Let's start off this interactive demo with a quick tour showing some of what Interana can do. Don't worry if you don't quite understand everything here — there's more context and explanation in future sections. For now, please just follow each step and take a few moments to reflect on what you're seeing. Consider how you would accomplish something similar today by asking your data team, using your current analytical tools, or writing SQL.
If you only have a few minutes now, focus on completing this tour. Come back when you have more time.
Start from the landing dashboard
If you've just logged in, you should see the default landing dashboard:
You can get back to the landing demo at any time by clicking the Interana logo in the upper left corner of the screen:
The long image on the right is a screenshot of the entire dashboard. Once here, take a minute to scroll up and down the dashboard. You'll see some of the different chart types Interana supports, plus get a high-level overview of Wikipedia changes.
Diving deep into our analytical capabilities
Next step is to dive deep into Interana's most powerful capabilities and highlight some of what makes Interana special. From the dashboard, scroll down until you reach the table chart titled Contentious Articles ordered by Number of Editors (last 28 days). It should look something like this:
We're going to explore how this chart was built. Click the compass icon in the bottom right corner of the chart. This will bring you into the Interana Explorer interface, showing the Query Builder on the left side of the page that looks like this:
We instantly went from a chart on the dashboard to the Explorer, where you can see the results in more detail, directly interact with the chart, and learn exactly how the query was built. The data is coming from the last time the dashboard was refreshed, and you can refresh it now by again clicking the GO button at the top:
This will recalculate the query on the freshest data and give you the result in seconds. A big part of Interana's power is how quickly you can iterate on your queries and follow your intuition when exploring the data. Next, take a closer look at the query builder details:
Let's break it down:
- We're using the Wikipedia dataset and displaying our query results in a simple table view.
- The query spans the last 28 days.
- We're reporting the following two measures:
- The number of unique users who interacted with a contentious article.
- The sum of a custom metric that tracks the number of times a contentious article has been edited.
- The results in the table will be split by the name of the article.
- The statistics are shown for the articles that are in the cohort of contentious articles.
Examine the named expressions
The query is using two custom objects, a metric and a cohort. Move your mouse over to the little circled "i" next to the cohort. It'll turn blue. Click it to bring up the associated tool tip that explains the cohort:
It'll open up a box that includes an automatic definition of the named expression, plus any description supplied by the author at the top:
One other super power of tool tips is the ability to jump directly to the object definition. Click the Edit link to open a box that lets you see how the cohort was defined:
We'll get into the meaning of each of these areas later on. Just notice that the cohort is defined for each article, and checks that the article meets certain conditions (the filter) to be included in the cohort. The other interesting bit about the cohort criteria is that the article was part of a Contentious Articles funnel and reached step 6 at least once.
Click that Cancel button and return to the Explorer.
Let's take a look at the metric in the same way. Click the tool tip link next to the metric:
and then click the Edit link:
Did you notice that the metric is again defined for each article, and that the metric is also using the cohort to figure out which articles to count?
Interana lets you easily use one named expression within another, creating powerful and flexible query components without writing any code. Importantly, these named expressions are simply definitions. There's no work done until the query is run — no indexing or pre-aggregation to waste resources, take time, and slow down the pace of trying out new queries.
Click that Cancel button to return to the Explorer.
Examine the funnel definition
Funnels are a powerful tool for identifying meaningful sequences of steps that actors take on their journeys through the system. Interana can rapidly compute these funnels across all the actors in the dataset — calculating useful metrics for each step and the funnel as a whole.
We've seen that the cohort was partly defined using a funnel, and the metric was using the cohort. So both of them depend on a funnel named Contentious Articles. Let's take a closer look at that funnel. Click the funnel icon (vertical lines of decreasing size) on the left-most navigation panel:
This will open up a new panel that lets you select which funnels to examine. If the All view isn't already selected, click it, and then click on the Contention Articles funnel:
The funnel will take a few seconds to resolve, and then you'll see an interactive visualization that looks something like this:
The grey bars are the steps of the funnel, and the blue lines are sized proportionally to the median time between steps. The steps include statistics on the number of actors matching that step and the dropoff from the earlier step. The lines show the median elapsed time between the surrounding steps.
Hover your mouse over the steps and times to get a sense of the statistics:
Now let's take a look at how the funnel was built. Click on the funnel control box in the upper right, and once the panel opens select copy to open a copy of the funnel definition:
You'll now see a box that looks like this:
The funnel is defined as a sequence of six steps, each of which is looking for event characteristics that represent the step being reached. Toward the bottom of the funnel definition are settings, including the time period within which all the steps should occur. We'll get into the details later, but for now just notice that the steps are simple to define based on the dataset columns and values. There can be as many steps in the funnel as needed, and you can repeat a step if looking to find an event that happens multiple times. All this with no coding.
Click the Cancel button to close the funnel definition.
This was just a quick tour of some of what you'll learn during this live demo. Head back to the landing dashboard by clicking the Interana logo again. The next step is to learn about Interana Living Dashboards in more depth.
Keep in mind that you're using a shared demo system meant for learning by everybody. The dashboards and objects you create will stick around for a while, but we will periodically clean up the system and remove stale accounts.