Data With a Narrative: Understanding Data Storytelling

In the past few years, data has become an increasingly valuable resource for making business decisions. But cold, hard data doesn’t always offer valuable insights right away. Data needs to be corralled into a cohesive narrative, and that’s where data storytelling comes into the picture. 

Data storytelling is an underrated part of the larger data insights process. While most data professionals understand the importance of various data analysis techniques, not enough attention is paid to creating a compelling narrative from their findings. In this article, we’ll find out why effective data storytelling is so important and how you can learn to do it well. 

What Is Data Storytelling?

Data storytelling is the process of narrativizing data analysis so that its insights can be understood by a wide audience. While not everyone can quickly comprehend data, most can follow a visual story that summarizes findings from the analytics process. 

Why Is Data Storytelling Important?

Let’s take a look at an example to see why data storytelling is important. Here are some earth surface temperature data from a Kaggle dataset.

This is what your average dataset looks like—several rows and columns with various data points. Analyzing this dataset can produce valuable insights, but it won’t change what the output looks like. To the average viewer, it will remain a collection of rows and columns that don’t make a lot of sense. 

And that’s why data-driven storytelling is so important. It transforms complex data into universally comprehensible stories with a solid narrative. 

Essential Parts of a Data Story

Here are the components that contribute to an effective data story:

Data

You can’t have a data story if you don’t have any data. So the bedrock of the data storytelling process is clean data that can be analyzed to produce actionable business insights. 

The places from where you source your data will depend on the nature of the project that you’re working on. For example, if you’re trying to use data analytics to make better fantasy football picks, then you should use the Football Database. There are various sites that offer data sets that you can analyze for fun. 

Now that you have your dataset, it’s time to do some data analysis. That means that you connect your data to a specific business problem, clean the data so that there are no erroneous or missing entries, and carry out your analysis. 

At the end of this process, you will find that you’ve produced insights that weren’t available when you started with a raw dataset. If you work in the professional world, this will contribute to the business decision-making process. But in order to convince your organization about your findings, you need something more than just those insights. 

Narrative

One doesn’t usually associate a narrative with the data process, but the two are more intertwined than you might first think. Here are a few elements that contribute to a good story, data or otherwise: 

Setting

A setting is the foundation of any strong narrative, and data storytelling is no different. In this specific case, that means that you explain the context of your project, its business significance, and any specific problems that it is looking to address. 

Imagine that you’ve been given data from various marketing campaigns to determine why there is a lag in the sales of a specific product during a certain time of the year. The setting is the problem statement as it has been outlined, the product, and the nature of the marketing campaigns (marketing channels, nature of creatives, ad spends, etc.). 

Characters

You can’t have a story without characters. The same goes for the story that you’re trying to tell with data. The characters in this particular case are the subjects who contribute to your data and the various stakeholders who care about this particular data project. 

Let’s return to the example of the marketing campaign. Our characters are going to be the target demographic of those campaigns. The list of characters might also include members of the marketing team who contribute to the creation of those campaigns. 

Conflict

Stories are driven by conflict. Every project that you will work on is driven by some kind of conflict, usually one that involves a pressing business problem. To further the example we’ve been using, the conflict could be defined as ineffective marketing campaigns. In another case, it might be the need to explore new markets or enhance employee productivity. 

Describing the conflict helps your audience gain a clear understanding of the goals of the project. It tells them exactly what you’re trying to achieve and what kind of impediments might be in your way. 

Resolution

The resolution is your answer to the conflict that you’ve established in the previous step. This resolution must, of course, be backed by the data analysis that you’ve already conducted. 

Remember that your resolution needs to tie in with all of the other elements of your narrative. It must work in the setting that you’ve established, be relevant to your characters, and be appropriate to the conflict that you’re dealing with. 

Returning to our example, we see that certain marketing campaigns aren’t working as well as a company wants them to. Your data might reveal that the problem is that the specific marketing channels that have been chosen don’t cater to the target audience. In that case, your resolution will be to select different marketing channels based on their ability to reach that demographic. 

