DOANH NGHIỆP CHÚNG TÔI ĐÀO TẠO
” Khóa học Oracle BI Publisher 11g: Fundamentals (chính hãng Oracle) dành cho doanh nghiệp, tập đoàn lớn, khối chính phủ, các ngân hàng tại Việt Nam ”
Thời gian học
Số học viên
Linh hoạt theo
Tùy thuộc vào
NỘI DUNG KHÓA HỌC
- INTRODUCTION TO DAMA
- What is data management and why is it critical.
- What are the different disciplines of data management?
- DAMA & the DMBoK 2.0, and its relationship with other frameworks (TOGAF/COBIT…).
- Overview of available professional certifications focusing on DAMA CDMP.
- DATA GOVERNANCE
- What is Data Governance and why it is important. A typical data governance reference model.
- The main data governance roles: owner, steward, custodian.
- The role of the Data Governance Office (DGO) and its relationship with the PMO.
- What is the difference between Data Governance and IT Governance, and does it matter?
- Overview of the Data Management implications of a selection of other regulations.
- The key steps that organizations can take to prepare for compliance with current and future regulations.
- How to get started with data governance and sustaining and building data governance.
- DATA LIFECYCLE MANAGEMENT
- Proactive planning for the management of data across its lifecycle.
- Differences between data life cycle and a Systems Development Lifecycle (SDLC).
- Data governance touch points throughout the data lifecycle.
- METADATA MANAGEMENT
- What is metadata and why it is important?
- Types of metadata, their uses and their sources.
- Metadata and business glossaries. What is the connection?
- How metadata provides the essential glue for data governance and metadata standards.
- DG MINI PROJECT
- Starting the Data Governance Program, what you must get in place early. How to produce a realistic business case for DG linked to business objectives?
- DOCUMENT RECORDS & CONTENT MANAGEMENT
- Why document and records management is important.
- Taxonomy vs. ontology… what’s the difference.
- Legal and regulatory considerations impacting records and content management.
- DATA MODELING BASICS
- Types of data models, their use and how they interrelate.
- The development and exploitation of data models, ranging from enterprise, through conceptual to logical, physical and dimensional.
- Maturity assessment to consider the way in which models are utilized in the enterprise and their integration in the System Development Life Cycle (SDLC).
- Data modeling and big data.
- Why data modeling plays a critical part in data governance and BP case study.
- DATA QUALITY MANAGEMENT
- The different facets of data quality, and why validity is often confused with quality.
- The policies, procedures, metrics, technology and resources for ensuring data quality.
- A data quality reference model and how to apply it.
- Why data quality management and data governance are interconnected and case studies.
- DATA OPERATIONS MANAGEMENT
- Core roles and considerations for data operations.
- Good data operations practices.
- DATA RISK & SECURITY
- Identification of threats and the adoption of defenses to prevent unauthorized access, use or loss of data and particularly abuse of personal data.
- Identification of risks (not just security) to data and its use.
- Data management considerations for different regulations, e.g. GDPR, BCBS239.
- The role of data governance in data security management.
- MASTER & REFERENCE DATA MANAGEMENT
- The differences between reference and master data.
- Identification and management of master data across the enterprise.
- 4 generic MDM architectures and their suitability in different cases.
- How to incrementally implement MDM to align with business priorities.
- Statoil (Equinor) case study.
- DATA WAREHOUSING, BUSINESS INTELLIGENCE & DATA ANALYTICS
- What is data warehousing and business intelligence and why do we need it.
- The major data warehouse architectures (Inmon & Kimball).
- Introduction to dimensional data modeling.
- Why master data management fails without adequate data governance.
- Data analytics and machine learning and data visualization.
- DATA INTEGRATION & INTEROPERABILITY
- What are the business (and technology) issues that data integration is seeking to address?
- Data integration and data interoperability – What’s the difference?
- Different styles of data integration and interoperability, their applicability and implications.
- The approaches and guidelines for provision of data integration and access.
- DAMA CERTIFICATION-FIRST LEVEL
- Students will have the opportunity to sit the CDMP Data Quality specialist exam at the end of this course to attain DAMA Certified Data Quality Professional designation and a credit towards attainment of a full CDMP at Practitioner or Master Level.
Upon completion of the course, participants will be able to:
- Different categories of challenges
- Appreciate concepts including lifecycle management, normalization
- Dimensional modeling and data virtualization and appreciate why they are important
- Understand the critical roles of master data management and data governance and how to effectively apply them
- Understand the different facets (dimensions) of data quality and explore a workable data quality framework
- Describe the major considerations for successful data governance and how it can be introduced in bite-sized pieces
- Understand the different types of data models and their applicability
- Attend the DAMA certification exam.
Khoá Đào Tạo Dành Cho Doanh Nghiệp
Học phí: Liên hệ
Số Điện Thoại: 0986.882.818
GIẢNG VIÊN TẠI INDA
Ảnh Thực Tế
- Data Architect, Information Architect, Data Modeler
- Business Subject Matter Expert
- BI Team Lead, BI Team Implementer
- Data Miner, Data Scientist
- Big Data Specialists
- Sprint, Scrum Master , Project Lead, Agile Lead
Chúng tôi có hai hình thức học: Offline (tại nơi doanh nghiệp yêu cầu) và Online (Trực tiếp với giảng viên qua Team/ Zoom/ Google Meet) Theo yêu cầu của doanh nghiệp
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