Storytelling with Data: a book review and my takeaways

As a child, I loved telling stories. I’d take my favorite book and TV characters and create a world where they would oh-so-conveniently meet. Say, a magical anime girl wanders Narnia until she encounters the now-villainous Power Rangers.

As an adult in the corporate world, I still want to tell stories. But now I find that people are more critical of which stories I tell them.

It must be in the form of numbers, they said.

It’s a data-driven world, they said.

In Cole Nussbaumer Knaflic’s book Storytelling with Data, she argues we can do just that: tell stories with numbers.

language + math = data storytelling

She takes traditional storytelling concepts then re-interprets them for “adult-appropriate” tables and charts. She teaches us to edit our charts, the same way authors do their stories, by borrowing principles of visual design.

My key takeaways from the book can be found below (click for larger size), but they can be summarized as follows:

  1. Context is king. The form your data will take depends on your audience and what you want them to do with the data.
  2. Choose the right graph to best express the key message (I’ve made a flowchart in my notes to help with that).
  3. Following on #1, design around this message.
  4. Present your data as you would a story, with a beginning, middle, and end.
“Storytelling with Data” notes, by dannaisadork

P.S. Sorry about the terrible handwriting. My normal penmanship’s already pretty bad, but writing on a tablet made it worse!


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How much will Valentine’s Day cost me?

J.Lo may say “Love Don’t Cost a Thing”, but Americans are planning to spend $136.57 on Valentine’s Day anyway.

How much does Valentine's Day cost?

When I chanced upon the results of NRF’s Valentine’s Day Spending Survey I knew it was the perfect material for this week’s post. Data from a reliable source that’s actually relevant and interesting? Ah, be still my geeky heart.

In today’s post, I talk through the thought process in coming up with an infographic like the one I made above.


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I measured my productivity for a month. Here’s what I learned.

I’m not a morning person.

Given the choice, I’d rather sleep in and work after lunch. Like most people though, I don’t have that choice.

But am I under-performing because my brain isn’t fully awake yet?

I have a typical 9 to 5 job. I often come in earlier to accommodate my global team’s timezone differences (but offset by leaving early as well). I’d come in all bleary-eyed, head floating in the clouds, rushing to get my first shot of caffeine.

At one point I questioned,

Am I under-performing because my brain isn’t fully awake yet? Am I selling myself short just because I’m forcing myself to work against what’s natural?

I asked a few colleagues and even my manager, and they assured me I wasn’t under-performing at all.

Paranoid as I am though, I decided to put numbers to my feelings so I could make some logical analyses.

For 3 to 4 weeks I measured my productivity, hour by hour, by evaluating my energy, focus, and motivation with a number between 1 to 10.

I limited myself to weekdays because I knew my weekends were too spontaneous to measure. I also wrote little notes to give context to my scores, such as “drank coffee” or “back-to-back calls”.

The data by the month’s end was revealing.



Morning productivity measured

Since I have to have a morning cup of joe, all three traits start to climb after breakfast until they peak at around 9 AM. From there, the three diverge.

Energy is consistently high while motivation and focus start to dip. I suspect focus relates to a caffeine crash, while the other two to my morning schedule.

Why? Because my mornings are usually reserved for meetings. Whether its physical meetings where I hop from room to room and floor to floor, or virtual meetings where I talk to people over the phone or video.

This is my most physical part of the day, hence the high energy.

Yet, its also in those meetings that issues come up. Hearing bad news is never a good way to start the day. It’s a possible source of demotivation.


Continue reading “I measured my productivity for a month. Here’s what I learned.”

Danna on Data

It’s been a while since I’ve talked about my data analysis self-study.

I’ve been trying this and that, but haven’t felt anything was worth writing about. I mean, who would want to know that I tried something and failed, right?

Oh wait. Me. I would want to know.

When I’m about to try something new, like skincare or a restaurant, I look up blogs for reviews. I try to see if I can relate to the blogger and put myself in their shoes–Would I have failed as well?

It saves me a lot of effort because someone else has already gone through the experience for me.

That’s why I’m writing about all my data science-related updates so far, incomplete and disorganized as they are. Maybe it’ll help.

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