How to Evaluate the Visualization of Your Predictive Analysis Data - dummies

How to Evaluate the Visualization of Your Predictive Analysis Data

By Anasse Bari, Mohamed Chaouchi, Tommy Jung

There are several ways to visualize data; but what defines a good visualization? The short answer: Whatever gets the meaning across is your best choice. To help you find that best choice, these four criteria can use to judge your visualization. This is not a comprehensive list, but it should point you toward the best visualization to drive your idea home.

How relevant is this picture?

Your data visualization must have a clear, well-defined purpose — have a goal in mind and convey a clear idea of how to get there. That purpose could be the answering of the business need that brought you to apply predictive analytics in the first place. A subsidiary, immediately practical purpose could be your need to convey complex ideas through visualization.

To answer both needs, first keep in mind that the data presented in the visualization has to be relevant to the overall theme of your analytics project. (That relevance won’t be far to seek; your analytical project started with selecting the relevant data to feed into your predictive model.)

With the theme in mind, the next step is to create a narrative that presents the relevant data, highlights the results that point toward the goal, and uses a relevant visualization medium. (If your company has a room that’s ideal for, say, PowerPoint presentations, consider that a big hint.)

How interpretable is the picture?

If you apply analytics to your data, build a predictive model, and then display your analytical results visually, you should be able to derive well-defined interpretations from your visualizations. Deriving those meaningful interpretations leads, in turn, to deriving insights, and that’s the linchpin for the whole predictive analytics process.

The story you tell via your visualization medium must be clear and unambiguous. A roomful of conflicting interpretations is usually a sign that something is amiss. To keep the interpretation of the visualization on track, be sure you keep it firmly in line with the model’s output — which in turn aligns the whole effort with the business questions that prompted the predictive analytics quest.

In cases where a visualization might allow several interpretations, those interpretations should converge to tell the same story in the end. As with many undertakings, multiple interpretations are often possible. Try to anticipate, discuss, and tweak them beforehand until they all convey the same underlying idea or support the same overarching concept.

Is the picture simple enough?

A visualization that’s too complex can be misleading or confusing. To achieve simplicity, your visualization needs clarity and elegance.

You should always aim for clarity by adding as many legends (guides to what the parts of the image mean) as needed, and making them as clear as possible. You can use legends to define all the symbols, figures, axes, colors, data ranges, and other graphical components you have in your visualization.

Choosing the right combination of colors and objects to represent your data can enhance elegance. The medium you choose to present your data is also critical. The medium refers to the images, graphs, and charts in your presentations, in additions to the conference room, and to the visual aids you use to present your analytical results, such as TV screen, white board, or projector.

As a rule, the simpler the visualization and the more straightforward its meaning is, the better it is. You know you’ve succeeded when the visualization does the talking for you.

Does the picture lead to new insights?

Your visualization should add something new to your predictive analytics project. Ideally, it should help you find new insights that were not known before.

During the building of your predictive analytics model, you can use visualization to fine-tune the output of your model, examine the data, and plot the result of the analysis. Visualization can be your guide to discovering new insights, or discerning and learning new relationships among items of data in the sea of data you’re analyzing.

Visualization should help you seal the deal and erase any doubts about the analysis; it should support the findings and the output of the model. If it does so effectively, then presenting these findings to management will help them embrace and act upon the results.