What are some tips for creating data visualizations with complex and messy data sets?

Data visualizations can help you communicate insights, patterns, and trends from complex and messy data sets. However, creating effective and engaging data visualizations is not always easy. You need to consider the audience, the purpose, the data quality, the design principles, and the tools you use. In this article, we will share some tips for creating data visualizations with complex and messy data sets.

1 Understand your audience and purpose

Before you start creating any data visualization, you need to ask yourself two questions: who is your audience and what is your purpose? Your audience determines the level of detail, the language, and the format of your data visualization. Your purpose determines the type, the message, and the action of your data visualization. For example, if your audience is technical and your purpose is to explore the data, you might use a scatter plot with many variables and annotations. If your audience is non-technical and your purpose is to persuade them, you might use a bar chart with few variables and captions.

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Shaurya Kuchhal

Hiring in AI, Sales, Dev | Founder UpSolve Solutions | Building Enterprise Grade AI
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Understanding your audience involves knowing their demographics, interests, needs, and preferences. Consider their technical proficiency, cultural context, and accessibility requirements to ensure your visualizations resonate and convey insights effectively to your specific audience. It is a key element of successful visualization strategy.

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Chris Hawkinson, NACD.DC, MBA, MSc

Board and Executive Decision Governance | Enterprise AI Accountability | Fractional CDO | NACD.DC
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We have to be careful on audience and level of detail. It isn’t necessarily about summary and transactions, but which metrics. I have known CEOs who want transactional detail, but only margin, and to drill down to revenue and cost when an anomaly is detected. With digital literacy on the rise, more people want to answer the next level questions themselves without calling/emailing/IM someone.

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Vignesh Coumarane
(edited)
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A data set can answer multiple questions with numerous visualizations but the audience is the key. An excellent visual narrative can be worthless if not useful to the audience and purpose. In an organization, based on the hierarchy of the user, the level of detail varies. A dashboard view of the same dataset varies between a C-level executive and a technical colleague. It's important for the analysts to build reports in such a way that it serves different audiences at the same time and be self-sufficient.

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2 Clean and prepare your data

One of the biggest challenges of creating data visualizations with complex and messy data sets is dealing with data quality issues. You might have missing values, outliers, duplicates, errors, or inconsistencies in your data. These can affect the accuracy, the clarity, and the credibility of your data visualization. Therefore, you need to clean and prepare your data before you visualize it. You can use tools like Excel, Python, or R to perform data cleaning tasks such as filtering, sorting, aggregating, merging, or transforming your data.

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Vignesh Coumarane
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Real-world data is no good data. The main challenge an analyst faces with any data set in the exploration phase is data cleaning. There are multiple ways of cleaning the dataset to address duplicates, errors, and missing data. Fixing these quality issues may entirely provide the users with a new perception of the dataset results. So, once the analysts are able to understand the data set, and comprehend and know the business value of the KPIs, it can drastically have a positive impact in the cleaning/data exploration phase.

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3 Choose the right type of visualization

Another challenge of creating data visualizations with complex and messy data sets is choosing the right type of visualization for your data and your purpose. You need to consider the characteristics of your data, such as the number of variables, the scale, the distribution, and the relationship among them. You also need to consider the message you want to convey, such as the comparison, the correlation, the distribution, or the composition of your data. You can use guides like the Data Visualization Catalogue or the Chart Chooser to help you choose the right type of visualization for your data and your purpose.

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Vignesh Coumarane
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The choice of visualization type (e.g., bar chart, line chart, scatter plot, etc.) should also align with the data you're presenting and the message you want to convey. Different types of data require different visualization approaches. Also when choosing a visualization, consider the complexity of your data, your report's audience, and the message you want to convey. Avoid clutter and use clear labels, titles, and legends to ensure your report's visualizations are easy to interpret. Combining different types of visualizations can also enhance report comprehensibility, allowing readers to gain a comprehensive understanding of the data.

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4 Follow the design principles

When creating a data visualization, you should follow some design principles to make it effective and engaging. Simplicity is key - use only the elements that are necessary and remove any clutter or noise that distracts from your message. Contrast can help highlight the most important or relevant information, while alignment and consistency should be used to create a sense of order, balance, harmony, and cohesion. Grids, margins, axes, colors, shapes, sizes, or fonts can all help achieve this.

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5 Use the right tools

The last tip for creating data visualizations with complex and messy data sets is to use the right tools for your needs and preferences. There are many tools available for creating data visualizations, such as Tableau, Power BI, Qlik, or D3.js. Each tool has its own features, advantages, and disadvantages. You need to consider the functionality, the usability, the cost, and the compatibility of the tools you use. You also need to learn how to use the tools effectively and efficiently. You can use online tutorials, courses, or books to learn how to use the tools you choose.

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6 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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Vignesh Coumarane
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It's also important to have proper documentation for these visualizations so that the users can comprehend the assumptions and outcomes.

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What are some tips for creating data visualizations with complex and messy data sets?

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2

3

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1 Understand your audience and purpose

2 Clean and prepare your data

3 Choose the right type of visualization

4 Follow the design principles

5 Use the right tools

6 Here’s what else to consider

Data Analytics

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What are some tips for creating data visualizations with complex and messy data sets?

1

2

3

4

5

6

1 Understand your audience and purpose

2 Clean and prepare your data

3 Choose the right type of visualization

4 Follow the design principles

5 Use the right tools

6 Here’s what else to consider

Data Analytics

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