The Value of Mastering Data Visualization & Storytelling for Data Scientists

All of the analysis you perform and the insights you generate as a data scientist would not be worth your time if it weren't for storytelling and visualization. Simply presenting numbers and data from your analysis rarely helps accomplish anything for you. The people you are reporting to have numerous questions that can only be answered through in-depth data visualization and storytelling.

Imagine a weather forecaster walking in to tell people about an approaching blizzard. Their warning won't have any impact on the audience if they don't use appropriate visuals and storytelling techniques. Hence, forecasters use graphics and interactive methods to keep viewers hooked and informed.

Both visualization and storytelling techniques help data scientists communicate the insights they have achieved after their analysis. These analyses and insights are meant to help improve decision making for everyone involved.

"The ways in which organizations deliver business analytics insights are evolving, notably in the rising use of what is called data storytelling," states James Richardson, Senior Director Analyst at Gartner.

Data scientists need to realize that not everyone in the organization they work for will be able to understand data and analytics in a manner that they do. Hence, they have to apply visualization and storytelling techniques that can actually serve the purpose here.

Many organizations have realized the importance of data visualization and are investing in implementing it. However, they are still facing challenges in representing data in a manner that can genuinely optimize business value.

The lack of storytelling and visualization in a data presentation can actually slow down the process of data scientists, leading to a reduction in the quality of research being done as well.

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Why You Must Be a Good Storyteller as a Data Scientist?

Storytelling is just as much a part of a data scientist's job as any other technical aspect. Gartner has described storytelling as being a non-technical trend that is extremely critical to the success of a data science program.

Whenever you are presenting data in front of an audience, you need to gather and offer context to them. Context is what builds value that surrounds insights generated through data. Through their storytelling skills, data scientists can help build significant context and can effectively explain their ideas to everyone present.

A good storytelling narrative can help make data and analytics a lot more accessible. Storytelling has been part of marketing for a long, long time and has served a significant purpose. A good storyteller isn't just meant to evoke emotions and responses from the audience, but also wishes the audience to learn and comprehend what is being displayed or presented. As a data scientist, you are serving the latter purpose more than the former one. You want your audience to understand the significance of what you are presenting. You want them to learn from the insights and make informed business decisions based on them. 

The insights generated from the analysis are quite strategic for the organization running the data science campaign. You need to gather and convey your data in a reasonable manner as it is these insights that usually make or break the deal for a sound data strategy. Many important and strategic business decisions could be hanging on a thread, waiting on the insights from analysis.

Storytelling is, at its core, an extension of business intelligence, as it combines the visualization of data with a powerful narrative for pushing your point forward. Data scientists and CDO's can utilize storytelling as a non-technical data science tool to share a powerful data vision, generating calls to action and influencing decision making.

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Data Visualization Supports Storytelling

Have you ever heard of the term "actionable insights?" Do you know what separates insights from actionable insights? The presence of visualization when the data is presented! Data visualization can help turn any insight into actionable insight for managers and organizations to work on when they are creating the best possible strategies.

To transition your insights into actionable insights, you need to begin by making a clear and visual representation of your data. A picture says a thousand words; the old adage holds true when it comes to showing data to a specific audience. You can sum up a lot of verbal info within a picture and show it to your intended audience. Images are a strong medium to support storytelling, period.

For instance, if you were talking about pipe leaks across a particular city, you can show pictorial evidence to pinpoint areas where leakages are more common. You can then follow the pictorial evidence to create a narrative of what could lead to an increase in pipeline leakages in that particular area.

Following the example above, data visualization can seriously help you to reveal all connections, patterns, and trends present within your data. The ultimate objective is to promote data literacy, and data visualization helps do just that.

Data Visualization is also a powerful and effective means of covering tons of information in formats that are easily digestible for an audience. When presented in its raw form, data can be quite nearly impossible for people to understand. Numbers can also reduce interest, which is why not many people would want to wrap their heads around raw data. Visualization can break down data into digestible formats, which are easy for most people to keep track of. This eventually increases the impact of information and helps communicate your message.

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Types of Data Visualization

There are numerous charts and bars that you can use to visualize your data. Some of these are of intermediary level, and you might already be familiar with them. Others require tools and equipment for better comprehension.

To choose the best type of data visualization, you need first to know the story that you are telling alongside the visualization. You also need to see the purpose you want to achieve through that specific visualization. You could be doing any of these three things by visualizing your data:

  1. Convey data composition
  2. Compare value sets
  3. Trend analysis

Once you have decided what you are doing with your visualization, you can ask yourself a list of final questions to get started. You can use these questions to understand better the purpose behind whichever visualization tool you use.

  1. Is it communicating the right messages?
  2. Is it interactive, engaging, and understandable to non-data scientists?
  3. Is the information accessible to everyone?
  4. Is it conveying what matters?
  5. Is the visual effectively connecting to the data story you want to tell?

The answers you have to these questions can eventually help you determine the best way forward with your visualization strategy. Remember, good visualization should complement your storytelling.

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Final Thoughts

Storytelling and data visualization are linked together when it comes to success in presenting data. Both of them are non-technical data science skills that you should attempt to master for the successful presentation of data. Together, both of these skills can help bridge the disconnect between knowledge and data. To become a successful data scientist, you need to master all facets that come with this job.

Simplilearn provides numerous data visualization educational paths and certifications to augment a data scientist's skills. If you want to get comprehensive training in this exciting field, you can take the Data Scientist Master's Program, which includes a Tableau Certification Training Course. You can also learn how to present data in meaningful ways through our Business Analytics Certification Training with Excel, where you can learn how to present data through various kinds of intuitive charts. Check it out now to see which Simplilearn learning path is best for you in your career.

About the Author

Ronald Van LoonRonald Van Loon

Named by Onalytica as the world's #1 influencer in Data and Analytics, Automation, and the Future Economy (Tech), Ronald is the CEO of Intelligent World and one of the top thought leaders in Data Science and Digital Transformation.

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