Step 1 - Discovery

It all begins with a conversation

In design, the process of asking probing questions in order to surface deeper meaning and underlying information is called discovery. Discovery is always the first step in my work process. I like to ask a lot of questions and from experience I’ve found that it’s a good idea to ask as many questions as you need upfront. That way, once the project is underway you can work without always having to stop to get clarification on what you should be doing.

In a data visualization project specifically, one of the most important initial steps is to gather information about the situation that’s being looked into so that you can understand context around the data we’re going to look at.

When starting a new project, I make sure that I completely understand the questions that are being asked by stakeholders. Specifically, I make sure that I have a firm grasp of the following:

• What do we hope to learn from our data?

• Why is this information important to us?

• What problem are we trying to solve?

-and-

• What else is going on that makes this an area of particular concern for us right now? (Is there a particular pain point or urgent situation that needs an immediate solution?)

The answers to these questions not only help guide my investigation but they also allow me to place my findings into context and deliver a final analysis that is practical and actually useful.

Step 2 - Prep

Clean up and organize the data

Cleaning up the data is an unglamorous but crucial job. In a typical data analysis project, up to 80% of the work takes place in this step. There’s a specific format that a dataset needs to be in before it can be properly analyzed. Getting your data into this format is known as data wrangling and it’s about as much fun as it sounds. The good news is, once you get this part right, everything else gets a lot easier. Having a clean, well-organized data set means that we can now ask real questions of our data—and get back coherent answers.

Step 3 - Explore

Exploratory data analysis

Armed with our clean data set, it’s now time to explore what’s in it.

Based on the needs of the particular project, I select one or more of the following data tools from my repertoire:

Step 4 - Explain

Explanatory data analysis

Next, I report out the insights and key takeaways I’ve uncovered. I make sure that I've addressed all of the stakeholders' questions and I take care to place the data I’m reporting on into context.

Step 5 - Empower

Empower stakeholders to be able to further explore the data on their own

Finally, I provide resources that empower stakeholders to conduct their own self-directed inquiry.

I typically do this by building data dashboards. A data dashboard is a user-friendly, interactive, data tool that allows anyone to quickly gain a better understanding of their data. They provide a high-level overview of the data in an easy to understand visual format. At the same time, they also allow individuals to drill down deeper into the data to take a closer look at the areas that are of particular interest to them. Dashboards allow leaders to monitor progress and spot trends as they develop.