How journalists tell stories with data

G. Elliott Morris , Data journalist and US correspondent, The EconomistMarch 1, 2022
Prepared for a talk to the Data Analytics Symposium at Queens University, Charlotte, North Carolina1 / 46

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Background...From a small town on a barrier island in south TexasPopulation 4,123 (2020)
86% white, 7% Hispanic, 4% "two or more"
Median household income of $57,910
11% poverty rate

I went to college at the University of Texas at AustinStudied government and history
With some statistics and computer science
Graduated in 2018, hired by The Economist in the fall of my senior year

I've always been interested in politics and policy, only really became fascinated with data in the second semester of collegeRead a book called Big Data by Kenn Cukier andViktor Mayer-Schönberger
In 2016, built a forecasting model for the US presidential primaries and general election
The rest, as they say, is history

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[ Data ]  journalism4 / 46

BIG DATA

^{small data}

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AT&T, Verizon, Comcast

FitBit, iRobot, Phillips, Sony

Tesla, Kia

Indeed

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90%

Of all data worldwide was created in the last 2 years

Google estimates there will be roughly 175 ZB of data 2025. That's about 175 billion times the size of the storage on your laptop.

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Use #1. Actionable business insights

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Use #1. Actionable business insights

Use #2. Telling stories?

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[ Data journalism ]11 / 46

Data journalism today (perceptions):12 / 46

Data journalism today (perceptions):1. Complex modeling12 / 46

Data journalism today (perceptions):1. Complex modeling2. Large datasets "big(ger) data"12 / 46

Data journalism today (perceptions):1. Complex modeling2. Large datasets "big(ger) data"3. Resource-intensive learners and pipelines12 / 46

https://www.economist.com/graphic-detail/coronavirus-excess-deaths-estimates

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https://www.economist.com/graphic-detail/2021/04/10/our-house-price-forecast-expects-the-global-rally-to-lose-steam

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https://projects.economist.com/us-2020-forecast/president

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Data journalism today (for real):16 / 46

Data journalism today (for real):Most stories use small data16 / 46

Data journalism today (for real):

Most stories use small data

Datasets from academic studies

Single time-series from other companies or websites

Polls or social surveys

(Extracts of) Census data

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Data journalism today (for real):

The bread and butter of our daily work is improving stories with original data analysis and visualization.

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What do all of these examples have in common?24 / 46

What do all of these examples have in common?1. Examples of empirical journalism: or, "data in service of stories"124 / 46

What do all of these examples have in common?1. Examples of empirical journalism: or, "data in service of stories"12. Not "data science" or "computational journalism"24 / 46

What do all of these examples have in common?

1. Examples of empirical journalism: or, "data in service of stories"¹

2. Not "data science" or "computational journalism"

Lesson: Data journalism does not need to be complex to tell a story. In fact, most of the time, simpler is better.

[1] See: Sarah Cohen, "Numbers in the Newsroom"

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" In fact, most of the time, simpler is better. "

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" In fact, most of the time, simpler is better. "

Lower signal-to-noise ratio

The "curse of dimensionality"

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Data analytics is not data scienceNeither is data journalism28 / 46

Data analytics is not data science

Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions.

Data scientists on the other hand, design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis.

Data (or "empirical") journalists do both of these things, but usually proceed from the former to the latter only when necessary

and only when the data and modeling processes can supply enough precision.

Source: Northwestern

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Data journalism in a nutshell

Most stories fall into one of 3 categories:

1. Small data that add context to a story

2. Visualizations explaining findings, meeting readers where they are

3. Complex statistical models or big(-ish) data that help us decipher relationships between variables and quantify uncertainty

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80% of stories fall in bucket 1-231 / 46

Case studies32 / 46

Example 1: House redistricting

Narrative:

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Example 1: House redistricting

Data:

https://projects.fivethirtyeight.com/redistricting-2022-maps/

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Example 1: House redistricting

Original analysis:

https://www.economist.com/united-states/democrats-have-fared-surprisingly-well-in-congress-new-maps/21807593

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Example 1: House redistricting

Original analysis

https://www.economist.com/united-states/democrats-have-fared-surprisingly-well-in-congress-new-maps/21807593

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Example 2: News in 2021

https://www.economist.com/graphic-detail/2021/12/18/2021s-biggest-stories-were-covid-19-and-americas-presidential-transition

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Example 3: Covid-19 forecasts

Predictions for December 2021

Actual values for Dec 2021

https://covid19forecasthub.org/reports/single_page.html?state=US&week=2022-01-04

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Example 3: Covid-19 forecasts

Predictions for January 2022

Actual values for Jan 2022

Unpredictable = bad use of modeling for storytelling

https://covid19forecasthub.org/reports/single_page.html?state=US&week=2022-01-04

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Example 3: Covid-19 forecasts

Think of alternative questions: not "what's going to happen next?" but "what's happening now?"

Who isn't vaccinated? Where are hospitals overrun? What counties are experiencing worse outbreaks?

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Three principles41 / 46

#1. Explore, but don't blindly follow the data

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#2. Visualization can be the whole story!

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#3. Big data is not a silver bullet. Don't ask questions the data cannot answer

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Three principles

1. Explore, but don't blindly follow the data

2. Visualization can be the whole story!

3. Big data is not a silver bullet. Don't ask questions the data cannot answer.

If you keep these three things in mind you will be a successful data analyst/journalist/scientist/whatever.

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Thank you!

Questions?

Website: gelliottmorris.com

Twitter: @gelliottmorris

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How journalists tell stories with data

G. Elliott Morris , Data journalist and US correspondent, The Economist

March 1, 2022Prepared for a talk to the Data Analytics Symposium at Queens University, Charlotte, North Carolina

Background...

[ Data ] journalism

BIG DATA

AT&T, Verizon, Comcast

FitBit, iRobot, Phillips, Sony

Tesla, Kia

Indeed

90%

Of all data worldwide was created in the last 2 years

Use #1. Actionable business insights

Use #1. Actionable business insights

Use #2. Telling stories?

[ Data journalism ]

Data journalism today (perceptions):

Data journalism today (perceptions):

1. Complex modeling

Data journalism today (perceptions):

1. Complex modeling

2. Large datasets "big(ger) data"

Data journalism today (perceptions):

1. Complex modeling

2. Large datasets "big(ger) data"

3. Resource-intensive learners and pipelines

Data journalism today (for real):

Data journalism today (for real):

Most stories use small data

Data journalism today (for real):

Most stories use small data

Data journalism today (for real):

The bread and butter of our daily work is improving stories with original data analysis and visualization.

What do all of these examples have in common?

What do all of these examples have in common?

1. Examples of empirical journalism: or, "data in service of stories"1

What do all of these examples have in common?

1. Examples of empirical journalism: or, "data in service of stories"1

2. Not "data science" or "computational journalism"

What do all of these examples have in common?

1. Examples of empirical journalism: or, "data in service of stories"1

2. Not "data science" or "computational journalism"

Lesson: Data journalism does not need to be complex to tell a story. In fact, most of the time, simpler is better.

" In fact, most of the time, simpler is better. "

" In fact, most of the time, simpler is better. "

Lower signal-to-noise ratio

The "curse of dimensionality"

Data analytics is not data science

Neither is data journalism

Data analytics is not data science

Data journalism in a nutshell

1. Small data that add context to a story

2. Visualizations explaining findings, meeting readers where they are

3. Complex statistical models or big(-ish) data that help us decipher relationships between variables and quantify uncertainty

80% of stories fall in bucket 1-2

Case studies

Example 1: House redistricting

Narrative:

Example 1: House redistricting

Data:

Example 1: House redistricting

Original analysis:

Example 1: House redistricting

Original analysis

Example 2: News in 2021

Example 3: Covid-19 forecasts

Example 3: Covid-19 forecasts

Example 3: Covid-19 forecasts

Three principles

#1. Explore, but don't blindly follow the data

#2. Visualization can be the whole story!

#3. Big data is not a silver bullet. Don't ask questions the data cannot answer

Three principles

1. Explore, but don't blindly follow the data

2. Visualization can be the whole story!

3. Big data is not a silver bullet. Don't ask questions the data cannot answer.

Thank you!

Questions?

Help

March 1, 2022
Prepared for a talk to the Data Analytics Symposium at Queens University, Charlotte, North Carolina

1. Examples of empirical journalism: or, "data in service of stories"¹

1. Examples of empirical journalism: or, "data in service of stories"¹

1. Examples of empirical journalism: or, "data in service of stories"¹