Business Intelligence, 2nd Edition

The Savvy Manager's Guide

 
Business Intelligence, 2nd Edition,David Loshin,ISBN9780123858894
 
 
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Morgan Kaufmann

9780123858894

9780123858900

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This completely updated best seller is a must read for anyone who wants an understanding of business intelligence, business management disciplines, data warehousing, and how all of the pieces work together.

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Key Features

  • Guides managers through developing, administering, or simply understanding business intelligence technology
  • Keeps pace with the changes in best practices, tools, methods and processes used to transform an organization’s data into actionable knowledge
  • Contains a handy, quick-reference to technologies and terminology.

Description

Business Intelligence: The Savvy Managers Guide, Second Edition, discusses the objectives and practices for designing and deploying a business intelligence (BI) program. It looks at the basics of a BI program, from the value of information and the mechanics of planning for success to data model infrastructure, data preparation, data analysis, integration, knowledge discovery, and the actual use of discovered knowledge. Organized into 21 chapters, this book begins with an overview of the kind of knowledge that can be exposed and exploited through the use of BI. It then proceeds with a discussion of information use in the context of how value is created within an organization, how BI can improve the ways of doing business, and organizational preparedness for exploiting the results of a BI program. It also looks at some of the critical factors to be taken into account in the planning and execution of a successful BI program. In addition, the reader is introduced to considerations for developing the BI roadmap, the platforms for analysis such as data warehouses, and the concepts of business metadata. Other chapters focus on data preparation and data discovery, the business rules approach, and data mining techniques and predictive analytics. Finally, emerging technologies such as text analytics and sentiment analysis are considered. This book will be valuable to data management and BI professionals, including senior and middle-level managers, Chief Information Officers and Chief Data Officers, senior business executives and business staff members, database or software engineers, and business analysts.

Readership

IT managers, analysts, consultants, MIS students, data managers

David Loshin

David Loshin is President of Knowledge Integrity, Inc., a company specializing in data management consulting. The author of numerous books on performance computing and data management, including “Master Data Management" (2008) and “Business Intelligence - The Savvy Manager’s Guide" (2003), and creator of courses and tutorials on all facets of data management best practices, David is often looked to for thought leadership in the information management industry.

Affiliations and Expertise

President, Knowledge Integrity Incorporated, Silver Spring, MD, USA

View additional works by David Loshin

Business Intelligence, 2nd Edition


Preface

Foreword

1 Business Intelligence and Information Exploitation

    Improving the Decision-Making Process

    Why a Business Intelligence Program?

