A dashboard is …

A dashboard is …


A dashboard is well hard to define. When you hear the word dashboard what do you think of? Could it be something similar to the instrument panel in your car? Do you think of gauges and slides? Or maybe for you any report is a dashboard. So what is a dashboard?


According to dictionary.com a dashboard as :


“a user interface or Web page that gives a current summary, usually in graphic, easy-to-read form, of key information relating to progress and performance, especially of a business or website: Our managers use an interactive dashboard to monitor employee data. The project dashboard shows all tasks assigned to your team.”


Dashboards can mean many things but to me it means a graphic representation of summarized data. Agreeing on what constitutes a dashboard in your organization is important as it sets the consumer expectations for design, performance, aesthetics and functionality. All really important things you should be considering as part of your requirements gathering process. Plus, these requirements will also help you select the correct BI tool for the job. For example, if all you want for your dashboard is to be delivered via pdf on Mondays, you aren’t going to bother to design a sophisticated Design Studio application because you can just get by with a Webi or Crystal Report.  If you want active filtering or other interactions, then you are going to most likely go down the Lumira or Design Studio route.


Selecting the correct tool is important not just for being able to deliver the appropriate UX experience but also has a significant impact on your development time. Being able to develop something in Webi or Lumira for example can have a significantly smaller development life cycle as compared to Design Studio.


In summary, you can call anything a dashboard but you understand the following requirements in order to make the best tool choice and set the correct expectations leading to greater user adoption and project success.


  1. Data expectations: summary vs. detail level
  2. Interaction: filtering, changing values, drill down
  3. Deliver model: LaunchPad, mobile, email, etc
  4. Graphics: certain tools have limits on visual options
  5. Pretty factor: how advanced does the UX need to be


Overall I just ask that you remember being pretty is meaningless if the data is inaccurate and being accurate is meaningless if the data doesn’t lead to action.


Happy developing!

Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.