CDMP-DAMA: DATA MANAGEMENT FUNDAMENTALS by DAMA.org
This course, designed for professionals responsible for managing and governing corporate data, helps consolidate the foundations of various information disciplines across the entir...
This course, designed for professionals responsible for managing and governing corporate data, helps consolidate the foundations of various information disciplines across the entire Data Management lifecycle (from Data Governance to Data Quality, from Data Modeling to Metadata Management, from Data Architecture to Data Integration, from Data Risk Management to Data Security and Operations), and prepares participants to take the main professional certification levels in the sector, including, specifically, the CDMP DAMA Data Management Fundamentals certification.
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Business and IT professionals at all levels, such as project or unit managers or executives; data and enterprise architects; data consultants; data modelers; BI and data warehouse developers; data and business analysts; DBAs; technical staff required to operate and manipulate data; and, in general, anyone who needs to delve deeper into and/or implement broad-spectrum data management systems or methods within their organization.
No prior knowledge required
31
– Data and its value for the company and the community
– Data Management lexicon and main definitions
– Introduction to the DAMA DMBoK2 framework
– Regulatory landscape and notes on other data management and governance frameworks
– Tools and approaches for the continuous improvement of Data Management: the Data Management Maturity Assessment (DMMA)
– What is meant by Data Governance and why is it important
– Designing and implementing Data Governance
– Typical roles and responsibilities in data management
– Building a “Data Management Organization”
– Understanding and outlining the path from data to knowledge
– Data vs. Information and Data vs. Metadata
– The different types of metadata and their management
– Data Lineage
– Master and Reference Data
– What is meant by “data quality”
– Dimensions, measures and metrics
– Managing data quality
– Main theories, frameworks and standards
– Data security in a multidimensional perspective
– Rationales and principles for the ethical treatment of data
– Data Security vs. Information Security vs. Data Protection
– Risks, threats and vulnerabilities in data management
– Main techniques and methods for data security
– The role of Data Architecture in an Organization and its place in the Enterprise Architecture
– The Enterprise Data Model and Data Flow Design
– The importance of the Data Model
– Components, schemas and levels of detail of Data Models
– Designing, implementing and evaluating a Data Model
– Definitions, principles and underlying elements
– Types of databases and processes for their management
– Data integration processes, techniques and methods
– Data interoperability processes, techniques and methods
– Overview of the characteristics of the Data Warehouse
– Data Warehouse vs. Data Lake
– Business Intelligence for data-driven decisions
– Definitions of Big Data and Data Science
– Architectures for Big Data
– Processes and techniques for “exploiting” Big Data through Data Science
– Management of unstructured/semi-structured data, documents, records and contents
– Controlled vocabularies: from vocabularies to ontologies
Recap and final exam simulation with the teacher
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