From the course: Introduction to Career Skills in Data Analytics

Gathering requirements for visualizations

From the course: Introduction to Career Skills in Data Analytics

Gathering requirements for visualizations

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- We have all heard the stories where the entrepreneur designs their life-changing app on a napkin and then moves on to greatness. Well, guess what? You can apply the same approach to your visuals. Maybe it's not a napkin, but I can tell you from my experience, even starting with your customer and a napkin is better than guessing at the visual representation of the data on your own. People never know what they want in a dashboard or report until they can see what you see, and if that's all in your head, well no one is a mind reader. The best way to express your ideas is to create a mockup of the dashboard. Just lay out different objects, like a table, matrix or stack chart, add a few filters on the image, this will help everyone get on the same page about the design. And if it's multiple pages with navigation, then wireframing helps communicate the navigation of information before you build it. Wireframing allows you to build out a skeleton of the pages, it doesn't have to be designed with all the colors and final graphics, it's just a sketch. The mockup might have a little more visual styling than the wireframe, but even just a few minutes of investing time into these together will reduce tons of back and forth on the design process. There are many ways to produce mockups and wireframes, we can thank all the software developers and UX designers for these sets of tools. If you are newer, it might be hard to visualize the visuals needed because you may still be trying to determine the right visual for the data. You can look for inspiration through samples that you can find available in the software, like Power BI has a whole set of dashboards you can play around with to get started. In addition to getting on the same page about the look of the dashboard, we must consider other requirements. Be sure you're documenting these in every meeting and then following up with notes to all stakeholders afterwards. A few items to always address are, what type of filters do we need on the data? That way you're not bringing in more than what's needed. Example would be a 100 year old company doesn't need 100 years of data in the dashboard. I would call this a hard filter, that's because you handle this type of filter at the data level. What type of filters are needed for the consumer? Which is the user of the dashboard. What might they search and filter? These are soft filters and they're meant to be interactive. Common filters might be years and dates. If it's dedicated to products, it will likely have a product filter. And if it's dedicated to customers, it will have some customer filters. Never fail to find out who this dashboard actually is for, and also determine if they have the permissions to the data and the correct licensing to use the dashboard. Visualization is as much an art as it is a science, and these requirements are pretty standard to every type of visualization project. And you'll discover there are many more, but if you start with these, you'll be designing better dashboards right from the beginning.

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