    Taking Advantage of the Information Asset

    Business Intelligence and Program Success

    Business Intelligence Defined

         Turning Data into Information

         The Flash of Insight: Turning Information into Knowledge

         Turning Knowledge into Actionable Plans

    Actionable Intelligence

    The Analytics Spectrum

    Taming the Information Explosion

    Considerations

    Continuing Your Business Intelligence Education

    Endnotes

2 The Value of Business Intelligence

    Value Drivers and Information Use

    Performance Metrics and Key Performance Indicators

    Using Actionable Knowledge

    Horizontal Use Cases for Business Intelligence

         Customer Analysis

         Revenue Generation

         Human Resources and Staff Utilization

         Product Management, Productivity Analysis, and Cost Management

         Operations

         Finance and Risk Management

         Supply Chain Management

         Sales Channel Analytics

         Behavior Analysis

    Vertical Use Cases for Business Intelligence

    Business Intelligence Adds Value

3 Planning for Success

    Introduction

    Organizational Preparedness for Business Intelligence and Analytics

         Organizational Readiness

         Management Readiness

         Operational Readiness

         Preparing for Change

    Initial Steps in Starting a Business Intelligence Program

         Championship and Management Sponsorship

         Establishing High-Level Goals and Setting Expectations

         Partnering

         Establishing a Vision

         Developing a Plan

    Bridging the Gaps Between Information Technology and the Business Users

         Dichotomy

         Partnering

    Knowing the Different Types of Business Intelligence Users

         Analytic Databases and Information Consumers

    Business Intelligence Success Factors: A Deeper Dive

         Strong, Dedicated Management

         Setting Appropriate Expectations

         Establishing Metrics for Success

         Building a Strong Team

         Understanding the Technology

         Data Architecture

         Using Quality Information

         Enterprise Data Integration

         Benefitting from Reuse

         Managing Scope

         Scalability

    More on Building Your Team

    Strategic Versus Tactical Planning

         Long-Term Goals

         Short-Term Success

    Summary

    Endnotes

4 Developing Your Business Intelligence Roadmap

    A Business Intelligence Strategy: Vision to Blueprint

    Review: The Business Intelligence and Analytics Spectrum

         Incremental Improvement

         Maintaining the Business Focus

    The Business Intelligence Roadmap: Example Phasing

    Planning the Business Intelligence Plan

5 The Business Intelligence Environment

    Aspects of a Business Intelligence and Analytics Platform and Strategy

         Fundamentals and Requirements

         Business Intelligence Architecture and Design

         Data Preparation

         Data Integration

         Business Intelligence Platforms

         Analysis

         Delivery and Presentation

    The Organizational Business Intelligence Framework

         Business Process Modeling and Analytic Needs Assessment

         Metadata Management

         Data Modeling

         Data Profiling

         Data Quality

         Business Rules

         Database Management System

         Data Integration

         Analytical Platforms

         ‘‘Canned’’ Reporting

         Dimensional Analysis

         Ad Hoc/Self-Service

         Search

         Text Analysis

         Predictive Analytics

         Dashboards and Other Visualization Techniques

         System/Network Management

         Data Security

    Services and System Evolution

         Architecture and Integration

         Data Governance

         Migration

         Analytical Service Providers

         Training

         Strategic Management

    Management Issues

    Additional Considerations

6 Business Processes and Information Flow

    Analytical Information Needs and Information Flows

         The Value of Modeling Information Flow

         Process Design Versus Application Implementation

         Benefits of the Business Process Model

    Information Processing and Information Flow

         Transaction Processing

         Operational Processing

         Batch Processing

         Analytical Processing

    The Information Flow Model

         Information Flow: Processing Stages

         Information Flow: Directed Channels

         Data Payload Characteristics

    Practical Use

         Information Valuation

         Root Cause Analysis

         Operational Performance Improvement

    Modeling Frameworks

         Integrated Definition Language

         Use Case Modeling

         Unified Modeling Language

         Business Process Model and Notation (BPMN)

    Management Issues

    Deeper Dives

7 Data Requirements Analysis

    Introduction

    Business Uses of Information

    Metrics: Facts, Qualifiers, and Models

         The Fact/Qualifier Matrix

         The Metrics Model

    What is Data Requirements Analysis?

         Identify the Business Context

         Conduct Information Consumer Interviews

         Synthesize Requirements

         Develop Source-to-Target Mapping

    Assessing Suitability

    Summary

8 Data Warehouses and the Technical Business Intelligence Architecture

    Introduction

    Data Modeling and Analytics

         Transaction Processing and Data Modeling

         Dimensional Models

         Using the Dimensional Model for Business Intelligence

    The Data Warehouse

    Analytical Platforms

         Data Marts

         OLAP and Cubes

         Predictive Analytics Engines

    Operational Data Stores

    Management

         The Religion of Data Warehousing

         The Technology Trap and the Strategic Plan

         Working with Vendors

    Do You Really Need a Data Warehouse?

    Summary

9 Metadata

    What is Metadata?

