Screen LibraryImplementing analytics : a blueprint for design, development, and adoption /
Implementing Analytics demystifies the concept, technology and application of analytics and Big Data and breaks its implementation down to repeatable and manageable steps, making it ready for widespread adoption. Implementing Analytics simplifies and helps democratize an otherwise very specialized d...
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| Main Authors: | |
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| Corporate Authors: | |
| Published: |
Elsevier Science,
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| Publisher Address: | Burlington : |
| Publication Dates: | 2013. |
| Literature type: | eBook |
| Language: | English |
| Series: |
Morgan Kaufmann Series on Business Intelligence. |
| Subjects: | |
| Online Access: |
http://www.sciencedirect.com/science/book/9780124016965 |
| Summary: |
Implementing Analytics demystifies the concept, technology and application of analytics and Big Data and breaks its implementation down to repeatable and manageable steps, making it ready for widespread adoption. Implementing Analytics simplifies and helps democratize an otherwise very specialized discipline to foster business efficiency and innovation without investing in multi-million dollar technology and manpower. Shows how existing technology within an enterprise can be used to build analytics solutions Helps formalize analytics. |
| Carrier Form: | 1 online resource (211 pages) |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: |
9780124016811 0124016812 |
| Index Number: | T57 |
| CLC: | N945.1 |
| Contents: | Machine generated contents note: ch. 1 Defining Analytics -- The Hype -- The Challenge of Definition -- Definition 1: Business Value Perspective -- Definition 2: Technical Implementation Perspective -- Analytics Techniques -- Algorithm versus Analytics Model -- Forecasting -- Descriptive Analytics -- Predictive Analytics -- Decision Optimization -- Conclusion of Definition -- ch. 2 Information Continuum -- Building Blocks of the Information Continuum -- Theoretical Foundation in Data Sciences -- Tools, Techniques, and Technology -- Skilled Human Resources -- Innovation and Need -- Information Continuum Levels -- Search and Lookup -- Counts and Lists -- Operational Reporting -- Summary Reporting -- Historical (Snapshot) Reporting -- Metrics, KPIs, and Thresholds -- Analytical Applications -- Analytics Models -- Decision Strategies -- Monitoring and Tuning-Governance -- Summary -- ch. 3 Using Analytics -- Healthcare -- Emergency Room Visit -- Patients with the Same Disease -- Customer Relationship Management -- Customer Segmentation -- Propensity to Buy -- Human Resource -- Employee Attrition -- Resume Matching -- Consumer Risk -- Borrower Default -- Insurance -- Probability of a Claim -- Telecommunication -- Call Usage Patterns -- Higher Education -- Admission and Acceptance -- Manufacturing -- Predicting Warranty Claims -- Analyzing Warranty Claims -- Energy and Utilities -- The New Power Management Challenge -- Fraud Detection -- Benefits Fraud -- Credit Card Fraud -- Patterns of Problems -- How Much Data -- Performance or Derived Variables -- ch. 4 Performance Variables and Model Development -- Performance Variables -- What are Performance Variables? -- Designing Performance Variables -- Working Example -- Model Development -- What is a Model -- Model and Characteristics in Predictive Modeling -- Model and Characteristics in Descriptive Modeling -- Model Validation and Tuning -- Champion-Challenger: A Culture of Constant Innovation -- ch. 5 Automated Decisions and Business Innovation -- Automated Decisions -- Decision Strategy -- Business Rules in Business Operations -- Decision Automation and Business Rules -- Joint Business and Analytics Sessions for Decision Strategies -- Examples of Decision Strategy -- Decision Automation and Intelligent Systems -- Learning versus Applying -- Strategy Integration Methods -- Strategy Evaluation -- Retrospective Processing -- Reprocessing -- Champion-Challenger Strategies -- Business Process Innovation -- ch. 6 Governance: Monitoring and Tuning of Analytics Solutions -- Analytics and Automated Decisions -- The Risk of Automated Decisions -- Monitoring Layer -- Audit and Control Framework -- Organization and Process -- Audit Datamart -- Control Definition -- Reporting and Action -- ch. 7 Analytics Adoption Roadmap -- Learning from Success of Data Warehousing -- Lesson 1: Simplification -- Lesson 2: Quick Results -- Lesson 3: Evangelize -- Lesson 4: Efficient Data Acquisition -- Lesson 5: Holistic View -- Lesson 6: Data Management -- The Pilot -- Business Problem -- Management Attention and Champion -- The Project -- Results, Roadshow, and Case for Wider Adoption -- ch. 8 Requirements Gathering for Analytics Projects -- Purpose of Requirements -- Requirements: Historical Perspective -- Calculations -- Process Automation -- Analytical and Reporting Systems -- Analytics and Decision Strategy -- Requirements Extraction -- Problem Statement and Goal -- Data Requirements -- Model and Decision Strategy Requirements -- Business Process Integration Requirements -- ch. 9 Analytics Implementation Methodology -- Centralized versus Decentralized -- Centralized Approach -- Decentralized Approach -- A Hybrid Approach -- Building on the Data Warehouse -- Methodology -- Requirements -- Analysis -- Design -- Implementation -- Deployment -- Execution and Monitoring -- ch. 10 Analytics Organization and Architecture -- Organizational Structure -- BICC Organization Chart -- Roles and Responsibilities -- Skills Summary -- Technical Components in Analytics Solutions -- Analytics Datamart -- ch. 11 Big Data, Hadoop, and Cloud Computing -- Big Data -- Velocity -- Variety -- Volume -- Big Data Implementation Challenge -- Hadoop -- Hadoop Technology Stack -- Hadoop Solution Architecture -- Hadoop as an Analytical Engine -- Cloud Computing (For Analytics) -- Disintegration in Cloud Computing -- Analytics in Cloud Computing. |