Современный менеджмент данных

2024/2025

Статус: Маго-лего

Кто читает: Департамент бизнес-информатики

Когда читается: 3, 4 модуль

Охват аудитории: для всех кампусов НИУ ВШЭ

Преподаватели: Панфилов Петр Борисович

Язык: английский

Кредиты: 6

Full Syllabus Ask Question

Abstract

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

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 management 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

Pre-exam based on homework
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 creates conceptual data model for homework
Student creates list or roles, list of data assets, role to asset matrix in CRUD terms
Student creates logical data model for homework
Student describes a list of data-sources fo his/her conceptual data model
Student describes business model of chosen company for homework
Student describes master-data standards
Student describes multidimensional model fo BI solution

Course Contents

Data management overview
Data governance
Data architecture management
Data developing
Data operations management
Data security management
Master data management
Data warehousing and business intelligence management, Data quality management
Document and content management
Meta-data management. Modern technologies and tool for data management

Assessment Elements

Exam
A test of 25 multiple-choice questions covering all topics covered in the course. Each question is worth 4% of the total exam score, for a maximum of 100 points for the exam.
Homework 1: Business Process Modelling
Student must take any available modeling tool (e.g., Bee-Up Tool from OMiLAB's repository of open modeling tools or other free [UML] modeling tools such as Visual Paradigm, etc.) and conduct experiments on completing "Build a new model" excercise to familiarise with the interface and functionality of the chosen tool.
Class participation
Student's activity in attending classes and participation in discussions during lectures and group work presentations. Participation/attendance may be monitored throughout the course at all stages and modules and added to the semester grade formula, accounting for up to 10% of students' overall seminar grade.
Homework 2: Data Modeling
Homework 3: Physical data model and database
Experimenting with data modeling tools and data base management systems
Homework 4: Master Data Management (Golden Record)
Experiments with Master Data Management solutions
Homework 5: Data Security (CRUD)
Experiments with CRUD matrix building tools
Homework 6: DWH and BI
Building Multidimensional Model for Business Intelligence
Project Defense
Term project on Building and managing data infrastructure for a business case

Interim Assessment

2024/2025 4th module
0.1 * Class participation + 0.1 * Exam + 0.1 * Homework 1: Business Process Modelling + 0.1 * Homework 2: Data Modeling + 0.1 * Homework 3: Physical data model and database + 0.1 * Homework 4: Master Data Management (Golden Record) + 0.1 * Homework 5: Data Security (CRUD) + 0.1 * Homework 6: DWH and BI + 0.2 * Project Defense

Bibliography

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

Authors

PANFILOV PETR BORISOVICH
Shliapnev MAKSIM VALEREVICH
Beklarian Armen Levonovich

Course Syllabus