    The Origin and Utility of Metadata

    Types of Metadata

         Structural Metadata

         Technical Metadata

         Reference Metadata

         Operational Metadata

         Information Architecture

         Analytical Metadata

         Semantic Metadata

         Business Metadata

    Semantic Metadata Processes for Business Analytics

         Management of Nonpersistent Data Elements

         Business Term Glossary

         Managing Synonyms

         Developing the Business Concept Registry

         Mappings from Concept to Use

         Semantic Hierarchy Management

         Considerations: Entity Concepts and Master Dimensions

    Further Considerations

    Using Metadata Tools

10 Data Profiling

    Establishing Usability of Candidate Data Sources

    Data Profiling Activities

    Data Model Inference

         Simple Type Inference

         Table Model Inference and Relational Model Inference

    Attribute Analysis

         Range Analysis

         Sparseness

         Format Evaluation

         Cardinality and Uniqueness

         Frequency Distribution

         Value Absence

         Abstract Type Analysis

         Overloading Analysis

    Relationship Analysis

         Domain Analysis

         Functional Dependency

         Key Relationships

    Management Issues

    Summary

11 Business Rules

    The Value Proposition of Business Rules

         Encapsulation of Successful Business Logic

         Componentized Development

         Speed of Implementation

         Ease of Modification and Rapid Response to Change

         Reuse

         Persistence of Encapsulation of Business Knowledge

         Enhanced Capability Through the Declarative Model

    The Business Rules Approach

    The Definition of a Business Rule

         Rule Basics

         Definitions and Specifications

         Assertions

         Constraints

         Guidelines

         Actions

         Triggers

         Inference

    Business Rule Systems

         Rules Definition

         Rules Engines

    Sources of Business Rules

         People

         Documentation

         Laws and Standards

         Application Code

         Turning Language into Rules

         The Value of Subject Matter Experts

    Management Issues

         Political Issues

         Limitations of the Approach

    To Learn More

    Endnotes

12 Data Quality

    Good Decisions Rely on Quality Information

    The Virtuous Cycle of Data Quality

    Types of Data Flaws

         Inconsistent Number of Records

         Data Element Granularity Issues

         Invalid Values

         Transcription Errors

         Forms and Floating Data

         Absent and (Implicitly) Null Values

         Unstructured and Semistructured Values

         Formatting Errors

         Flawed Transformation Rules

         Attribute Overloading

         Unit of Measure Errors

    Business Impacts of Data Flaws

    Dimensions of Data Quality

    Data Quality Assessment

         Preparation and Data Analysis

         Data Assessment

         Synthesis of Assessment Results

         Review with Business Data Consumers

    Data Quality Rules

         Conceptual Domains and Value Domains

         Mappings

         Null Conformance Rules

         Value Restriction Rules

         Domain and Mapping Membership Rules

         Completeness and Exemption Rules

         Consistency Rules

    Continuous Data Quality Monitoring and Improvement

    Considerations Regarding Data Quality for Business Analytics

         Quality Versus Correctness

         Precision of Quality

         Supplier Management

         Data Correction Versus Process Correction

    Data Cleansing

         Parsing

         Standardization

         Abbreviation Expansion

         Data Correction

         Updating Missing Fields

         Duplicate Analysis and Elimination Using Identity Resolution

    Summary

13 Data Integration

    Improving Data Accessibility

         Accessibility

         Latency

         Drivers for Data Integration

    Extraction/Transformation/Loading

         Staging Architecture

         Extraction

         Transformation

         Loading

         ETL Scalability

         Extract, Load, and Transform (ELT)

    Data Latency and Data Synchrony

         The Pervasiveness of the Data Access Bottleneck

         Characteristics Governing Data Synchrony

    Data Replication and Change Data Capture

    Data Federation and Virtualization

    Data Integration and Cloud Computing

    Information Protection

    More on Merge/Purge and Record Consolidation

         Scoring Precision and Application Context

         Elimination of Duplicates

         Merge/Purge

         Householding

         Reliability of Automated Linkage

    Thoughts on Data Stewardship and Governance for Integration

14 High-Performance Business Intelligence

    The Need for Speed

    The Value of Parallelism

         The Need for Scalable Systems

         Parallelism and Granularity

         Scalability

         Task Parallelism

         Pipeline Parallelism

         Data Parallelism

         Combinations

    Parallel Processing Systems

    Symmetric Multiprocessing

         Multicore Machines

         Massively Parallel Processing

         Networks of Workstations

         Cloud Computing

         Hybrid Architectures

    Parallelism and Business Intelligence

         Query Processing

         Data Profiling

         Extract, Transform, Load

    Performance Platforms and Analytical Appliances

         Architectural Approaches for Analytical Systems

         Shared-Nothing

         Shared-Disk

         Shared-Everything

    Data Layouts and Performance

         Performance Characteristics of a Row-Oriented Data Layout

         Column-oriented Data Management Systems

    MapReduce and Hadoop

         What is MapReduce?

