Visão Geral

Este Curso Introduction to Data Governance and Assessing Data Maturity, ajuda você a entender a Governança de Dados e avaliar sua Maturidade de Dados. Ele usa os padrões de governança de dados IEC/ISO 38505, escritos para organizações que desejam desenvolver e implementar uma estratégia de dados. Por exemplo, essa estratégia pode ser parte de um plano para fornecer novos serviços ou produtos aos seus clientes.

Objetivo

• Aprenda as diferentes terminologias de dados: OLTP e OLAP, painéis e scorecards.
• Entenda o que são dados e o histórico dos dados.
• Aprenda sobre os diferentes níveis de maturidade de dados.
• Entenda como tomar decisões com dados, relatar os dados e distribuí-los.
• Entenda quando descartar dados e por quê.

Pre-Requisitos

• Os participantes devem ter uma compreensão básica do que são dados e da função da análise de dados.
• Introdução à Análise de Dados .

Materiais

Conteúdo Programatico

A World of Data

1. What Is Data?
2. Health and Census Data
3. Traditional and Modern Data-Heavy Industries
4. Data Quality Disasters
5. How Good and Bad Data Impacts Your Business
6. Why Poor-Quality Data Exists
7. Data and the Digital Revolution
8. Five Key Drivers for Data Awareness
9. Causes of Poor Data Quality
10. Components of a Data Solution
11. Data Products: Terminology
12. Data Products: OLTP and OLAP
13. Data Products: Dashboards and Scorecards
14. Data Products: Extract, Transform, Load, and Reporting Tools
15. “Big” Data
16. Data and Digital

Introducing Data Governance & Data Management

1. Data Governance Or Data Management?
2. Data Governance Standards
3. Other Definitions of Data Governance
4. Data Governance Responsibilities
5. Data Management
6. ISO Standards In This Course
7. ISO/DIS 37000 Wheel: The Centrality Of Governance
8. ISO/DIS 37000*:
9. Guidance for the Governance of Organizations
10. ISO/IEC 38500:2015
11. Governance of IT for The Organization
12. Data Governance
13. The Definitions and Focus of Data Governance
14. Other Definitions of Data Governance
15. Six Use Cases for Data Governance
16. Data Governance: Areas of Focus
17. Who Manages Data Governance?

Working with Data

1. Trust Issues
2. Impact On Directors
3. Opportunities For Data-Driven Business
4. The Risk of Unintended Outcomes
5. Regulation Of Data
6. Maturity Assessment: Creating the Vision
7. Data Maturity Levels
8. Level 1: Data Aware
9. Level 2: Data Capable
10. Level 3: Data Adept
11. Level 4: Data Informed
12. Level 5: Data Pioneer
13. Discussion Point: Driving Data Initiatives Forward
14. What Are Business Drivers?
15. Top-Down Data Analysis
16. Enterprise Information Architecture
17. Designing Standardized Business Metadata
18. Bottom-Up Source Data Analysis
19. Source Data Selection
20. Data Selection Issues
21. Extract, Transform, and Load (ETL)
22. Examples: Specifying ETL Rules
23. Data Technologies
24. Enabling Technologies That Generate Data
25. Moving Data to a Data Warehouse
26. Developing the Data Warehouse
27. Denormalization
28. OLTP vs. OLAP
29. OLAP Terminology
30. Value of OLAP
31. Identifying Enterprise Systems and Data
32. How Is Enterprise Data Stored?
33. Unstructured Data
34. Technical Infrastructure
35. Issues: Structured and Unstructured Data

Principles & Components of Data Governance

1. Principles & Components of Data Governance
2. The Principles of Data Governance
3. Components of Data Governance
4. The Data Governance Paradox
5. The Data Governance Paradox: The Hidden Data Factory
6. Barriers to Data Governance
7. When Data Governance Fails
8. Setting Governance Goals and Objectives
9. Creating Your Data Governance Vision

Building a Data Strategy

1. Your Key Data Challenges
2. Data Management Metrics
3. Business Culture
4. Effective Business Communication
5. Selling Data Governance
6. Business Culture Change and Data Governance
7. Creating a Cultural Change Plan
8. Communications Strategies for a Cultural Change Plan
9. Using a Motivation Model
10. Using a Motivation Model
11. Tools & Technology
12. Building a Data Governance Business Case
13. What Are Business Drivers?
14. Building a Data Governance Business Case:
15. Costs and Benefits
16. Building a Data Governance Business Case:
17. Implementation Plan
18. Five Data Governance Models
19. Pros & Cons of Each Model
20. Using The Right Model for Your Organization

Building or Adopting a Data Governance Framework

1. Data Governance Framework
2. Data Governance Framework Examples: DGI
3. Data Governance Framework Examples: McKinsey
4. Data Governance Framework: Overview
5. The Data Governance Framework: Vision
6. The Data Governance Framework: Strategy
7. The Data Governance Framework: Organization & People
8. Implementing a Data Governance Framework
9. What Does a Data Governance Framework Provide?
10. The Benefits of a Data Governance Framework
11. DGF Deliverables & Actions Checklist
12. Data Governance Framework

Data Management Processes

1. The Data Governance Roadmap
2. Data Governance Capability Mapping
3. The Data Governance Roadmap Checklist
4. Hints & Tips for Developing Roadmaps
5. Organizing Practical Data Management
6. Identifying and Designing Data Management Processes
7. Data Ownership & Stewardship
8. What are Stakeholders?
9. Types of Requirements Errors
10. Why Do We Care?
11. Defining Organizational Goals and Objectives
12. Identifying Your Key Data
13. Data Management and Measurement
14. Quality Measures for Data
15. Baselines and Improvement Targets
16. Data Governance & Management Toolset
17. Data Improvement Plans
18. The Role of IT in Data Governance & Management
19. Issue Logging

Data Management & Accountability

1. Dealing with Connectivity
2. Issues With a Connected World
3. Addressing Issues with the Connected World
4. Examples of Issues with the Connected World
5. The Data Accountability Map
6. Data Accountability
7. Data Collection Governance Issues
8. Collecting Data
9. The Quality of Data Collected
10. Storing Data
11. Decide
12. The Governance Of Decision Making
13. Bias in the Decision-Making Process
14. Automated Decision-Making
15. Reporting on Data
16. Strategy for Reporting on Data
17. Data Classification for Reports
18. Distribution of Data
19. Aspects of Data Security
20. Models For Data Distribution:
21. Example: ENISA* Traffic Light Protocol
22. Why Dispose of Data?
23. Candidates For Data Disposal
24. Policy For Data Disposal
25. Creating Reports to Identify Disposable Data
26. External Triggers For Data Disposal
27. Permanent Removal
28. Archiving Data
29. Balancing Value, Risk, and Constraint