member spotlight: Pris Lam
The following is a guest post from Pris Lam. Pris is a regular contributor to the #SWDchallenges and the Tableau community. To see examples of Pris’ creations, check out her community gallery and her website. Keep reading to learn about how Pris broke into the data visualization field—despite coming from a non-data background—and don’t forget to pop over to the community to chat with her directly as part of our latest Member Spotlight.
Each month, we select someone from the community to highlight through our Member Spotlight. These are people who contribute in ways that foster an even more connected and diverse learning environment. Our goal is to help you get to know your fellow community members a little better and learn from one another.
Open my resume/CV, and any recruiter will tell you that I am not your “traditional” data analyst...if there such a thing.
Currently, I am a Data Analytics Consultant at the Data School Australia; I am a Tableau Desktop Certified Professional who has, on three separate occasions, had my work selected as the Tableau Public Viz of the Day. But before that, my work included being Country Head of Operations for a youth-led social enterprise, Learning Specialist for a government body, and extensive experience in facilitation, training, design, and presentation.
Given my unconventional pathway into data, I wanted to share my tips for people who already work with, or are interested in working with, data and want to break into the data visualisation field.
There are many pathways to learning data visualisation.
When we think about learning data visualisation, the ideas that come to mind might be some form of classroom-based instruction—completing coursework or attaining a full degree at an educational institute. As someone who went down the informal education route, I can attest to there being many alternative ways to learning data visualisation, each with its pros and cons.
Learning outcomes at an accredited educational institute are standardized and cover a full series of skills and knowledge, making them a perfect general introduction to data visualisation or a benchmarked qualification. Formal education can also provide a strong foundation in theory, ethics, and interdisciplinary studies that are not often part of independent or on-the-job courses of learning. On the other hand, not everyone has the time or resources to commit to a full formal course of study, and some people may simply prefer a more pragmatic and focused approach to their training.
I was looking for an applied, tool-based learning method, which led me to the Data School Australia, where I am part of the second cohort. Here we undergo intensive full-time training for four months in Alteryx, Tableau, and focus on soft skills required for consultancy. We then embark on four sets of six-month client placements. I’ve found client placements to be extremely useful for understanding how workplaces store and display data, why they do so, and ways to successfully improve how data is visualised and analyzed.
However, although I’ve found informal education, such as client placements, to be more flexible and practical, they can lack the structure required to ensure consistent learning outcomes. The quality of your learning experience can depend on the people around you and the work undertaken at the client site.
Another tip I recommend, in addition to either academic or vocational training, is to participate in community initiatives such as the #SWDchallenge, Makeover Monday, Iron Quest, Sports Viz Sunday (to name a few). I’ve found the consistent practice and community-sourced feedback to be great for identifying points of improvement as well as gathering inspiration.
Formal, informal, and independent approaches to learning data visualisation are all valid, and can be used in conjunction with one another. Whether you pursue one, some, or all of them comes down to your individual preference, resources, and goals.
Focus on what you need to learn based on your career goals.
With data science being one of the biggest in-demand industries and a continually changing environment, it’s easy to feel overwhelmed with lists of skills or knowledge you seemingly must-have. However, there are many career pathways in data itself.
To identify what you need to learn career-wise, you could work backward from your career goals—whether it be a role or area of work you’re interested in. What do you see yourself doing in one, five, and ten years?
Once you have identified where you want to be, you could look up people in similar roles or fields of work; look at their career progression for some pointers on how you can get to where you want to be. At the same time, note the essential data visualisations skills or knowledge they have that helped shape their data career.
The difference between being a ‘good’ or a ‘great’ data visualiser lies in your soft skills.
It’s common to focus on the technical skills and knowledge you are missing when starting or changing careers. However, designing and delivering great data visualisations that fulfill a client’s needs requires more than technical skills.
A large component of the end-to-end process includes soft skill-driven tasks. These include asking the right questions, communicating your understanding, building positive relationships with your audiences, and facilitating solution acceptance and positive collaboration.
Capitalizing off my past experiences has been essential in designing great data visualisations that solve business problems. Instead of seeing a career change as starting from scratch, build off your existing expertise. Ask yourself: what do I currently know how to do, what soft skills were required, how would these skill sets be used in the data visualisation process? By doing so, you can increase your competitive advantage and use your unique past experiences as a platform to boost your data visualisation career.
Pris shares great advice for those looking to advance their data careers or simply get started in the field. The good news is that everyone can learn to communicate better with data! To learn more from Pris, head over to the community, and chat with her directly this month.
There are plenty of noteworthy members in the community—too many to spotlight at once. This program is part of our larger efforts to find new ways to share multiple voices and experiences. As you discover great work and ideas in the community, be sure to give appropriate kudos and spread the word!