Connected data is all around us – financial transactions, communications records, and IT networks, to name a few. By visualizing this complex data, you can reveal unexpected connections and hidden patterns your business needs to understand.
From Fortune 500 companies to governments and small businesses, we’ve helped over 300 customers develop powerful graph visualization applications with KeyLines, our powerful SDK. It can visualize data from virtually any source and turn it into valuable insight.
In this post we’re going to demonstrate the value of graph visualization and why it should be a core part of your web application – it’s not just pretty pictures.
Graph visualization, sometimes called ‘network visualization’ or ‘link analysis’, is the process of visually presenting networks of connected entities using a a node-link model. It enables analysts to intuitively identify trends, outliers and patterns of behavior, helping them make the right decisions, quickly.
Graph visualization is often overlooked or viewed as a non-essential, but it should be a must-have and not a nice-to-have. We’re going to demonstrate why it’s the most effective way to uncover, explore, understand and communicate insight from complex data.
Let’s take a closer look at some of the benefits.
There are many ways to present data to your users, but the node-link structure instantly makes sense, even to people who’ve never worked with graphs before.
This chart, for example, represents a network of businesses and people associated with Donald Trump. Even someone with no data analysis experience can follow the connections and pick out some interesting patterns.
Read this blog for more on how we used graph visualization to explore the TrumpWorld dataset.
With graph visualization, it’s easy to explore relationships and uncover trends, insights and network structures in graph data. This means your analyst tools can be used by the widest possible audience.
The node-link model of graph visualization can be applied to pretty much any dataset. As long as there’s an interesting relationship in your data somewhere, you’ll find value in graph visualization.
Here, we’ve used the GitHub API to visualize how Neo4j built and maintained a project – data that wouldn’t conventionally be presented as a graph. See this blog for more on how we did this.
Whether your users need to visualize email communication, credit card fraud, IT networks, or data that isn’t inherently ‘graphy’ – graph visualization can help. It brings a new dimension to any dataset, revealing insight that would otherwise go undiscovered.
Our brains struggle with processing data, but are great at spotting patterns if the data is presented in a tangible format. Armed with visualizations, we can spot trends and outliers very effectively.
For example, in this chart – representing email data collected by US federal investigators during the Enron collapse – we can quickly identify patterns in the network. Influential nodes are sized larger, making them stand out instantly, and clusters of closely connected nodes are easy to spot.
Integrating graph visualization into your web application will help your users gain insight into their data quickly. Analysts often need to make fast decisions, based on a deep understanding of data. Graph visualization empowers them do that with confidence.
Exploring network data interactively allows users to gain deeper knowledge, understand context and ask more questions, compared to static visualization.
The example below shows a corporation’s IT network. Exploring the data interactively and incrementally uncovers insight that would be impossible to see in a flat, tabular format without losing context or detail.
Now that we’ve shown some of the high-level benefits of graph visualization you’re probably ready to give it a go. There are plenty of reasons why KeyLines should be your graph visualization engine of choice. Take a look at the benefits here.
Ready to supercharge your data analysis with graph visualization? Sign up for a free KeyLines trial here.