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ColoredCow’s central analytics brain — data architecture, metric definitions, investigation playbooks, dashboard principles, and insight-generation frameworks.

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ColoredCow · data-analytics-core

Purpose

data-analytics-core is the single source of truth for the Data Analytics vertical at ColoredCow. It defines how we think, design, structure, validate, and deliver analytics across all clients and internal initiatives.

This repository is the operating system behind our analytics work. It contains the principles, processes, mental models, templates, and frameworks that guide:

  • how data flows from source → warehouse → dashboards
  • how metrics are defined
  • how insights are generated
  • how investigations are conducted
  • how dashboards are designed
  • how reporting is standardized

This is not a collection of dashboards or SQL models. This is the blueprint behind all analytics.


Repository Structure

01 — Foundation

Core principles behind analytics at ColoredCow:

  • mission & philosophy
  • glossary
  • data quality rules
  • ethics & privacy
  • levels of analytics (reporting → insights → action → outcomes)

02 — Architecture

How data moves, lives, and is governed:

  • data sources overview
  • pipeline & warehouse architecture
  • naming conventions
  • folder structure
  • access control guidelines

03 — Methodologies

How we think and how we work:

Investigation

  • asking better questions
  • SQL debugging and validation
  • confidence vs evidence
  • model validation patterns

Visualization

  • dashboard design principles
  • business metric definitions
  • color & chart usage rules
  • storytelling with data

04 — Data Models

Reusable, cross-client data structures:

  • ecommerce models (orders, customers, products, cohorts)
  • marketing analytics (campaigns, attribution, funnels)
  • operational models (ticketing, lifecycle)
  • cross-client standards

05 — Processes

Repeatable systems that ensure reliability:

  • data onboarding
  • scheduled jobs
  • warehouse update workflows
  • reporting cadence
  • archiving
  • QA & verification

06 — Templates

Standardized formats for:

  • insight writing
  • RCA (analytics-specific)
  • dashboard specification
  • client reporting

Plus GitHub issue templates for all data-related requests.

07 — Examples

Real, anonymized examples to learn from:

  • sample RCAs
  • sample insights
  • dashboard screenshots
  • worked examples

How to Use This Repository

1. Start with the Foundation

If you are new here, begin with /01-foundation. It defines the mental model and expectations.

2. All analytics decisions must be documented

Use GitHub Issues for:

  • metric definitions
  • naming updates
  • data model changes
  • dashboard specs
  • RCA investigations
  • pipeline enhancements

Every important decision remains traceable and reviewable.

3. Use feature branches

  • No direct commits to main
  • Every change → branch → PR
  • At least one maintainer review required

This maintains clarity, consistency, and quality.

4. Treat this repo as the “brain,” not the “output”

Dashboards, SQL models, pipelines, and reports live elsewhere. This repo holds the thinking behind them.


👥 Ownership & Review Process

Owner

  • Pokhi (Data Analytics & Engineering Strategy)

Core Maintainers

  • Analytics Leads
  • BI Developers
  • Data Engineering

Changes that impact standards, naming, or data models require at least one maintainer approval.


Why This Repo Matters

This repository ensures that analytics at ColoredCow is:

  • consistent
  • reliable
  • high-quality
  • scalable
  • client-ready
  • culture-driven

It allows us to build analytics as a repeatable, independent vertical — not dependent on any individual.

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ColoredCow’s central analytics brain — data architecture, metric definitions, investigation playbooks, dashboard principles, and insight-generation frameworks.

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