Data Literacy Bootcamp - 2 Day Remote Program

Day 1 - Theory (Data literacy foundations + why data projects fail)

Module 1 - Data literacy fundamentals

• What data literacy means in real workplaces
• Metric vs KPI vs target
• Dimensions, segments, cohorts
• The “one sentence rule” for any KPI: definition + time period + segment + comparison

Module 2 - Data quality and trust

• The 6 practical checks: accuracy, completeness, consistency, timeliness, uniqueness, validity
• Fitness for use - how quality depends on decision risk
• Common reasons teams lose trust in data (and how to fix it)

Module 3 - Dashboard and chart literacy

• How to read dashboards without getting fooled
• The most common traps: missing definitions, wrong time windows, averages hiding segments, misleading axes
• How to ask better questions in meetings (without sounding “difficult”)

Module 4 - Reasoning with data

• Correlation vs causation (and what to do instead)
• Simple segmentation thinking - finding drivers, not just totals

Module 5 - Why data projects succeed or fail

• The real reasons data initiatives fail: unclear ownership and definitions
• weak adoption and training
• poor governance and trust
• misaligned incentives and “metric gaming”
• Success patterns: clear KPI ownership, agreed definitions, simple governance, strong enablement

Day 1 deliverables

• KPI Definition Card (for one KPI from your business)
• Dashboard Review Checklist
• Data Quality Triage Sheet

Day 2 - Practice (Power BI data consumption + basic reporting)

Module 6 - Power BI basics for data consumers

• Power BI concepts in plain language: dataset vs report vs dashboard
• How filters, slicers, drill-down, and interactions work
• How to validate what you’re seeing (time window, segments, definitions)
  

Module 7 - Working with data in Power BI (practical)

• Importing or connecting to a provided dataset
• Basic data model view - what relationships mean (high level)
• Creating basic measures (simple examples only - no heavy DAX)
• Understanding common reporting mistakes that cause wrong numbers
  

Module 8 - Building a clean report (hands-on)

• Choosing the right visuals for the question
• Building a basic report page:
• KPI cards
• trend chart
• category comparison
• slicers for segmentation
• Visual design habits for clarity (titles, labels, units, avoiding clutter)

Module 9 - Telling a story with a report

• Turning charts into decisions: Context - Insight - Impact - Action
• Writing a simple insight summary
• Presenting results with assumptions and limitations

Capstone - Mini reporting project

Participants will build and present:

• 1 Power BI report (1-2 pages)
• 3 insights with at least one segmentation
• 1 recommendation with next step and owner
  

Day 2 deliverables

• Power BI report file
• Insight summary (short write-up)
• Capstone presentation (3-5 minutes per participant/team)