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What is the best way to reduce data redundancy in data storytelling?

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Data redundancy is the duplication of data in a database or a data visualization. It can cause confusion, inconsistency, and inefficiency in data storytelling. Data redundancy can also make your data visualization look cluttered, boring, or misleading. In this article, you will learn what are the common causes and types of data redundancy, and what are the best ways to reduce data redundancy in data storytelling.

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1 Causes of data redundancy

Data redundancy can be caused by a variety of factors, such as poor data quality or entry errors, a lack of data standards, multiple data sources or formats, unnecessary or repetitive elements or attributes, and ineffective data integration or aggregation. To prevent this from happening, it is essential to guarantee that your data is precise, consistent, and relevant. Additionally, you should adhere to data quality best practices like validating, cleaning, and transforming your data before using it for data visualization.

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2 Types of data redundancy

Data redundancy can be classified into two main types: controlled and uncontrolled. Controlled data redundancy is intentional and beneficial, while uncontrolled data redundancy is accidental and harmful.

Controlled data redundancy is when you deliberately duplicate or repeat some data to improve data reliability, availability, or performance. For example, you might use backup copies, indexes, or summary tables to enhance data security, accessibility, or speed.

Uncontrolled data redundancy is when you unintentionally duplicate or repeat some data due to poor data design, management, or communication. For example, you might have duplicate records, overlapping fields, or conflicting values in your data that create confusion, inconsistency, or errors.

To reduce data redundancy, you need to identify and eliminate uncontrolled data redundancy, and optimize and justify controlled data redundancy.

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3 Effects of data redundancy

Data redundancy can have a detrimental impact on your data storytelling, such as reducing data clarity and readability, increasing complexity and maintenance, wasting resources, compromising integrity and quality, and misrepresenting insights and messages. To reduce data redundancy, you should evaluate and measure its effects on your data storytelling. Metrics like data size, accuracy, completeness, timeliness, and relevance can be used to assess the quality and performance of your data.

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4 Strategies to reduce data redundancy

When it comes to reducing data redundancy in your data storytelling, there are several strategies you can employ, such as using data visualization best practices , applying principles, implementing techniques, and leveraging tools. For instance, you can choose the right chart type and use appropriate colors and labels to reduce cognitive load and maximize data-ink ratio. Additionally, you can filter, group, aggregate, or summarize your data to highlight the most important or relevant information. Lastly, there are software, libraries, and frameworks that can help you create effective and efficient data visualizations with minimal coding or manual work. Ultimately, selecting and applying the most suitable strategy for your data storytelling goal and audience is essential for reducing data redundancy.

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Tai Chen Wei

Analyst | Microsoft Power BI Data Analyst ? Google Project Management ? SQL ? Alteryx Designer Core ? LSSGB | Registered Pharmacist
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One thing that I found helpful is to initiate your data storytelling by articulating the central message or insight you intend to communicate. This initial step enables you to concentrate on the pivotal elements and prevent the redundancy of non-contributory information.

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5 Examples of reducing data redundancy

Illustrating how to reduce data redundancy in data storytelling, let's look at some examples of data visualizations that have been improved by minimizing data redundancy. For instance, a pie chart that initially had too many slices, colors, and labels was made easier to read and compare by removing some of the elements. Similarly, a bar chart with too many bars, axes, and grid lines was made clearer when these elements were reduced. Furthermore, a line chart that was initially too busy and overwhelming was simplified when fewer lines, legends, and annotations were used. To reduce data redundancy in your own visualizations, you need to compare and contrast your data before and after applying the strategies and techniques discussed above.

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Linus Bech

Data & Analytics Lead | Driving Strategic Insights for Growth
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One of the most effective strategies I've found for reducing data clutter is to create contrast in your visuals. By limiting the color palette to just two colors, you can focus your audience's attention on what's most important: a bold color to highlight the main point, and a more neutral, subtle color like gray for the rest. Another technique to consider is combining data points. Grouping data points with low volume or similar attributes can create a clearer, more impactful visualization. This allows for easier one-to-one comparisons and helps the audience better understand the data without oversimplifying it.

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6 Benefits of reducing data redundancy

Reducing data redundancy in data storytelling can bring many benefits, such as improving comprehension and communication of the data, simplifying analysis and interpretation, saving resources and costs, and strengthening insights and messages. To achieve this, you should recognize and demonstrate the value of data redundancy for your data storytelling project.

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Henny Speelman

Founder of the A.R.T. model. Follow me to learn everything about data, -visualizations, -storytelling, and AI. Keynote speaker & Trainer.
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Redundancy in data can be a double-edged sword. In my early days, I recall creating a dashboard for a logistics division that included multiple similar metrics, thinking more data would offer comprehensive insights. Instead, it overwhelmed the team and diluted the key messages. Some benefits from my experience or reducing redundancy: → Improved clarity: Less clutter means your audience can easily grasp the key insights you want to convey. → Efficiency in resources: Fewer redundant data points mean less time spent on analysis, saving both time and cognitive power. → Sharper Insights: By focusing on unique, crucial data points, you're likely to uncover insights that might have been buried otherwise.

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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 is the best way to reduce data redundancy in data storytelling?

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1 Causes of data redundancy

2 Types of data redundancy

3 Effects of data redundancy

4 Strategies to reduce data redundancy

5 Examples of reducing data redundancy

6 Benefits of reducing data redundancy

7 Here’s what else to consider

Data Visualization

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What is the best way to reduce data redundancy in data storytelling?

1

2

3

4

5

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7

1 Causes of data redundancy

2 Types of data redundancy

3 Effects of data redundancy

4 Strategies to reduce data redundancy

5 Examples of reducing data redundancy

6 Benefits of reducing data redundancy

7 Here’s what else to consider

Data Visualization

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