Today, data visualization is “all the rage,” but what does that really mean? After all, visualizations should not be misinterpreted as the end product of an analysis. In reality, the end product is the impact showcased and resulting recommendations made based on a solid analysis, whether manually or created via big data tools. Visualizations, however, have given rise to an enticing and informative way to present results that simplify a set of complex findings and display it in a way that garners attention. As a result, mastering these skills has become a critical element in the creation of decision-support tools.
Visualizations are so important because they help you to see triangulated data and information more clearly and more quickly, attract the attention of stakeholders and your readers, and help to answer questions and show impact with a high level of clarity. Most analysts may agree that one of the most challenging aspects of their jobs is indeed, presenting results that attract attention, while being complex in nature, easy to decipher and able to influence decisions.
Here are five guiding principles you can apply in order to produce best practice effective impactful visualizations.
1. Know your audience
It’s important to understand who your audience is as there are several types of stakeholders at all levels of an organization.
2. Don’t overdo it
Create visualizations that are short and concise. Typically, executives do not want to see more than one or two pages. Refer to appendices and sources.
3. Connect the dots
Create visualizations that are multidimensional in nature by building relationships between data points.
4. Streamline visuals
Do not over complicate with too many views, or colors or text.
5. Make a timeline
Determine the level of frequency for dissemination.
As you start to storyboard your visualizations there are many questions to keep in mind: Have you determined who your stakeholders are? What question or issue are your graphics trying to address? What is your overall message to the reader and can it be effectively communicated? What is the storyline and how does that come through in the visualization?
Also consider which tools best fit the intended purpose of the impact analysis: for “who” and “what” questions, use a qualitative representation; for “how much” questions, use charts; for a “where” or a position in space-type query, use graphs; for “when” or position-in-time queries, use a timeline; for “how” or “cause and effect,” use flowcharts; and for “why” questions or deduction and predictions, use multivariate plots.
Creating visualizations for the sake of displaying information without the foundation of solid analytics is not data visualization; it is the process of creating pretty pictures. Ensure that you focus on the analysis first and the resulting impact and then apply the use of big data tools to ensure your analysis is robust. These are the most important aspects of decision-support. Building skills in the area of data visualization is what packs the punch and ensures that those recommendations are well presented and recognized by stakeholder audiences that stand to benefit from the findings and recommendations.