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Managing Information Quality Increasing the Value of Information in Knowledge intensive Products and Processes 2nd Edition by Martin J Eppler ISBN 3540314083 9783540314080

SKU: BELL-2170798

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Managing Information Quality Increasing the Value of Information in Knowledge intensive Products and Processes 2nd Edition by Martin J Eppler ISBN 3540314083 9783540314080 instant download after payment.

Publisher: Springer

File Extension: PDF

File size: 3.45 MB

Pages: 408

Author: Martin J. Eppler

ISBN: 3540314083, 9783540314080

Language: English

Year: 2006

Edition: 2nd

Product desciption

Managing Information Quality Increasing the Value of Information in Knowledge intensive Products and Processes 2nd Edition by Martin J Eppler ISBN 3540314083 9783540314080 by Martin J. Eppler 3540314083, 9783540314080 instant download after payment.

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Product details:

ISBN 10: 3540314083
ISBN 13: 9783540314080
Author: Martin J Eppler

It still holds true: information is not always the answer. Information is often part of the problem. While the main goal of information in the business place is to - able adequate decisions and actions, it can also lead to numerous negative effects: it can confuse, block creativity, or it can lead to hectic activism, stress and fatigue. Information can distract and divert attention, and it may even delay important - cisions – the paralysis by analysis. Strategies to avoid these dysfunctional effects of information can be divided into sender-based strategies and receiver-based strategies. In my previous research, I have looked at receiver-based strategies that outline effective ways of dealing with information overload. This book, by c- trast, analyzes sender-based strategies that aim at making content actionable by increasing its information quality. By offering relevant and sound information in a convenient and reliable manner, managers and information providers ranging from analysts to CEOs can not only optimize communication, they can also improve their reputation, employee satisfaction and customer loyalty. In a time where - formation has become a commodity or even a nuisance, this is a valuable strategy. The main premise of this book is consequently that information quality has - ready become a (if not the) decisive factor of the information economy. Many companies, however, are not managing this factor systematically.

Managing Information Quality Increasing the Value of Information in Knowledge intensive Products and Processes 2nd Table of contents:

Part I: The Strategic Imperative of Information Quality

Chapter 1: The Value of Information in the Modern Enterprise
- 1.1 Information as a Business Asset: Beyond Data
- 1.2 Knowledge-Intensive Products and Services: Definition and Examples
- 1.3 The Data-Information-Knowledge-Wisdom (DIKW) Hierarchy
- 1.4 The Cost of Poor Information Quality: Tangible and Intangible Impacts
- 1.5 The Benefits of High Information Quality: Competitive Advantage, Innovation, Efficiency
Chapter 2: Understanding Information Quality (IQ)
- 2.1 Defining Information Quality: Dimensions and Attributes (Accuracy, Completeness, Consistency, Timeliness, Relevance, etc.)
- 2.2 Objective vs. Subjective IQ Perceptions
- 2.3 IQ vs. Data Quality (DQ): Delineation and Interrelation
- 2.4 The Contextual Nature of Information Quality
- 2.5 Frameworks for Understanding IQ (e.g., Wang and Strong's Framework)
Chapter 3: Information Quality as a Strategic Imperative
- 3.1 Aligning IQ with Business Strategy and Objectives
- 3.2 IQ in Digital Transformation and Industry 4.0
- 3.3 The Role of IQ in Decision-Making Processes
- 3.4 Competitive Advantage through Superior Information Assets
- 3.5 Building a Culture of Information Quality

