In this lesson we will discuss how a Google Analytics account is structured,
and the implications this has for data collection, processing and reporting.
A Google Analytics account is simply a logical way for a business to group data from all
of its digital assets together.
There are also certain configuration settings that you apply to your entire account, like
managing the users who have access.
Within each account, you can have one or more properties that independently collect data.
Each property is assigned a unique tracking ID
that tells Google Analytics exactly which data should be collected, stored and reported
together.
Typically you create separate accounts for unique businesses or distinct business units.
Then you can create unique properties within that account for the different websites,
mobile applications, or other digital assets that belong to the business.
This approach makes it easy to view the data from an individual digital asset.
However, it doesn't allow you to view data in aggregate for multiple assets
since the data for properties is physically stored and reported in different places.
If you do decide you want to view data for two assets together instead of separately,
you must adjust your implementation to collect the data together in a single property
using the same tracking code across both assets.
For example, if you use this technique to track two different websites with the same
tracking code,
Google Analytics collects, and reports on, both websites in the same property.
This is commonly called rollup reporting.
Refer to the resources in this lesson for more details on rollup reporting.
For each property, you have the option to create different views of your data.
A view lets you define a unique perspective of the data from a parent property.
You use the configuration settings in your account to define each view.
Usually you create multiple views for each property in order to protect and manage your
data.
For example, if your organization has different sales teams for various geographic regions,
you might want to create a specific view for each region.
Then give each sales team access to the relevant view.
That way, your sales team members can easily see just the data that is important to them.
We recommend that you have at least three views for each property.
By default, you have one unfiltered view that is automatically generated when you create
a property.
Don't apply any settings or configurations to this view since it is the backup for your
data.
Second, you should have a master view.
This view should have all of the settings needed to transform your data into useful
information.
Finally, you should have a test view.
If you need to make changes to your configuration test them using this view first.
Once you know the impact to the data you can then apply the same change to your master
view.
This type of account and view structure helps protect your data.
Remember, once Google Analytics processes data from your website or app the data can
never be changed.
So, if you have a bad configuration setting, and Google Analytics processes your data,
you could have inaccurate data.
It's also important to know that once you delete a view it's gone forever.
So having a backup view, like the unfiltered data view, is very useful.
One more tip: when you create a new view
Google Analytics does not automatically copy any of the historical data in the original
view to the new view.
You'll only have data from the date you create the view onward.
To help understand how you would organize your account structure in real life, let's
take a look at an example for our fictional outdoor company.
Let's say our company has developed a website and a mobile application for visitors to shop.
In this scenario, we would create one property for the mobile store and one property for
the website.
Remember, within each property, we should always have an unfiltered data view.
This is the view that collects all of the data for the property and has no configuration
settings.
In addition to the unfiltered data view,
we should create a master view that has been configured to match our business needs from
our measurement plan.
For example, for the store website, we could create a filtered view that excludes any data
from our internal IP address.
This data represents traffic from our employees, which we don't want mingled with our customer
data
And, as mentioned previously, we should have a test view to try new settings.
For the mobile app, we would also have an unfiltered data view, a master view and a
test view.
In addition, we could create one view that only shows tablet traffic and another that
only shows smartphone traffic.
Again, the structure of your reporting views is entirely up to you,
but always remember to keep an unfiltered view that you never delete.
Because the structure of your properties and views affects your ability to achieve your
long term reporting goals,
we strongly recommend that you develop your measurement plan prior to setting up your
account, properties and views.
Now, let's have you practice working with properties and views in Google Analytics.
Visit the account you created previously,
and set up your properties and views according to the instructions in the activity.