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The Art of CTO Technology Tree is an interactive AoE-style progression map that visualises maturity across engineering domains — from ad-hoc practices to elite capability — with actionable steps, effort estimates, and cross-domain dependencies.

Tech Tree · Data

Data & Analytics Maturity

Advance your data capability from manual spreadsheets to a self-learning intelligence platform. Each node represents a concrete data engineering or analytics practice with steps, effort estimates, and the dependencies that mirror real-world data stack evolution.

Maturity tiers

  1. Spreadsheets

    Data lives in spreadsheets and people's heads. Reporting is manual, slow, and inconsistently defined.

  2. Warehouse

    A central data warehouse consolidates sources. Dashboards replace spreadsheets. Analysts self-serve structured reports.

  3. Real-time

    Streaming pipelines deliver fresh data in seconds. Operational dashboards react instantly to business events.

  4. ML / AI

    Machine learning models augment decisions, personalise experiences, and surface insights no human analyst would find at scale.

Tracks

  • Collection

    How data is captured, ingested, and made available for downstream use.

  • Storage

    Where data lives, how it is organised, and how it is governed.

  • Processing

    How raw data is cleaned, joined, aggregated, and modelled for analysis.

  • Intelligence

    How data drives decisions — from human dashboards to automated ML systems.

All capabilities (15)

Spreadsheets

  • Executive Dashboards

    A small set of consistent dashboards gives leadership a daily view of business health — replacing ad-hoc spreadsheet pulls.

    dashboards · bi · reporting · executive

  • Operational Database Exports

    Production database tables are exported on a schedule so analysts can query business data without hitting the live transactional system.

    export · etl · database · foundation

  • Product Event Tracking

    User interactions are captured as structured events with a consistent schema. This is the foundation every downstream analytics capability depends on.

    tracking · events · analytics · foundation

  • Shared Metric Definitions

    Key business metrics (revenue, DAU, conversion) are defined once in a central document and agreed across teams. Eliminates the "which number is right?" debate.

    metrics · governance · alignment

Warehouse

  • Cloud Data Warehouse

    A columnar, cloud-native warehouse (Snowflake, BigQuery, Redshift) centralises all analytical data with scalable compute separate from storage.

    warehouse · snowflake · bigquery · redshift

  • dbt Data Models

    SQL transformations are version-controlled, tested, and documented using dbt. Analysts own the transformation layer with software engineering discipline.

    dbt · sql · transformation · data-modelling

  • ETL / ELT Pipelines

    Automated pipelines extract data from all sources, load it into the warehouse, and transform it into analytics-ready models on a reliable schedule.

    etl · elt · airflow · fivetran · pipeline

  • Self-Service Analytics

    Non-technical stakeholders can answer their own data questions using a governed BI layer — without waiting for an analyst or writing SQL.

    self-serve · bi · semantic-layer · looker

Real-time

  • A/B Testing Platform

    Product changes are validated through randomised controlled experiments with statistical rigour. Every significant feature launch is an experiment with a clear success metric.

    ab-testing · experimentation · statsig · product

  • Data Lake

    Raw, unprocessed data from all sources lands in object storage in open formats (Parquet, Iceberg). The lake is the foundation for ML training datasets and ad-hoc exploration.

    data-lake · parquet · iceberg · s3

  • Real-Time Analytics

    Business-critical dashboards refresh in seconds using a real-time OLAP engine, enabling operational decisions based on the current state of the business.

    real-time · olap · clickhouse · druid

  • Streaming Ingestion

    Events flow from producers to the analytics layer in seconds via a durable message stream. Batch ETL is complemented or replaced for high-velocity sources.

    streaming · kafka · kinesis · real-time

ML / AI

  • Feature Store

    A centralised store serves pre-computed features to both offline training pipelines and online inference endpoints — eliminating training-serving skew.

    feature-store · mlops · feast · tecton

  • ML Training Pipeline

    Model training is automated, reproducible, and tracked. Every experiment logs hyperparameters, metrics, and artefacts so results are comparable and models are promotable.

    ml · mlops · mlflow · training

  • Recommendation Engine

    A production recommendation system personalises content, products, or actions for each user, increasing engagement and conversion at scale.

    recommendations · personalisation · ml · ranking

Interactive view

Other tech trees

Frequently Asked Questions

What is a technology tree?

A technology tree (tech tree) is a visual progression map inspired by strategy games like Age of Empires. It shows capabilities organised by domain (columns) and maturity level (rows), with dependency lines showing what must be achieved before advancing. Each node includes effort estimates, actionable steps, and links to relevant tools.

How do I use the tech tree for my organisation?

Select an organisational tree (like Engineering Org Maturity or Security & Compliance), then mark nodes as completed based on your current state. The tree automatically highlights what is available to work on next based on prerequisites. Click any available node to see the concrete steps required to achieve it.