How to backup your Universal Analytics data before it is permanently deleted in June 2024

Your historical data is an invaluable asset that helps you understand long-term trends, make strategic decisions, and benchmark your current performance against historical data. Losing access to this data means losing access to your website's historic knowledge, which might be really important. For that reason, it is critical to decide right now which data you want to back up and where you want to keep that data so that it is easily accessible in the future.

Google's BigQuery offers a robust solution for efficiently archiving large datasets. The other options include Google Sheets, Excel or any other data warehouse you may already have in place. The process for how to get that data there will vary; we recommend working with BigQuery as Google has made moving the data here pretty straightforward. Moving your Universal Analytics data to BigQuery before June 30, 2024, ensures that your historical data continues to serve your business without interruption. Here's how you can start:

1. Evaluate your data needs: Determine which data points are crucial for your business. Not all data might be necessary for future use, so select the most relevant datasets to keep your costs and storage optimised. For example, do you really need all of your historical data, or will the last 2-5 years be enough? Do you need demographic and device data, including browser versions, etc.? Or would traffic and revenue by channel and an overview of your key events over time be enough?
2. What price on historical data: Archiving your data in BigQuery is an investment, but what is the cost of not having access to crucial historical data? The cost of extracting your UA data will depend on the volume and complexity of the data you wish to preserve. Many tools exist to help with that, at varying price points and usability.
3. Plan for ongoing costs: While storing data in BigQuery is cost-effective (approximately £0.70 per month for 100 million rows), you should also consider the costs associated with querying this data, especially if you plan to integrate it with dashboards or other tools for regular use.
4. Choose the method of export: Universal Analytics data needs to be exported via the API if you want to do anything at scale. Simply hitting ‘export to Excel’ won’t cut it. Most agencies have a custom solution to pipeline UA data into BigQuery, and a few tools are available. We use a tool called ‘Analytics Canvas’, which provides technical support with the extraction and table creation in BQ. We then simply stitch it all together for our clients.
5. Seek help: Chat with us if you need help determining how to proceed with data archiving or preserving the best data sets. Our data experts can guide you through the process, ensuring that you make the most of your data archive strategy.  

Please don't wait until it's too late. We expect high demand, creating bottlenecks in June, so we highly recommend getting this done in May 2024. Start archiving your Universal Analytics data in BigQuery to avoid disruptions or disappointment!

If you need assistance or have questions about the process, our team is ready to help. Contact us for a consultation to explore your data backup needs and to discuss any specifics related to your business context.