Hi there,
Welcome to the fifth and final tutorial of your Data Analytics for Beginners Course 🎈I hope you’re starting to feel like a bona fide data analyst!
You certainly should be, considering everything you’ve accomplished so far 😎 You’ve expertly applied key data analysis techniques to a real dataset, from data cleaning and calculating descriptive statistics, to creating pivot tables and data visualizations. That’s pretty impressive 🤩
Most importantly, you’ve gleaned lots of valuable insights from your data—insights that can be used to help improve the NY Citi Bike service. In this tutorial, you’ll learn how to communicate your findings and present them to key stakeholders in a way that’s meaningful and actionable. This is what’s known as storytelling with data.
By the end of this tutorial, you’ll be able to:
- Explain the importance of storytelling with data
- Effectively tell a story with your NY Citi Bike data
- Create a presentation deck, showing all your key findings and turning them into actionable advice for NY Citi Bike stakeholders
As always, our tutorial will comprise some essential theory followed by a practical exercise. And, at the very end of the tutorial, you’ll find a final quiz which will revisit everything you’ve learned throughout the Data Analytics for Beginners Short Course. If you score over 70%, you’ll get 5% off our full Data Analytics Program 😍
- What is storytelling with data and why is it important?
- How to tell a story with data
- Practical exercise: Creating and sharing your data story
- Key takeaways and next steps
So, one last time…Let’s go! 🏃
1. What is storytelling with data and why is it important?
As a data analyst, your goal is to make data meaningful. You take raw data, analyze it, and draw out insights that can have a real-world impact. When you uncover these insights, it’s your job to communicate them in a way that means something. You need others to not only understand your findings, but to care about and act upon them. You do this by building a narrative or a story around your data 📚
The power (and science) of storytelling
Since the dawn of time, storytelling has been one of the most fundamental—and powerful—methods of human communication. In fact, it’s in the way our brains are wired.
Studies have shown that if we’re presented purely with information or facts, only the language processing areas of the brain are activated.
If we’re being told a story, on the other hand, we engage lots of different areas of the brain—the language processing areas, but also any other part of the brain that would be activated if we were personally experiencing the events of the story 🤯
Storytelling makes things more relatable, more vivid, and more memorable. So, as a data analyst looking to build a connection between your data and the people you’re sharing it with, storytelling is a critical skill.
A story about data storytelling
We’ve just told you that storytelling is important. But, as we’ve learned, we can relay this message much more effectively with a story. Here’s a powerful anecdote on the importance of data storytelling, shared by Brent Dykes, data analytics expert and author of Effective Data Storytelling.
In 1846, a Hungarian doctor named Ignaz Semmelweis was appointed as an assistant at a hospital in Vienna. The hospital had two maternity clinics: one for training doctors, and one for training midwives. Sadly, many mothers who came to the hospital were dying of a mysterious illness known as childbed fever.
Looking at the numbers, Semmelweis noticed a rather shocking trend: the doctors’ clinic had an average mortality rate of 9.9%, compared to the 3.9% mortality rate of the midwives’ clinic. Upon further investigation (and some gory events we won’t go into here), Semmelweis realized the reason for the significantly higher mortality rate in the doctors’ clinic. Essentially, doctors were performing autopsies in the morning before attending to patients on the maternity ward, spreading deadly contaminants to the mothers on the ward.
Based on these findings, Semmelweis introduced a handwashing policy which decreased the mortality rate on the doctors’ ward by 82%. Despite this, many doctors failed to adhere to the handwashing policy, and Semmelweis faced harsh criticism and resistance 😔
So what went wrong?
Semmelweis had accurate, valuable, and actionable insights from his data analysis, but that wasn’t enough to convince the medical community to change their behavior.
Something clearly went awry in the communication of his findings. Semmelweis wasn’t able to successfully convey his insights in a way that resonated with his audience. His data and life-saving insights were rejected, and he was discredited as a medical professional.
When it comes to data analysis, it’s not enough to simply present your findings. You need to put them into context and build a meaningful narrative around your data; a narrative that resonates with your audience and speaks their language. Only then can you get buy-in from key stakeholders and make sure that your findings turn into actions and solutions.
2. How to tell a story with data
There are three key components of data storytelling:
- Data: This entails all the insights you’ve gathered from your analysis—for example, the top 20 Citi Bike pick-up locations, and the finding that the most frequent users of the Citi Bike service fall into the 35-44 age range (to name just a few!) 📈
- Visualizations: We learned all about data viz in tutorial four, so you’re already familiar with what a powerful storytelling tool it can be. Visualizations are key to making the findings of your analysis understandable for a broad audience 📊
- Narrative: This is what puts your findings into context and makes them meaningful for your audience. With data storytelling, your narrative should introduce the topic (for example, the challenge or questions you set out to answer), present your findings and their relevance, and conclude with a specific call to action 🎬
So how can you devise a compelling data story? Let’s take a look.
