Why Visualize Networks?

How network visualization can improve your data to insight journey

What is a network?

In the context of network visualization, a network is a dataset whose structure contains interconnected entities. When they are visualized, the entities are called ‘nodes’ and the connections are called ‘links’.

Tabular data shows us entities in isolation (these will be our nodes), but it’s hard to understand the dynamics and connections within our data

Tabular data shows us entities in isolation (these will be our nodes), but it’s hard to understand the dynamics and connections within our data.
Networks help us see how entities in data are connected to one another, adding a new dimension to our understanding. Here we’ve added the links
Networks help us see how entities in data are connected to one another, adding a new dimension to our understanding. Here we’ve added the links.
Applying a layout to our network helps to untangle the connections and starts to show some kind of structure
Applying a layout to our network helps to untangle the connections and starts to show some kind of structure.
Using analysis measures and some design touches, our network becomes a valuable analysis tool, revealing patterns and dynamics completely missing when looking at flat data
Using analysis measures and some design touches, our network becomes a valuable analysis tool, revealing patterns and dynamics completely missing when looking at flat data.

Why networks?

Looking beyond ‘flat’ data to study connections and networks is useful for so many reasons.

  1. A more accurate model: The network structure more accurately represents real life. There are connections everywhere – not just between people or machines, but between any entities. By seeing these all as part of one interconnected system, data becomes richer, more realistic and more informative.
  2. See connections and context: Networks allow us to focus on the connections and context of data, as much as on the data elements themselves. Often these two can provide more insight and highlight trends more clearly than looking just at the data as separate elements.
  3. Complexity simplified: The network structure also neatly simplifies very complex situations. Vast amounts of data, including semi-structured, incomplete and varied data from disparate sources, can be summarised as a network in a way that’s not possible with a flat, tabular data format.
  4. Find patterns and ignore noise: Networks help us understand connections in aggregate and individually, meaning we can focus closely on the data of interest – finding the insight in our ‘big data’.
  5. Make inferences and predictions: Finally, we can sometimes use the connections in our network data to infer missing information. With a strong understanding of our network and a knowledge of simple concepts, such as triadic closure, and contagion, we can hypothesise about connections that might not be explicit in the data, helping us make predictions about the future.

Why visualize networks?

Network visualization software, like KeyLines, allows us to more easily understand the connections in data:

  1. Faster, more efficient decision making: By combining machine power with human’s ability to recognize visual patterns, network visualization brings more efficient processes and faster decisions – helping you take the correct action, sooner. KeyLines also permits real-time visualization, aiding the discovery of problems as they occur, speeding up response times.
  2. More accurate decisions: By visualizing complex data within its context, we can also perform more rounded data analysis based on a 360-degree view. The decisions driven by this understanding are likely to be better and more accurate – helping to avoid costly mistakes.
  3. Gain richer intelligence:Network visualization allows us to understand the dynamics in our data at any level – node by node, network by network, or as an overall system. This provides richer intelligence.
  4. Make data accessible to all: KeyLines network visualizations are interactive and 100% customizable. This makes them intuitive, impactful and enjoyable to use, helping you to put data analysis in the hands of all users.
  5. Improve reporting processes: Finally, network visualizations are easier to understand than data alone. This makes reporting simpler and more effective – either by sharing visualizations as PNG files or as interactive charts. KeyLines makes it easier to share your findings with colleagues with accessible, visual evidence.

When can network visualization be used?

KeyLines customers have applied network visualization to a whole range of real-world business and intelligence problems, from combatting terrorism to understanding Twitter networks.

To read some of these examples, visit our use case pages, or download more resources.

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