This tutorial is designed to give an overview to the Insights panel. The Insights panel allows you to collect cards saving key aspects of your analysis alongside text, charts and descriptive statistics. To start creating insights, we need a dataset and we need to create a project using it. We will be working with the same Fictional-Employees.csv dataset we used in our Setting Up tutorials. We'll use this to create a project focused on clustering employees based on their characteristic scores.

After the project has been built we will use the Graph and Compare panel of your project to save some insights before heading over to the Insights panel to have a look around. We'll add context to the insights you've saved using resizable elements like charts, text and descriptive statistics. Then, we'll look at reordering our insights and presenting them within the Insights panel. Finally, we will walk through the processes of exporting and presenting insights.

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"The purpose of computing is insight, not numbers."

- Richard Hamming

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Your project's Insights panel presents snapshots of your analysis in customizable cards. It gives you a space to save important findings as you progress with your project. Insights cards can contain charts, text and statistics and act like slides in a presentation. You can reorder and present insight cards within a project's Insights panel. On top of this, clicking play at the bottom of an insights card will take you straight to the point in your analysis where you saved the insight.  The Insights panel has been designed to allow you to report on your analysis and offers the ability to export insights individually or as a PDF report.

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Step 1. Quick Project Setup

If you followed our Setting Up introductions and have already setup a project clustering the Fictional Employees dataset using their characteristics as factors, move onto step 2.

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Download the dataset here.

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Otherwise, make sure you have the Fictional-Employees.csv file downloaded to your computer. Upload this to the workspace of your Personal team in Graphext and select it to bring up the project setup wizard on the right-hand side of the screen. For a more detailed tutorial on datasets, have a look at our Intro to Datasets tutorial.

Choose Employees as your analysis type and then Clustering. Then, inside of the Clusters and Network Creation menu, set the Performance Level variable as your target and set the 10 characteristic variables as your factors.

Finally, inside the Network Visualisation menu, set Name as the variable to identify your nodes by and add Gender to the list of pinned variables. That's it. Continue to name your project something like Fictional Employees: Clustering and execute it. Head back to the Projects panel of your Graphext workspace and hold on for a couple of minutes whilst Graphext builds your project.

For a more detailed tutorial on setting up a project, follow our tutorial Intro to Projects.

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Step 2. Creating Insights

Now that you’ve built your project, find it and open it inside of the Projects dashboard belonging to your Personal Graphext team. Then, head straight over to the Insights panel of the project and take in the empty space. There is nothing here and won’t be until you create some insights. You can create insights from the Graph, Trends or Compare panels of your project as well as saving the variable charts inside of your sidebars as insights. After you capture and insight, it gets collected here.

Because Fictional Employees doesn't have a date variable, we can't use the Trends panel. Instead, let's create some insights using the areas of our project that allow us to do so; the variable sidebar charts as well as the Graph and Compare panels.

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Graph Insights

Currently, your Graph represents 100% of your data. Insights are generally more insightful when you find patterns in sub-communities. Let's use filtering to select specific groups of employees in our data. Change the Absolute / Relative dropdown menu so that Relative representation is active. We want to use relative representation to display our data points in proportion to the selection that we've made.

Now, find Coachability from your right sidebar and drag your cursor to select the values for 8, 9 and 10. The employees now displayed in your Graph are all employees with a high Coachability score. Notice from the Performance Level chart in your left sidebar that 50% of these employees have a high Performance Level.

Let's save this Graph view as an insight. From the top of your Graph, select the icon that looks like a line graph with a spark flying out the end. Now, name your insight 50% of Very Coachable Employees are High Performers and click Save Insight. That's it! Head over to the Insights panel to see the snapshot of the Graph, the Coachability and Umap_Cluster variable charts nested within your first insight.

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Variable Insights

Clear your previous filter so that data in your Graph represents 100% of your employees. Let's move on to investigate a specific cluster in your dataset and find out why these employees have been grouped together.

With your representation still set to Relative, select Cluster 4 from the Umap_Cluster variable chart at the bottom of your right sidebar. Once again your Graph has changed to present only employees in this cluster. Browse through the variable charts and notice the Competitiveness values for employees in this cluster are grouped towards the higher end of the range. In fact, it seems pretty certain that employees in this cluster were grouped together in part because they are very competitive.

Click the 3 dots on the far right of the Competitiveness variable card and choose Save as Insight from the menu list. Now, name your insight Employees in Cluster 4 are very Competitive and save the insight.

