How can data visualization professionals grow their careers?

A good data visualisation person acts as an analytics “translator” for the organisation. Anyone who is interested in a role in visualisation must simultaneously build knowledge of the business and financial ecosystem. The intent is to derive meaningful, actionable insights and not just bamboozle colleagues with statistics and graphs

The most effective and efficient method for acquiring proficiency in data visualization is to study and emulate experts in the field. I consistently track sources like Visual Capitalist, Statista, and Tableau Public for this purpose. Engaging in regular visualization exercises fosters creativity in this domain. Moreover, valuable insights into general principles can be gained from books like "Storytelling with Data.

To grow your career as a data visualisation professional, continuously learn, build a portfolio, get involved in the community, take on leadership roles, be creative, focus on quality, be a good communicator and consider specialising.

I would summarize my approach to following four steps: 1. Visit LinkedIn and search for data visualization contests (e.g., Maven Data Challenge, DataDNA Data Challenge). 2. Explore their latest data challenge, understand the use case, obtain the dataset, and create a dashboard. 3. I am more of a tableau person. So, I would publish my work on the Tableau Public gallery. Power BI users can use platforms like NovyPro for their portfolio. 4. Share the published design as a LinkedIn post to reach a wider audience.

Building a portfolio is such a powerful way of showing people your best work. There is also an added advantage of potential hiring managers becoming interested in you just by seeing your portfolio. One crucial point related to a portfolio is to ensure that only "your best work" is in your portfolio. If you have a hundred dashboards, but only ten are amazing, then only include those ten. Including too much "noise" in your portfolio will dilute the experience of the person looking at it. Think about how you want others to perceive your work. Put yourself in the shoes of your audience.

People who want to advance should learn as many visualization platforms as possible. The two industry leaders are Tableau and Power BI, but any SME should also have advanced Excel skills. Utilizing R and Python for building models that feed visualizations would only add a cherry on top.

Sharing my best practices on platforms like LinkedIn or other social media channels has proven to be a transformative experience, yielding numerous benefits. Firstly, the act of sharing not only showcases my expertise but also establishes me as a contributor within my professional community. This visibility often attracts valuable connections and fosters a network of like-minded individuals. Additionally, seeking feedback on my work opens the door to constructive criticism and diverse perspectives, enabling continuous improvement. The exchange of insights and ideas through these platforms not only enhances the quality of my work but also cultivates a culture of collaboration and knowledge-sharing within the broader professional community.

Feedback is a valuable means to enhance your work. Numerous online platforms provide opportunities for collaboration with the #datafam on various projects and for soliciting feedback. Some notable projects, such as Makeover Monday and Sports Sunday, allow you to share your work and receive feedback from the community. Additionally, you can publish your work on Tableau Public and request feedback from fellow community members. This approach is an excellent way to facilitate learning.

Another great way to get indirect feedback on your reports is to participate in monthly data challenges. If you are one of the finalist with the Maven Analytics challenge, your report will be analysed and feedback provided by the team.

Since there are some different capabilities from each data visualization tools, you must try more than one tool : - Power BI - Tableau - Looker studio (previously known as Google Data Studio) - Python (with Plotly express) - or other tools like ArcGIS which will come in handy when visualize location data

Integrating the principles of UI/UX design into data visualization and dashboarding can significantly enhance the overall user experience and comprehension of complex information. A well-crafted UI/UX design ensures that the interface is intuitive, visually appealing, and user-friendly, allowing individuals to interact seamlessly with data. Thoughtful user interface design enables users to navigate through various datasets effortlessly, while effective user experience design ensures that the presented information is not only visually accessible but also easy to interpret. By prioritizing UI/UX concepts, data visualizations become more engaging, fostering user engagement and promoting data-driven decision-making.

Being able to translate your insights to dashboards and visualizations show your data analysis skills to start with and a layman being able to understand your visuals show your DV skills. Start with Excel to test your skills, then slowly move to Python and R. Learn different tools like Looker, Tableau and Power BI and then directly integrate your query results to these BI tools and build amazing visuals. Read, experiment and explore - These three key things will help you mind map and be able to present your analysis better.

Let me share my dataviz journey: 1. Follow the people Connect with data viz people on social: Tableau Visionaries, including authors of book, data journalists of mainstream media. 2. Get the basics It's important to understand the basics of the tools and best practices: Tableau, R. 3. Community projects Get involved in the community projects: Makover Monday, Maven. 4. Dataset Look for a dataset of your interest: personal data, Kaggle. 5. Inspiration Get inspired from the vizzes on Tableau Public, Visual Capitalist. 6. Showcase and Portfolio Showcase your work on the social media platforms: LinkedIn, Tableau Public. 7. Feedback Be open to feedback, the best way to improve your skills. 8. Test your skills Test your knowledge: certification.

Understanding technical skills will only you get so far in your data visualization career - the real power comes from constantly expanding and contributing back to a growing network. The people you meet today and the community you choose to support can pay back huge dividends in your professional growth in completely unexpected ways. I know firsthand as someone who has so many unique opportunities - including starting and growing my own data analytics consultancy - networking is how you add rocket fuel to your career.

Networking and collaborating becomes very important in this field as their is so much to learn everyday. It becomes almost impossible to work on each and every use case or challenging scenario in your project. But collaborating and networking can open doors to a pool of use cases and challenges. It becomes an important source of learning.

Although it would be great if important data spoke for itself, visualizations help the message stay in people's minds. -Beautiful tables, charts, and figures tend to be shared and cited more often. Attention to aesthetics can go a long way. -It can be useful to create data visualizations that would still work in black, white, and greyscale for figures likely to be printed in case there is limited access to a color printer. -If you need to plot two lines on the same chart, plot one of the lines thicker than the other. Over the thicker line, plot the thinner line in a different style or color. That way, you can see both lines at the same time even if they overlap.

Data visualization is indeed about „story telling“, beside mastering the technical skills (which is constantly evolving), good visuals help to convey the message faster and more intuitive to the data consumers and decision makers.

As a headhunter that specializes in the data field, the best data visualization specialists/ data analysts I have met tend to have a blend of qualities such as: -A great communicator with a story to tell. -Curiosity and passion to learn the business -Work super close with the Users to understand their challenges and making something that is truly beneficial to them. -Multi-tasking, job prioritization skills -Knows at least 3 mainstream BI tools. Surely you don't want to be seen as a purely tech person.