How Data Scientists Can Bridge The Gap Between Businesses And Technology

Company executives now recognize the power of data. They know that they must leverage this power if they want to remain competitive. The challenge is that making data useful requires very technical skills. Data scientists must employ technologies such as data analytics and artificial intelligence in order to process available data.

At the other end of the spectrum are the business areas that rely on the information that can be gleaned from these data sources in their decision-making processes.

In its ‘raw’ form, this data isn’t very useful. As a result, it’s up to data scientists to bridge that gap by presenting data in a way that makes sense to business professionals.

Data Science: Potential Value Vs. Practical Value

Data is full of potential value. Like a not yet assembled piece of furniture from IKEA, something must be done with it before it is actually useful. One way that data scientists can do this is by using data storytelling.

Data scientists who have this skill are likely to be valued over data scientists who do not. In this article, we’ll explore the value of data storytelling, then examine the potential downsides that data scientists might face.

What is Data Storytelling?

Udacity published a list of 8 skills that people should have if they’re interested in working as data scientists. One of these is ‘Data Visualization & Communication. This is further described as the ability to communicate data in ways that make sense to both technical and non-technical audiences. Data storytelling is a methodology that involves using data as a tool to communicate important information to your audience.

Business professionals need data to be communicated to them in a way that enables them to make the best possible decisions.

Good data storytelling does that. It makes the information you gain from data analysis relevant, and easy to understand. Some of the benefits of data storytelling include:

  • Provide Actionable Information to Decision Makers.
  • Persuade Stakeholders With Data They Can Understand.
  • Present Information in Real-Time to Audience Members.
  • Customize Information to Different Business Areas.
  • Continually Refine Data to Answer Relevant Questions.

How to Use Data Storytelling

Data storytelling makes information accessible to a wider audience. It has a purpose in marketing, but can also be used internally to help decision-makers make sense of the information provided to them.

Know Your Audience

You should understand the importance of getting to know your audience before determining how to use data to assist them. Who you are communicating with should drive how you are communicating with them.

Each business area will have different needs and competencies when it comes to understanding data. C-level staff may want short summaries that cover a broad range of topics. On the other hand, department heads will want you to take a deeper dive into data that specifically impacts their business area.

It’s also important to understand what your audience needs to know. Assumptions can lead to frustration and wasted time. Of course, this can be challenging.

Data can be used to answer questions for sure. On the other hand, it can produce new information that reveals questions that your audience didn’t know they had to begin with.

Use Data From Multiple Sources

The stories you tell should be based on multiple sources of data. First-party data is the information you obtain from your owned sources. This includes customer information, transaction histories, and customer service records. Second-party data is data from another entity that is collected directly from its customers.

This information isn’t aggregated in any way, so you have to be cognizant of the importance of data privacy. Finally, third party data is data that you purchase from third parties that are in the business of collecting and aggregating data from a variety of sources.

By incorporating data from multiple sources you can:

  • Gain deeper insights.
  • Validate or challenge your own data reveals.
  • Find relevant stories that don’t exist in your own data sets.

Use Simple But Impactful Visualizations

People are best able to understand and recall information if you use visuals. However, what you want to avoid is using overly complex imagery. Keep things simple, and focus on a single point so that your data storytelling is relevant and easy to understand.

Create a Relevant Narrative

You should not bombard people with facts and figures. Don’t ignore the storytelling part of data storytelling. It’s the story that is engaging, and that creates relevance.

Look for existing case studies or other content that is relevant to your data. Show where the information you’ve gathered supports or contradicts that. Also, don’t be hesitant to look for narratives from other sources as well.

Final Thoughts: Let Stakeholders Participate in The Storytelling Process

Without intimate familiarity with a particular business area, it can be difficult to determine which data is important or to identify the stories within that data. People working in various business areas cannot only be passive consumers of data.

Yes, as a data scientist, it is your job to make data accessible, but you need their inputs to understand what is relevant, and where to dig deeper. Keep in mind that they may use the data you provide to them to further communicate with their customers.

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