Data impact investigation and visualization
Introduction to Data Impact Investigation
Understanding the importance of data impact investigation in data-driven organizations Exploring the key concepts and objectives of data impact investigation Overview of common scenarios requiring data impact investigation (e.g., data quality issues, system changes, business decisions) Introduction to techniques and methodologies for conducting data impact analysis
Review case studies and real-world examples showcasing the importance of data impact investigation in various industries Identify different types of data impact scenarios and their potential consequences on business operations Discuss strategies for prioritizing data impact investigation tasks based on business impact and urgency Set up a sample dataset and scenario for hands-on investigation activities throughout the bootcamp
Data Profiling and Analysis
Understanding the role of data profiling in data impact investigation Exploring techniques for profiling data to identify anomalies, inconsistencies, and patterns Overview of common data profiling tools and methodologies Techniques for data sampling, statistical analysis, and data visualization in data profiling
Conduct data profiling on the sample dataset to identify potential data quality issues and anomalies Use data profiling tools (e.g., Apache Metron, Talend Data Quality) to generate summary statistics, histograms, and frequency distributions Analyze profiling results to identify patterns, outliers, and potential areas of concern Document findings and observations from the data profiling process for further investigation
Impact Analysis and Root Cause Investigation
Techniques for conducting impact analysis to assess the consequences of data issues or changes Understanding the importance of root cause investigation in identifying the underlying reasons for data impacts Overview of common root cause analysis methodologies (e.g., 5 Whys, Ishikawa diagrams) Strategies for tracing data lineage and dependencies to identify affected systems and processes
Perform impact analysis on the identified data quality issues or changes in the sample dataset Trace data lineage and dependencies to identify downstream systems and processes affected by the data issues Use root cause analysis techniques to investigate the underlying reasons for the data impacts Collaborate with relevant stakeholders (e.g., data engineers, business analysts) to gather insights and perspectives on the data impacts
Data Visualization Techniques
Introduction to data visualization and its role in communicating insights and findings from data impact investigation Overview of common data visualization techniques and best practices Understanding the principles of effective data visualization design (e.g., simplicity, clarity, accuracy) Introduction to data visualization tools and libraries (e.g., Tableau, Matplotlib, ggplot)
Design and create visualizations to represent findings from the data impact investigation process Choose appropriate visualization types (e.g., bar charts, scatter plots, line graphs) based on the nature of the data and the insights to be communicated Use data visualization tools to create interactive dashboards and reports for presenting investigation findings Incorporate storytelling techniques to effectively communicate insights and recommendations derived from the data visualization
Presentation and Reporting
Strategies for presenting investigation findings and recommendations to stakeholders Importance of clear and concise reporting in conveying the results of data impact investigation Tips for structuring investigation reports and presentations for different audiences (e.g., technical vs. non-technical) Techniques for fostering collaboration and alignment among stakeholders based on investigation findings
Prepare a presentation or report summarizing the findings and recommendations from the data impact investigation conducted throughout the bootcamp Structure the presentation/report to include an overview of the investigation objectives, methodology, key findings, root causes, and recommendations Practice delivering the presentation to simulate a real-world scenario, incorporating feedback and questions from the audience Discuss strategies for effectively communicating investigation findings and engaging stakeholders in follow-up actions and decisions