Graph visualization: fixing data hairballs

Part 2 looks at ‘the snowstorm‘, and part 3 explores ‘the starburst‘, but first we’ll focus on ‘the hairball’ – a problem that affects many large datasets.

You’ve invested in the latest and greatest big data technology stack. You’ve curated a fantastic data source. You’re absolutely convinced that within this data lie insights that’ll give your end users ultimate power. All they need to do is load it into a visualization platform to reveal beautiful interconnected structures that blur the boundaries between data science and art.

So you evaluate a technology like our powerful graph visualization SDKs, load your data, and wait with bated breath for the results.

None of these will help your users uncover threats or find insight. The difference between success and failure hinges on what you do next.

Do you accept that this is the nature of your data, advise your users to boost their hardware and leave them to it? Or do you spend hours trying to understand the root causes of the problems in your data and designing a visual investigation tool that really works? Why do these beasts appear? What aspects of the underlying data give rise to them? And what can you do about it when the underlying data is not yours to control?

Leave a Comment