5 Best Practices for Data Visualization

1. 5 Best Practices for Data Visualization As our world is increasingly filled with data, both companies and small businesses alike are looking for ways to evaluate vast amounts of data. Data visualizations present data in a graphical format so that business stakeholders can better understand complex data findings. However, there are things as good and bad data visualizations. To not to distract or mislead viewers, here are a few best practices to keep in mind, so your data visualization is useful and clear. Know your audience. To make sure that your visualization is helpful, start by analyzing who your audience is. What type of questions do they care about, and what answers is your visualization is delivering? What other problems does it inspire? Keep in mind that not all end-users will see the same information in the same way. For example, a chief financial officer and a sales manager will have diverse ways of understanding profitability on a probability dashboard, so it’s essential to ensure that you’re answering the question from the proper perspective. Follow a methodology. Describe a process by which you obtain your design requirements, design visuals, collect your data, and free them. Only a perfectly defined methodology will ensure constant quality improvement and continuous data visuals quality. Classify your dashboard. There are three different types of dashboards: strategic/executive, analytical, and operational. Let’s take a look at each of these: Strategic/Executive: This kind of dashboard offers a high-level view of the question or analysis line that is usually answered in a particular, routine way and presents KPIs in a modestly interactive way. Analytical: This kind of dashboard offers a highly interactive view and provides a different variety of investigative approaches to a particular central topic with limited corollary contextual views. Operational: This kind of dashboard is a commonly updated answer to the question or inquiry line, which often monitors all operational concerns in response to events on an ad-hoc basis.

2. Profile your data: Different visual features work better with diverse data types. For example, distribute plots function well with two bits of quantitative data, while line graphs and line diagrams are a perfect fit for ordinal information. Here’s a brief look at each type of data. Ordinal: Data that has a logical sequence such as silver, gold, and bronze medals Categorical: Data that belongs in a similar category, e.g. North America, Europe, and Asia Quantitative:Data that describes “how much” of something there is, e.g. $1 million in sales, 20° Celsius, 150 defects Design iteratively. Don’t wait until all of your necessities are fulfilled. Visualization should be done in a manner that is clear to customers with the ability to deliver the best services. To accomplish this, get a big chunk of requirements and begin designing concept proofs and prototypes right away. Then, obtain feedback in an interactive setting and revise consequently. Make sure that you are avoiding analysis paralysis that tends to happen to those who are familiar with old project management approaches. Data visualization can build an engaged data-driven business culture that allows employees to access BI capabilities that take the business forward. You can further support this engagement by sending metric-driven notifications and planned email reports. Remember, people generally respond better to visuals that tell a clear story than long descriptions that needs interpretation. Source: visualization/ https://onlineschoolofanalytics.wordpress.com/2019/03/04/5-best-practices-for-data-