Advanced Data Management
- 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
- 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
- Data management overviewThe concept of data management within the overall concept of the enterprise and information technology. Detailed overview of data management.
- Data governanceData governance is the exercise of authority and control (planning. monitoring and enforcement) over the management of data assets.
- Data architecture managementData 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 developingData 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 managementData operations management is the development. maintenance and support of structured data. It includes two sub-functions: database support and data technology management.
- Data security managementData 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 managementReference and Master data management is the ongoing reconciliation and maintenance of reference data and master data.
- Data warehousing and business intelligence management, Data quality managementData 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 managementDocument 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 managementMetadata 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.
- HomeworkStudent must prepare at least 3 first parts of homework: Business model, Conceptual data model and Logical Data Model.
- 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
- 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