Technology | Databases
How Graph Databases Help Visualise Data
How Graph Databases Help Visualise DataBack
Last week we took a look at graph databases and the mechanics that make them super useful in the modern world. This week we’d like to take a look at how graph databases can enable data visualisation in some really interesting ways.
Due to the fact that they focus on the connections between data points, rather than the data points themselves, potential visualisation options are varied compared to your average pie or bar chart.
How do graph databases work?
For a full rundown on how graph databases work, we suggest you check out last week’s article: Graph Databases – Why relationships need real focus. Just in case you don’t have the time, here’s a quick refresher.
Traditional relational databases focus on having data points organised into a predetermined structure, meaning relationship queries have to be run between larger datasets. Graph databases store the relationship information at an individual record level, making understanding the relationship between data points much, much easier.
There’s three bits of terminology that are most important when looking at graph databases. Nodes contain data, edges link between nodes, and properties describe attributes that nodes and edges have. Every graph database is made up of nodes, connected by edges, all described by properties.
Why should we visualise data?
We, human beings, have evolved to recognise patterns. In primitive times, knowing the significance of an unfamiliar visual could mean the difference between life and death. That skill hasn’t gone away, but it does have different applications now.
Alpha-numeric characters are a relatively recent introduction into our world and not something we instinctively recognise as important. When they’re lined up neatly they can be fascinating, but they might not spark the brain in the same way a visual indicator would.
By taking data that our conscious brain wants to understand but our unconscious tries its best to ignore (so it can keep an eye out for predators and the like), and giving it visual representation, we can make flat information come to life.
On a less evolutionary level, data visualisation can act as a shorthand. You can represent a decrease in house prices year-on-year by presenting pages of data, collated, referenced, and indexed. Or, you can present a line graph, charting the same information in an easily digestible fashion.
How do graph databases help us visualise data?
Relational databases give us access to the kind of charts and graphs you’ve seen a million times. Percentages and totals, growth and decline all represented as lines, bars or tasty pies. Graph databases give us the chance to see how data points relate to each other.
Abstract visualisations using graph databases
An abstract visualisation of the data within a graph database would use the nodes, edges and attributes themselves to dictate the visualisation. This is very much about putting down the information you’ve recorded, as it’s been recorded.
Nodes with many edges might be larger, leading off to nodes connected by fewer edges, and then just a single edge, with each node decreasing in size. This is a simple level of visualisation, but already you can see how it might help in conveying the meaning behind that data.
If you think about census data for example, a large central node might be a city. Then areas within that city might be smaller nodes. Going further, you can have even smaller nodes representing families, couples or single people. Instantly you can see, using this sort of visual, demographic breakdowns and how they relate to location.
Comprehensive visualisations using graph databases
With a bit of extra effort, you can provide context for your graph database visualisations. Thinking of the above example, instead of just having the location and census data mapped out on a blank background, how about mapping it out on a map?
Again, this gives the pattern recognition instinct in our minds a helping hand. If we’re familiar with the map, we can instantly start placing those nodes in context. We understand that London relates to this node, or Liverpool to that one.
Thinking about it in terms of a business, you could have nodes representing different departments mapped out onto the floor plan of your office. You could show at a glance how finance is flowing to marketing or development. You could visually understand where staff engagement is at its highest and start analysing the edges to see what relationships between data points matter most in making your workplace run as smoothly as possible.
It’s really one of those times where the phrase “the only limit is your imagination” is true. With some creative thinking, you can turn unappealing data into a visually stimulating presentation, easy to understand and instantly engaging.