With the impending shutdown of Universal Analytics, businesses are under pressure to safeguard their historical data before it’s irretrievably deleted on July 1. This guide outlines a strategic approach to ensure you capture and store vital analytics data effectively.
Key Steps Overview: This guide will cover:
- Planning your data archival strategy.
- Choosing the right tools and methods for data extraction.
- Verifying the completeness of your data archive.
Phase 1: Strategic Planning Before diving into data archiving, it’s crucial to identify what data is essential for your business:
- Identify Key Data: Focus on crucial metrics like conversion and sales data that you frequently use for decision-making.
- Determine Data Range: Decide on the historical range. While you may have data spanning back to the mid-2000s, consider archiving at least from 2018 to cover pre-pandemic trends.
- Review Frequency: Reflect on how often you utilize this data to determine the best archival intervals, whether monthly or quarterly.
Phase 2: Selecting an Archival Method Choose a data archiving solution that aligns with your technical capabilities and resource availability:
- Manual Downloads:
- Pros: Simple and cost-free.
- Cons: Labor-intensive and limited to 5,000 rows per download.
- Process: Manually export data from Universal Analytics for each required metric and time frame.
- Google Sheets via Add-on:
- Pros: User-friendly and integrates directly with Google Analytics.
- Cons: Prone to data sampling issues; limited by Google Sheets’ cell count restrictions.
- Setup: Utilize the Google Analytics add-on in Google Sheets to automate data downloads, adjusting settings to minimize sampling.
- API Utilization:
- Pros: Efficient for large data sets.
- Cons: Requires programming knowledge; subject to API quotas.
- Implementation: Develop custom scripts using the Google Analytics API to fetch and store data programmatically.
- BigQuery Integration:
- Pros: Ideal for complex queries and large datasets.
- Cons: Setup complexity; potential costs.
- Approach: For Analytics 360 users, employ Google’s native export feature to BigQuery. Non-360 users should configure data transfer via tools like Supermetrics to BigQuery.
Phase 3: Validation and Completion After transferring your data, undertake a thorough review to confirm the integrity and completeness of the archived data:
- Cross-Verify: Match archived data with your records in Universal Analytics to ensure no critical data is missing.
- Final Checks: Before the July 1 deadline, ensure all planned data is securely archived and easily accessible for future analysis.
Archiving your Universal Analytics data is crucial to retain valuable insights and maintain compliance with reporting requirements. By following this guide, you can ensure that your business’s historical data is preserved accurately and efficiently.
For more detailed guidance or personalized assistance with your data archiving needs, contact Clever Clicks today! Subscribe to our newsletter for more insights and updates on navigating the digital analytics landscape.