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Digital Marketing Analytics and Experimentation

Data Preparation and Organization

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Data preparation is the foundation of analytics; without clean, organized, and complete data, every insight you produce will be unreliable.

Stages of Data Preparation

  1. Consolidate your data sources: gather data from all platforms (GA4, CRM, ad platforms, e-commerce tools) into one structured place;
  2. Clean up the data: remove duplicates, fix typos, align formats, and standardize naming across all files and platforms;
  3. Check for missing or broken tracking: verify that UTMs, pixels, events, and integrations are working so no data gets lost;
  4. Use AI to speed up cleaning and pattern detection: leverage tools like ChatGPT ADA, MonkeyLearn, or Google Sheets AI to automate repetitive cleanup tasks;
  5. Build a long-term organizational system: create naming conventions, folder hierarchies, and documentation to keep data structured and understandable over time.
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Match the step to its purpose:

→ Ensures unified cross-channel reporting;
 → Removes duplicates and corrects inconsistencies;
 → Detects missing tags, broken tracking, or gaps;
 → Maintains long-term organizational clarity;
 → Speeds up pattern detection and data standardization.

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Data Preparation and Organization

Data preparation is the foundation of analytics; without clean, organized, and complete data, every insight you produce will be unreliable.

Stages of Data Preparation

  1. Consolidate your data sources: gather data from all platforms (GA4, CRM, ad platforms, e-commerce tools) into one structured place;
  2. Clean up the data: remove duplicates, fix typos, align formats, and standardize naming across all files and platforms;
  3. Check for missing or broken tracking: verify that UTMs, pixels, events, and integrations are working so no data gets lost;
  4. Use AI to speed up cleaning and pattern detection: leverage tools like ChatGPT ADA, MonkeyLearn, or Google Sheets AI to automate repetitive cleanup tasks;
  5. Build a long-term organizational system: create naming conventions, folder hierarchies, and documentation to keep data structured and understandable over time.
Все було зрозуміло?

Як ми можемо покращити це?

Дякуємо за ваш відгук!

Секція 3. Розділ 1
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