Visuals

Visuals are an essential aspect of data storytelling, as they convey large amounts of information quickly and keep readers engaged. Here are a few things you should keep in mind when coming up with your visual elements: 

Appropriate Visualizations

The kind of data visualizations you choose in each case will depend on the kind of data you’re working with. For example, column charts are a good choice when you want to compare numerical values, whereas a histogram works better when you want to track a specific data trend over time. 

Colors in Moderation

There is a tendency to make visualizations colorful in the hopes of this leading to a more attractive appearance. But using too many colors can confuse readers and distract them from the broader story. So make sure that you use a few colors and choose neutral tones as much as possible. 

Familiar Formatting

The whole idea of using visuals is to make it easy for viewers to quickly glean the information they need. You can contribute to that goal by using familiar layouts and formatting throughout your story. For example, if you have multiple graphs in your story, use the same formatting and colors across them so that they’re easy to parse quickly. 

Data Storytelling: Tips, Tricks, and Things To Remember

Just like the stories you watch on TV or read in books, a data narrative needs to have a structure. Where do you want your audience to begin? Where does the story end? How will you get them there? Here are a few veteran moves you can use to put all those things together in your data story:

Include the Right Details

Don’t overload your audience with information right off the bat. By keeping things succinct, you can guide users toward meaningful conclusions. For example, this table provides too many details about each sales opportunity, but the bar chart shows the metric the department truly cares about—open opportunities—in a more visual manner.

Provide the Right Context

Everything you include in your data story should relate to your organization’s larger goals, and your dashboard or data visualization should highlight the correlations between each metric and these KPIs. The chart below shows actual website traffic alongside the target numbers to provide important context. This is an example of how you can contextualize your data in light of larger organizational goals.

Provide Actionable Information

Your audience should know why a specific chart or graph is important without any guesswork. Additionally, it should be easy for them to see how the data relates to their role in the organization. 

Let’s look at an example based on the visualization below. Viewers can quickly figure out that the chart displays educational assessments across different subjects at Eisenhower Elementary. It’s also easy to get an idea of the comparative performance between subjects just by looking at this visualization. That makes it a lot easier to take actionable steps based on the data. 

Data Storytelling Tools

There are various data visualization tools that are used by data storytelling professionals. Let’s take a look at the most popular ones out there. 

Tableau

If you’ve ever gone about researching data visualization tools, then you’ve no doubt heard of Tableau, a powerful tool that can integrate data from a myriad of sources. It’s also a relatively intuitive software to learn. 

Infogram

Data storytelling is a domain that’s not limited to just designers anymore, and that’s thanks to tools like Infogram. This is a piece of software with a simple drag-and-drop interface that you can use to build dashboards, infographics, and more. An added advantage is that you can use Infogram to produce interactive visualizations, which is a great way to create a more engaging experience for your audience. 

Datawrapper

Datawrapper is a visualization tool built specifically for news stories. You can easily copy and paste your data into the tool to create tables and maps that enrich news pieces. These visualizations can easily be embedded on news sites with an embed code and can also be exported as a PNG, PDF, or SVG. 

D3.js

D3.js is a Javascript library that you can use to create visualizations in data-driven documents. Since this is a Javascript-based tool, you can also easily add transitions and transformations to bring your data to life. 

Data Storytelling Examples

Here are a few examples that you can use as inspiration to create your own data stories. 

We Feel Fine

We Feel Fine is a large-scale experiment in visualizing human emotions. How does it do that? Well, the tool trawls blogs on the Internet every 10 minutes and conducts a sentiment analysis on them to figure out the dominant emotions of each post. The results visualizations are a visually rich, data-driven exploration of human emotions in real-time. 

Spotify Fan Study

Given its position as one of the most widely used music streaming services out there, Spotify has vast amounts of data at its disposal. The company used some of that data to produce the Fan Study data visualization. This is a website where musical artists can get a data-driven insight into what Spotify has been able to gather about listeners’ preferences and tastes. The site shows artists how they can expand their reach, make a splash with new releases, and improve merch sales. 

