Did you know that up to two-thirds of the neurons in your brain can work to help you make sense of an image? And that while many people find it impossible to pick out trends from simple lists of numbers, they find it easy to see those trends when the numbers are shown by lengths of lines, heights of bars, or hues of color?
Data analytics visualization uses these ideas to show you the output of data analytics in ways that make it easier to grasp. Line charts, bar charts, and pie charts are examples that may already be familiar to you, but there are also many more options for displaying information to tell a story simply and effectively. Ideally, an application to do this should then be usable by novices, as well as by experts.
How to Get the Message Across
Data analytics visualization is about making things obvious. The ground rules for making a good visualization are simple, too:
- Show the data analytics output while being succinct and without distorting what it says.
- Get the viewer to think about the meaning rather than the prettiness of the graphics.
- Encourage the viewer to visually compare different aspects of the data analytic.
- Make the data analytics available at different levels of visualization, from top-level to fine detail.
- Make the visualization serve a clear purpose, like exploration, description, or recommendation.
- Create the visualization so that it also works as a stand-alone graphic too.
Function First, Form Afterwards
The basic design rule about function and form applies, with form following function. First, be clear in your own mind about the business intelligence message that you want to get across. For example, is your purpose to show how well your advertising works for different media and products (and therefore how to optimize your ad spending)? Or do you want to highlight how production team size affects your manufacturing quality and efficiency (and explore options for reorganization)? Afterward, choose the form of your data analytics visualization to make the message as clear as possible.
What to Expect From a Data Analytics Visualization Program
Software for data analytics visualization should help you do the things described above, by offering:
- Ease of use in selecting datasets and algorithms, and creating visualizations from them.
- Wide choice of visualization formats, with simple, fast toggling between different formats to find what works best for the visualization in question.
- Possibilities of interactivity to let viewers change the visualization by themselves, for example, by drilling down into deeper levels of detail.
For a good visualization, you may need to work with the underlying data that is being analyzed. If you are working with one or two simple spreadsheets, you may have everything you need in your spreadsheet application. On the other hand, if you are working with complex, multiple data sources of different types, data may need to be cleaned and modeled, before meaningful analysis and visualization can take place. Some visualization tools are limited in their ability to change the data they use or the way they use it because they have no data preparation or advanced analysis functions. If these limitations are too much, consider an integrated solution that offers these possibilities, as well as a good visualization frontend.
The Impact of Big Data Analytics
Big data analytics will further test the effectiveness of data analytics visualization tools. With its three ‘V’s of volume, variety, and velocity, big data can rapidly outstrip the abilities of some reporting and visualization tools to offer “actionable insights.” Yet at the same time, as big data is getting bigger and coming at organizations faster, there is pressure on the time to visualization to be shorter. Forcing users to rely on the IT department creates a bottleneck that slows what should be a stream of actionable insights to a trickle.
Towards Data Analytics Democracy
The way forward is the self-service tool for data analytics visualization that lets non-technical users get the results they need by themselves. More generally, simple ways to “mash up” data from a wide range of sources, ask analytics questions in natural human language, and share visualizations with coworkers are becoming increasingly important as enterprises seek to adapt to rapidly changing markets and business needs.
The advantages of the right data analytics visualization now and into the future are numerous. Business trends will become easier to spot. For instance, a suitable graphic could highlight how consumer preferences for shopping locations are changing according to city size, helping an enterprise to plan its future retailing activities.
More creative data exploration will be possible, as non-technical users ask ad hoc questions. From a visualization showing which products sell best, they might then look for the other products that are most often ordered together with the best-sellers.
Communication of insights to others will be easier. Does your CEO “get” scatter charts about manufacturing costs and profitability from different factories? A click or two, and you’re ready to present. Does a major account insist on pivot tables to see how your company plans to maintain or replace its installed base of PCs and servers? Click again, and it’s a done deal.
Easy, Effective Data Analytics Visualization is Here Today
In a scene in the science-fiction film “The Matrix,” team member Cypher sits in front of several computer screens. He watches an endlessly scrolling display of thousands of data symbols, all in the same font, size, and color. When the new team member Neo asks him what he is doing, Cypher explains that in his mind’s eye, he is simultaneously visualizing the faces and bodies of the people that the symbols collectively represent — a great example of Hollywood scriptwriting imagination! In the real world, data analytics visualization, fortunately, does not need such superhuman powers.