In the last post of this blog series on being successful with digital analytics, I talked about how better documentation can help your end users understand your implementation. While you may take your implementation for granted and know all of its ins and outs, your business users don’t have that same luxury. Just a small amount of effort towards documentation can go a long way. In this post, we’ll discuss how better implementation governance can also help your end users.
When I say implementation governance, I am referring to how organized your implementation is and how much of an effort you have made to make it easy for your users to navigate the analytics tool. Most digital analytics tools provide some administration features that can be used to maintain the implementation, but many organizations fail to take advantage of them.
One of the biggest mistakes I see organizations make in the governance area is having inconsistent data sets across different websites or mobile apps. It is best practice to have your different analytics data sets mirror each other so you can easily merge data and re-use implementation components like reports, dashboards, segments, etc. Here you can see an example of an organization using the same data slot for different things, which will likely haunt them down the road.
Dashboard organization is something else you can do to help your users. Most tools allow you to create dashboards, but I suggest that you create a dashboard of dashboards and make that the starting point for end users. This dashboard of dashboards is like a table of contents of your implementation and can be configured to make it easy for end users to find what they are looking for. Here is an example of this concept that I saw at an Adobe Analytics event:
Another thing you can do to help your users is providing a way to denote which implementation items are “approved” or “blessed” by the analytics team. It is not uncommon for users to look at segments or calculated metrics and see hundreds or thousands of them. Sometimes there are even ones with the same names!
In these situations, it is impossible for end users to discern which ones have been vetted and should be used. To remedy this, some analytics tools have a concept of “approved” metrics or segments where an administrator can flag them as being correct. Here is what it looks like in Adobe Analytics:
If your tool doesn’t offer this feature, the other old-school option is to simply create a fake user login and use the name “Analytics Team” and save all of the reports, dashboards, calculated metrics and segments that are official under that ID. If users see the owner as “Analytics Team” they know that it can be trusted.
Speaking of duplicate items and having way too many metrics and segments, I recommend that you review all items in your implementation for stuff that can be removed at least every six months. Since most analytics tools do a poor job of telling you where these items are used, I wouldn’t delete them, but if you find stuff that you think should be deleted, change its name to something like “TO BE DELETED ON 6/1/20 — “ in the beginning of its name. Then tell your users that if they ever see something with that notation to contact you and you will help them swap it for the official version of the item.
Another feature that has made its way to some analytics tools is tags. Tags are ways to classify implementation items in a way that is similar to tags for blog or social media posts. For example, if you have a bunch of metrics or segments related to mobile apps, you can tag them as “mobile” and later search for them by tag. This can be very useful if you have an especially large analytics implementation.
One clever way to use tags is to include all of the elements contained within a calculated metric or segment as tags. For example, if you have a segment that includes a metric and a dimension, you can tag it with their data point names. Doing this allows you to later filter items by data point name and find any items that include that data point:
These are just some of the ways that putting some forethought into your analytics implementation administration can help your end users.
Your homework for this post is to:
- Create a dashboard table of contents. Don’t forget to include a link to a dashboard that contains your data dictionary from the previous post.
- Pick a method to communicate which implementation items are “approved” and mark all approved items for your users.
- Go through all of your implementation items and either remove old, outdated items or duplicate items or mark them for future deletion so you can work towards their removal.
- If your analytics tool has tags, try tagging as much as you can and see if that helps users find what they are looking for. If you have a large implementation consider adding the data point names as tags to make it easier for users to narrow down what they are looking for.
In the next post we will talk about the imprtance of executive support and how to attain it.