• Overview of DAMA-DMBOK2 and its approach to Data Warehousing and BI

• The role of data warehousing in modern analytics ecosystems

• Key definitions: OLTP vs. OLAP, Data Warehouse vs. Data Lake

Activities:

• Group discussion: Identify strategic reporting needs in your organisation

• Exercise: Match business questions to BI tools or warehouse layers

• Traditional architecture: source systems, ETL, staging, warehouse, marts

• Comparison of Inmon (CIF) and Kimball (dimensional) models

• Modern approaches: cloud warehousing, real-time ingestion, data lakehouses

Activities:

• Draw a hybrid warehouse architecture diagram based on a case study

• Review pros and cons of cloud vs. on-premise DW solutions

• Star vs. Snowflake schemas

• Fact tables and dimension tables

• Slowly Changing Dimensions (Types 1, 2, 3)

• Declaring the “grain” and designing conformed dimensions

Activities:

• Design a dimensional model for a retail or finance use case

• Identify dimension types and choose appropriate SCD handling

• Overview of the Extract, Transform, Load (ETL) process

• Data staging, profiling, and cleansing

• Automation, orchestration, and ETL tools (e.g., Informatica, SSIS, Talend)

Activities:

• Build a simple ETL pipeline using a visual tool or template

• Troubleshoot data quality issues in sample ETL output

• BI platforms overview: Power BI, Tableau, Qlik, Looker

• Dashboards, KPIs, ad hoc queries, and scorecards

• OLAP vs. self-service BI

Activities:

• Use a BI tool to create an interactive dashboard

• Create a wireframe of an executive-level BI report

• Importance of high-quality data in decision-making

• Data profiling and validation techniques

• Handling duplicates, inconsistencies, and missing data

Activities:

• Perform a data profiling exercise on a sample dataset

• Create a data quality checklist for BI input feeds

• Business glossary vs. technical metadata

• Data lineage and traceability in reports

• Roles of data stewards and governance teams in BI

Activities:

• Map metadata flow from source to report

• Draft metadata documentation for a sample dataset

• Query performance tuning and indexing strategies

• Use of aggregates, caching, and materialised views

• Partitioning, parallel processing, and archiving strategies

Activities:

• Tune a slow-performing query and measure improvement

• Propose a partitioning scheme for a large fact table

• Cloud data warehouses: Snowflake, BigQuery, Redshift

• Data lakes and lakehouse architectures

• Real-time streaming and ELT vs. ETL

Activities:

• Compare three major cloud warehouse providers

• Group debate: Is ELT replacing traditional ETL?

• Best practices for designing effective dashboards

• Visualisation types: heatmaps, bullet graphs, sparklines, fan charts

• Descriptive, predictive, and prescriptive analytics

Activities:

• Redesign an ineffective dashboard for better insight

• Build a simple predictive analysis using BI tool plugins

• Role-based access control and data masking in BI

• Securing sensitive data in reports and dashboards

• Compliance with GDPR, HIPAA, and industry regulations

Activities:

• Create a data access control matrix

• Identify risks in a hypothetical BI deployment scenario

• DW/BI in finance, healthcare, retail, and public sector

• Success factors and common pitfalls in implementation

• Reviewing ROI and business value

Activities:

• Present a mini-case study of a real or fictional BI project

• Reflect on what would make it sustainable and scalable

• DataOps, ML-integrated BI, automation and AI

• Augmented analytics and natural language BI

• The future of enterprise data platforms

Activities:

• Research and present one emerging BI trend

• Draft a roadmap for your team to explore next-gen BI

• Recap of key DAMA-DMBOK2 principles covered

• Common CDMP® exam question styles and preparation tips

• Final guidance for the DAMA International 32567 exam

Activities:

• Practice multiple-choice questions

• Final group Q&A: Consolidate your DW/BI action plan