| Course Type | Elective Course |
|---|
| Course Level | Bachelor's Degree |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 5 |
|---|
| Name of Lecturer(s) |
|
| Learning Outcomes |
At the end of this course, the students;
- Define the concepts of business intelligence, data literacy, and data-driven organizational culture.
- Explain the cognitive biases of the human mind in decision-making processes and how these biases can be overcome using business intelligence.
- Analyze, through case studies, how gathered data is transformed into a strategic competitive tool.
- Plan internal authorization and access policies by classifying a company's trade secrets and intangible assets.
- Develop proactive risk management strategies by identifying external threats to corporate information, such as digital footprints and social engineering.
|
| Mode of Delivery | Face to face |
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| Prerequisites | No |
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| Recommended Optional Programme Components | |
|---|
| Course Description | The primary objective of this course is to examine how data is transformed into a strategic competitive tool in the modern business world, along with the managerial dimensions of business intelligence systems. Rather than focusing solely on technical software processes, the course will explore in detail concepts such as the human psychology (cognitive biases) that affects data-driven decision-making, data literacy, and analytical organizational culture. Furthermore, the management of acquired corporate knowledge and intangible assets against internal and external threats will be addressed from a strategic leadership perspective, accompanied by case studies. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Introduction to Business Intelligence |
|
2nd Week |
Data Literacy and the Four Levels of Analytics |
|
3rd Week |
Decision Science: The Mind and Cognitive Biases |
|
4th Week |
Data-Driven Organizational Culture and the "HiPPO" Effect |
|
5th Week |
Competing on Analytics and the DELTA Model |
|
6th Week |
Information Architecture and the "Need-to-Know" Principle |
|
7th Week |
MIDTERM EXAM |
|
8th Week |
Automation Bias and Human-AI Collaboration |
|
9th Week |
Visual Perception and Directive Design |
|
10th Week |
"Choice Architecture" and Nudging |
|
11th Week |
Ecosystem Risks and Digital Footprints |
|
12th Week |
Elicitation and Information Security |
|
13th Week |
Protection of Trade Secrets and Intellectual Property Rights Strategies |
|
14th Week |
Data-Driven Decision Making: Case Studies and Problem Solving/Global Leadership: Managing Technology and "Left of Boom" |
|
| Recommended or Required Reading | Anderson, C. (2015). Creating a data-driven organization: Practical advice from the trenches. O'Reilly Media.
Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning. Harvard Business School Press.
Few, S. (2013). Information dashboard design: Displaying data for at-a-glance monitoring (2nd ed.). Analytics Press.
|
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Discussion
- Case Study
- Practice
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Total(%) |
|
30 |
| Contribution of In-term Studies to Overall Grade(%) |
|
30 |
| Contribution of Final Examination to Overall Grade(%) |
|
70 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
2 |
2 |
| Preparation for Quiz |
5 |
2 |
10 |
| Individual or group work |
15 |
3 |
45 |
| Preparation for Final exam |
1 |
20 |
20 |
| Course hours |
15 |
3 |
45 |
| Preparation for Midterm exam |
1 |
20 |
20 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
2 |
2 |
| Homework |
|
|
|
| Quiz |
5 |
2 |
10 |
| Total Work Load | 154 |
| Total Work Load / 30 (h) | 5.13 |
| ECTS Credit of the Course | 5 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
