Data Analytics Visualization for the People, by the People

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.

Data analytics visualization is about making things obvious. The ground rules for making a good visualization are simple, too:

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.

Software for data analytics visualization should help you do the things described above, by offering:

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.

Big data analytics will further test the effectiveness of data analytics visualization tools.