Information Lifecycle Management

Term: 
Spring
Credits: 
2.0
ECTS credits: 
4.0
Status: 
Elective
Course Description: 

Type: elective

Business decisions frequently require appropriate supporting information. However, information may be extracted from a variety of entry points, including both internal and external sources and information quality may be questionable or simply unknown. This course discusses information quality issues and necessary support processes to track data pieces and corresponding context from creation to deletion. The course emphasizes ‘the fitness for use’ definition and aims to prepare participants for the most common issues in organizations regarding information handling. The building of ‘one truth’ for decision support purposes is also a key concern in many organizations. Among other topics, information workflows and modeling tools (e.g. Data Flow Diagrams) for organizations will be reviewed. The course discusses the consolidation of information into data warehouses and the necessary processes of extract-transform-load. Topics covered: Information flow, data flow, DFDs. Data structures. Well-structured business data. ILM as policy and process. Data warehousing, OLAP. The extract-transform-load workflow. Data manipulation tools. Text analytics tools. Search. Entity resolution. Data warehousing

Learning Outcomes: 

INTENDED LEARNING OUTCOMES

ASSESSMENT

Knowledge and Understanding

  1. Understand Information Life Cycle Management and its implications
  2. Understand how quality information can enhance decision making and organizational performance
  3. Describe tools and techniques for business decision support
  4. Identify information-related problems and opportunities and identify strategis and tactics for addressing these challenges

Final Exam

Intellectual Skills

  1. Recognize critical factors in a problem
  2. Develop a structure for analyzing problems
  3. Carry out a cogent analysis
  4. Present the analysis and insights on a problem

Final Exam and Team Work

Practical Skills

  1. Design, build, test, and use meaningful spreadsheets to present and solve quantitative problems
  2. Carry out sensitivity, data, regression, and optimization analysis
  3. Use pivot tables to aggregate, visualize and drill down into data

Final Exam and Team Work

Transferable Skills

  1. Translate descriptions of situations into formal models, and investigate those models in an organized fashion
  2. Extract insights from models, and use those insights to communicate, persuade and motivate change

Final Exam

Assessment: 

Final exam usually consists of two parts.

  • Part A) Individual, MS Excel-based model building for a business problem, using given data sources (70%)
  • Part B) Team based exercise, typically either a short case or a situational exercise. Includes information flow modelling, decision making based on information, and recommendations to improve ILM. E.g. the team has to act as consultants to a private health firm, or to a leasing company. Background information with some data is given. The lecturer plays the role of BDM (Business Decision Maker). (30%)
Prerequisites: 

None.

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