Overview of Analytics capabilities in Intune

A lot has changed from the traditional on premise managed workplace to the modern workplace managed via cloud power. You no longer have to worry about infrastructure, you can work securely from anywhere and you save money. But where do we go from here? The topic of analytics and user experience is becoming increasingly important. The goal is to reduce problems or to detect them at an early stage. The cloud offers you limitless possibilities for this. In this blog we want to take a look at what Intune currently delivers out of the box and how you can build solutions on it.


There are several levels of analytics. The simplest type of analytics is also called descriptive where you look into the past e.g. via simple reports and can understand what happened. But there are also more advanced types like predictive and prescriptive. Prescriptive is in my opinion still very far away but the topic predictive analytics is becoming more and more important. We want to make forecasting and detect errors before they occur or before they become significant.

There are also several ways to establish analytics capabilities. There are cases where you can use out of the box capabilities such as endpoint analytics, but there are also cases that are so specific that it is important to have data and develop your own solutions based on the data. In this blog we want to take a look at what Intune delivers out of the box in terms of analytics solutions, but also what data is provided to work with.

As you can see from my Ignite summary post, microsoft has also announced to develop more and more in this direction with Intune in the future.

Endpoint Analytics

With Endpoint Analytics, Intune offers a very powerful tool. Endpoint analytics includes several reports such as Application reliability this report gives you an overview of which applications in your environment crash often. You can also break this down to individual devices and see how device models or OS versions behave differently. This possibility is also offered by the Startup performance report which shows the processes that slow down the startup of the devices or what are the reasons for reboot like updates, blue screen of death, user trigger,… In addition, there is also the Work from anywhere report that shows you how well prepared you are for productive from anywhere. With this report you get a basic overview and some hints what you can improve to increase the user experience. But you can also export most of the information shown in the reports via Graph. How you can get this data I explained in detail in this blog:

But the most important thing that Endpoint Analytics offers is the Proactive Remediation script feature. With this feature you can not only detect and monitor problems via a detection script, but you can also directly fix them via a remediation script. Here are a few examples from my blogs:


Intune offers a lot of reports that are decentralized in the different categories but there is also a separate report section. The reports help you to see which apps currently have installation problems, which config profiles are on error, the deployment state of update rings and much more.

These repeorts can be used for troubleshooting in case of an error or for proactive monitoring but let’s be honest who looks at reports regularly? The solution is to let the computer do the monitoring. Everything you see in the reports can also be queried via graph and using Azure Services or custom developments, this data can be analyzed and notified in case of a problem. You can find some blog posts on how this works on my site.


As mentioned in Endpoint Analytics, you can query and process almost everything you see in the Intune portal with graph. For example, you can analyse the error rate of app installations or how secure the complaint state of your devices is. Here you can. checkout this blogs from me:

In addition, the device object contains a lot of useful information for further processing, e.g. detection of devices with less free storage. I used a lot of this information in the Intune Device Troubleshooter tool. You can try it out. Open the graph explorer and execute the following query:


There is also a possibility to use graph to export whole reports. How this works is realy good explained here;


Intune has a connection to log analytics and it is possible to collect log entries about changes to configurations, complaints status or device status and to evaluate them via the powerful KQL language or to create dashboards via workbooks. Of course, these logs can also be evaluated with the help of machine learning and thus anomalies can be detected or they can be used to check whether named conventions are followed in configurations. There are endless examples and instructions on what you can do with the logs.

A good resource is for example the github repository from Ugur Koc

Data warehouse

With the Data warehouse microsoft offers a very cool source to access data from Intune and this even with a history. The data warehouse can be used to create evaluations or reports or to learn how certain values have changed over time. I went into more detail about the data warehouse in a blog of mine and also showed you how to create a PowerBi dashboard. In this blog you can also find an example dashboard to download


Intune currently offers a lot of possibilities to work with data and already has some out of the box analytics possibilities. But there is still room for improvement. For analytics and artificial intelligence, data is the gold. There could be more raw data available or there could be more possibilities like alerting or more out of the box analytics. But I am sure we will see a lot more in this area in the coming years.
If you want a deep dive into a topic around analytics then let me know and I’m happy to blog about it and share my knowledge.

2 thoughts on “Overview of Analytics capabilities in Intune

  1. The Internet of Things and artificial intelligence have a remarkably synergistic relationship. We can say that artificial intelligence is the brain of business, and the Internet of Things is the body. Artificial intelligence, especially machine learning, is capable of evaluating options, learning from experience, and making smart business decisions. The Internet of Things, like the body, makes it possible to feel and act. The IoT provides both the data that AI needs and the physical means to act on AI decisions.
    Thanks for the work you’ve done!
    it was very helpful to read! Keep it up


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