Plan: Well- Established TELCOs Leverage Data Warehousing and Analytics to Stay on Top in a Competitive Industry.ģ.3. Opening Vignette: Targeting Tax Fraud with Business Intelligence and Data Warehousing.ģ.2. Provide for Guided Analytics.Ĭhapter 3: Descriptive Analytics II: Business Intelligence and Data Warehousing.ģ.1. Wrap the Dashboard Metrics with Contextual Metadata.
Information Dashboards.Īpplication Case 2.7: Dallas Cowboys Score Big with Tableau and Teknion.Īpplication Case 2.8: Visual Analytics Helps Energy Supplier Make Better Connections. High-Powered Visual Analytics Environments.Ģ.11. Which Chart or Graph Should You Use?.Ģ.10. What Are the Most Important Assumptions in Linear Regression?.Īpplication Case 2.4: Predicting NCAA Bowl Game Outcomes.Īpplication Case 2.5: Flood of Paper Ends at FEMA.Ī Brief History of Data Visualization.Īpplication Case 2.6: Macfarlan Smith Improves Operational Performance Insight with Tableau Online. How Do We Know If the Model Is Good Enough?. How Do We Develop the Linear Regression Model?. The Shape of a Distribution.Īpplication Case 2.3: Town of Cary Uses Analytics to Analyze Data from Sensors, Assess Demand, and Detect Problems.Ģ.6 Regression Modeling for Inferential Statistics. Measures of Dispersion (May Also Be Called Measures of Spread Decentrality). Statistical Modeling for Business Analytics.ĭescriptive Statistics for Descriptive Analytics. The Art and Science of Data Preprocessing.Īpplication Case 2.2: Improving Student Retention with Data-Driven Analytics.Ģ.5. A Simple Taxonomy of Data.Īpplication Case 2.1: Medical Device Company Ensures Product Quality While Saving Money.Ģ.4. Opening Vignette: SiriusXM Attracts and Engages a New Generation of Radio Consumers with Data-Driven Marketing.Ģ.3.
#Business intelligence application developer software
An Overview of the Analytics Ecosystem.ĭata Management Infrastructure Providers.Īnalytics-Focused Software Developers.Īpplication Developers: Industry Specific or General.Īnalytics Industry Analysts and Influencers.Īcademic Institutions and Certification Agencies.Īnalytics User Organizations.Ĭhapter 2: Descriptive Analytics I: Nature of Data, Statistical Modeling, and Visualization.Ģ.1. Transaction Processing versus Analytic Processing.Īppropriate Planning and Alignment with the Business Strategy.ĭeveloping or Acquiring BI Systems.Īpplication Case 1.2: Silvaris Increases Business with Visual Analysis and Real-Time Reporting Capabilities.Īpplication Case 1.5: A Specialty Steel Bar Company Uses Analytics to Determine Available-to-Promise Dates.Īnalytics Applications in Healthcare-Humana Examples.Īnalytics in the Retail Value Chain.ġ.8.
#Business intelligence application developer drivers
The Origins and Drivers of BI.Īpplication Case 1.1: Sabre Helps Its Clients Through Dashboards and Analytics. Evolution of Computerized Decision Support to Analytics/Data Science.ġ.4. Changing Business Environments and Evolving Needs for Decision Support and Analytics.ġ.3. Opening Vignette: Sports Analytics-An Exciting Frontier for Learning and Understanding Applications of Analytics.ġ.2. Chapter 1: An Overview of Business Intelligence, Analytics, and Data Science.ġ.1.