• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

Advanced Data Management

Academic Year
Instruction in English
ECTS credits
Course type:
Elective course
1 year, 3, 4 module


Course Syllabus


"Advanced Data Management" is an elective course taken in the third and fourth modules of the Master’s program. The course is designed to give general vision and understanding of data management process in the key of applicability for various size-businesses. The key focus is on achieving business value in the bounds of corporate strategy with the aid of data management and big data technologies. In the first part of the course we review high-level data management processes as corporate functions which serves to business targets and needs. These processes are aligned with corporate strategy. Also, students will learn broad scope of second-level data-management functions and the environmental elements that influence on data management function. The second part of the course covers every data-management function as architecture, development, operations management, security, master data, data warehousing and business intelligence, document management, meta-data, quality of data. This is the main part of the course. The third part of the course gives review of modern approaches of data management methodologies and advanced data management tools. The students are supposed to be familiar with database architecture, some of the algorithmic languages (like Python), SQL, general understanding of business architecture and some management models. The duration of the course is two modules. The course is taught in English and worth 5 credits. At the end of the course students will take an exam.
Learning Objectives

Learning Objectives

  • Be aware of: • the needs, applicability and basic concepts of data management; • the ways of corresponding data management targets with corporate strategy; • the lifecycle of data in business, data management processes, data management projects; • the scope of responsibility and ability of data managers and data specialists
  • Be able: • to understand targets, corporate and functional strategies of business; • to select and develop data management functions required for implementation business strategy; • to plan and develop data management projects; • to build efficient team of data managers and specialists to develop and support data man-agement projects and functions
  • Learn how to: • build a data model of business; • find business problems and need in the scope of data management; • generate business value from data management process; • lower costs of data management functions without losing a quality; • correspond business needs with regulators requirements
Expected Learning Outcomes

Expected Learning Outcomes

  • Student chose one business-model and company type for homework
  • Student chose whether he/she will take a test or make a presentation. Student chose a presentation theme
  • Student describes business model of chosen company for homework
  • Student creates conceptual data model for homework
  • Student creates logical data model for homework
  • Student creates list or roles, list of data assets, role to asset matrix in CRUD terms
  • Student describes master-data standards
  • Student describes multidimensional model fo BI solution
  • Student describes a list of data-sources fo his/her conceptual data model
  • Pre-exam based on homework
Course Contents

Course Contents

  • Data management overview
    The concept of data management within the overall concept of the enterprise and information technology. Detailed overview of data management.
  • Data governance
    Data governance is the exercise of authority and control (planning. monitoring and enforcement) over the management of data assets.
  • Data architecture management
    Data architecture is an integrated set of specification artifacts used to define data requirements. guide integration and control of data assets. and align data investments with business strategy.
  • Data developing
    Data development is the analysis, design, implementation, deployment and maintenance of data so-lutions to maximize the value of the data resources to the enterprise. Data development is the subset of pro-ject activities within the system development lifecycle (SDLC) focused on defining data requirements, de-signing the data solution components and implementing these components.
  • Data operations management
    Data operations management is the development. maintenance and support of structured data. It includes two sub-functions: database support and data technology management.
  • Data security management
    Data Security Management is the planning, development and execution of security policies and pro-cedures to provide proper authentication, authorization, access and auditing of data and information assets.
  • Master data management
    Reference and Master data management is the ongoing reconciliation and maintenance of reference data and master data.
  • Data warehousing and business intelligence management, Data quality management
    Data Warehousing and Business Intelligence Management (DW-BIM) is the collection, integration and presentation of data to knowledge Workers for business analysis and decision-making. Data Quality Management (DQM) is a critical support process in organizational change management.
  • Document and content management
    Document and Content Management is the control over capture, storage, access and use of data and information stored outside relational databases.
  • Meta-data management. Modern technologies and tool for data management
    Metadata management is the set of processes that ensure proper creation, storage, integration and control to support associated usage of meta-data. A review of modern data management perspective researches, concepts and tools.
Assessment Elements

Assessment Elements

  • non-blocking Attendance
  • non-blocking Homework
    Student must prepare at least 3 first parts of homework: Business model, Conceptual data model and Logical Data Model.
  • non-blocking Exam
Interim Assessment

Interim Assessment

  • Interim assessment (4 module)
    0.1 * Attendance + 0.2 * Exam + 0.7 * Homework


Recommended Core Bibliography

  • Enfield, R. (2010). Reviewing your organisation’s approach to data management. Journal of Securities Operations & Custody, 3(2), 122–130. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=bsu&AN=53774483
  • Harrington, J. L. Relational database design and implementation. – Morgan Kaufmann, 2016. – 441 pp.
  • Teorey, T. J. et al. Database modeling and design: logical design. – Morgan Kaufmann, 2011. – 352 pp.
  • Барсегян А., Куприянов М., Степаненко В., Холод И. Технологии анализа данных: Data Mining, Text Mining, Visual Mining, OLAP. 2 изд., Санкт-Петербург: БХВ-Петербург, 2008 г. , 384 с. ISBN 5-94157-991-8

Recommended Additional Bibliography

  • Alexander Osterwalder, Er Osterwalder, Mathias Rossi, & Minyue Dong. (2002). The Business Model Handbook for Developing Countries. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.63A7BE39
  • Celko, J. (2006). Joe Celko’s Analytics and OLAP in SQL. San Francisco, Calif: Morgan Kaufmann. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=195632
  • Khadam, U., Iqbal, M. M., Alruily, M., Al Ghamdi, M. A., Ramzan, M., & Almotiri, S. H. (2020). Text Data Security and Privacy in the Internet of Things: Threats, Challenges, and Future Directions. Wireless Communications & Mobile Computing, 1–15. https://doi.org/10.1155/2020/7105625
  • Love, J. S. (2018). Sociolegal And Empirical Legal Research - Research Data Management. https://doi.org/10.5281/zenodo.1200550
  • Petrov, A., & O’Reilly for Higher Education (Firm). (2019). Database Internals : A Deep Dive Into How Distributed Data Systems Work (Vol. First edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2250514
  • Plattner, H., & Zeier, A. (2012). In-Memory Data Management : Technology and Applications. Berlin: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=535046