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Data Storytelling with Tableau: Effectively Sharing Insights"

Ideal for professionals, analysts, and marketers. This article provides information for journalists, and researchers, and gives a brief overview how to communicate with data using tableau. Data has the power to change the world, but the difficulties and tools at hand now necessitate a blend of analytical, aesthetic, and creative communication. Since the communicator needs to comprehend the audience's requirements and motives, empathy is crucial. For simple data discovery and communication, Tableau Software delivers a visualization querying engine and user interface. The communicator must, however, be aware of the best techniques in each circumstance to use Tableau to its maximum potential.

The user is responsible for adhering to best practices; the software only provides guidance. Sharing data analysis insights can be difficult, but data storytelling can make the data more understandable and lead to better action and outcomes. This strategy guarantees a streamlined and effective communication method for your company. When creating a message, there are a lot of options and choices to consider, so it's important to know what options are available and how successful they are.

A greater comprehension of the world is made possible through effective data transmission, which also inspires action. It stands out from other data discovery phases because it starts with a question and concludes with a shared insight. The total experience improves comprehension and inspires action on newly acquired knowledge.

How communicating with data?

In the data discovery process, which starts with a specific question and concludes with a common insight, communicating data is an essential stage. Data collection, organizing, and exploration are steps in the process of developing insights. The message used to communicate quantitative statements to others is composed of these insights. Each phase in the entire data discovery process helps to construct the message, making it an iterative process.



Types of Communication Problems

To communicate information successfully, consider technical, semantic, and effectiveness issues, concentrating on accurate transmission, precise meaning conveyed, and effective impact on the desired behavior.

Even though technology has improved, it still encounters technical problems including poor print quality, faulty audio, and insufficient screen resolution. Due to different hardware, operating systems, and software, it can be difficult to guarantee that the message is retained. Semantic issues occur when symbols are not recognized by the recipient or when messages are encoded using the incorrect display kinds. When everything is fine, but the recipient doesn't care or respond in any way, the communication fails. This is known as the "why?" problem.


Six Principles of Communicating Data

Iterative, creative, and involving multiple loops are three of the six principles to be kept in mind when solving communication issues. Crafting and fine-tuning messages as part of the creative process of communicating necessitate a thorough evaluation of the entire procedure.

1. Know your goal

2. Use the right data

3. Select suitable visualizations

4. Design for aesthetics

5. Choose an effective medium and channel

6. Check the results


1. Know your goal

Information and message are not interchangeable terms since information refers to the pool of potential messages chosen by the source, whilst message refers to the selected message to be communicated. Choosing the appropriate message is essential in a world where information is expanding quickly. To establish trust in data storytelling, confirm data sources, outcomes, and data in graphs, charts, and displays. Additionally, identify the source and validate it.

However, it's crucial to be aware of your aim before selecting your message. You may do this by answering a few crucial questions up front:


  • With whom are you attempting to communicate?

  • What do you want them to understand?

  • Why? What action do you want them to take?



The objectives for sharing data may vary among fields, but clearly stating them is essential. Write down your responses to the three questions and hold off on additional research until you are certain of your replies.

2. Use the Right Data

Sometimes when communicating data, less is more. For instance, can spur a whole company to improve customer service. A story on mislabeled fish containers, however, demonstrates that there are instances when little is more. Listeners may be misinformed and misled by small sample sizes. Since Shannon and Weaver caution that a crowded audience can lead to inaccuracy and confusion, overloading them with information can undermine the message.

Statistics can be deceptive and susceptible to logical fallacies; hence it is essential to choose data ethically and based on strong epistemology. Avoiding these icebergs can help the message and the experience.

3. Select Suitable Visualizations

Data is transformed into abstract representations, such as size, color, or shape, during encoding. Avoiding semantic issues requires an understanding of how the human mind uses these representations. Pioneers in information visualization have determined that position is the best format for all forms of data. While color hue enhances effectiveness, length, angle, and area lessen it. Considering the audience's comprehension, aesthetics, media, channel, and impact can help you choose the best visualization kind.

Several things are instantly clear:

  • For all data types, the position is the most efficient encoding method.

  • The effectiveness of length, angle, and area decreases from quantitative to nominal to ordinal.

  • The effectiveness of color hue grows from ordinal to quantitative.

Example for inappropriate visualization:

Example for appropiate visualization:



4. Design for Aesthetics

In visualizations, aesthetics is frequently viewed as superfluous since they might confuse viewers and distort the data. It is crucial to eliminate any aesthetic components to prevent this. Aesthetic components should not impair intellect, but they can increase interest and memory. Visualizations can be more successful in accomplishing their objectives by minimizing distracting aspects.

Every data visualization has a variety of aesthetic components, and when making them, people frequently commit the following errors:

· Bad color combinations

· Obtrusive typefaces

· A variety of fonts

· Careless alignment

· Angled or vertical labels

· Backgrounds with deep hues

· Thick outlines or grids

· Ineffective graphics and clip art

· Accepting most software defaults sloppily

Example for Poor Aesthestics:



Example for Good Aesthestics:




When it comes to design, less is more. If you know a talented graphic artist, invite her for coffee and ask for her opinion. Paying even a small amount of attention to how your data visualizations look can mean the difference between being ignored and arousing interest, or between being quickly forgotten and being remembered for some time to come. Design is a separate discipline that you could spend a lifetime learning about and perfecting.


5.Choose an Effective Medium and Channel

Data communication requires choosing the message's medium and channel, which is essential for reaching the target audience and achieving objectives. Pointing and waving of the arms are essential components. Simple static charts may not have much of an effect on the target audience, but presenting a mix of static and dynamic visuals live can have a big impact. Cost and impact are trade-offs, with significant stakes for small businesses and a large audience. These choices are influenced by the objective and the intended audience.

6. Check the Results

Include feedback loops and checkpoints to evaluate progress and modify objectives. To gauge audience reaction, pose questions like "RUI" (Reach, Understanding, Impact). By doing so, you can improve your message, convey information clearly, and show your audience that you appreciate them.


Summary

We saw sharing data as both an essential element in a wider data discovery process and a crucial form of communication in general. We also considered three issues that can obstruct effective data communication; this is a technological issue. both the effectiveness and the conceptual issues. Finally, we have six guiding concepts to address these issues and accomplish our goals. Regardless of the technology or method used, these six guidelines software are employed.




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