Data visualization is the term used to describe technology that lets end users see visual representations of data in order to help them gain a clearer understanding of the information, put it in a business context and use it to meet business objectives. Visualized data can be displayed in business intelligence (BI) dashboards and performance scorecards that give users a high-level view of corporate information, metrics and key performance indicators (KPIs). But models alone aren’t effective unless they are able to help clearly interpret and understand the results. So to ensure that your data visualization interface does more than simply provide great looking visuals, here are a few design-phase best practices to follow:
Start with user needs
The best way to develop user-focused analytics is to start by finding out what the user’s needs are, working backwards to create an interface that effectively and efficiently supports those needs. Ultimately, this will lead to the analytics for the interface. By including end-users early on in the process — even if they don’t always know what they really want or need — you increase your chances of designing a well-suited interface. On the front-end, users may not be able to describe how a good interface will look and operate, but they certainly know what a bad one is.
By including users in the planning process, designers can identify successes early on and be looped into needs and/or challenges they may have never anticipated without user involvement. It’s also helpful to include a diverse set of users to weigh in on the design phase, as varying skills and abilities can impact how broad usability will potentially be.
Determine how the results will be used
Analytic interfaces should be developed out of a deep understanding of how the results will be used. When working with users in the design phase, discuss the design based on the varying roles within the organization, allowing sensory cues to determine actions for key or critical information.
Leverage the human ability to quickly understand and draw relationships by size, position, colors and other visual and sensory means. Pictures can accomplish immediately what numbers require a considerable amount of time and processing to accomplish.
Provide actionable analytics
Effective analytics produce results that drive what step or what multiple steps should be taken. Your interface should alert users that something should be done or it should make real-time answers available. For example, rectangles can be designed to indicate an increase in warehouse deliveries. A larger rectangle can represent an increase in deliveries and a smaller rectangle can represent a decrease. The color of the rectangle might represent forecasted versus actual deliveries, and so on. The point is the information should be easy to understand and interpret and spur users to take some action.
If your organization is looking to harness the power of data visualization tools, contact the experts at Infovision to see how we can help find the BI solution that’s right for you.