Data Analysis

Data Analysis for BA

Support decisions with evidence, not intuition.

What is Data Analysis?

Definition: Systematic process of collecting, cleaning, analyzing, and interpreting data to answer business questions and inform decisions.

Why it matters:

• Intuition wrong 30-40% of time (cognitive biases)
• Data reveals truth hidden from observation
• Numbers convince executives better than opinions
• Baseline data allows measuring progress

When to Use:

• ✅ Validating assumptions (do 50% of users really want this?)
• ✅ Prioritizing features (which matters most to customers?)
• ✅ Measuring success (are we meeting our KPIs?)
• ✅ Identifying problems (where is quality failing?)
• ✅ Comparing options (Option A or Option B?)

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4 Levels of Analytics

Level 1: Descriptive Analytics (What happened?)

Summarize historical data.

Question: "How many users signed up last month?"
Data: User signup logs
Analysis: Count signups by cohort
Output: 5,234 signups (↑12% vs previous month)

Question: "Which features get used most?"
Data: Feature usage logs
Analysis: Count usage by feature
Output: Feature A: 85%, Feature B: 45%, Feature C: 12%

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Level 2: Diagnostic Analytics (Why happened?)

Understand causes behind observations.

Question: "Why did signups drop last week?"
Data: Signups, marketing spend, conversion rates, technical incidents
Analysis: Correlate events with signup dips
Output: Correlation with:
  - Reduced ad spend (-$10K) = -800 signups expected
  - Website downtime (2 hours) = -50 signups lost
  Root cause: Marketing reduced budget, not technical issue

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Level 3: Predictive Analytics (What will happen?)

Forecast future based on patterns.

Question: "How many users will churn next quarter?"
Historical Data: Churn patterns by cohort, feature adoption, support tickets
Analysis: Identify predictive factors (inactivity >30 days = 70% churn)
Output: Predicted 300 users (±20%) will churn next quarter
Action: Design retention campaign for inactive users

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Level 4: Prescriptive Analytics (What should we do?)

Recommend specific actions based on data.

Question: "Which feature should we build to reduce churn?"
Data: Churn reasons, feature requests, competitive analysis
Analysis: Feature A prevents 200 churns, costs $50K, ROI: 4x
         Feature B prevents 80 churns, costs $30K, ROI: 2.7x
Output: Recommendation: Build Feature A first

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5-Step Data Analysis Process

Step 1: Define Question Clearly

Bad questions:

• ❌ "What do users want?" (too vague)
• ❌ "Are we doing well?" (no comparison)
• ❌ "Should we pivot?" (unclear metrics)

Good questions:

• ✅ "What % of users use feature X regularly?" (specific, measurable)
• ✅ "Are retention rates higher for cohort A vs B?" (comparative)
• ✅ "If we reduce price by 20%, how many new users do we gain?" (specific impact)

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Step 2: Identify & Collect Data

Data sources:

• Product data (usage, events, features)
• Business data (revenue, churn, support)
• User data (surveys, interviews, NPS)
• Market data (competitors, industry trends)
• Operational data (costs, resources, timeline)

Quality check:

• ✅ Is data accurate? (check for obvious errors)
• ✅ Is it complete? (missing data?)
• ✅ Is it timely? (recent enough?)
• ✅ Is it relevant? (answers your question?)

See: references/data-collection.md

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Step 3: Analyze Data

Common analysis techniques:

• Trend Analysis: How values change over time
• Cohort Analysis: Compare groups (early vs late users)
• Correlation: Do two variables move together?
• Segmentation: Break data into meaningful groups
• Benchmarking: Compare against standard or competitor

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Step 4: Visualize & Interpret

Present data clearly so insights jump out.

Chart types:

• Trend: Line chart (usage over time)
• Comparison: Bar chart (Feature A vs B usage)
• Part-to-whole: Pie chart (% of each feature)
• Distribution: Histogram (spread of values)
• Relationship: Scatter plot (correlation between variables)

Interpretation:

• What does data show?
• What's surprising or unexpected?
• What explains the pattern?
• What could be wrong with this interpretation?

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Step 5: Communicate & Act

Present findings so stakeholders understand and believe them.

1. START WITH FINDING:
   "Users with fewer than 3 logins per month have 80% churn rate"

2. PROVIDE EVIDENCE:
   "Based on 10,000 users over 6 months"

3. EXPLAIN IMPLICATION:
   "This means inactive users are at extreme risk"

4. RECOMMEND ACTION:
   "Design re-engagement campaign for <3 logins/month users"

5. PROPOSE MEASUREMENT:
   "Track churn rate improvement monthly"

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Common Data Analysis Pitfalls

❌ Correlation ≠ Causation

Observation: High support tickets correlate with high usage
False conclusion: High usage causes support tickets
Better analysis: High usage reveals more bugs → more tickets
                 Need: Better QA, not less usage

❌ Survivor Bias

Observation: Surviving customers have high satisfaction
False conclusion: Our product is great
Missing data: Unhappy customers already left
Better analysis: Include churned customer feedback

❌ Simpson's Paradox

Situation: Feature A better than B overall, but worse in each segment
Example: Men prefer A (70% vs 60%), Women prefer A (65% vs 50%)
        But overall: B beats A (65% vs 60%)
Why: Different group sizes skew overall results
Fix: Analyze by segment, not aggregate

❌ P-Hacking / Multiple Comparisons

Problem: Test 100 hypotheses, some will be significant by chance
        Claim victory on random findings
Fix: Pre-specify hypothesis before analysis
    Validate findings on new data

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Tools & Templates

• 📋 Analysis Plan Template: assets/analysis-plan-template.md
• 📋 Data Collection Checklist: assets/data-collection-checklist.md
• 📋 Findings Presentation Template: assets/findings-presentation-template.md
• 📋 Cohort Analysis Template: assets/cohort-analysis-template.md
• 📋 Statistical Significance Calculator: assets/significance-calculator.md

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Usage Examples

/data-analysis "What % of users adopt new feature within 2 weeks?"
/data-analysis "Which pricing point maximizes revenue?" "Have price sensitivity survey"
/data-analysis "Why did churn increase 15% last month?"

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BA Standards & References

Based on:

• Statistics: Hypothesis testing, confidence intervals, significance
• Analytics: Descriptive, diagnostic, predictive, prescriptive
• Causal Inference: How to identify true causes
• User Research: Quantitative research methods

Detailed Frameworks:

• 📚 references/data-collection.md - Finding, gathering, and validating data
• 📚 references/analysis-techniques.md - Trend, cohort, segmentation, correlation analysis
• 📚 references/statistical-thinking.md - Confidence, significance, and limitations
• 📚 references/visualization.md - Choosing right charts and telling story with data
• 📚 references/common-pitfalls.md - Bias, correlation, and interpretation mistakes

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: GiangGiangTran
• Source: GiangGiangTran/ba-skills
• License: MIT

Install and usage instructions live in the source repository linked above.