Data Analysis

Overview

A comprehensive data analysis and storytelling skill optimized for financial, SaaS, and RevOps contexts. This skill provides structured workflows for turning raw data into actionable insights with full transparency on analytical decisions, bias awareness, and progressive disclosure reporting.

Workflow Overview

Every analysis follows a 7-phase process:

1. SETUP    → Initialize Marimo notebook (run init_marimo_notebook.py)
2. INGEST   → Load data, document sources and assumptions
3. EXPLORE  → EDA with logged decisions (why this viz, why this filter)
4. MODEL    → If needed, with interpretable-first approach
5. INTERPRET → Apply bias checklist, hedge appropriately
6. WISHLIST → Document data gaps and proxies used
7. OUTPUT   → Generate appropriate tier (slides/report/notebook)

Decision Logging Protocol

Every analytical choice must be logged. This creates an audit trail and enables reproducibility.

What to Log

Decision Type	Example	Log Format
Data filtering	Removed 47 records with null revenue	FILTER: [reason] - [count] records affected
Metric choice	Used logo churn vs revenue churn	METRIC: [chosen] over [alternative] because [reason]
Visualization	Line chart for time series	VIZ: [type] because [reason]
Assumption	Assumed linear growth for projection	ASSUMPTION: [statement] - confidence: [H/M/L]
Proxy used	Used support tickets as NPS proxy	PROXY: [proxy] for [missing data] - quality: [S/M/W]

Log Format in Notebook

# === DECISION LOG ===
# FILTER: Excluded trial accounts - 1,247 records removed
# METRIC: NRR over GRR because expansion is significant factor
# ASSUMPTION: Q4 seasonality similar to prior year - confidence: M
# PROXY: Support ticket sentiment for NPS - quality: Weak

Analysis Workflow Details

Phase 1: Setup

Run the initialization script to create a new Marimo notebook with pre-built scaffolding:

python scripts/init_marimo_notebook.py <notebook_name>

This creates a .py file with:

• Decision log cell (markdown)
• Data loading template
• EDA template cells
• Bias checklist cell

Phase 2: Data Ingestion

When loading data:

1. Document the source (file path, API, database query)
2. Record row/column counts
3. Note any immediate data quality issues
4. Log assumptions about data freshness

# === DATA SOURCE ===
# Source: sales_data_2024.csv
# Loaded: 2024-01-15
# Records: 15,847 rows x 23 columns
# Note: Data through 2024-01-10, 5-day lag from source system

Phase 3: Exploratory Data Analysis

Follow this EDA checklist:

• Distribution of key numeric variables
• Missing value patterns
• Outlier detection
• Time series patterns (if applicable)
• Segment breakdowns
• Correlation exploration

Log every visualization choice and filtering decision.

Phase 4: Modeling (If Needed)

Prioritize interpretability:

1. First choice: Descriptive statistics, cohort analysis
2. Second choice: Linear regression, decision trees
3. Last resort: Complex ML (document why simpler won't work)

Always provide:

• Model assumptions
• Feature importance / coefficients
• Confidence intervals
• What the model cannot tell us

Phase 5: Interpretation

Before finalizing insights, run the bias checklist. See references/biases.md for full checklist.

Quick check:

• Survivorship bias: Am I only looking at "survivors"?
• Simpson's paradox: Do segment trends differ from aggregate?
• Selection bias: Is my sample representative?
• Small-n warning: Is sample size sufficient for claims?

Hedge appropriately:

• Use "suggests" not "proves"
• State confidence levels
• Note what additional data would strengthen conclusions

Phase 5.5: Validation Gate (MANDATORY)

⚠️ GATE: Before proceeding to output, you MUST run the data quality validation checklist.

