data-collection-plan

name	data-collection-plan
description	Plans data collection strategies with tracking requirements, privacy compliance, storage recommendations, and implementation steps. Use when building your business data infrastructure.
allowed-tools	Read Write Glob
metadata	{"author":"matthewhitcham","version":"1.0"}

Data Collection Plan

When to Use This Skill

Use this skill when you need to:

• Plan what data to collect across your business operations
• Design tracking implementations for web, product, or marketing
• Ensure data collection complies with privacy regulations
• Create a data architecture plan for a growing business

DO NOT use this skill for data analysis, dashboard building, or database engineering. This is for planning what to collect, why, and how.

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Core Principle

COLLECT DATA WITH PURPOSE — EVERY DATA POINT MUST ANSWER A BUSINESS QUESTION OR DRIVE A DECISION. COLLECTING "JUST IN CASE" CREATES LIABILITY WITHOUT VALUE.

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Phase 1: Brief

Required Inputs

Input	What to Ask	Default
Business type	"What does your business do?"	Must be provided
Key decisions	"What decisions do you wish you had better data for?"	Must be provided
Current data	"What data do you already collect? Where does it live?"	Minimal — starting fresh
Channels	"Where do you interact with customers? (website, app, email, social, in-person)"	Website and email
Privacy requirements	"Where are your customers? (affects GDPR, CCPA, etc.)"	US-based customers
Technical resources	"Do you have a developer or will you self-implement?"	Self-implement with no-code tools

GATE: Confirm brief before proceeding.

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Phase 2: Design

Data Collection Framework

Organize by business function:

1. Marketing data — traffic sources, campaign performance, ad spend, conversions
2. Product/website data — user behavior, feature usage, engagement metrics
3. Sales data — leads, pipeline stages, close rates, deal values
4. Customer data — profiles, preferences, purchase history, support interactions
5. Financial data — revenue, costs, margins, cash flow
6. Operational data — fulfillment times, error rates, capacity utilization

Data Priority Matrix

For each data point, assess:

• Value: How much does this data improve decisions? (High/Med/Low)
• Effort: How hard is it to collect? (High/Med/Low)
• Privacy risk: Does this involve personal data? (Yes/No)

Start with high-value, low-effort, low-risk data.

GATE: Present the prioritized data collection plan and wait for approval.

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Phase 3: Build

Deliverables

1. Data Collection Inventory

Data Point	Source	Collection Method	Storage	Frequency	Privacy Level
Page views	Website	GA4	Google	Real-time	Low
Email signups	Forms	Zapier → CRM	CRM	Real-time	Medium
Revenue	Stripe	API sync	Spreadsheet	Daily	High

2. Implementation Guide

• Step-by-step setup for each data collection tool
• Integration connections (what feeds into what)
• Testing procedures to verify data is flowing

3. Privacy Compliance Checklist

• Privacy policy updated to reflect data collection
• Cookie consent banner implemented (if required)
• Data processing records documented
• Data retention periods defined
• User data deletion process established
• Third-party data sharing disclosed

4. Data Quality Rules

• Naming conventions for events and fields
• Validation rules for data entry
• Deduplication strategy for customer records
• Regular audit schedule (monthly data quality check)

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Phase 4: Polish

Data Stack Diagram

Create a simple map showing: data sources → collection tools → storage → reporting tools.

90-Day Implementation Roadmap

• Month 1: Core tracking (website analytics, basic CRM)
• Month 2: Marketing attribution (UTMs, conversion tracking)
• Month 3: Customer data enrichment (behavior tracking, segmentation)

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Example 1: E-commerce Business

Priority data: Traffic sources, conversion events, AOV, cart abandonment rate, customer purchase history, email engagement. Tools: GA4, Shopify analytics, Klaviyo.

Example 2: Service Business

Priority data: Lead sources, consultation bookings, close rate, project profitability, client satisfaction scores. Tools: GA4, CRM (HubSpot free), Google Sheets for financials.

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Anti-Patterns

• Collecting everything — more data means more privacy risk, more storage cost, and more noise. Be selective.
• No privacy consideration — collecting personal data without proper consent and documentation is a legal and financial risk.
• Data silos — customer data in 5 tools that do not talk to each other makes analysis impossible. Plan integrations from the start.
• No naming conventions — email_signup, EmailSignup, email-signup, and signup_email in different tools creates chaos. Standardize.
• Collecting without reviewing — data nobody looks at is worse than no data because it creates a false sense of being data-driven.

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Recovery

• User overwhelmed by options: Start with 3 data points that directly answer their most important business question. Expand later.
• Privacy concerns paralyze action: Focus on aggregated, non-personal data first (page views, conversion rates). Add personal data collection only with proper consent mechanisms.
• No technical ability: Recommend no-code tools (Zapier, Google Forms, native platform analytics). Most essential data collection requires zero code.
• Data already scattered: Begin with an inventory of existing data and create a consolidation plan before adding new collection.