How can Data Visualization professionals improve their relationships with managers and supervisors?

Asking the hard questions up front like "Why? So what? Who cares?" can be tough for those starting out in this career, but they are also fundamental. The more you ask questions and peel back the layers, the more you understand the task at hand and what's relevant to the end users. You won't get this right 100% of the time, but with time and practice, you're able to be more comfortable and capable doing this core, fundamental part of the development lifecycle.

To improve their relationships with managers and supervisors, data visualization professionals can focus on effective communication and collaboration. They should strive to understand the goals and priorities of their managers and supervisors, and align their visualization efforts accordingly. Regularly seeking feedback and involving them in the visualization process can foster a sense of ownership and engagement. Additionally, demonstrating the value and impact of data visualizations through tangible results and success stories can help build trust and credibility. By actively listening, adapting to feedback, and proactively addressing concerns, data visualization professionals can cultivate strong relationships with their managers

Be human. Period. Okay, LinkedIn just told me I need to write a minimum of 125 chapters. Let me elaborate: No matter the nature of your job or the tools you deal with every day, human connection is what differentiates us from AI. The more human you are, the better the bond you will have with the team or counterpart, and the more likely you will achieve outcomes that far exceed expectations.

A great example I have seen is when data visuals are used to tell a story that connects with the audience. Once you understand the goal of the audience and connect the data with a relatable example or problem they are looking to solve, you captivate your audience's attention.

All data professionals, not only those specializing in data visualization, should work on building strong relationships with their managers and supervisors. This means communicating effectively, not just within the team but also with higher-ups. To make data projects successful, it's crucial for managers to fully understand what's being created, who it's for, and what problem it's solving. When everyone is on the same page, it reduces confusion and saves time when the results don't exactly match what was expected. In simpler terms, better communication and understanding lead to more efficient and productive data projects.

I think is key to remember that Data Visualization is in great part a design discipline and as such it is paramount to keep in mind the design process, which includes constant iterations. This should be implemented at each stage, from draft and prototyping until the final product.

A clear feedback and communication plan is essential in data visualization projects. This should be agreed upon before completing the project, perhaps on a regular basis. Since some managers can be quite particular about the visual results, data visualization professionals should aim to keep them updated on work progress even before the project is finished. This proactive approach can help ensure alignment and reduce misunderstandings.

Explaining data visuals can be challenging, particularly when managers have varying skill levels in creating them. However, I've learned that simplicity is the key to success. The simpler the visuals, the easier it becomes to explain the concept behind their creation, especially when dealing with diverse skill levels.

Besides explaining your choices, be open-minded to change some of them, if the information understanding is not going to be affected or, perhaps even better, is going to be made easier. Data visualization professionals may have more education and expertise in better chart choices, but they do not always know how the analyses are made or how people consume data. It is important to keep communication open and invest in increasing data literacy and data culture in the company

This step is equally important. While dealing with my stakeholders, we often face difficulty in the 'why this graph is chosen' part. Often, there is brainstorming and open conversation with managers. Once, we tell our reasoning and are open to their logic, a midway can be achieved where data is visualized in the best appropriate manner.

Being open to feedback is key in data visualization. Adopt an Agile approach, welcoming input for iterative improvement. Listen carefully to managers, thank them for their insights, and adapt your work accordingly. If you disagree, offer a respectful and constructive counter-argument, backed up by data or best practices in UX design.

It is also very important to stress the need for a gradual and well managed change processes on visuals. When introducing new or updating features in a dashboard arising from an intelligence analyst's end, analytics managers must communicate this effectively, eliciting feedback until adoption viz-a-viz understanding of change goal is achieved.

Do not forget that data visualization is the product to be used. Especially in the field of management, where dashboards are common. If a user complains that the dashboard is unclear, and difficult to use and asks for export to Excel (!), it is time to ask for feedback and listen carefully - what to improve, so the final data product is actually useful to them. Also, if the user who looks at the same dashboard daily asks for some visual candy - a nice donut chart, or more colours across the rainbow – let's then forget about best-practices for a moment and give them what they need. Remember - you are not the user of your data product - they are. So, sometimes just listen to what they ask for.

To enhance the relationship between managers and data visualization designers, involve them in the design process from the beginning. Instead of viewing them only as project validators or budget-owners, recognize them as experts with deep business knowledge. Their insights can offer context and a holistic perspective, helping developers craft the most pertinent data visualizations without missing essential details.

In my professional journey, I've come to realize the significance of merging business acumen with analytical prowess. By integrating this knowledge with our analytical skills, we don't just visualize data, but also narrate the story behind it. This approach ensures that our data representations are not just figures on a chart but meaningful insights that resonate with our business goals. It's this union of expertise and analysis that steers us towards our shared objectives, ensuring that our strategies are data-driven and aligned with the company's vision.

It’s really I deal and very important for managers to be engaged and be involved with team members at every steps and process of the project, instead of only settling on reviewing and validating projects.

In my experience, soft skills are equally as important as technical skills in BI / Data Visualization roles. The confidence and ability to articulate your decisions and rationale tempered with the humility to understand and appreciate constructive feedback is paramount.

The key thing is focusing on making business users successful with your data visualization product & and then trying to leverage your manager/supervisor to make it happen. This will open a new pathway to work together & and in sense-making your manager & and yourself successful in whatever you are doing. My 2 cent...