Training Curriculum

The Xendat Data Academy offers a comprehensive curriculum of courses in data and knowledge management. IT and Business professionals can learn about enterprise data management strategy, data governance, ontology & knowledge management, data quality, metadata management and data cataloguing, master data management, reference data management, information lifecycle management, and data interoperability. With these skills, professional graduates will be prepared to take on leadership roles in data management and help organizations make the most of their data assets.

The Data Academy curriculum is designed to give data professionals the skills and knowledge they need to be successful in data management. The courses are taught by experienced industry experts who have a wealth of experience in data management. Data professionals will learn about the latest trends and best practices in data management, and they will have the opportunity to apply what they have learned in a real-world setting.

The Data Academy curriculum is divided into 7 main areas. Each of these areas offers a variety of courses that cover different aspects of data and knowledge management.

Data Management Strategy:

In this area, data professionals will learn about the different approaches to data management and how to develop an effective data management strategy, framework, roadmap, and implementation. They will also learn about the different tools and techniques that can be used to implement a data management strategy.

Data Governance:

In this area, data professionals will learn about the principles of data governance and how to apply them in a real-world setting. They will learn about the foundational principals of data governance and stewardship. They will also learn about the different tools such as Collibra Data Governance Center and technologies that can be used to support data governance.

Data Quality:

In this area, data professionals will learn about the different dimensions of data quality and how to assess and improve data quality. They will also learn about the different tools such as Informatica IDQ, Atacama, Collibra DQ, and techniques that can be used to manage data quality.

Metadata & Data Catalog Management:

In this area, data professionals will learn about the different aspects of data cataloging strategy and implementation and how to manage metadata. They will also learn about the different tools such as Collibra Data Catalog, Informatica ECD, Alation, and techniques that can be used to store, retrieve, and manage metadata.

Data Catalog Management

Master Data Management:

In this area, data professionals will learn about the different types of master data and how to manage master data assets in their organization. They will learn about master data management framework and architecture. They will also learn about the different tools and techniques that can be used to store, retrieve, and manage master data.

Reference Data Management:

In this area, data professionals will learn about the different types of reference data and how to manage reference data within an organization that deals with internal and external reference data. They will also learn about the different tools and techniques that can be used to store, retrieve, and manage reference data.

Information Lifecycle Management:

In this area, data professionals will learn about the different stages of the information lifecycle and how to manage information assets throughout its lifecycle such as data retention, tagging, masking, deidentification, and access/use. They will also learn about the different tools and techniques that can be used to store, retrieve, and manage information.

Data Interoperability:

In this area, data professionals will learn about the different approaches to data interoperability and how to apply them in a real-world setting. They will also learn about the different tools and technologies that can be used to support data interoperability.

Ontology & Knowledge Management:

In this area, data professionals will learn about the different approaches to data interoperability and how to apply them in a real-world setting. They will also learn about the different tools and technologies that can be used to support data interoperability.