MODERN BUSINESS
23 buys cocoa-butter lotion, a purse
large enough to double as a diaper bag,
zinc and magnesium supplements, and
a bright blue rug. Target can predict
she has an 87% chance of being
pregnant and will have her baby in five
months. With that knowledge, they
can then send brochures directly to
the woman, encouraging pregnancyrelated purchases.
As Target discovers, though, you have
to be careful with this knowledge. Their
analysis results in a high-school girl
receiving advertisements
of maternity clothing and nursery
furniture. Her father is enraged and
complains to a Target manager about
the obvious mistake. A few days later
the manager calls to apologise again
to the father. A little sheepishly, the dad
admits he’s had a conversation with his
daughter and she is due in August.
Whether you are telling the story of
the root causes during analysis or
exploring the possible stories that
explain strong correlations, it’s the
stories that help the analyst make
meaning out of what they discover
and see whether the data supports,
complicates or refutes the story.
AFTER DATA ANALYSIS
As I’ve said, there’s often a chasm
between an analyst’s insight and the
decision-makers who need to have this
insight. Part of the challenge stems
from the background disciplines of the
analysts. They typically are steeped in
mathematics, statistics and IT, and are
more comfortable digging into the data
than conversing with decision-makers
about what they’ve found. On the flip
side, decision-makers often assume
that people trained in STEM disciplines
are poor communicators. But the more
I work with engineers, technologists
and the many other flavours of numbercruncher, the more I’ve learned that
32 ModernBusiness
October 2016
many do just fine communicating their
discoveries once they have the basic
skills under their belts. And they are
keen to learn.
When you add to that the simple fact
that we are all storytellers, helping
analysts convey their findings in
interesting and compelling ways
using story techniques becomes a
straightforward task.
SPOTTING STORIES
The first thing you need to have clear
in your mind is exactly what we mean
when we say ‘story’. This is vital
because you won’t get all the benefits
of sharing stories unless what you’re
sharing is actually a story. I’ve offered
a definition in Putting Stories to Work
and also on our blog here and here.
But in a nutshell, you can tell whether
something is a story if it has the
following characteristics.
A story begins with a time marker or
a place marker. So when you hear
someone say, “A couple of days ago…”
or “Last year…” or “A while back…”, then
it’s likely to be the beginning of a story.
It could also start with a place, like, “We
were next to the river…”
A story has a series of events
connected in a way that infers
causality: this happened a couple of
days ago, but then that happened, and
as a result this happened.
A story has people in it who are doing
things. It’s a giveaway when you hear
dialogue, as dialogue can only be
delivered in a story.
Finally, in a story, something
unanticipated happens. When the
audience hears it, they are a little
surprised. It’s news to them.
With these simple giveaways, you can
now spot stories. So what types of
stories should you tell?
There are three story types and one
story technique you should consider
using in data storytelling.
THE DATA STORY
When you have a time series and the
data does something unanticipated,
then you can tell a story about it – a
data story.
Here’s an example. From the 1920s to
the 1930s in Norway, deaths from heart
disease steadily rose.15 Then in 1939
they plummeted and stayed low until
1945, after which they quickly began to
rise again. So why would that happen?
(Figure 6)
Well, in 1939 the Nazis occupied
Norway and confiscated all of its
livestock, forcing the Norwegians
to live off a plant-based diet for the
duration of World War II. This diet
reversed the death rate from heart
disease. When the war ended in 1945,
livestock returned and meat and dairy
was added back into the Norwegian
diet, and heart disease came back.
The story about Procter & Gamble and
its Pamper strategy is also a data story.
The data story typically has this basic
structure:
• In the past…
• Then something happened…
• As a result…
One of the ways to present a data story
is to share a high-level version of the
narrative and then ask the audience
what they think is happening. This is
like presenting the audience with a
mystery to be solved and asking them
to be the detectives (we love mystery
stories16). When they come to the right
answer, you can show them the full
data story. Now they own the results