Business Intelligence, 2nd Edition

The Savvy Manager's Guide

Business Intelligence, 2nd Edition,David Loshin,ISBN9780123858894


Morgan Kaufmann




229 X 152

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.


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.


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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