Business Intelligence

Course guarantor:

Lecturer:

Matej Mojzeš

Tutor:

Matej Mojzeš

Supervisor:

Department of Software Engineering

Synopsis:

The aim of the course is to introduce students to Business Intelligence (BI) as a key discipline for supporting decision-making in organizations in the era of digitalization and the development of artificial intelligence. The course focuses on the design, implementation and use of analytical data solutions that connect classical BI principles (data warehouses, reporting, OLAP) with modern approaches, such as cloud BI solutions, self-service analytics, real-time BI and AI-assisted (augmented) analytics.

Special emphasis is placed on the practical use of artificial intelligence in BI tools - automatic detection of patterns and anomalies, generation of analytical knowledge (so-called insights), work with natural language (NLQ/NLG), recommendation of metrics and visualizations and support for managerial decision-making.

Requirements:

During the semester, students will solve assignments whose topics will be individually designed to follow up on their previously solved tasks (semester thesis, BP, DP) and will typically consist of implementing their own BI system component.

The assessment will be given after the presentation (defense) and submission of the solution with documentation.

Syllabus of lectures:

1. BI in the AI era

- BI principles

- The role of BI in a modern organization

- BI vs. Data Science vs. Data Engineering

- Shift from reporting to decision intelligence (operational / managerial / strategic decision-making)

2. Production systems vs. analytical and decision-making systems

- OLTP vs. analytical systems

- Latency, historization, operational decision-making

3. Data integration for BI (ETL / ELT in the modern sense)

- Data sources

- Transformation process logic

- Batch vs. stream in the context of BI

4. Data warehouse as the basis of BI

- Facts, dimensions, granularity

- Semantic layer as a key element of BI

- Separation of physical and logical model

5. Data marketplaces and domain analytics

- Thematic data products

- BI as a service for business domains

- Self-service BI and user autonomy management

6. Reporting, dashboards and analytical storytelling

- KPIs, metrics and business context

- Visualization supporting decisions

- Automated comment generation (NLG)

7. OLAP and interactive analytics

- Multidimensional analysis

- Drill-down, slice & dice

- Combination of OLAP and AI-assistance

8. Data mining

- Segmentation, association and trend analysis as tools to support data interpretation

- The role of data mining as a supporting layer of BI, emphasis on interpretability of results

9. AI-assisted (augmented) analytics

- Automatic detection anomalies

- Insight discovery

- Natural Language Query (NLQ)

10. Integrating advanced analytical methods into BI

- Statistical, optimization and predictive calculations

- Separation of computational logic from BI presentation

- BI as a consumer of analytical services

11. BI, knowledge and decision-making

- Knowledge Management

- Data information insight decision

12. Metadata, governance and responsible BI

- Data cataloging, lineage

- Transparency of AI-insights

- Ethical aspects of BI and AI

13. Data Quality Assurance

- Dimensions and data quality management in BI

- Data quality monitoring and reporting

- Practical approaches to improving data quality

Syllabus of tutorials:

1. Introduction to the BI environment and practical project

- Architecture of BI solutions using open-source tools and Python

- Due to the time allocation of the course, this is not about in-depth programming, but about a practical demonstration of architectural and analytical principles of BI solutions

2. Analytical data modeling

- Design and implementation of fact and dimensional tables

3. Data integration and transformation for BI

- ETL/ELT logic, business rules, data validation

4. Data warehouse and analytical layer in practice

- Analytical database structure, historical data, time dimensions

5. Definition of metrics and semantic layer

- KPI and analytical metrics, consistency of definitions, separation of computational logic from presentation

6. Reporting and basic dashboards

- Management overviews, time comparisons, interpretation of results

7. Advanced visualization and analytical storytelling

- Contextualization of analytical outputs, annotations, automated summaries of analytical findings

8. OLAP and interactive analytics

- Multidimensional views, drill-down, slice & dice, exploratory analytical scenarios

9. Data mining in BI---exploratory analytical methods

- Segmentation, association, trend analysis, emphasis on interpretability of results

10. AI-assisted analytics

- Anomaly detection, insight discovery, Natural Language Query (NLQ)

11. Integration of advanced analytical calculations

- BI as a consumer of analytical calculations, presentation of results in a BI context

12. Metadata, governance and data quality

- Data documentation, lineage, data quality monitoring and its impact on BI

13. Presentation of student projects

Study Objective:

Knowledge: The student will gain an understanding of the different characteristics of production, analytical and decision-making data systems and will adopt the principles of BI as a tool to support analytical and management activities in the organization. They will become familiar with the architecture of BI solutions, integration and transformation processes, analytical data modeling, reporting, OLAP analysis, metadata management and data quality assurance. Special attention is paid to the use of AI-assisted analytical functions in the BI environment, their benefits, limits and interpretability of results.

Skills: The student will be able to design, implement and evaluate BI solutions with an emphasis on business benefits, consistency of analytical metrics, interpretation of analytical outputs and decision support. They will gain the ability to integrate advanced analytical calculations and algorithms (used in other subjects) into the BI environment, work with AI-assisted functions of BI tools and critically assess data quality and credibility of analytical findings.

Study materials:

Required literature:

[1] KIMBALL, Ralph. The data warehouse lifecycle toolkit. 2nd ed. Indianapolis, IN: Wiley Pub., c2008, 636 s. ISBN

04-701-4977-9.

[2] VERCELLIS, Carlo. Business intelligence: data mining and optimization for decision making. Chichester, U.K.:

Wiley, 2009, 417 s. ISBN 04-705-1139-7.

Recommended literature:

[1] LANGIT, Lynn. Smart business intelligence solutions with Microsoft SQL Server 2008. Redmond, Wash.: Microsoft,

c2009, 765 s. ISBN 07-356-2580-8.

Note:

Time-table for winter semester 2025/2026:

Time-table is not available yet

Time-table for summer semester 2025/2026:

Time-table is not available yet

The course is a part of the following study plans:

• Aplikované matematicko-stochastické metody (elective course)
• Aplikace informatiky v přírodních vědách (elective course)