A step-by-step guide to data storytelling
Data storytelling isn’t just about creating visualizations and sharing them. It requires a structured approach, and consideration of various factors. While there is no set formula for telling the story of your data, here are some steps you can follow:
A. Identify your audience 🎭
To create an engaging story, you first need to understand exactly who you are trying to engage. Who are you presenting your insights to? Why should they care about the data and your findings? What problem or challenge will the data help them to address? What insights from your analysis will matter most to them?
B. Construct a compelling narrative 🧑🎤
When sharing your insights, you don’t just want to explain them; you want to take your audience on a journey. To build a narrative:
- Start by setting the scene: What’s the context behind your analysis? Why did you analyze this data in the first place? What was the problem or challenge you set out to solve, and why does it matter?
- Present and discuss your findings: What did your analysis tell you? What are the main points you’ll share with your audience? What answers can you provide to the original questions or challenges you set out to investigate? Remember that not all the data used in your analysis will be relevant to the story you want to tell, so it’s important to pick out and highlight the key points. Do you remember that Cole Knaflic quote we shared in tutorial four? About how it’s tempting to share everything with your audience, but much more powerful to “concentrate on the pearls”? That’s what you’re doing in this step: picking out the pearls 💎
- Provide action points and solutions: Based on your analysis, what actions can be taken moving forward? What advice can you give to your audience? How can they utilize the data you’re showing them, and what will be the impact?
C. Create and organize your data visualizations 📊
You may have already created data visualizations as part of your analysis (as we did in tutorial four). If these visualizations already illustrate the key points you want to convey, it’s a case of organizing them and deciding how you’ll present them—for example, figuring out the order in which they’ll be presented to your audience. Otherwise, you may need to create additional visualizations in order to convey your data “pearls.”
D. Share your data story 🚀
With a compelling narrative in place, there’s only one thing left to do: Share it! We recommend building out a presentation deck, which brings us nicely to the practical part of the tutorial…
3. Practical exercise: Creating and sharing your data story
At the beginning of the course, you embarked on a noble mission to analyze data collected by Citi Bike, with the goal of helping key stakeholders make smart, data-driven decisions. You’re now going to turn your findings into a meaningful story, complete with action points and suggestions—just as a real data analyst would 😎
In tutorials one to four, you analyzed data to answer the following questions:
- What are the most popular pick-up locations across the city for Citi Bike rental?
- How does the average trip duration vary across different age groups?
- Which age group rents the most bikes?
- How does bike rental vary across the two user groups (one-time users vs long-term subscribers) on different days of the week?
- Does user age impact the average bike trip duration?
With the overall project in mind, you’ll now craft your data story and compile it all together into a Google Slides presentation deck. We’ll follow the steps we laid out in section two.
You’ll need the following tools for this exercise: Pen and paper, or a blank Google Doc.
Got your blank canvas at the ready and your dataset open? Then let’s begin ✍️
Psst! Here’s the final version of the dataset, complete with data visualizations. If you got stuck in the previous tutorial, just make a copy of this file and you’ll have everything you need to proceed with the next practical exercise. We’ve called this version of the dataset “New York Citi Bikes_Final.”
1. Identify your audience
Who will you share your data analysis with? Why should the data matter to them? How will it impact their work? Jot down your thoughts on paper, or in your Google Doc. You might come up with something like this:
My main audience is business stakeholders who work at NY Citi Bike. This includes representatives from the marketing, sales, product, and customer service departments, as well as the CEO. The data will help them to understand how people use the Citi Bike service, which will allow them to deliver more effective, targeted marketing campaigns and to plan where to install more bikes.
This won’t be featured in your final presentation, so it doesn’t need to be particularly elegant—it just helps to keep your audience in mind as you craft your narrative.
2. Construct a compelling narrative
You’re now going to fill in a table to form your narrative. So, either with pen and paper or in your Google Doc, sketch out or insert a table like this one:
Figure 1.
*Tip💡 If you’re using Google Sheets, you can use this example sheet with the tables above.*
The first step in constructing your narrative is to set the scene, considering the questions or challenges you set out to answer. We’ve already laid out the five key questions you investigated for NY Citi Bike, so that part is done for you!
To complete your narrative, fill in:
(a) The answer or insight(s) you found in relation to each question, and
(b) An action point (or action points) for each insight.
