Data drop downs are useful for filtering different information so the user can see how one group performs compared to another. Tables, charts, maps, and thermometers are good visualizations that help show how data is spread over a specific group. Sliders and gauges help isolate specific ranges of data; for example, if the gauge or slider is for maximum speed for a car you could limit the range to cars that can only reach between 100-120 mph. Crosstabs give a more precise look at the data because instead of getting a visual of a group of data the user is getting exact numbers instead.
There are two main types of data a user could be working with. Those types are categorical and quantitative. Categorical data is sorted into specific groups while quantitative is actual numbers and quantities. While normally a user will be working with a combination of the two, it starts with whether or not the data is additive or non-additive. This refers to whether or not adding the data makes sense or only showing the difference between the data is the point. For example, a time series is non additive, so if data is being used to show a time series then points or lines should be implemented. An example of additive data would be data showing a groups grade or age, in which case then a bar chart should be used instead.
Before designing you should have a clear idea of whether your dashboard is an analytic dashboard or a monitoring dashboard. A monitoring dashboard is used for real time operations and can alert whomever on changes from normal output. This type of dashboard would be used with someone like a stockbroker who needs constant updates with the market's changes. An analytic dashboard helps provide users with the information needed to understand trends and why certain things are occurring by making comparisons across time and multiple variables. This type of dashboard can be used by pretty much any business but for example a bakery, because they could better understand why certain items are selling better than others.
They could see this with tools such as sliders and charts so that the information could be presented visually and could also be filtered depending on the user's preference. Both of these dashboard's follow different design principles. If the user was to create a monitoring dashboard the layout would be simple, with only a few charts showing the exact data that would be deviating from the norm. It needs to be a simple as possible so the problem can be fixed. If an analytic dashboard was created it would be much more complex with more information. The data included also needs to ability to be seen in detail so the use of charts and drill-down information must be prevalent. The use of sliders, gauges, and drop-down menus help even more so because large quantities of data can then be filtered making it easier for the user to discover information.
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