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Analygo CORE Framework

website·Indexed 31 days ago

https://analygo.co/

A comprehensive data analytics framework consisting of four phases: Collect, Organize, Refine, and Expose. The documentation provides technical guides on data collection (Piwik PRO, Matomo, GTM, server-side tracking), data organization (BigQuery, Dataform, Python notebooks), privacy compliance (Consent Mode v2), and data refinement and exposure.

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What's inside Analygo

  1. Overview of Matomo interface and reporting

    Matomo provides several built-in reporting sections to analyze user behavior:

    • Dashboard: A customizable overview of account analytics.
    • Visitors: High-level user data and KPIs over time.
    • Behavior: Detailed user interactions and events.
    • Acquisition: Traffic source and generation analysis.
    • Ecommerce: Sales data (requires ecommerce tracking configuration).
    • Goals: Tracking of key interactions/conversions.
    • Funnels: Customizable journey tracking (e.g., purchase or application flows).
    • Forms: Analysis of website form submissions.
    • Media: Performance tracking for video and audio content.
    • Custom Reports: User-defined reporting views.

  2. Overview of Server-Side Tracking Transformations

    Transformations in server-side tracking allow developers to control the data sent to vendors by allowing, excluding, or modifying event parameters. Crucially, transformations do not modify the original event; instead, they create a modified copy of the event to be sent to the destination, ensuring the original event data remains intact for other tags.

  4. Overview of server-side tracking transformations

    Transformations in server-side tracking allow developers to allow, exclude, or modify data within an event to meet business requirements before the data is sent to a destination vendor. Crucially, transformations do not modify the original event; instead, they create a modified copy of the event to be sent to the destination, ensuring the original event data remains intact for other tags.

  5. Overview of the Organize phase in the CORE framework

    The Organize phase transforms raw collected data into structured, accessible formats to enable scalable analytics. It balances two primary processing methods: batch processing via data warehouses/lakes for historical analysis and real-time processing via stream layers for time-sensitive use cases.

  6. Overview of Matomo interface and reporting sections

    Matomo provides several specialized reporting sections:

    • Dashboard: Customizable overview of the analytics account.
    • Visitors: User data and KPIs over time.
    • Behavior: User interactions and events.
    • Acquisition: Traffic generation sources.
    • Ecommerce: Sales data (requires ecommerce tracking configuration).
    • Goals: Key interactions on the website.
    • Funnels: Customizable tracking for user journeys (e.g., purchase or application flows).
    • Forms: Analysis of website form submissions.
    • Media: Performance tracking for audio and video content.
    • Custom Reports: User-defined reporting views.

  7. Overview of Dataform for BigQuery transformations

    Dataform is a dedicated transformation tool used to convert raw, unusable data (such as GA4 raw exports with nested fields) into production-ready tables. It provides several advantages over using plain SQL in BigQuery, including version control for collaboration, the ability to sequence and manage dependencies between tables, and the creation of reusable code blocks to reduce duplication.

  8. Implement the Organize phase of the CORE framework

    The Organize phase transforms raw collected data into structured, accessible formats to support scalable analytics. It involves two primary processing paths:

    1. Analytics Warehouse / Data Lake: Used for centralized historical data storage, batch processing of large volumes, and cost-optimized storage tiers.
    2. Real-Time Stream Layer: Used for low-latency, sub-second processing of high-volume event streams to power real-time dashboards and alerts.

  9. Understand the GA4 BigQuery data hierarchy

    When GA4 is linked to BigQuery, data is organized in a three-tier hierarchy:

    1. Project: The top-level container created during BigQuery account setup.
    2. Dataset: A bucket within the project where GA4 tables are stored. The dataset follows the naming convention analytics_<your_project_ID>.
    3. Tables: Individual tables within the dataset that store specific types of data (Events or Users).

  10. Implement Basic Consent Mode for Google Ads

    Basic Consent Mode is a stricter implementation where Google tags are completely blocked from firing unless the user has explicitly granted consent.

    To implement:

    1. Enable Consent Overview in the Google Tag Manager admin section.
    2. Configure the Google Ads tag settings to require consent. If consent is not granted, the tag will be blocked and will not send any data (including pings) to Google.

  11. Deliverables and artifacts for data organization

    A successful data organization phase should result in the following outcomes and artifacts:

    Required Outcomes:

    • Configured data warehouse or data lake.
    • Designed data pipeline architecture.
    • Implemented ETL (Extract, Transform, Load) processes.
    • Established data quality monitoring and security access controls.
    • Real-time stream processing setup (if applicable).

    Key Artifacts:

    • Data Warehouse Schema: The defined structure for historical data.
    • ETL Pipelines: The actual processes moving and transforming data.
    • Stream Processing Setup: The infrastructure for real-time data.
    • Data Catalog: Documentation of available datasets and schemas.
    • Data Quality Reports: Dashboards for monitoring and validation.

  12. Configure Consent Mode v2 in Google Tag Manager (GTM)

    To manage consent settings within GTM:

    1. Enable Consent Overview: Navigate to GTM container > Admin > Container settings > Enable consent overview. This allows you to manage all tag consent settings from a single dashboard.
    2. Using CMP Templates: If using a CMP template (e.g., CookieYes), ensure ad_user_data and ad_personalization are enabled in the template settings. If these parameters are missing, try deleting and re-adding the template from the GTM library.
    3. Tag-Level Configuration:
      • For Advanced Mode: Set the tag to "No additional consent required."
      • For Basic Mode: Require specific consent types before the tag executes, or use a blocking trigger.