Data Storytelling Resources

Here are a few resources that can help you learn more about data storytelling: 

Online Courses

• Data Storytelling and Data Visualization – Udemy
• Data Storytelling for Business – StoryIQ
• Data Science Bootcamp – Springboard
• Storytelling and Persuading using Data and Digital Technologies – edX

YouTube

• Storytelling with Data
• Effective Data Storytelling – Lights On Data
• Crafting Stories with Data – Google Career Certificates

Books and Websites

• DataStory – Explain Data and Inspire Action Through Story
• Effective Data Storytelling
• Information is Beautiful
• Junk Charts

Experts to Follow on Social Media

• Michael DeBenigno
• Amber Thomas
• Brent Dykes

Making a Career in Data Storytelling

A career in data storytelling can be exciting if you’re passionate about making data-driven insights comprehensible to a large audience. Here’s how you can go about making a career in this field. 

What Does a Data Storyteller Do?

A data storyteller’s job lies at the intersection of data, business, and design. These are all disciplines that you must have some familiarity with if you want to work in this area. 

It’s essential that data storytellers understand the foundational aspects of the business context of their organization. This will help you better grasp the problems that the data team is trying to solve and why certain resolutions have been proposed. 

You do, of course, need to have a strong understanding of how data is employed in business decision-making, as your work will entail interpreting results from the data analysis process and deciding which of those results needs to be highlighted for a specific audience. 

Finally, you need to know what constitutes good design. This doesn’t mean that you need to have a degree in design or be able to illustrate digital mosaics from scratch. Rather, data storytellers have a strong grasp of the basics: clean layouts, intuitive formatting, and concise textual elements. 

Job Roles and Titles To Explore

There are a few different titles you’ll come across when looking for data storytelling jobs. Here’s what each of them entails. 

Insights Analyst

This is a job role that’s equal parts data and storytelling. You’ll spend your time producing key insights based on the requirements of the projects and visualizing them for a non-technical audience. 

Data Visualizer

Data visualizers work specifically on transforming insights from the data analysis process into visuals. This job is less heavy on data-specific work and requires a strong understanding of tools like Tableau and Adobe Photoshop. 

Data Analyst

Some data analyst jobs require skills in data storytelling. While your job will primarily involve data analysis, you will also need to be able to work with designers to produce presentations for different stakeholders. 

General Prerequisites

These skills are all prerequisites to landing a job in data storytelling: 

• Data concepts: A fundamental understanding of the value of data in business decision-making and the analysis process. 
• Business and strategy: Understanding the market context in which an organization functions and its business goals. 
• Design: Knowledge of how humans process visual information and how to effectively communicate with design. 
• Creativity: The ability to come up with innovative ways to communicate data insights to an audience. 

Data Storytelling FAQs

We’ve got the answers to your most frequently asked questions.

Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.

In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you. 

Is Data Science A Good Career?

Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.

10 Data Science Careers To Consider

Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.

Data Scientist

Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. 

General Requirements

A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. 

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.

Average Salary

The average salary of a data scientist in the US is $156,363 per year.

Data Analyst

A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.

A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.

General Requirements

To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills. 

For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.

Average Salary

The average base salary of a data analyst is $76,892 per year.

Business Analyst

Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. 

A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.

General Requirements

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.

Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.

Average Salary

A business analyst can earn an average of $84,435 per year.

Database Administrator

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. 

A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.

General Requirements

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. 

Average Salary

Database administrators earn an average salary of $77,391 annually.

Data Engineer

Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).

General Requirements

Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. 

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.

Average Salary

Data engineers earn a whooping average salary of $125,180 per year.

Database Architect

A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. 

Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.

General Requirements

If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java. 

If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.

Average Salary

Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.

Machine Learning Engineer

A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. 

General Requirements

As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.

Average Salary

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.

Quantitative Analyst

Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. 

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.

General Requirements

This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. 

Average Salary

A quantitative analyst earns an average of $173,307 per year.

Data Mining Specialist

A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.

General Requirements

If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. 

Average Salary

Data mining specialists earn $109,023 per year.

Data Visualisation Engineer

Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context. 

General Requirements

Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.

Average Salary

The average annual pay of a data visualization engineer is $103,031.

Resources To Find Data Science Jobs

The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.

Job Boards

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. 

Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.

Online Communities

Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.

Network And LinkedIn

Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.

FAQs about Data Science Careers

We answer your most frequently asked questions.

Do I Need A Degree For Data Science?

A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.

Does Data Science Need Coding?

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.

Is Data Science A Lot Of Math?

It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.

What Skills Do You Need To Land an Entry-Level Data Science Position?

To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!