This is not optional. Run through references/data-quality-validator.md before finalizing:

Critical Patterns Checklist:

• Market Context: Are YoY changes compared to market/baseline?
• Weighting Sensitivity: If using composite scores, tested 5-6 weight scenarios?
• Bootstrap CIs: For small samples, generated P10/P50/P90 ranges?
• Survivorship Quantified: Data_Availability column added? Coverage % reported?

Statistical Checks:

• Sample sizes disclosed with confidence intervals?
• Multiple comparisons accounted for?
• Cherry-picked baselines avoided?

Logic Checks:

• Correlation not claimed as causation?
• Alternative explanations considered?
• Back-of-envelope validation passed?

Methodology Note on Time Horizons: When assessing skill vs luck (e.g., sales rep performance, investment returns):

• Longer time horizons (3+ years) reveal inconsistency that short windows hide
• More periods = higher bar for "likely skill"
• A rep who is "top 10%" for 2 quarters could easily be luck
• A rep who is "top 10%" for 12 quarters is more likely skill
• Always state the number of periods analyzed and what that implies for confidence

Do not proceed to Phase 6/7 until this checklist is complete.

Phase 6: Data Wishlisting

Document gaps and proxies. See references/data-wishlisting.md for patterns.

Format:

## Data Wishlist

| Missing Data | Proxy Used | Quality | Impact on Analysis |
|--------------|------------|---------|-------------------|
| Customer NPS | Support sentiment | Weak | Core finding, needs validation |
| True LTV | 12-month value | Moderate | Acceptable for segmentation |

Phase 7: Output Generation

Choose output tier based on audience and purpose:

Tier	When to Use	Tool
Slides	Executive summary, board deck	generate_pptx_summary.py
Report	Detailed findings, stakeholder review	Markdown/PDF
Notebook	Full analysis, data team handoff	Marimo .py file

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Data Cleaning Workflow

For messy data that needs cleaning before analysis:

1. Profile the Data

python scripts/profile_data.py <csv_file> --output data_quality_report.md

This generates:

• Column-level statistics (nulls, uniques, types)
• Data quality score (A-F grading)
• Suspicious pattern detection
• Suggested cleaning steps

2. Apply Cleaning Patterns

Reference references/data-cleaning.md for:

• Missing value strategies (drop, impute, flag)
• Outlier detection methods (IQR, z-score, domain rules)
• Common transforms (pivot, melt, merge patterns)
• Type coercion recipes
• Deduplication patterns

3. Handle Datetime Issues

Reference references/datetime-handling.md for:

• Timezone conversion patterns
• Date parsing for mixed formats
• Fiscal calendar handling (FY vs CY)
• Period aggregation (daily → weekly → monthly)
• Business day calculations

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Dashboard Building Workflow

For interactive monitoring dashboards:

1. Initialize Dashboard

python scripts/init_dashboard.py <dashboard_name>

This creates a Marimo dashboard with:

• KPI cards row
• Filter sidebar (segment, date range, period)
• Time series trend chart
• Summary data table
• Responsive layout

2. Apply Dashboard Patterns

Reference references/dashboard-patterns.md for:

• Marimo layout patterns (sidebar, tabs, grid)
• KPI card templates with sparklines
• Filter/slider patterns for interactivity
• Data table styling and formatting
• Time series with range selection
• Refresh patterns for live data

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Data Quality Validation Workflow

Before presenting or accepting analytical claims:

Run the Data Quality Validation Checklist

Reference references/data-quality-validator.md for comprehensive checklists:

Statistical Sins:

• P-hacking / multiple comparisons
• Small sample extrapolation
• Missing confidence intervals
• Cherry-picked baselines

Chart Crimes:

• Truncated y-axis
• Dual y-axis manipulation
• 3D charts
• Misleading scales

Logic Fallacies:

• Correlation ≠ causation
• Ecological fallacy
• Base rate neglect
• Survivorship bias

Sanity Checks:

• Does this pass the smell test?
• Back-of-envelope validation
• Historical comparison
• Cross-source validation