         A Common Example

    Assessing Architectural Suitability for Application Performance

         Architectural Suitability

         Questions for Selecting a Performance Architecture

    Endnote

15 Deriving Insight from Collections of Data

    Introduction

    Customer Profiles and Customer Behavior

         Customer Knowledge and Customer Profiles

         Customer Behavior

         Developing Behavior Models

         Influencing Change

    Customer Lifetime Value

    Demographics, Psychographics, Geographics

         Demographics

         Psychographics

         Developing the Customer Profiles

    Geographic Data

         Geographical Clusters

    Behavior Analysis

         Tracking Web Activities

         Customer Behavior Patterns

    Consideration When Drawing Inferences

16 Creating Business Value through Location-Based Intelligence

    The Business Value of Location

    Demystifying Geography: Address Versus Location

    Geocoding and Geographic Enhancement

    Fundamentals of Location-Based Intelligence for Operational Uses

         Mailing Accuracy

         Finding the Nearest Location

         Nonaddressable Locations

         Eligibility and Provisioning

         Location-Based Content Provision

         Horizontal Analysis

         Vertical Applications

    Geographic Data Services

         Geocoding

         Reverse Geocoding

         Address Cleansing and Standardization

         Data Enhancement

         Mapping

         Inclusion

         Distance

         Routing

         Proximity Matching

    Challenges and Considerations

    Where to Next?

17 Knowledge Discovery and Data Mining for Predictive Analytics

    Business Drivers

    Data Mining, Data Warehousing, Big Data

    The Virtuous Cycle

         Identify the Business Problem

         Mine the Data for Actionable Information

         Take the Action

         Measure Results

    Directed Versus Undirected Knowledge Discovery

    Six Basic Data Mining Activities

         Clustering and Segmentation

         Classification

         Estimation

         Prediction

         Affinity Grouping

         Description

    Data Mining Techniques

         Market Basket Analysis

         Memory-based Reasoning

         Cluster Detection

         Link Analysis

         Rule Induction Using Decision Trees

         Rule Induction Using Association Rules

         Neural Networks

    Technology Expectations

    Summary

18 Repurposing Publicly Available Data

    Using Publicly Available Data: Some Challenges

    Public Data

         Reference Information

         Individual Information

         Business/Legal Entity Information

         Legal Information

         Government Demographic Databases

         Content Data Sets

         Streamed or Filtered Data Sets

    Data Resources

         Original Source

         Data Aggregators

    The Myth of Privacy

         Fear of Invasion

         The Value and Cost of Privacy

    Information Protection and Privacy Concerns

         Data Retention

         Drawing Inferences

         Economic Disadvantages

         Managing Risk

         The ‘‘Privacy’’ Policy

         The Good News for Business Intelligence

    Finding and Using Open Data Sets

19 Knowledge Delivery

    Review: The Business Intelligence User Types

    Standard Reports

    Interactive Analysis and Ad Hoc Querying

    Parameterized Reports and Self-Service Reporting

    Dimensional Analysis

    Alerts/Notifications

    Visualization: Charts, Graphs, Widgets

    Scorecards and Dashboards

    Geographic Visualization

    Integrated Analytics

    Considerations: Optimizing the Presentation for the Right Message

20 Emerging Business Intelligence Trends

    Search as a Business Intelligence Technique

    Text Analysis

    Entity Recognition and Entity Extraction

    Sentiment Analysis

    Mobile Business Intelligence

    Event Stream Processing

    Embedded Predictive Analytic Models

    Big Data Analytics

    Considerations

    Endnote

21 Quick Reference Guide

    Analytics Appliance

    Business Analytics

    Business Intelligence

    Business Rules

    Dashboards and Scorecards

    Data Cleansing

    Data Enhancement

    Data Governance

    Data Integration

    Data Mart

    Data Mining

    Data Modeling

    Data Profiling

    Data Quality

    Data Warehouse

    Dimensional Modeling

    ELT (Extract, Load, Transform)

    ETL (Extract, Transform, Load)

    Event Stream Processing

    Hadoop and MapReduce

    Location Intelligence and Geographic Analytics

    Metadata and Metadata Management

    Mobile Business Intelligence

    Online Analytical Processing (OLAP)

    Parallel and Distributed Computing

    Query and Reporting

    Endnotes

Bibliography

Index






 
 
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