DATA STEWARDSHIP BODY OF KNOWLEDGE (DSBOK)

Transcription

1 DATA STEWARDSHIP BODY OF KNOWLEDGE (DSBOK) Release 2.2 August This document was created in collaboration of the leading experts and educators in the field and members of the Certified Data Steward (CDS) Advisory Council. All rights reserved. Reproduction in whole or part prohibited except by written permission. Product and company names mentioned herein may be trademarks of their respective companies.

2 1. Data Stewardship Fundamentals 1.1. Definitions Stewardship Data Stewardship 1.2. Data Stewardship Organizations Kinds of Data Stewards Kinds of Business Stewards Kinds of IT Stewards Differences of Business and IT Stewards Roles and Responsibilities Reporting and Relationships 1.3. Data Steward Characteristics Knowledge and Experience Skills and Competencies Data Skills Technical Skills Human Skills Facilitation Skills Communication Skills 2. Data Management Processes 2.1. Architectural Processes Enterprise and Subject Area Modeling Data Mapping and Consolidation Data Flow 2.2. Utilization Processes (CRUD) Create Processes and Practices Retrieval and Reporting Processes and Practices Update Processes and Practices Delete and Archive Processes and Practices Business Process Management 2.3. Custodial Processes Database Administration Security Administration and Access Authorization Backup, Recovery, and Business Continuity 3. Information Management Concepts 3.1. Types of Data and Information Operational and Analytical Event and Reference Structured and Unstructured Page 1

3 Transactional Data Master Data 3.2. Types of Data Stores Application Databases Departmental and End-User Databases Data Warehouses and Data Marts Operational Data Stores Master Data Hubs 3.3. Common Uses of Data Record Keeping and Audit Trail Reporting and Information Measurement and Monitoring Analysis and Discovery 3.4. Business Data Flow The Business Value of Data Data Sources and Data Acquisition Data Providers and Consumers Data Flow through Organizations and Systems Data Conversion and Consolidation Data Replication and Redundancy Data Sharing and Interfaces Data Disposal and Destruction 3.5. Information Management Disciplines Data Modeling Metadata Management Content Management Enterprise Information Management Data Quality Data Governance Data Integration Data Warehousing Master Data Management Business Intelligence Business Analytics Performance Management Data Mining Predictive Analytics 4. Data Quality 4.1. Quality Management Basics Quality Perspectives Expectations Purpose Specifications Page 2

4 Defects Quality Management Terminology Quality Control (QC) Quality Assurance (QA) Waste and rework Inspection, Correction, Prevention Quality Management Methodologies Total Quality Management (TQM) Statistical Process Control (SPC) Six Sigma 4.2. Data Quality Concepts and Principles Data Quality Definitions Defect Free Conform to Specifications Fit to Purpose Meet Customer Expectations Common Causes of Data Quality Problems Costs and Benefits of Data Quality 4.3. Data Quality Dimensions Content Quality (Correctness) Accuracy Completeness Precision Granularity Consistency Structural Quality (Integrity) Identity Reference Cardinality Dependency Inheritance Domain of Values Temporal Quality (Timeliness) Currency Retention Continuity Precedence Business Quality (Value) Aligned Trusted Understandable Reliable Compliant Useful Usage Quality (Usability) Available Accessible Navigable Recoverable Page 3

5 Secure Private Presentation Quality (Communication) Clear Organized Non-Ambiguous Tool-Integrated Media-Fit 4.4. Data Quality Processes and Projects Data Quality Assessment Root Cause Analysis Data Cleansing Data Enrichment 4.5. Data Quality in IT Processes and Projects Data Quality in Application and Database Projects Data Quality in Data Conversion and Consolidation Projects Data Quality in Data Sharing and Data Interfaces 5. Data Governance 5.1. Data Governance Basics Definitions Purpose What to Govern and What not to Govern? 5.2. Components of Data Governance Governance Goals Quality Security Compliance Standardization Usage Value Business Impact Governance Elements Roles Responsibilities Decision Authority Accountability Policies Quality Sensitivity Privacy Security Access Retention Disposal Page 4

6 Standards Naming Definition Architecture Transfer Integration Technology Data Governance Enabling Data Quality Workflow Collaboration 5.3. Data Governance Programs Business Drivers for Governance Legal Regulatory Financial Operational Competitive Data Governance Roles Sponsors Owners Data Stewards Custodians Stakeholders Data Officers Councils, Committees, Competency Centers 5.4. Executing Data Governance Getting Started Funding and Sponsorship Day-to-Day Governance Sustaining Governance Monitoring Governance: Measures, Metrics, Maturity Evolving Governance Stakeholder Communications 6. Metadata Management 6.1. Metadata Concepts Metadata Definition Roles of Metadata Classify Describe Guide Control Types of Metadata Business Metadata Technical Metadata Page 5

7 Metadata Standards and Practices 6.2. Data Modeling Data Model Principles Types of Data Models ER vs. Dimensional Logical vs. Physical Who and Why for each Type of Model Complementary Models Subject Area Models State Transition Models Reading Data Models 6.3. Data Profiling Roles of Profiling Support of Data Quality Management Support of Master Data Management Support of Data Migration Support of Data Integration Profiling Techniques Column Profiling Profiling Data Models Profiling Time-Dependent Data Subject Profiling Profiling State-Transition Models Attribute Dependency Profiling Dynamic Data Profiling Profiling Challenges 7. Master Data Management 7.1. Master Data Management (MDM) Basics Master Data Definition Sources of Master Data Types of Master Data MDM Objectives MDM Architectures and Styles Costs and Benefits of MDM MDM Challenges and Best Practices 7.2. Data Parsing, Matching, and De-Duplication Data Parsing and Standardization Personal and Business Name Postal Addresses Geocoding Date and Time Data Matching and De-Duplication Deterministic vs. Probabilistic Matching Rules Page 6

8 Data Survivorship Types of Matching Tools 7.3. External Reference Data Address Directories Business Directories DUNS and EPC 7.4. Global Data Format Differences across Countries and Regions Address Formats Personal Names Business Names Phone Numbers Job Titles Special Characters and Diacritics Regional Alphabets Character Sets Code Pages and Unicode Cultural and Legal Issues Privacy Laws Cultural Differences Best Practices of Working with Global Data Note: While this document goes into four levels of detail, the fourth level is not exhaustive and in many cases only includes some examples for topic clarification. Page 7