Enhance your dashboard design with data visualization

In our earlier blog post, visual network analysis dashboards that work, we used established visualization design theories to build an interactive analysis tool for your dashboard reporting system. We explored the data as a network and a timeline, using clever styling and intelligent grouping to uncover high-level patterns.

This time we’ll make your dashboard even better at sharing context and insight. We’ll use advanced features of KeyLines and KronoGraph – including powerful graph algorithms and pattern of life analysis – to understand specific data connections.

Effective use of color

Last time, we used our visual network analysis tool to create a simplified view of the email traffic inside an organization. Chart entities and timelines were grouped by department.

A KeyLines and KronoGraph visualization tool showing combined nodes and heatmap timelines
So far in our visual analysis tool we’ve used combined nodes (combos) and timeline heatmaps

My focus is on finding out who the HR team emailed most frequently. Let’s see if we can represent the different departments more clearly using custom styling.

Color can be hard to get right, but it’s an effective way to categorize sources and show how they interrelate. For practical tips, read our blog post on choosing colors for your data visualization.

Adding custom colors transforms the timelines. Emails from each department immediately stand out – blue for HR, green for Finance, and so on.

A KronoGraph timeline showing color-coded departments to help identify email communication patterns
Color-coded departments help us identify email communication patterns

Here’s where the patterns become much easier to spot. Right away, I can see that Sharolyn Baumgartner in Finance mostly emails others in her department, because the heatmap representing her emails is almost all green.

A KronoGraph timeline showing the number of emails sent to Sharolyn’s teammates in Finance
The more green a timeline looks, the greater the number of emails sent to Sharolyn’s teammates in Finance

Sammy Chupp in the Management team also sends a lot of email traffic to Finance, which isn’t something I could pick up easily just by looking at the network view.

A KronoGraph timeline showing the number of emails sent by manager Sammy to Finance
Color and opacity reveal email patterns at-a-glance

Powerful graph analysis

For the graph visualization part of my visual network analysis, I want something that’ll give me at-a-glance insight into my email traffic data.

Using advanced graph analytics, I’ve resized the combined nodes representing each department to show the relative communication between them. I’ve also sized the directional links between departments according to the volume of traffic sent.

A KeyLines network chart showing department nodes sized by volume of email traffic
Sizing nodes and links by volume of traffic – one of the sophisticated graph analytics options available in our visualization toolkits

Management is clearly far more communicative than the other departments. The reasonable amount of traffic between it and Finance is in line with the email pattern we spotted earlier for manager, Sammy Chupp.

But back to our goal: to find out who HR talks to. I select the blue HR timeline combo on the right of the network chart. The heatmap for every other timeline changes to shades of blue, so we can focus on communication with HR across the organization.

A KronoGraph timeline filtered to show how much email traffic the HR department receives
Who talks to HR? It’s easier to analyze this by focusing solely on their communications with other departments

The stronger the blue opacity, the more emails an individual receives from HR. We see that Finance talks to HR often, especially Josue Cogdill. Perhaps Josue organizes payroll and gets details from HR on new starters, leavers or salary changes?

Notice that a couple of people in IT speak to HR frequently too. Again, maybe HR are sharing information on new starters or leavers and managing their hardware and security permissions?

Visual network analysis search

In our interactive visual network analysis tool, there’s more scope to provide context compared with a traditional dashboard view.

I noticed from the timeline view that Thi Veiga in Marketing has a reasonable amount of communication with HR. Who are they communicating with exactly?

I’ve found Thi in the network view: they’re here in the chart as part of the Marketing group combo.

A KeyLines network chart showing one of the nodes inside a combo plus the other nodes it's linked to
Combos declutter your network chart but you can reveal the nodes inside at any point

Notice that even though Thi’s part of a combo, I can still see the direct links representing emails to and from other groups. For more information on customizing node groups, see 5 (quirky) ways to style combos for maximum impact.

The links in the chart are aggregated, so they give a nice high-level summary of what’s happening. For a much more granular level, I turn to my timeline view.

