Data Storytelling 1: Who this is for
Who this is for, you're not as good as you think, but i know excel, the real costs of poor storytelling
Let’s find out really quickly if this is for you, or not. Here’s a quick twitter thread I made which has signs of people who have bad data story telling:
https://twitter.com/BowTied_Raptor/status/1761780277200752863
Who this is for
Data storytelling is an essential skill that transcends various professional roles, particularly in fields where data is a pivotal aspect of decision-making and communication. Here's a look at who can benefit most from honing their data storytelling skills:
Data Analysts: They need to transform complex data sets into *understandable* insights that can drive decisions. Good storytelling skills allow them to communicate their findings effectively, making their insights actionable.
Data Scientists: They need to *convey* the significance of their models and findings in a way that is accessible to all stakeholders.
Machine Learning Engineers: the real value of their work is realized only when they can *explain* how these models work and their implications in a clear and engaging manner.
Researchers: Researchers deal with a wealth of data that needs to be communicated to a broad audience. Effective storytelling can make their findings more impactful and *widely understood*.
Also, the most common thing that interviewers will ask you when you are applying for a job is “So, tell me about a project you worked on”, if this makes you uncomfortable, then this is also for you.
If you are currently working in a data role, but whenever you are communicating your findings with the C-Suite, you notice you basically need your manager to step in, and help explain what you are talking about (basically every time you present), then this is also for you.
You’re not as good as you think
Here’s some real talk: you're probably not as good at data storytelling as you think. There are people who are naturally gifted at crafting stories – they're the creative types. Then, there are those who excel in math and coding – the logical thinkers.
What about data storytelling?
It's like asking you to be both, not exactly easy.
On top of that nobody is born a master of data storytelling. It's not about talent, it's about the practice. The main thing that separates the mediocre from the masters is the relentless dedication to improving their skills.
And here's another thing: unlike coding or math, where you've got endless resources to learn from, data storytelling is a bit of a clownfiesta. There aren't as many structured guides or courses, which means a lot of what you learn comes from trial and error in your job.
So, if you're sitting there thinking you've got this data storytelling thing down, you might want to think again. It's a skill that demands constant practice and learning. If you're struggling or feel like you're always leaning on others to articulate your points, that's normal.
The key is to keep at it and not get complacent.
But I know Excel
“But I know Excel”
> Congratulations, so does everyone with a pulse. Nowadays, knowing MS Office is the expectation, not the exception.
Jokes aside, think about it: companies expect you to navigate VPNs and remote desktops for remote work. They assume you'll use Outlook for emails, Word for reports, PowerPoint for presentations. So why on earth would your Excel skills be impressive?
Here’s the reality: Excel is just the starting point. Employers are looking for proficiency in tools like Power BI, Python, and R. These are the skills that make you stand out in a sea of candidates. So, if you're banking on Excel to impress in a data role, it's time for a dose of reality. Excel is good, but it's not the golden ticket. The real game-changers are the more advanced, specialized tools that are shaping the future of data analysis and storytelling.
The Real cost of poor storytelling
Here’s a question for you: Can bad data storytelling skills actually cost you your job? As a data professional, your main gig is to build models & gain insights. Although candidates love to say they are building models because it’s their passion, reality is, we know you’re here because of the $$$.
Here's the business angle: companies invest in your skills with the expectation that the insights you provide will boost their profits. They're not shelling out salaries just for fun. They want returns on their investment in you.
Now, imagine if your storytelling skills are trash tier. What if your analysis is so poorly communicated that no one in your organization trusts or even understands it? If your insights aren't being used, they're not contributing to the company's success. In the reality of business, if you're not adding value, you are expendable.
In other words, in the Profit/Loss Statement, you are in the loss
It's harsh but true. If your data storytelling is weak and your work isn't driving results, your role in the company becomes questionable. Why would a company continue to pay for a service that doesn't benefit them? This is the reality check for anyone in data roles: your ability to communicate insights effectively is just as crucial as your technical skills. Without it, your job security could be on the line.
What you’ll learn
People who have good storytelling generally tend to be good at the following:
Understanding the Importance of Context:
*Context is king* in data storytelling. It's not about the numbers; it's about the why and how. Knowing the bigger picture turns data from mere numbers into something the suites actually care about.
Starting with the Conclusion:
In data storytelling, you lead with the punchline. Begin with the conclusion to immediately capture interest and set the stage for the supporting data.
Avoiding Clutter Like the Plague:
Whether it's too much data, too many words, or overly complex visuals, clutter only serves to confuse. Strive for simplicity and clarity.
Capturing and Holding Attention:
You need to engage your audience. Pay attention to their body language and facial expressions – these are your cues to adjust and captivate. It's about making your presentation resonate, not just present information.
Thinking Like a Product Designer:
Your model and presentation are your products. Think like a designer: How user-friendly is your presentation? Is the information laid out logically? Is it visually appealing? This mindset ensures that your 'product' is both useful and used.
Understanding Good vs. Bad Visuals:
Not all visuals are created equal. Learning the difference between good and bad visuals is crucial. Good visuals support and clarify your story, while bad ones distract and confuse.
You’ll also want to read this post: Power BI visuals to get a solid understanding on it.
Analyzing Case Studies:
Real-world examples are invaluable. Analyzing case studies of both successful and failed data storytelling can offer insights into what works, what doesn't, and why. And we’ll wrap up the series with this.