Image created by Tim Urban of waitbutwhy.com.
Image created by Tim Urban of waitbutwhy.com.

Data Driven Digest for January 5: Life and Expectations

Welcome to 2016!  Wrapping up 2015 and making resolutions for the year ahead is a good opportunity to consider the passage of time – and in particular, how much is left to each of us. We’re presenting some of the best visualizations of lifespans and life expectancy.

So haul that bag of empty champagne bottles and eggnog cartons to the recycling bin, pour yourself a nice glass of kale juice, and enjoy these links for the New Year.

“Like Sands Through the Hourglass…”

It’s natural to wonder how many years more we’ll live. In fact, it’s an important calculation, when planning for retirement. Figuring out how long a whole population will live is a solvable problem – in fact, statisticians have been forecasting life expectancy for nearly a century. And, the news is generally good:  Life expectancies are going up in nearly every country around the world.

Life-expectancy
Chart created by Nathan Yau of Flowingdata.com.

But how do you figure out how many years are left to you, personally? (Short of consulting a fortune-teller, a process we don’t recommend as the conclusions are generally not data-driven.)

UCLA-trained statistician Nathan Yau of the excellent blog Flowing Data came up with a visualization that looks a bit like a pachinko game.

It runs multiple simulations predicting your likely age at death (based on age, gender, and Social Security Administration data) by showing little balls dropping off a slide to hit a range of potential remaining lifespans, everything from “you could die tomorrow” to “you could live to 100.” As the simulations pile up, they peak at the likeliest point.

Credit: Nathan Yau at Flowingdata.com
Credit: Nathan Yau at Flowingdata.com

One of the advantages of Yau’s simulator is that it doesn’t provide just one answer, the way many calculators do that ask about your age, gender, race, health habits, and so forth. Instead, it uses the “Monte Carlo” method of multiple randomized trials to get an aggregated answer.

Plus, the little rolling, bouncing balls are visually compelling.  (That’s academic-ese for “They’re fun to watch!”)

“Visually compelling” is the key.  As flesh-and-blood creatures, we can’t operate entirely in the abstract. It’s one thing to be told you can expect to live X years more; seeing that information as an image somehow has more impact in terms of motivating us to action.

That’s why the approach taken by Wait But Why blogger Tim Urban is so striking despite being so simple.  He started with the assumption that we’ll each live to 90 years old – optimistic, but doable. Then he rendered that lifespan as a series of squares, one per year.

Image created by Tim Urban of waitbutwhy.com.
Credit: Image created by Tim Urban of waitbutwhy.com.

What makes Urban’s analysis memorable – and a bit chilling – is when he illustrates the remaining years of life as the events in that life – baseball games, trips to the beach, Chinese dumplings, days with aging parents or friends.  Here, he figures that 34 of his 90 expected winter ski trips are already behind him, leaving only 56 to go.

Stepping back, he comes to three conclusions:

1) Living in the same place as the people you love matters. I probably have 10X the time left with the people who live in my city as I do with the people who live somewhere else.

2) Priorities matter. Your remaining face time with any person depends largely on where that person falls on your list of life priorities. Make sure this list is set by you—not by unconscious inertia.

3) Quality time matters. If you’re in your last 10% of time with someone you love, keep that fact in the front of your mind when you’re with them and treat that time as what it actually is: precious.

Spending Time on Embedded Analytics

Since we’re looking ahead to the New Year, on Tuesday, Jan. 12, we’re hosting a webinar featuring TDWI Research Director Fern Halper, discussing Operationalizing and Embedding Analytics for Action.

Halper points out that analytics need to be embedded into your systems so they can provide answers right where and when they’re needed. Uses include support for logistics, asset management, customer call centers, and recommendation engines—to name just a few.  Dial in – you’ll learn something!

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Fern Halper

We share our favorite data-driven observations and visualizations every week here.  What topics would you like to read about?  Please leave suggestions and questions in the comment area below.

Recent Data Driven Digests:

December 22: The Passage of Time in Sun, Stone, and Stars

December 18: The Data Awakens

December 11: Holiday Lights

About Stannie Holt

Stannie Holt
Stannie Holt is a Marketing Content Writer at OpenText. She has over 20 years' experience as a journalist, market research analyst, and content marketing expert in the fields of enterprise business software, machine learning, e-discovery, and analytics.