Drilling down into event sequences

So far we’ve used the timeline heatmaps to give us a summary view of the data. Let’s take this analysis further and explore individual events in detail.

As I zoom in, I get a detailed breakdown of each individual connection that tells me when emails were sent and received, and by whom.

A KronoGraph timeline showing a zoomed-in view of email traffic
Transition quickly from a high-level overview of communications to a detailed timeline of individual email events

From this I learn that Thi has a lot of communication with Cori Brian and Vito Tony in HR. I can zoom smoothly into the timeline for a more detailed picture.

A KronoGraph timeline and a KeyLines network chart side-by-side showing an individual's email behavior
Focus on the connections of a single entity in both timeline and network views

In KronoGraph, I can zoom into the detail in different ways. I can use my scroll wheel to smoothly bring the details into view. I can also double-click on a particular range in the bars at the top or bottom to zoom to a specific duration. Alternatively, I can marquee zoom to drag across a range and focus on that.

Advanced chart styling with donuts

Our toolkits offer thousands of ways to visualize the same data. Earlier we saw how link widths in our network chart gave us an idea of the volume of communication between departments. Another smart way to do this is through donuts on nodes.

Donuts represent numeric proportions of data on a node. They’re a clever technique for giving users more information about the data underlying the graph view. For best practice advice, see using donut nodes.

Here we’ve used them to give a categorical breakdown of emails between departments.

A KeyLines network chart showing departments as combo nodes with donuts representing numerical proportions of email traffic
If you show proportions of email traffic between departments as donuts instead, you avoid overwhelming users with masses of numeric data

Using our familiar departmental color coding, we can see by the large green segment on the Management node’s donut that they’re more likely to have contact with Finance than any other department. Donuts make it much easier to compare and contrast the levels of inter-departmental communication for each group.

We can add other useful cues to reveal further detail on demand, such as tooltips on individual segments revealing actual numbers of emails. That’s often the kind of thing you’d see on a busy dashboard, but we want to avoid overwhelming the user with too many figures up front. Instead, let users seek out the information they need, exactly when they need it.

Investigating patterns in timelines

Timeline visualization makes powerful analysis methods possible. Here, we’ll focus on pattern of life analysis: a clever technique that helps investigators understand typical behaviors.

When you visualize events over regular time durations (hours of the day, days of the week, etc) similar patterns of behavior emerge. Users can build a valuable picture of what a typical routine looks like for that individual. And once they understand a person’s usual behaviors, they can easily spot anomalies worth investigating further.

Here I’ve focused on a single day of email communications.

A KeyLines network chart and KronoGraph timeline app showing a day of email communications to make patterns easier to spot
KronoGraph wraps data to whichever scale you choose and reveals what typical activity looks like

We can see that some people are emailing in the early hours, but then there’s an obvious gap with only 2 people communicating (Floy again!).

A KronoGraph timeline app showing two individuals with unusual email patterns
Scale wrapping makes it much easier to spot outliers in the data

Back to our focus on HR’s communication patterns. Again, I can use my zoom options to focus on specific emails. It’s a valuable tool for cyber security analysts who identify suspect alerts, then comb methodically through every piece of communication transmitted, millisecond by millisecond.

Why are there HR emails firing off just after midnight? Are they automated? With every new view of the data, we uncover more insight and reveal potential areas for further investigation.

A KronoGraph timeline app focusing on detailed patterns of email traffic between midnight and 2am
With timelines, users can look beyond aggregated views of the data to see specific events, how they unfolded, and how they’re connected.

Build your best visualization tool

Our innovative technology builds interactive visual network analysis tools to complement any data dashboard. Accurate summaries, detail-on-demand and complex analytical capabilities give your users insights that are hard to spot in a static presentation of numbers and bar charts.

Ready to make data visualization an effective part of your dashboard reporting process? Request a free trial

More from our blog

Visit our blog

Registered in England and Wales with Company Number 07625370 | VAT Number 113 1740 61
6-8 Hills Road, Cambridge, CB2 1JP. All material © Cambridge Intelligence 2021.
Read our Privacy Policy.