---

Excel Output Workflow

For exporting analysis results to Excel with proper formulas and formatting:

Financial Model Standards

Reference references/xlsx-patterns.md for:

• Color coding convention (blue=inputs, black=formulas, green=cross-sheet links)
• Number formatting standards (currency, percentages, multiples)
• Formula construction rules (use formulas, not hardcoded values)
• Common formula patterns for analysis

Verification

After creating Excel files with formulas, always recalculate:

python scripts/recalc.py output.xlsx

This ensures:

• All formulas are calculated (openpyxl doesn't evaluate formulas)
• Zero formula errors (#REF!, #DIV/0!, etc.)
• JSON output shows any errors to fix

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PDF Handling Workflow

For extracting data from PDFs or creating PDF reports:

Extracting Data

Reference references/pdf-patterns.md for:

• Text extraction (pypdf, pdfplumber)
• Table extraction to DataFrame
• OCR for scanned documents
• Command-line tools (pdftotext, qpdf)

Creating Reports

Reference references/pdf-patterns.md for:

• Basic report creation with reportlab
• Professional reports with sections and tables
• Embedding matplotlib charts in PDFs
• Merge/split operations

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Reference Files

Load these as needed during analysis:

Reference	When to Use
references/metrics.md	Calculating SaaS/RevOps metrics
references/biases.md	Interpretation phase, before finalizing insights
references/report-templates.md	Structuring output (pyramid vs consulting style)
references/visualization-guide.md	Choosing chart types, avoiding anti-patterns
references/data-wishlisting.md	Documenting gaps, rating proxy quality
references/data-cleaning.md	Data quality checks, cleaning patterns
references/datetime-handling.md	Timezone, parsing, fiscal calendars
references/dashboard-patterns.md	Marimo layouts, KPIs, interactivity
references/data-quality-validator.md	Data quality validation, detecting issues
references/xlsx-patterns.md	Excel output, financial model standards, formulas
references/pdf-patterns.md	PDF extraction, report creation, manipulation

Scripts

Script	Purpose	Usage
scripts/init_marimo_notebook.py	Initialize analysis workspace	python scripts/init_marimo_notebook.py <name>
scripts/generate_pptx_summary.py	Create slide deck from findings	python scripts/generate_pptx_summary.py <config.json>
scripts/profile_data.py	Generate data quality report	python scripts/profile_data.py <csv_file>
scripts/init_dashboard.py	Scaffold interactive dashboard	python scripts/init_dashboard.py <name>
scripts/recalc.py	Recalculate Excel formulas	python scripts/recalc.py <xlsx_file>

Technology Stack

Tool	Purpose	Why
Marimo	Notebook environment	Pure Python files, reactive, git-friendly
pandas	Data manipulation	Reliable LLM code generation, mature ecosystem
Matplotlib/Seaborn	Visualization	Publication-quality, static, well-supported
python-pptx	Slide generation	Programmatic PowerPoint creation
openpyxl	Excel files	Formulas, formatting, financial models
pypdf/pdfplumber	PDF handling	Extract text, tables; create reports
reportlab	PDF creation	Professional PDF reports

Example Invocations

Revenue analysis:

"Analyze our ARR trends by segment and identify drivers of growth/churn"

Pipeline analytics:

"Build a win rate analysis by deal size and sales rep"

Cohort analysis:

"Create a retention cohort analysis for customers acquired in 2023"

Forecasting:

"Project next quarter revenue based on current pipeline"

Board deck:

"Create an executive summary deck of our key SaaS metrics"

Data cleaning:

"Clean this messy CSV and profile the data quality"

Dashboard:

"Build a dashboard to monitor our key SaaS metrics"

Data validation:

"Validate these findings before I present them"

Excel output:

"Export this analysis to Excel with proper formulas and formatting"

PDF extraction:

"Extract the tables from this quarterly report PDF"

Financial model:

"Create a revenue projection model in Excel with scenario inputs"