by Konstantin Tskhay​

August 21st

Once Upon a Time:
Why Are We So Obsessed with Stories in Data Analytics Anyway?

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Hey All,

Over the last week, I finally saw Deadpool and Wolverine, which was a really fun movie. But, like most (ahem, all) Marvel movies, this one had a very little story.

It went something like this:

• Deadpool was trying to live an everyday life
• Something happened, and the universe started to collapse
• He found Wolverine to save the world
• They united and, through a bunch of kapow, saved the world
• Everyone is happy

What makes this movie is pure action.

But... Let's backtrack and talk about story plots and how to use them in action.

by the way, I also teach data storytelling as a bonus in my Practical People Analytics Course.

Let's dive in.

The Anatomy of a Story

Just like humans, stories have anatomy.

And just like human anatomy, they can be graphed.

In fact, Kurt Vonnegut was one of those people who decided to spend time graphing different stories.

At the base of it, Kurt talked about the time in the story: each story has a beginning and an end. What changes in the story as a function of time are the good fortune and the ill fortune.

So, this simple framework is a mathematical function spanning a relatively simple plane:

Now that we are all on the same plane let's review a few stories.

Man in the Hole

In this story, we start doing pretty well for ourselves. Then, things do not go so well, and suddenly, we are faced with a problem that generates tons of nasty things. We must overcome these challenges to emerge on the other end and do well overall.

This narrative can be easily seen in Schitt's Creek, where the family starts in an affluent situation, falls from grace, and loses it all. They have to rebuild everything, including family connections and fortune. Surprise, they are doing quite well at the end.

This can also be seen often in the venture world via a J-curve.

In people analytics, when change is introduced, employee engagement dips because people don't like the change very much at first. Yet, once they get to know the change, they start reaping the benefits of the new world.

On a chart, it looks like this:

Here is an example of a hero journey in data storytelling:

• You start with the status quo
• You then highlight a misfortune (drop in sales)
• Your intervention is your hero: you show how it's effective
• 9 months later, your sales are higher than before

Boy Meets Girl

This one is like a rom-com plot.

They meet, fall in love, have a miscommunication, and suffer; the miscommunication is resolved, and they are happily ever after.

In business, it's a classic turnaround story:

• Apple initially enjoyed great success with the launch of the Apple II and the Macintosh, establishing itself as an innovative company.
• In the mid-1990s, Apple faced challenges. It struggled with declining market share, dull products, and internal conflicts, leading to financial difficulties and the perception that it might not survive.
• Steve Jobs' return in 1997 marked the beginning of a remarkable turnaround. Under his leadership, Apple launched groundbreaking products like the iMac, iPod, iPhone, and iPad, leading to its current position as one of the world's most valuable and influential companies.

Here is what it looks like:

Not bad, eh? How can we apply it to people analytics?

You can see this story in many organizations around the pandemic.

Things were good before the pandemic, with the company's products being developed and distributed quickly. Once the pandemic hit, the way people live and work changed. Successful companies have redeployed their people resources to take market share in a new way (Zoom, for example) and become dominant players.

It's also a reminder that there is an excellent opportunity in every crisis.

So, don't let a good crisis go to waste.

The final one is The Miracle Story.

This is your New Testament or Cinderella, where the hero stats relatively low and moves up over time via a quick series of milestone events. Then, once they reach their peak, everything falls apart. The story then rapidly changes direction to end up in Valhalla.

Here is an example we all know:

NVIDIA was founded in 1993 as a relatively small player in the competitive semiconductor industry. Initially, it faced significant challenges in establishing itself among more prominent companies like Intel and AMD.

NVIDIA began to gain recognition with the release of its GeForce series of graphics processing units (GPUs). The company's focus on innovation led to incremental successes, particularly in the gaming sector. Each new product release further solidified its position in the market.

But then... NVIDIA faced challenges, such as the 2008 financial crisis, which impacted consumer spending and the tech industry. Additionally, in the mid-2010s, the cryptocurrency market caused extreme volatility in GPU demand, leading to fluctuations in NVIDIA's stock price and revenue.

So... NVIDIA’s shifts towards artificial intelligence (AI) and data centers marked its significant rebound. The company leveraged its GPU technology for deep learning, positioning itself as a leader in AI hardware. The rise of AI and machine learning created enormous demand for NVIDIA’s products, leading to exponential growth in revenue and valuation.

The difference from the previous arc is a dramatic turnaround, in which the leaders make a huge bet on what will make things great again.

Here, you really need to use metrics to tell your story. Show a dramatic decline and a dramatic change that was immediate after the intervention.

But all of this is retrospective...

Yes, all of the data storytelling gurus will tell you that this is the way to describe what happens in the data. But... no one has time for that anymore (unless we make time specifically to gain a full, wholesome understanding of the narrative).

Instead, we focus on short snippets of valuable information that inform decision-making.

It's shifting focus from Lord of the Rings to a Marvel Movie. Sure, the first one is scenic but has very little action. The other is all action plus a few bits of empty dialogue.

This is not to say tell stories without context. But when you do, keep them focused, straight to the point, and driving action.

See you later,

K

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Whenever you’re ready, there are 2 ways I can help you:

#1

If you’re still looking to get started in People Analytics, I recommend starting with my affordable course:

​Practical People Analytics: Build data-driven HR programs to 10x your professional effectiveness, business impact, and career. This comprehensive course will teach you everything from building an HR dashboard for business results to driving growth through more advanced analytics (i.e., regression). Join your peers today! ​
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#2

If you are looking for support in your human capital programs, such as engagement, retention, and compensation & benefits, and want to take a more data-driven approach, contact me at Tskhay & Associates for consulting services. Or simply reply to this email!