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Regular version of the site

Advanced Data Management

Academic Year
Instruction in English
ECTS credits
Delivered at:
Department of Innovation and Business in Information Technologies
Course type:
Elective course
1 year, 3, 4 module


Shlyapnev, Maxim

Course Syllabus


The program provides the contents of the course and describes the learning outcomes, competences and practical skills obtained upon completion of the course. It also sets pre-requisites for taking the course and provides criteria for assessing student’s performance. The program is designed for instructors teaching the course, teaching assistants and graduate students following educational track 38.04.05 "Business In-formatics", Master’s level. "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 func-tions 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 man-agement, 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 lan-guages (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 3 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 describes a list of data-sources fo his/her conceptual data model
  • 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
  • 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 Online test
    Examination format: The exam is taken multiple choice questions test with asynchronous proctoring. Asynchronous proctoring means that all the student's actions during the exam will be “watched” by the computer. The exam process is recorded and analyzed by artificial intelligence and a human (proctor). Please be careful and follow the instructions clearly! The platform: The exam is conducted on the StartExam platform. StartExam is an online platform for conducting test tasks of various levels of complexity. The link to pass the exam task will be available to the student in the RUZ. The computers must meet the following technical requirements: https://eduhseru-my.sharepoint.com/:b:/g/personal/vsukhomlinov_hse_ru/EUhZkYaRxQRLh9bSkXKptkUBjy7gGBj39W_pwqgqqNo_aA?e=fn0t9N A student is supposed to follow the requirements below: Prepare identification documents (а passport on a page with name and photo) for identification before the beginning of the examination task; Check your microphone, speakers or headphones, webcam, Internet connection (we recommend connecting your computer to the network with a cable, if possible); Disable applications on the computer's task other than the browser that will be used to log in to the StartExam program. If one of the necessary requirements for participation in the exam cannot be met, a student is obliged to inform a program manager 7 days before the exam date to decide on the student's participation in the exams. Important rules: All rules are available in exam regulations using asynchronous proctoring technology in the framework of intermediate certification. Additional rules: The test will be taken in Google Docs online service. Prepare your google id and send it to teacher earlier that 2 days before exam. Connection failures: A short-term connection failure during the exam is considered to be the loss of a student's network connection with the StartExam platform for no longer than 5 minutes per exam. A long-term connection failure during the exam is considered to be the loss of a student's network connection with the StartExam platform for longer than 5 minutes per exam and will be the basis for the decision to terminate the exam. In case of long-term connection failure in the StartExam platform during the examination task, the student must record the fact of connection failure (screenshot, a response from the Internet provider). Then contact the program manager with an explanatory note about the incident to decide on retaking the exam.
  • blocking Homework
    Student must prepare at least 3 first parts of homework: Business model, Conceptual data model and Logical Data Model.
  • non-blocking Attendance
    There are 12 lessons (lectures and practice) during the course period. Attendance of each lesson gives 10/12 points.
Interim Assessment

Interim Assessment

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


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