Learning Objectives

• Define business problem linked to data
• Create data strategy and plan
• Understand business impact
• Business domain selected
• Problem clearly defined
• Data sources identified
• Stakeholder map created

Business Problem Framework

• Business Domain: Finance, retail, healthcare, etc.
• Problem: Specific pain point or opportunity
• Impact: Measurable business value (revenue, cost, risk)
• Data Available: What data exists and what's needed
• Success Criteria: How will we measure success?

Data Strategy Components

1. Data Sources: Internal (DB, logs, CRM) and External (public APIs, purchased data)
2. Data Collection Plan: What, how, frequency, format
3. Data Governance: Quality, privacy, security, compliance
4. Analytics Approach: Descriptive, diagnostic, predictive, prescriptive

Q1: What makes a good business problem for a data project?

Specific (not vague), measurable impact, data available or collectable, aligned with business priorities, feasible timeline, actionable (results can drive decisions). Q2: How to estimate project ROI?

Cost: data acquisition, tools, personnel, time. Benefit: revenue increase, cost reduction, risk reduction. Calculate NPV/IRR of data investment. Q3: What are data governance considerations?

Data quality (accuracy, completeness), privacy (PII, GDPR), security (access control), retention, lineage (where data came from), documentation. Q4: How to create a project roadmap?

Phase 1: Data collection & EDA (2 weeks). Phase 2: Model development (3 weeks). Phase 3: Deployment (2 weeks). Phase 4: Monitoring & iteration (ongoing). Join Discord NextMilestone 2: Data Acquisition & Warehousing