Data Management Professional Certification

In the digital age, data is king. Data is increasingly seen as a corporate asset that can be used to make more-informed business decisions, improve marketing campaigns, optimise business operations, and reduce costs, all with the goal of increasing revenue and profits. Hence, it would follow if the data is precise, complete, coordinated, and predictable. It will contribute to the expansion of the organisation and, in case of the opposite, would become an excessively big liability; bad data organisation can lead to making harmful decisions because of incomplete information.

This 5-day interactive Data Management Professional Certification training course is a path to sustained and enhanced global employment opportunities over the next decade; it will remain relevant to the economy, and professionals must participate in ongoing professional development. Thus, it will provide the delegates with knowledge, skills, and experience in the DAMA field; it also gives data management professionals the opportunity to show professional growth that can enhance their personal and career goals.

Day One: Data Management Overview And Process

• Overview of the Certificate and Its Exam

• Data Management as a Discipline

• The Purpose and Scope of the DAMA International Data Management

• Data Management Drivers, Benefits, and Potential Pitfalls

• Enterprise Data Management Framework

• Data Management Function

Day Two: Data Governance & Data Quality Management

• The Role of the Data Governance in Successful Data Management

• Key Topic of Data Ethics & Compliance

• Understand the Various Data Quality Areas

• Explore Data Quality Policies and Procedures

• Linking Data Quality with Other Data Disciplines

• The Major Components to Leverage Reference and Master Data

Day Three: Data Warehouse, BI, Modeling & Metadata Management

• Concept of Data Warehousing & Business Intelligence (BI) Management

• Types of Data Warehouse and BI Platforms

• The Fundamentals of Big Data

• Features of Data Modeling

• The Differences between Data and Metadata Usage and Implementation of Metadata

Day Four: Data Architecture And Lifecycle Management – Data Security Management

• The Important Types of Architectures and Proactive Planning

• The Data Value Chain

• Differences between Lifecycles and Systems Development

• The Key Goals of Data Security Management

• Risk Assessment & Monitoring: Data Security Management

• Effective Document and Content Management 

Day Five: Data Storage And Operations Management & Data Integration And Interoperability

• The Effective Data Storage’s Components and Operations Management

• Data Technology Management

• Roles and Responsibilities

• Data, I&I Practices, and the Best Solutions

• Data Interchange & Data Replication

• Data Governance and Expanded Data Management Activities