Don‘t Let Your Historical Google Analytics Data Vanish - A Guide to Backing Up UA Before It‘s Too Late

Hey there!

If you‘ve been using Google Analytics for your website, you‘ve probably heard by now that the long-standing Universal Analytics platform (UA) will be shutting down on July 1, 2023. This means all your historical analytics data will stop processing on that date unless you take steps to back it up.

Losing access to years or decades of UA data would be a huge blow for understanding your website‘s performance and optimizing your digital marketing efforts.

As a fellow analytics enthusiast, I know you’ve invested heavily in compiling this data over time. So in this post, I’ll share my personal strategies and lessons learned for making sure not an ounce of your hard-won UA data gets deleted for good.

I’ll provide actionable steps for backing up your data using both manual methods and migration tools. My goal is to equip you with a tailored game plan so you can embark on this project with confidence.

Let’s dive in!

Why Your Historical UA Data Is Too Valuable To Lose

But first – why should you care about migrating your old UA data in the first place? Can’t you just rely on the new GA4 platform going forward?

While GA4 has some exciting new capabilities, the insights living within your UA account remain incredibly valuable:

You can analyze trends over time. UA gives you a long, continuous view of your website performance that is invaluable for spotting trends, opportunities, and threats as they evolve over months or years. GA4 starts you back at zero.

It captures seasonal or cyclical patterns. With UA data spanning many years, you can surface important seasonal or cyclical patterns in your business, like holiday spikes or summer lows. You can create predictive models from these insights.

Provides context and benchmarks. Your UA data establishes a performance baseline you can use to set goals and evaluate if your website is improving or declining over the long run.

Informs optimizations. Granular UA data can be used to optimize elements like channels, keywords, creatives, and more for future marketing campaigns.

Supplements other data. UA can be merged with CRM, financial, POS, and other systems to connect marketing to business outcomes. This unified view is powerful.

Meets compliance needs. Industries like financial services often must store user activity data for many years to meet regulations.

Enables advanced analysis. Broad historical data sets allow techniques like machine learning algorithms spot valuable patterns that would be impossible to see in GA4 alone.

In short, UA contains the unique digital blueprint of your website and marketing strategy over the years. It’s worth protecting. As analytics guru Avinash Kaushik wrote:

“Comparison with the past is an essential element of analytics. It is an unsound analytical practice to only have a view of the ‘now’.”

Let’s explore how to ensure you can continue comparing to the past once UA goes away.

Challenges With Backing Up Large UA Data Sets

Migrating your historical Google Analytics data sounds straightforward enough in theory. Unfortunately, the reality can get messy fast depending on the volume and complexity you’re dealing with.

Here are some pivotal factors to be aware of:

Massive data volumes – Sites with lots of traffic can accumulate gigantic UA data sets, sometimes spanning terabytes. This makes it incredibly difficult to export the full history without sampling or gaps.
Custom metrics and dimensions – If you’ve implemented custom metrics and dimensions in UA, these need to be carefully ported over to avoid losing that data.
Export size limits – UA‘s built-in exports to Excel or CSV run into row number limits, meaning you‘ll probably need to export in smaller batches.
API restrictions – UA query API calls have restrictions on number of dimensions, metrics, and date ranges you can request in one call.
Large queries = sampling – Massive data volumes lead UA to use sampling, where only a subset of sessions are returned. You need the full deal.
Fragmented data – Batch exporting can result in fragmented data that needs to be meticulously re-assembled into one contiguous set.
Tracking changes over time – Changes in tracking implementation on your site over the years can cause inconsistencies in the data.

As you can see, simply clicking an “Export All Data” button is wishful thinking. To do this right requires a strategic plan tailored to your site‘s size and complexity.

The good news? I’m going to share proven battle-tested migration approaches so you can tackle this project with confidence.

5 Recommended Ways To Back Up Your UA Data

Let’s explore some of the top techniques and tools for migrating your UA data to safety before the shutdown deadline:

1. Export Raw Data to CSV

The most basic built-in option UA gives you is to export your raw data to a CSV file. Here‘s how:

In the UA interface, open the reporting view you want to export.
Use the date picker to select the full date range you want to grab.
Click the "Export" button.
Choose CSV format.
Pick where to save the exported data on your computer.

This works well for quick backups of smaller data sets. But the major downsides are:

Row limits on exports requiring multiple CSVs.
Very time intensive and repetitive for large data.
Lack of customization or transformations.

2. Use Google BigQuery for Scalable Cloud Exports

If you have extensive UA data, Google recommends using BigQuery to both export and analyze it at scale in the cloud. Here are the steps for getting set up:

Create a BigQuery project and enable billing.
Make a dataset to hold your UA data.
Get an addon like Supermetrics to connect UA to BQ.
Authenticate the addon to pull your UA data.
Schedule ongoing automated exports into your dataset.
SQL it up and analyze away!

The benefits here are:

Handles data of any size without sampling.
Can transform and analyze natively within BigQuery.
Automated workflows to keep data in sync.

The downsides:

Need a paid BigQuery account and connector addon.
Steeper learning curve if you don‘t know SQL.

So if you have experience with BigQuery and large volumes of UA data, this can be a great solution.

3. Use a Migration Platform Like Segment

Segment is a popular customer data pipeline tool that moves data from all your apps and websites into one UI where it can be exported.

Here is how Segment can assist with UA migration:

Create a free Segment account.
Install their Google Analytics source integration.
Turn on historical data sync for analytics (with limits).
Set your Segment warehouse as the destination.
Export your now-consolidated data.