We’ve filled in the first row to start you off:
Figure 2.
Now, over to you to do the same for questions two, three, four, and five 😊
*Tip💡 You’ll want to include an answer or insight for each of the main questions asked, but you don’t need to show all the steps that led you there. So, for example, you might omit the descriptive statistics you calculated in tutorial three and focus instead on the insights that are most relevant and actionable*
How AI can help with storytelling
Provided you've stringently double-checked it, this is a great area where LLMs such as ChatGPT can help you out.
They can analyse the data and pull out a potentially different narrative to the one you initially came up with. On top of that, they can easily adjust the data storytelling based on the audience and context. Let's say that the audience for this data is actually representatives from the State of New York. ChatGPT could help come up with different action points based on the insights, and come up with different questions to ask in the first place.
3. Create and organize your data visualizations
As you know, data visualization is an extremely powerful storytelling tool. When it comes to crafting your data story and compiling all your findings, you want to use data visualizations to illustrate your key insights.
You produced all the visualizations you need in tutorial four, so now it’s just a case of gathering them in preparation for your final presentation.
So, returning to your table, add a new “Visualization” column, and make a note of the data visualization you’ll use to illustrate each main point. Again, we’ve completed the first row to start you off:
Figure 3.
In completing this table, you’ve pulled out the most relevant findings in relation to your original questions, determined how to present them visually, and turned them into possible action points. Your story is taking great shape 🕺
Now there’s just one final step left to complete…
4. Create a presentation to share your data story
Now, you’re going to turn your initial framework into a polished presentation that you could proudly share with Citi Bike stakeholders. We’ve created a template for you to fill in, so:
- Sign in to your Google account and open up this Google Slides presentation deck. Click “File → Make a copy” to create an editable version that you can work in.
- Using the framework you created in steps 1-4, and following the prompts on the presentation slides, fill out each slide.
All done? Excellent! You’ve successfully crafted a compelling data story—a critical step in the data analysis process. In the real world, you’d share your presentation with all key stakeholders at NY Citi Bike (such as the CEO, the marketing team, and product managers) and help them to plan their next steps accordingly ✨
You can take a look at how we structured our final Citi Bike presentation.
*Tip💡 You can even inject some AI into this part of the process, with useful tools such as the MagicSlides App. With it, you can plug ChatGPT straight into your Google Slides and use it to assist you with your work.*
So as you can see, data analysts aren’t just data wizards. They’re also brilliant storytellers and communicators! The role is varied, interesting, and rewarding: you get to inform key decisions and strategies, and see the real-world impact of your work. Pretty cool!
4. Key takeaways and next steps
That brings us to the end of the course! We’ve covered a lot of ground, and tackled some pretty complex topics—well done for sticking with it 🎉
Not only have you learned some of the key principles and theory behind data analytics; you’ve also analyzed a real dataset and produced valuable, actionable insights.
Let’s recap on everything you’ve achieved:
- In tutorial one, you learned what data analytics is and what a career in the field entails
- In tutorial two, you cleaned your NY Citi Bike dataset, removing duplicates and handling missing data
- In tutorial three, you conducted exploratory data analysis, calculating descriptive statistics and creating pivot tables in order to better understand your data
- In tutorial four, you created data visualizations in order to summarize and present your key findings in a succinct, accessible format
- In tutorial five, you crafted a compelling narrative for your data story, resulting in a polished, professional presentation, ready to share with stakeholders
Whew! You should be very proud of yourself. Whether or not you decide to pursue a career as a data analyst, you’ve certainly learned some valuable skills that can be applied to almost any career path. Data is everywhere, after all! 📈
So what now?
If you enjoyed stepping into the shoes of a data analyst, why not consider a career in the field? With the CareerFoundry Data Analytics Program, you’ll go from beginner to job-ready in under eight months. And, unlike this free Data Analytics for Beginners Course which you completed independently, the full program comes with extensive human support.
Our Dual Mentorship system mean that you’ll be assigned your very own mentor (a senior industry expert), a tutor (a course expert who works in the field), and a career specialist who will guide you every step of the way. You’ll also graduate with a fully-fledged data analytics portfolio 😍
If you’d like to learn more, have a read through our Data Analytics Program brochure and book a call with one of our expert program advisors. They’ll help you figure out if it’s the right option for you 😊
Want to learn more about making a career change with CareerFoundry? Check out our upcoming events, or tune in to our on-demand info session: Become a Data Analyst With CareerFoundry.
Want to keep exploring the wonderful world of data analytics? Head over to the CareerFoundry blog, Here are our top reads to get you started:
