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The Writer’s Dataset Is The World

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My friend Story Grid Editor Danielle Kiowski wrote a piece for Medium which included this provocative line: "the writer’s dataset is the world." 





I am a data scientist by day and a writer by night.

On the surface, these two occupations seem as different as… well, night and day.

I used to think that they were.

Data science is math. Writing is language.

Data science is logical. Writing is creative.

Data science is “left-brained.” Writing is “right-brained.”

I had all of these scripts about why these two fields were total opposites — but similarities began to emerge as I learned more and practiced in each area. Now I can see that, at the core, these disciplines are about the same essential skill: the ability to distill and communicate insights from a dataset. Let’s walk through an example dataset, and I’ll show you what I mean.

Find the signal in the noise

Take a look at this set of data points:

… and so on, for hundreds or thousands of rows.

Can you determine anything about this dataset from looking at it? Not much.

There’s too much input to hold everything in your mind at one time. You can’t find a pattern that way.

Now take a look at this:

Once we sum up the sales figures, we start to get an idea of what’s going on. Look at that seasonal trend — people are buying this product for the holidays.

Simple aggregations and visualizations help people to grasp the patterns of a dataset.

What does this have to do with writing?

While the analyst is making sense of a dataset, the writer’s dataset is the world.

Sometimes writers will write a slice of life piece jumbled with events that confuse and bore the reader.

“But that’s how life is!” they say, by way of justification.

And it is. But what makes an interesting world does not make an interesting story.

In real life, we take in only a tiny slice of what we experience (based not on curation but on heuristics that can lead us astray), we mull that over for hours/years/decades, and maybe we find meaning in the events that we’ve experienced. Readers need to experience events along with the characters in a story and understand the meaning of those events by the end of the experience in order to be satisfied with the story.

As Robert McKee writes in Story,


n life, moments that blaze with a fusion of idea and emotion are so rare, when they happen you think you’re having a religious experience. But whereas life separates meaning from emotion, art unites them.[… A] story well told gives you the very thing you cannot get from life: meaningful emotional experience. In life, experiences become meaningful with reflection in time. In art, they are meaningful now, at the instant they happen.

Cluttered, confused works may represent reality, but when readers try to take them in, they’re overwhelmed by unrelated events with no discernible pattern. They are deprived of that unity of meaning and experience that makes story special.

As a writer, your job is to illuminate the patterns that lie within the events by cutting away irrelevant events.

You can do this by creating your story with clean, crisp elements. Each character has one object of desire. The story revolves around one theme. This creates a strong spine for your story that your reader can follow through the work.

To you as the writer, it might seem simplistic to reduce people to one want; life to one idea. But for your readers, this makes people, who take a lifetime to understand, into characters that they can know as well as they know themselves.

This is the paradoxical nature of story: on the page, abstractions of reality seem more real than faithful representations.

Develop a point of view

Summing up the transactions is just the first step in our analysis. The objective of analyzing data is to answer questions (and perhaps pose new ones).

Suppose that we are trying to find out the characteristics of a successful store. In our dataset, we have store size. Let’s explore how the data looks when we cut it by store size.

We can divide the sum up by the size of the store, and an interesting pattern emerges.

You can see that medium and large stores have a lift during the holiday season, while small stores have relatively flat sales during the whole year. We might hypothesize that larger stores have the capacity to run holiday promotions, or have seasonal sections, while smaller stores do not.

We could do further research to find out if this is the case.

The additional research is informed and directed by the point of view that we have when we come to the dataset — we are researching store size because we asked that question and found something promising in that direction.

This is why a knowledge of the context of the dataset is so important — it helps the analyst to ask the right questions, setting the investigation onto a fruitful path.

What does this have to do with writing?

Readers connect with stories through characters. The most important character for reader engagement is the point-of-view character (or characters — you can have more than one).

This is the character whose thoughts the reader can hear. The reader gains empathy for the character by hearing their thoughts and understanding their object of desire.

The point of view that you choose for your story is critical for the development of your story because the reader evaluates the events of the story through the lens of the point of view.

Changing the point of view changes the story entirely. This is why retellings of classic stories with different points of view (Wicked, for example) can breathe new life into the stories. Through a different character’s eyes, different events may become important to the story, and existing events may take on a new meaning.

As you construct your story to create a meaningful experience for the reader, the point of view that you choose determines what that experience is and what meaning is created in it.

Take a stand

Let’s take it one step further. We know that large and medium stores have more sales toward the end of the year — but can we quantify that? What does it mean for individual purchases?
Look at this graph of transaction data from November and December.


The scattered dots are the data points. Each one is a transaction. They are plotted according to the transaction amount (the vertical axis) and the square footage of the store (the horizontal axis).

We can draw a trendline, which describes the general layout of the data:

The equation of the line indicates the relationship between the size of the store and the transaction amount. In these months, an additional square foot of store space corresponds to an additional $0.30 per transaction.

As you can see, there is a lot of variability in the individual data points. Very few of them lie directly on the line that is supposed to predict their locations. However, the line is the best fit for the full distribution of the dataset.

The prediction is not perfect, but it gives us useful ways to think about the relationships between input factors and outcomes.

What does this have to do with writing?

When you write, you tell an individual story — but one that encapsulates a truth that is true beyond the bounds of individual experience. Readers can read stories about cultures and times that are different from their own but find lessons about their own humanity.

Like the line in the dataset, the stories that a reader encounters won’t exactly mirror real life. But stories give us useful frameworks to understand how different choices and actions play out. We can use these analogies to inform our own decisions when we encounter similar situations.

This is why writing is so important. A story is not only for entertainment. Stories give us templates for life. They teach us how to make decisions; how to triumph; how to lose. They show us the truth buried in the chaos of this unpredictable world so that next time we look, we can see the pattern a bit more clearly.

Analyzing data and writing are essentially the same exercise: stripping away the chaos and clutter of reality until we are left with the truth.

But wait! you might say, That’s just one version of the truth!

That’s true. We could have looked at a different metric (average order value instead of sum) or different cuts (maybe customer characteristics). We could have used a different model to predict the relationship between store size and sales. But we didn’t. Those were all choices. There are as many ways to interpret a set of data points as there are analysts looking at that dataset.

There are as many ways to tell a story as there are writers.

And not all versions of the story are good, as we learn from the old chestnut: Statistics don’t lie, but statisticians do.

There are misleading, confusing, and just plain wrong ways to draw insights, whether from a dataset or from the experiences and ideas that create a story. The burden and the privilege of the artist and the analyst is the power to impact how others see the world.

Anyone can record facts; it takes courage and artistry to draw the line of truth.

By day and by night, I’ve chosen a life of drawing those lines.




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1 hour ago, Johne said:

You can do this by creating your story with clean, crisp elements. Each character has one object of desire.


That about sums it all up, Johne.  Fabulous article, Johne!

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Triple monitors. ;)


I frequently wait for my main application to generate document updates and I'm famously ADD so I look at my email while waiting. It takes little time to start a post, paste in a link and a summary statement, and close the browser tab. Easy. ;) (Of course, it's taken me many years to curate a list of writing resources which automatically dump into my Inbox but that's something you don't build in a day. I sign up for new things and when the first email arrives assign a new filter to dump it into a new bucket ('label,' in Gmail terms). It's a system I've honed over time.)

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