Enterprise Data Management

Course guarantor:

Lecturer:

Tutor:

Supervisor:

Department of Software Engineering

Synopsis:

The course will provide students with a practical overview of how large organizations process, store, and utilize data. The aim is to acquaint students in particular with modern approaches to data management, its links to enterprise and IT architecture, and technologies for processing enterprise data and metadata, including machine learning and artificial intelligence tools. Emphasis will be placed on real-world examples and on acquiring information applicable in the business environment. As the field of data processing and data technologies is exceptionally dynamic, the course content will be continuously adapted to current trends and the latest industry practices so that students gain the most relevant skills for modern large-scale organizational environments.

Requirements:

Syllabus of lectures:

1. The importance of data in large company. Role of data in a modern company. Manager and machine strategic decision-making.

2. Relationship of data architecture to other pilies of business architecture. Business layer of Data Management (DM).

3. (2) Data architecture. Data intensive application and data landscape.

4. Master Data Management. Traditional and historical approaches to Data Management.

5. Classic data processing and Business Intelligence. Data warehouses, analyses and reporting.

6. Modern corporate data platform as a three-lane highway. First speed bar - classic data warehouse, centralization, one version of truth.

7. Second speed lane - decentralization and democratization of data, business ownership, data products and concept of Data Mesh.

8. Third speed lane - DataOps - operation, integration, MLOPS.

9. Advanced analyses and involvement of artificial intelligence.

10. Role of data science in companies. Data Management as a multidisciplinary field. Introduction to CRISP-DM methodology.

11. Proces správy dat. Fáze ostrého-DM, jeho iterativní povaha. Správa a statistika dat.

12. Strojové učení. Aplikace AI při řešení úkolů správy dat.

Syllabus of tutorials:

Study Objective:

The course will provide students with a practical overview of how large organizations process, store, and utilize data. The aim is to acquaint students in particular with modern approaches to data management, its links to enterprise and IT architecture, and technologies for processing enterprise data and metadata, including machine learning and artificial intelligence tools. Emphasis will be placed on real-world examples and on acquiring information applicable in the business environment. As the field of data processing and data technologies is exceptionally dynamic, the course content will be continuously adapted to current trends and the latest industry practices so that students gain the most relevant skills for modern large-scale organizational environments.

Study materials:

1. Piethein Strengholt: Data Management at Scale: Best Practices for Enterprise Architecture 1st Edition. O'Reilly Media, 2020. ISBN 978-1492054788.

2. Dave Knifton: Enterprise Data Architecture: How to navigate its landscape. Paragon Publishing, 2014. ISBN 978-1782223269.

Note:

Further information:

bude doplněno

No time-table has been prepared for this course

The course is a part of the following study plans:

• Master specialization Software Engineering (PS)