Business Intelligence

Syllabus

Thematic definition of lectures: 1. Business intelligence. Identifying the need for BI implementation in the enterprise. Functions, position of BI in IS/IT architecture, BI concept, connection to other IS applications. BI stages, processes and tools. Critical success factors for BI in the enterprise, requirements for enterprise BI solutions. 2. Source systems. Data transformation and integration. Data integration approaches, data integration tools, achieving full data integration potential. 3. Data architecture. Data architecture requirements, data types and formats, data taxonomy, data models, enterprise data model, enterprise object model, enterprise conceptual model, enterprise conceptual entity model, multidimensional data model, semantic models, machine learning models. Effective data architecture. Data vs. information architecture. 4. Data warehouses. Data storage layer. Data warehouse environment, data warehouse architecture, data warehouse requirements, data warehouse user benefits. Temporary and operational data stores. Integration of data warehouses into IS and Cloud. Application of ETL process for data cleansing in data warehouses. Risks of data warehousing. Data warehouse security. Data mart. 5. KPIs and requirements for BI analytics services. Defining the business problem, setting objectives, defining research questions and hypotheses, determining data analysis, visualization and reporting requirements. 6. Data mining and data extraction. Data quality and Master Data Management. Data mining, data mining methods, data mining software tools. Data extraction - importance of source identification, data extraction techniques, logical and physical data extraction methods. Text and Web Mining. 7. Data enrichment. Data analysis layer. Data analysis - descriptive analysis, predictive analysis, prescriptive analysis, data analysis tools in BI, hierarchization and dependency search. Advanced data analytics and Machine Learning. 8. Querying - selection, query by attribute selection, relational, arithmetic operators, Venn diagram operators, geographic queries. Querying in traditional ETL process and flexible ELT process. 9. Data visualization and reporting. Data presentation layer - importance and roles of visualizations, dashboards and reporting in enterprise data management. Rules for effective reporting. Types and methods of reporting. Descriptive reporting, KPIs. Visualization techniques and tools as support for management. 10. Interpretation of data analysis results. Verification/falsification of hypotheses, formulation of answers to research questions and the business problem. Making management decisions based on the results of BI analysis. Assessing the success of BI analysis, evaluating feedback and adjusting the frequency of BI analysis. 11. Comprehensive view of the need to use Business intelligence solutions in the enterprise. Business intelligence solutions implementation projects in the enterprise - planning, implementation, control and feedback of the projects. Factors influencing the implementation of BI solutions in the enterprise. Evaluation of the success of implementing BI solutions in the enterprise. 12. Business intelligence strategy, objectives and outputs. Transformation plan. Parallel development paths and development stages of BI solutions. BI conceptual framework. BI lifecycle - lifecycle phases and elements of the BI lifecycle framework. 13. Business intelligence and Artificial Intelligence - integration of BI and Artificial Intelligence. Machine learning - forms of machine learning, predictive analytics and predictive applications. The relationship and impact of machine learning on Business intelligence. New challenges and trends in BI. Thematic definition of exercises: 1. Business Intelligence. BI application system requirements, BI application user environment and its customization, tools, data import/export, graphical outputs. 2. Data extraction from data sources. Data formats, structured and unstructured data. Connecting to data sources. Data preparation and cleansing, data formatting. 3. Data transformation. Data manipulation, dataset creation for Business Intelligence application. Preparation of test, training and validation dataset. Importing data into Business Intelligence application. Creating data model, session. 4. Data enrichment. Applying a systems approach to the ETL process. Identifying structures and elements for dataset enrichment. Implementation of data enrichment. 5. Defining data analysis requirements using BI applications. Defining the business problem, determining analytical questions, hypotheses and requirements for output reports as a basis for management decisions. 6. Data analysis. Algorithm selection - decision trees, regression, neural networks, Bayesian networks, etc. Methods of data analysis in Business intelligence applications Use of mathematical, logical, statistical functions in BI applications. 7. KPIs. Calculation of indicators using advanced data analysis methods - construction of measure, separate calculation formulas, correlations, regressions, factor analysis, anomaly detection. 8. Data visualization. Use of a wide range of visualization tools (graphs, maps, matrices, charts) to create graphical data outputs. 9. Data visualization and trend analysis. Use of BI application visualization tools to create graphical data outputs with visualization of trends and development of business indicators. 10. Dashboard. Creating dashboards - using the spectrum tool to create dashboards, grouping visualizations, filters, interactive dashboards and data stories. 11. Reporting. Parameterization of data outputs and their export. Configuration of report periodicity and templates. Concept of Real Time reporting. 12. Evaluation of data analysis. Interpretation of data outputs. Verification/falsification of hypotheses, finding answers to the research questions and the defined business problem. 13. Making managerial decisions. Managerial decision making based on data analysis from BI application. Analyzing the quality of analysis and data outputs. Evaluation of feedback and synthesis of insights gained. Setting the periodicity and need for continuous data evaluation. Continuous assessment - verification of knowledge and skills acquired by students during the semester.