Context as Currency: Defining Your Data Project Brief.
By: Jean-Paul Ventura
Tags: Flatiron School, Curiosity, Data Science Training, Audience Engagement, Projects

When undertaking a data science research project, it’s no secret that there is profound value to process when taking on visualization challenges. The use of a design workflow (what visualizations and why?) and purpose diagram (whats my starting question? What do I want to have communicated?) are critical tools for making good visualization decisions and ultimately establishing a meaningful connection with your audience.

In my experience as an Astronomer, Science Communicator and current Data Science student at the Flatiron School, visualizations often seem to take center stage in the practice of communication. While they are certainly very important, its the curiosity about the project context that pays in our ultimate pursuit to deliver meaning. After you have shaped the scope and nature of your project and have the origin and destination for your work generally defined, its time to consider the vision of your work or the birds-eye view of your project.

What is a brief?

In its most basic form, a brief represents a summary of the projects mission, goals, challenges, messaging and demographics. The term is commonly associated with the law, project management or graphic design industries but when it comes to visualization, the need to establish comprehensibility and the requirements of a project are just as important. A brief is composed to form the context of and vision for your work.

When working with clients or associates, it’s in everyones interest to have a mutual understanding of the project requirements and some agreement over the main deliverables. Whether you work with others on a team or are at the beginning of your career as a data professional, this stage can feel frustrating. At face value, it seems like some office busy work when what you really want is to get on with the rainbow-laden world of playing with the data and getting creative. Detailing and understanding the context of your project is too valuable a practice to overlook. All the decisions that follow in your work will be shaped around the definitions you establish now. Theres nothing wrong with some changes along the way but there are massive dividends to gain from achieving as much clarity as possible about who you’re talking to, what curiosities you’re addressing and who your audience really is.

Establishing project context

A worthwhile data visualization project should begin from the starting point of a curiosity. According to a the google dictionary definition, curiosity is “possessing a strong desire to know or learn something”. By establishing a sense of where your work originates in terms of curiosity, the main force that will shape your decision making as you detail your visualizations will be the desire to respond adequately to this intrigue. At the end of the day, the nature of this curiosity surrounding your projects context will vary depending on where it originated and who it is serving. In other words, what your question is and who your audience is.

Image credit: Josh Sorenson

Here are five scenarios where the details differ enough to offer different contextual challenges:

Personal intrigue — “ I wonder what … ”

Stakeholder intrigue — “He/She needs to know …”

Audience intrigue — “They will need to know …”

Anticipated Intrigue — “They might be interested in knowing …”

Potential intrigue — “There might be something interesting”

When starting out early in your career, I think the most likely scenarios one follows from are personal, audience, and potential. When the rubber hits the road and you are now a part of company X’s data science team or institution Y’s research group the central curiosity belongs the stakeholders. The key difference here from the rest is that the work commissioned or question prompted is known and therefor there is no anticipation of interest. However, not having to consider the context of information to visualize from square one isn’t necessarily streamline. Establishing an understanding of all the different players and their involvement will help in creating an arc of visualization that tells a complete story and is maximally useful. At the end of it all, our jobs as data scientists and researchers is to create value so give yourself every possible and relevant opportunity to reach your audience(s) and .

References & Sources

Data Visualization: A Handbook for Data Driven Design — Andy Kirk

https://www.import.io/post/what-is-data-visualization/