Part II: Foundational Elements of Information Quality Management

Chapter 4: Information Governance and Stewardship
- 4.1 Establishing Information Governance Structures
- 4.2 Roles and Responsibilities in IQ Management (Data Owners, Stewards, Custodians)
- 4.3 Policies, Standards, and Procedures for IQ
- 4.4 Data Ethics, Privacy, and Compliance (GDPR, HIPAA, etc.)
- 4.5 Data Lineage and Provenance
Chapter 5: Information Architecture and Design for Quality
- 5.1 Designing Information Systems for Quality from the Ground Up
- 5.2 Data Modeling and Schema Design for IQ
- 5.3 Master Data Management (MDM) and Reference Data Management
- 5.4 Metadata Management: The Key to Understanding Information
- 5.5 Data Integration Strategies and Their IQ Implications
Chapter 6: Information Acquisition and Capture
- 6.1 Sources of Information and Data Ingestion Challenges
- 6.2 Data Entry Controls and Validation
- 6.3 Automation in Data Capture (e.g., OCR, IoT Data)
- 6.4 Third-Party Data Acquisition and Due Diligence
- 6.5 The Human Element in Data Creation

Part III: Methodologies and Technologies for Enhancing Information Quality

Chapter 7: Information Quality Assessment and Measurement
- 7.1 Defining IQ Metrics and Key Performance Indicators (KPIs)
- 7.2 Manual vs. Automated IQ Assessment Techniques
- 7.3 Data Profiling and Discovery
- 7.4 Auditing Information Quality
- 7.5 Root Cause Analysis of IQ Problems
Chapter 8: Information Quality Improvement Techniques
- 8.1 Data Cleansing and Standardization
- 8.2 Data Matching and Deduplication
- 8.3 Data Enrichment and Augmentation
- 8.4 Data Transformation and Normalization
- 8.5 Error Correction and Exception Handling
Chapter 9: Information Quality Tools and Technologies
- 9.1 Data Quality Software Solutions
- 9.2 Master Data Management Platforms
- 9.3 Data Governance Tools
- 9.4 Business Process Management (BPM) Suites for IQ
- 9.5 Artificial Intelligence and Machine Learning in IQ Management

Part IV: Managing IQ in Specific Contexts and Knowledge-Intensive Processes

Chapter 10: Information Quality in Data Analytics and Business Intelligence
- 10.1 The "Garbage In, Garbage Out" Principle
- 10.2 IQ for Reporting, Dashboards, and Visualizations
- 10.3 Ensuring IQ for Predictive Analytics and Machine Learning Models
- 10.4 Data Warehousing and Data Lake IQ Considerations
Chapter 11: Information Quality in Customer-Centric Processes
- 11.1 CRM and Customer Data Quality
- 11.2 Impact of IQ on Marketing, Sales, and Service Effectiveness
- 11.3 Personalization and Customer Experience
- 11.4 Fraud Detection and Risk Management
Chapter 12: Information Quality in Supply Chain and Operations
- 12.1 IQ for Inventory Management and Logistics
- 12.2 Quality Control in Manufacturing and Production Processes
- 12.3 Supplier Data Quality and Relationship Management
- 12.4 Product Information Management (PIM) and Digital Product Passports
Chapter 13: Information Quality in Research & Development and Innovation
- 13.1 Managing Scientific Data Quality
- 13.2 IQ in Intellectual Property Management
- 13.3 Data Quality in Clinical Trials and Healthcare Research
- 13.4 Collaborative Research and Data Sharing Challenges

Part V: Implementing and Sustaining Information Quality Programs

Chapter 14: Developing an Information Quality Program
- 14.1 IQ Maturity Models (e.g., CMMI-DQ)
- 14.2 Phased Approach to IQ Program Implementation
- 14.3 Building a Business Case for IQ Investment
- 14.4 Change Management and Stakeholder Engagement
- 14.5 Establishing a Center of Excellence for Information Quality
Chapter 15: Future Trends and Challenges in Information Quality Management
- 15.1 AI and Automation: Opportunities and Risks for IQ
- 15.2 Data Mesh and Decentralized Data Architectures
- 15.3 The Rise of Data Literacy and Data Culture
- 15.4 Data Quality for Emerging Data Types (Streaming, Unstructured, Real-time)
- 15.5 Ethical AI and Trustworthy Data
- 15.6 The Evolving Role of the Information Quality Professional

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