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Compare Insights

Move over to the Compare panel of the project and select Performance Level as the variable you want to compare. Graphext will automatically generate a series of charts but for now they only represent employees with a high Performance Level. Add two more values into the chart so that you are comparing high performing employees with low and medium performing employees.

Compare charts are sequenced in order of their relevance to the difference between high, low and medium performing employees. These are really valuable charts and tell us lots about what distinguishes these groups. Let's save the first five charts collectively as an insight. Select the insight icon next to the representation dropdown menu. Then, name this insight The Variables that Best Express the Difference between High, Medium and Low Performing Employees.

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Now that we have saved the most important charts comparing employees in regard to their performance level, let's change the values in our Compare panel to represent a different variable.  Remove Medium performing employees from the charts and change the dropdown above High performing employees so that this now represents Umap_Cluster. Let's compare employees from Cluster 1 with all employees in the data. Using the variable dropdown, change the Low performing value so that it represents Everything in the dataset.

You can see from the first variable chart that employees in Cluster 1 have a much lower preparation score when compared with every employee in the dataset. Click the 3 dots from the top right of the chart to save this chart as an insight. Choose Save as Insight from the menu list and name your insight Employees in Cluster 1 have a Low Preparation Score.

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Step 3. Customizing Insights

Head over to the Insights panel and take a look through the insight cards that you've created! You can customize the appearance of insight cards so that they are ready for you to share.

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Deleting Elements

Take a look at the second insight that you created; Employees in Cluster 4 are very Competitive. This card features a large histogram representing the Umap_Cluster variable - something which isn't that useful considering we reference Cluster 4 in the title of the insight. Select the edit icon from the bottom of this insight card. Now, hovering over an element inside the card will bring up a trash icon allowing you to delete that element.

Hover over the Umap_Cluster chart and select the trash icon. It will immediately disappear from the insight leaving you an empty space to fill. Elements that you delete aren't permanently gone. You can add them back into cards using the tab that appears in the bottom after you've delete an element.

You can also see that we have a description block that isn't used and is taking up space. Delete that block from the card.

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Resizing and Moving Elements

Now that you've got some empty space to fill, let's resize the elements in place. You can resize any element by dragging the bottom right corner. Moving elements is a case of dragging the elements to another space in the card.

Drag the bottom right corner of the Competitiveness chart upwards and to the left to make it smaller. You want to make it small enough to be able to leave room for the title to run across the top of the card. Now, drag it down to the bottom of the chart and resize it again so that it runs across the full width of the card. Perfect. Finally, drag your title to the left hand side of the card.

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Adding Elements

Now that your title and chart are in place inside this insight, it makes sense to add some statistics to provide some context to the chart on display. Select the plus icon from the bottom left of your insight card and choose Statistics from the menu list. Then, selecting Coachability will add a table displaying the summary statistics for the Coachability variable. The first row in this table represents every employee in your data. The second row, highlighted by a blue bar, represents the values inside your selection.

Save your changes!

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Step 4. Reordering Insight Cards

Your insights are looking good although it makes sense to change the order of them so that when you come to present or share your findings, you start from the right place. To reorder insight cards, select the Filmstrip View icon from the top of your Insights panel. This brings up a sidebar showing your insight cards in sequence.

The third insight you created shows the compare charts best explaining the difference between High, Medium and Low performing employees. This is probably a good entry point for those approaching your analysis. From the sidebar drag the third insight to the top of the list. That's it. The order in which you present your insights forms a narrative. It's always useful to consider how others will interpret your analysis when deciding which order to present your findings in.

You can test this out by entering Presentation Mode. Select the Presentation Mode icon from the top left of your Insights panel. Now, scroll through the slides using your arrow keys. Does the ordering make sense? Good, now let's export our insights.

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Step 5. Exporting Insights

You can either export all of your insights as a PDF report or individually as image files. Insights that you export get downloaded to your computer so that you can edit them before sharing them.

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Exporting all Insights

To export all of the insights you've created, select Export from the top right of your Insights panel. The document that you'll download will take the name of your project and will be a PDF file. Once, you've selected Export, find a place to save the file on your computer!

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Exporting Individual Insights

The insight that you captured titled 50% of Very Coachable Employees are High Performers is a particularly interesting one. It makes sense to export this separately. Find this insight within your list of cards and select the export icon from the bottom right of the card. This will bring up an Exporting Insight window allowing you to customize the appearance of your card before you export it.

Let's set the background to be transparent. This makes it easy to further customize the insight card in another software tool. Click Export and save the file to your computer.

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