The upsides of this approach are:

Simple UI for bringing multiple data sources together.
Syncs back historical data into your warehouse solution.
Broad platform support beyond just UA.

Some limitations:

Limited on amount of backfill history.
Need a paid plan for some key functionality.
Light on transformations during migration.

So Segment is great if you want an easy cloud pipeline for standardizing your customer data from apps, sites, tools, etc.

4. Leverage an Analytics Migration Service

Since UA shutdown affects many major businesses, a crop of specialty migration services has emerged to help customers. These include:

Analytics Safe – Full service migration plus consulting on high-value data identification.
Piped Out – Advanced programmatic migration capabilities at scale.
Upped Game – Hands-on help from Google Analytics experts.
Electric.ai – Automated migrations with value-add data enrichment.
Pattern89 – Full service migration blended with advanced analysis and machine learning.

These services take the work out of UA migration for you. They can overcome limitations like sampling and stitch together massive data. If you can justify the cost, they may yield the best results hassle-free.

5. Roll Your Own Custom Migration Scripts

If you have advanced technical skills, you can programmatically migrate UA using the Measurement Protocol and Core Reporting API. The process looks something like:

Obtain API credentials for your UA view.
Design your migration data schema and transformations.
Script export automation using the APIs on a schedule.
Program any enrichment, combining batches, etc.
Output final migrated data to your destination warehouse.

Going the custom code route gives you maximum control and customizability for migrating data. But it requires significant upfront time investment for development and testing. Only tackle this if you have strong data engineering skills.

Key Factors To Consider When Choosing Your Migration Approach

With the basics of each method covered, let’s zoom out and discuss how you can pick the right path based on your unique situation:

What is your UA data volume? If you only have a few hundred thousand rows, CSV export may be reasonable. For billions of rows, BigQuery or an automated tool is better suited.
How complex is your data? Lots of custom dimensions and metrics? Complex behavior flows? If so, manual CSV migration will be incredibly painful.
What are your technical skills? If you‘re a SQL and data scripting pro, custom programming could work. Otherwise leverage turnkey SaaS tools.
Do you need data merged from other sources? If achieving a “single pane of glass” for customer data is important, investigate consolidation platforms like Segment.
What will you use the data for? If compliance and archival are the goals, storing raw CSVs may suffice. For continued analysis, transform it into an analytics-optimized data warehouse.
What is your budget? Some methods like paid migration services get you to the finish line faster but have higher costs. Factor this into your decision process.

By carefully weighing factors such as these, you can zero in on the best approach for you. Don‘t just blindly follow what works for others.

Expert Tips to Avoid Migration Pitfalls

Through my own trials and tribulations migrating UA data as an analytics practitioner, I’ve learned some helpful tips worth calling out explicitly:

Chunk up exports into smaller batches

Don‘t attempt one giant export. UA limits will likely cause failures. Export in smaller date ranges (monthly, daily etc).

Re-create custom dimensions and metrics

Ensure any custom fields are defined in the destination schema before migrating to prevent data loss.

Validate after migration

Spot check that your exported data matches the original UA tables and that no gaps, duplication, or anomalies occurred during migration.

Document your process

Record all steps you take, naming conventions, caveats uncovered, etc to preserve institutional knowledge.

Build in data enrichment

Augment the data during migration, like adding calculated metrics or integrating other datasets, to make it more valuable.

Pick a future-proof destination

Consider migrating from UA to a modern warehouse like BigQuery rather than just archiving CSVs.

Work with stakeholders

Involve other teams like IT and data scientists to ensure the migrated data works for cross-functional analytics needs.

Google Analytics 360 Customers Get Some Reliefs

Before we wrap up, it’s worth noting that the Analytics 360 suite, Google’s premium paid product, does get some additional allowances during the UA sunset:

Extended support timeline – 360 accounts will continue processing UA data until October 1, 2023 rather than July.
Free migration tools – 360 customers gain access to Google‘s internal Migration Hub and BigQuery exporter to assist with data migration.

So if you happen to be a 360 user, be sure to take advantage of these additional resources Google is providing to make your transition smoother.

But do note – 360 accounts are still losing UA in the end as well. So the same urgency applies to get your historical data backed up sooner than later.

Next Steps To Protect Your UA Data

Phew, we covered a lot of ground here!

By now, you should have a solid grasp of:

Why historical UA data is too valuable to abandon
Big challenges around migrating large Analytics datasets
A variety of methods and tools to tackle UA data migration
Key considerations for picking the right approach for your needs

Here are my suggested next steps:

Take stock of your situation – Log into UA and assess factors like your data volume, use of custom metrics/dimensions, untapped analysis opportunities, etc.

Build your migration plan – With your situation in mind, pick the migration approach that balances capabilities and cost. Outline all steps.

Execute the migration – Take the plunge and work methodically through getting your UA data successfully exported. Celebrate small wins!

Optimize the output – Now that your data is safe, focus on transforming and structuring it so it can power decisions for years to come.

I know tackling a migration like this can be daunting, especially on top of your usual analytics responsibilities. But I‘m confident that with strategic forethought and by applying best practices, you can achieve a successful UA data transition.

Future You will thank Past You big time for not losing touch with the historical website analytics context you’ve worked so hard to compile.

Let me know if any part of the migration journey is giving you headaches! I’m always happy to share tips and perspective. You’ve got this!