What are the most challenging aspects of data visualization for a new Data Visualization professional?

Data visualization is the art and science of transforming data into visual forms that communicate insights, patterns, and stories. As a new data visualization professional, you may face some challenges that require you to develop your skills, creativity, and mindset. In this article, we will explore some of the most common and difficult aspects of data visualization that you need to master to succeed in your career.

1 Know your audience

One of the first challenges of data visualization is to understand who your audience is and what they need from your visuals. Different audiences may have different levels of data literacy, expectations, preferences, and goals. You need to tailor your visualizations to suit your audience's context, background, and interests. For example, if you are creating a dashboard for a business executive, you may want to focus on the key metrics, trends, and actions that they care about, while using clear and simple charts and colors. If you are creating a report for a technical audience, you may want to provide more details, explanations, and references, while using more complex and sophisticated charts and techniques.

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Juan-Pierre Louw

✝️ | #powerbi101 | BI Consultant | 🎨 📊 Data Visualization & Modelling expert | Ready for your Data project | Microsoft enthusiast
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Designing accurate dashboards and reports start with initial requirement gathering from the respective stakeholders. Once you have obtained this, start designing the dashboard and report with the end user in mind. Management will require a quick overview, and possible drillthrough capability. Other users would need to see more detailed (granular) view of the data. Your report should not just contain visualizations that fill the page- your report should tell a Data story- which will allow your report consumers to make data driven decisions for the organisation.

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2 Choose the right tools

Another challenge of data visualization is to choose the right tools for your data and your goals. There are many tools available for data visualization, such as Excel, Tableau, Power BI, R, Python, D3.js, and more. Each tool has its own strengths, weaknesses, features, and learning curves. You need to evaluate the pros and cons of each tool and decide which one best suits your needs and preferences. For example, if you are working with large and complex data sets, you may want to use a tool that can handle big data and offer advanced analytics capabilities, such as R or Python. If you are working with simple and structured data sets, you may want to use a tool that can create interactive and attractive visuals quickly and easily, such as Tableau or Power BI.

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Juan-Pierre Louw

✝️ | #powerbi101 | BI Consultant | 🎨 📊 Data Visualization & Modelling expert | Ready for your Data project | Microsoft enthusiast
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As a PowerBI developer, the tool is easier to use than other BI tools and the learning path initially is not to hard. PowerBI has the expected standard visualizations to choose from, with various formatting options available. If you do not find the required visualization you can download approved 3rd party visuals within PowerBI- there will be paid and free versions available. (If you are using an organisational account you may be limited). You can also develop you own visualizations using Deneb or Charticulator- however you will need to learn how to do this. Additionally PowerBI does allow Python and R visuals within your report, so the visualization options available to you are endless

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3 Design with principles

A third challenge of data visualization is to design your visuals with principles that enhance their clarity, accuracy, and impact. Data visualization is not only about making your data look nice, but also about making your data meaningful and understandable. You need to follow some basic principles of design, such as choosing the right chart type, color scheme, layout, scale, labels, legends, and annotations. You also need to avoid some common pitfalls of design, such as misleading, cluttering, or distorting your data. You can learn more about these principles and pitfalls from books, blogs, courses, and experts in the field of data visualization.

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Juan-Pierre Louw

✝️ | #powerbi101 | BI Consultant | 🎨 📊 Data Visualization & Modelling expert | Ready for your Data project | Microsoft enthusiast
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There are a multitude of ways to apply design principles to your report. • Your report should have the correct context and answer key business questions. • Limit the amount of colours used in your report. • Use whitespace between visuals • Group certain visuals/ metrics together • Alignment of visuals will ensure a professional look • Place the most important visual in the top left corner. • Layout visuals in a Z format- as one reads from left to right (in western languages) • Design for end users with accessibility as well.

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4 Tell a story

A fourth challenge of data visualization is to tell a story with your data that engages your audience and delivers your message. Data visualization is not only about showing your data, but also about explaining your data and revealing its insights. You need to craft a narrative that guides your audience through your data and highlights the key points, questions, and conclusions that you want them to take away. You also need to use techniques that capture your audience's attention and emotion, such as using headlines, captions, annotations, transitions, and interactivity. You can learn more about these techniques and examples from books, blogs, podcasts, and TED talks on data storytelling.

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5 Test and iterate

A fifth challenge of data visualization is to test and iterate your visuals until they achieve your desired outcomes. Data visualization is not a one-time process, but a continuous cycle of improvement and feedback. You need to test your visuals with your audience and stakeholders and collect their opinions, suggestions, and criticisms. You also need to measure the effectiveness of your visuals using metrics, such as views, clicks, shares, comments, and conversions. You then need to use these inputs to refine and optimize your visuals and make them more relevant, useful, and persuasive. You can use tools, such as surveys, interviews, analytics, and A/B testing, to help you with this process.

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6 Learn and grow

A sixth challenge of data visualization is to learn and grow as a professional and as a person. Data visualization is a dynamic and evolving field that requires you to keep up with the latest trends, technologies, and best practices. You need to constantly update your knowledge, skills, and portfolio and seek new opportunities and challenges. You also need to develop your mindset and attitude and embrace curiosity, creativity, and collaboration. You can learn and grow by reading books, blogs, newsletters, and magazines, taking courses, webinars, and workshops, joining communities, networks, and events, and finding mentors, peers, and partners.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the most challenging aspects of data visualization for a new Data Visualization professional?

1

2

3

4

5

6

7

1 Know your audience

2 Choose the right tools

3 Design with principles

4 Tell a story

5 Test and iterate

6 Learn and grow

7 Here’s what else to consider

Data Visualization

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What are the most challenging aspects of data visualization for a new Data Visualization professional?

1

2

3

4

5

6

7

1 Know your audience

2 Choose the right tools

3 Design with principles

4 Tell a story

5 Test and iterate

6 Learn and grow

7 Here’s what else to consider

Data Visualization

Rate this article

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