CDMP-DAMA: DATA MANAGEMENT FUNDAMENTALS by DAMA.org (CDMP-DAMA: DATA MANAGEMENT FUNDAMENTALS by DAMA.org)

– 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