Advanced Data Management

Prerequisites:

• Basic computer science principles and skills
• Database theory basics
• Business process modeling

Courses:

• System Analysis&Organization Design
• Enterprise Architecture Modeling
• Advanced Data Analysis&Big Data for Business Intelligence

The course gives students understanding of the role and authority of data management, roles and responsibilities within data management, data architecture concepts, data management tools and techniques.

The course covers the basics of data management and also practical and effective approaches to managing corporate data to support an integrated and cost effective techniques of data and information resource development.

It content includes all stages of the data life cycle such as  data management planning; data collection or acquisition policies; quality control and assurance; metadata and data description; data preservation; data discovery, integration and analysis; publication and citation.

The course bases in general on Data Management Body of Knowledge (DAMA-DMBOK GuideTM).

Topics include:

• Data Architecture, Analysis and Design: information as a key business resource, the relationship between information and data, an enterprise-wide view of data, enterprise data architecture, development of the enterprise data model,  data modeling and specification, data model quality management;
• Database Management: database development,  database production support, data technology management;
• Meta Data Management: understanding of  meta data requirements, defining of  the meta data architecture, implementation of  managed meta data environment;
• Data Security Management: understanding of data privacy, confidentiality and security needs, definition of data privacy and confidentiality standards, implementation data security controls, audit data security;
• Data Quality Management: development and promotion of data quality awareness, data quality metrics definition, data quality requirements and business rules definition, analyzing/ profiling / measurement / monitoring of data quality, data quality service levels; identifying, escalation and resolving of data quality issues, conducting of clean-up campaigns; testing and validating data quality, data quality audit;
• Business Intelligence Management: business intelligence data needs understanding, BI architecture definition, business intelligence tools and user interfaces implementation, analytic applications implementation, supporting of  business intelligence activity;
• Document, Record and Content Management: Electronic Document, Physical Records, Information Content,  Multi-media systems,  Internet and Intranet-based systems, Object Management;
• Data Governance: data management planning, data management supervision and control.

1. Keith Gordon Principles of Data Management: Facilitating Information Sharing, BCS   Published: August 2007

2. Richard T. Watson Data Management: database and organization.  Fifth Edition, John Wiley & Sons, Inc., 2005

3. Modern Database Management (11th Edition) by Jeffrey A. Hoffer, V. Ramesh and Heikki Topi,  2012

4. Paul Beynon-Davies. Business Information Systems, 2009, Copyrighted material, Pallgrave Macmillan (Macmillan Publishers Ltd)

5. DAMA-DMBOK GuideTM (Data Management Body of Knowledge). Technics publications, LLC, 2009