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Mastering Data Visualization and Storytelling for Data Scientists

Updated: Mar 18

Without storytelling and visualization, all of the data analysis and insights you generate as a data scientist would be pointless. Putting figures and data from your analysis on the table rarely gets you far. The folks you're reporting to have many questions, and the only way to get answers is to use in-depth data visualization and narrative.

Imagine a weather forecaster entering the building to warn folks of an impending blizzard. Their warning will not affect the audience if they don't employ appropriate imagery and storytelling tactics. As a result, forecasters use graphics and interactive techniques to keep viewers engaged and informed.

Data scientists might use visualization and narrative strategies to communicate their conclusions after their investigation. These analyses and insights are intended to aid everyone involved in making better decisions.

Data scientists must accept that not everyone in their business can comprehend data and analytics like they do. As a result, they must employ visualization and storytelling approaches that are effective in this situation.

Many businesses have recognized the value of data visualization and are investing in its implementation. However, they continue to face difficulties in displaying data in a way that maximizes business value.

Data scientists' processes can be slowed by a lack of storytelling and visualization in data presentations, which can lead to a loss in the quality of research conducted.

Mastering Data Visualization

Why Must Data Scientist Be a Good Storyteller?

A data scientist's job includes storytelling just as much as any other technical aspect. According to Gartner, storytelling is a non-technical trend vitally important for a data science program's success.

When presenting statistics to an audience, you must first acquire information and provide context. The context that surrounds data-driven insights is what adds value. Data scientists can assist in providing important context and successfully communicate their ideas to everyone present by using their storytelling talents.

Data and analytics may be made much more approachable with the help of a solid storytelling narrative. Storytelling has long been a part of marketing and has played an important role. A skilled storyteller wants the audience to learn and understand what is being revealed or presented, not only to elicit their emotions and responses. You serve the latter goal more than the former as a data scientist. You want your audience to recognize the importance of what you're saying. You want them to take what they've learned and use it to make better business decisions.

The insights gained from the analysis are quite valuable for the organisation running the data science campaign. You must collect and communicate your data fairly, as these insights make or break a successful data strategy. Many crucial business decisions could be on a knife's edge, awaiting the analysis results.

Storytelling is a form of business intelligence at its foundation since it blends data visualization with a compelling narrative to make a point. Data scientists and CDOs can use storytelling as a non-technical data science technique to present a powerful data vision, generate calls to action, and influence decision-making.

Data Visualization Supports Storytelling

Do you know the difference between insights and actionable insights? When the data is provided, there is visualization! When developing the best potential strategy, data visualization may assist in turning any knowledge into actionable intelligence for managers and organizations to work on.

To turn your insights into actionable insights, start by creating a clear and visually appealing representation of your data. When presenting data to a specific audience, the old saying "a picture is worth a thousand words" remains true. You can condense much-spoken information into a single image and convey it to your target audience. Images are a powerful medium for supporting stories.

To understand more about data personas, please check out the following: What are the data personas in data literacy culture?

For example, when discussing pipe leaks in a certain city, you can use visual evidence to indicate regions where leaks are more common. Then, based on the visual evidence, you may piece together a story about what might be causing more pipeline leaks in that area.

As shown in the example above, data visualization may greatly assist you in revealing all of the relationships, patterns, and trends within your data. Data visualization aids in promoting data literacy, which is the ultimate goal for every business user

Data visualization is also a powerful and effective way of presenting a large amount of information in formats that are easy to understand. Data can be practically impossible to comprehend in its most basic form. Numbers can also detract from interest, which is why few individuals like to spend time deciphering raw data. Data can be broken down into digestible formats that are easy for most people to keep track of via visualization. This raises the impact of information and aids in communicating your message.

Types of Data Visualization

You can utilise various charts and bars to show your data. Some of these are intermediate-level, and you may be familiar with them already. Others involve the use of tools and equipment to gain a deeper understanding.

To select the appropriate type of data visualization, you must first understand the story you want to tell with the visualization. You must also visualize the goal you wish to achieve with that visualization. By visualizing your data, you could be accomplishing any of the following three things:

  • Trend analysis

  • Convey data composition

  • Compare value sets

Once you've decided what you will accomplish with your visualization, you can start by asking yourself a series of concluding questions. These questions can help you better comprehend the objective of whichever visualization tool you use.

  • Is it sending out the proper messages?

  • Is it entertaining, interactive, and understandable to people who aren't data scientists?

  • Is the information available to the general public?

  • Is it communicating what's important?

  • Is the graphic a good fit for the data storey you're trying to tell?

The answers to these questions will eventually aid you in determining the best course of action for your visualization strategy. Remember that good imagery should be used in conjunction with your storytelling.

In The End

When presenting data, storytelling and data visualization are inextricably interwoven. Both are non-technical data science abilities that you should strive to learn to convey data effectively. Both of these abilities, when combined, can aid in bridging the gap between knowledge and data. To be a successful data scientist, you must master all aspects of the job.

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