4 Steps To Preserve Your Adobe Analytics Data When Shifting To Google Analytics 360
Saying you lost all of your historical Adobe Analytics data is not something you want to tell your team after you transitioned to Google Analytics 360! If you worked through the 10 key transition questions when moving from Adobe Analytics to Google Analytics 360, then you know you need to take care of your data before you turn the Adobe lights off!
If you are thinking about the switch, or in the middle of one right now this article will make sure you are not losing anything (i.e. data!) along the way.
Google Analytics 360 Is a Clean Slate
In case you were wondering: no, the Google Analytics system knows nothing of your previous Adobe Analytics implementation. Also, Google is not concerned about preserving data from your Adobe implementation. Nothing in your new Google Analytics 360 account will “automagically” reflect any of your Adobe Analytics histories.
As far as Google is concerned, your world begins the day you go live on its platform. This means that once a transition is complete and Adobe Analytics is shut off, your historical data from Adobe is lost.
Here are a few steps you can take to preserve your Adobe Analytics history and be a hero for not forgetting to pack up your data before moving into that shiny new Google Analytics 360 home.
Step 1: Audit your current Adobe Analytics Setup
To ensure you maintain data continuity in a transition you need to audit your current Adobe Analytics setup. Document implementation details like tagging strategies and plans, that have been used in the past. Include what works, what did not work, and what has been lingering pain points.
If you have multiple Report Suites (RSIDs), you will also want to make sure implementation details for each are well documented, especially if they may have been managed by different teams. Usually, you can find variations between RSIDs because they were managed by different technical, analytics, and marketing teams within large enterprises.
This audit will ensure you have a firm grasp on the current implementation that will shape your understanding of what data should be exported from Adobe Analytics and the condition of that data once it is exported.
Step 2: Map Adobe Analytics Data To Google 360 Data
With a baseline of your current Adobe Analytics implementation in hand, you can start to map out your old implementation to the implementation with Google Analytics 360. One consideration teams are confronted with is how much of a shift in methodology and structure has occurred in a transition and how their data has been impacted. The typical situations are:
- teams adopt the past Adobe methodology to ensure continuity and consistency with past reporting and analysis standards
- teams make a break with the old implementation model used in Adobe and start with a “greenfield” approach defined by Google/Industry best practices
Regardless of your approach, with an audit you can make an informed decision as to the alignment between your historical Adobe Analytics implementation compared to Google Analytics 360 in the event you want to unify both sets of data. This also provides the foundation of your understanding of what type of Adobe exports you may want to employ.
Step 3: Liberate Your Adobe Analytics Data
Ok, there is one thing still missing — you don’t have your Adobe Analytics historical data. Your data is still stuck in the Adobe system in the San Jose data center. Time to liberate it.
Exporting Adobe Analytics Data
First, to start the process of exporting all your data from Adobe you will need to decide on what data you want. There are a few areas like how a report suite was set up and managed, number of report suites (RSIDs), tagging, and global RSID configurations that tend to make the process more or less complicated.
There are two primary methods of export:
- Adobe Data Feeds or Adobe Clickstream: Raw Event Level Data
- Adobe Data Warehouse Report Exports: Summarized Report Data
Adobe Clickstream data is the most comprehensive option available. However, Clickstream feeds can be difficult for teams if they are unfamiliar with them. Not only can the volume be large, but it is expansive. There can be 500+ columns in each hit data export file. Per Adobe, these are the two options available for delivery timing:
- Daily: “Data for each day is delivered after it is processed in a single zipped file, or in multiple zipped files each containing approximately 2 GB of uncompressed data. You receive a single delivery for each day.”
- Hourly: “Data for each hour is delivered in a single zipped file that contains all data received during that hour. You receive 24 separate deliveries for each day, with each file delivered after the data for that hour is processed.”
We came across a use case where there was about 1TB a day in Clickstream data being exported. That is an extreme case, in most cases, it will be < 100MB a day.
Adobe Data Warehouse exports are summarized and normalized. Since the data is summarized you lose the event level information. Compared to clickstream data, Adobe cares for QC issues to increase the fidelity of the data. This is especially true with calculated metrics like visits or page views. With Clickstream data you must do that yourself. In warehouse exports, it is done for you.
Also, the warehouse exports are user-defined which means you get to pick what data goes into each export. The more complex your reporting is, the greater the number of variations of these reports may need to be created for export.
A customer would need to contact Adobe to setup clickstream feeds for delivery. Warehouse data requires no Adobe support involvement. A user sets up one or more report definitions in Adobe UI and configures delivery to a specific location.
If possible, you will want to gather at least 13 months of historical data, if not more. This will allow you to more easily perform year over year comparisons and allow you to preserve data on previous campaign, program, or product performance.
Here are a few more posts on these processes:
Step 4. Audit and Test
While Adobe Analytics does a good job of export delivery, it makes sense to cross-check the files you are expecting to be delivered are in your possession. The simplest approach would be to create a manifest of all the files that Adobe should have delivered. Then, audit your export location for the files on the manifest.
If there are any that are missing, schedule the re-delivery of those files. The goal is to ensure all the data you expect to be delivered was in fact delivered. You may want to verify with Adobe or attempt a different export configuration (if you were using warehouse exports vs Clickstream) in the event of a delivery gap or error.
Another cross-check is to perform tests of export content to make sure the data reflect expectations. Poorly managed accounts, or badly implemented tagging, can reveal serious quality control (QC) issues with your Adobe Analytics implementation and data.
Typically customers do not realize they have QC issues until they start looking at the clickstream data (it is usually less obvious with warehouse exports). It is easier to diagnose these issues while your Adobe Analytics account is still active.
Lastly, if you do find data issues you may want to schedule a re-delivery of the data in question. If the inconsistent results persist after re-delivery, you can note the issues with that data. If you are leveraging Adobe Clickstream feeds, the QA process may be a little more involved depending on the precision in your tests.
Congrats!
Once this process is completed, you should have successfully exported all your historical data out of Adobe Analytics to a secure location of your choosing. Now that you preserved your historical data you can fuse Adobe Analytics data with Google Analytics 360 data to create a unified view of both.
If you have any questions or suggestions about transitioning from Adobe Analytics to Google Analytics 360 do not hesitate to reach out. You can also visit us at https://www.openbridge.com to learn how we are helping other companies with their data efforts.
