Senior executives and managers are being bombarded
with dashboards brimming with analytics. They struggle with data-driven
decision making because they don't know the story behind the data. But by
rethinking the way we use data and understanding our audience, we can create
meaningful stories that influence and engage the audience on both an emotional
and logical level.
“The ability to take data—to be able to understand it, to
process it, to extract value from it, to visualize it, to communicate it—that’s
going to be a hugely important skill in the next decades.”
-- Google’s Chief Economist Dr. Hal R.Varian
As data becomes increasingly ubiquitous,
companies are desperately searching for talent with these data skills. Many
of the heavily-recruited individuals with advanced degrees in economics,
mathematics, or statistics struggle with communicating their insights to others
effectively—essentially, telling the story of their numbers.
Below are some reasons why data story telling is important
Extract value
Once your business has
started collecting and combining all kinds of data, the next elusive step is to
extract value from it. Your data may hold tremendous amounts of potential value,
but not an ounce of value can be created unless insights are uncovered and
translated into actions or business outcomes. With the new breed of data
tools available it will be easier for people across business functions to
access and explore the data on their own. As a result, we’re going to see an
unprecedented number of insights being generated within companies than ever
before. However, unless we can improve the communication of these insights we
will also see a poorer insight-to-value conversion rate. If an insight isn’t
understood and isn’t compelling, no one will act on it and no change will occur.
And value will not be extracted from it , which is not good.
The need for
more data storytellers is only going to increase in the future. With
the shift towards more self-service capabilities in analytics and business
intelligence, the pool of people generating insights will expand beyond just
analysts and data scientists. It’s important to understand how these
different elements
combine and work together in data storytelling. When narrative is coupled with
data, it helps to explain to your audience what’s happening in the
data and why a particular insight is important.
Analytics
Analysis doesn’t have to be long and complex. The data collection and analysis process
can often be rigorous and time consuming. That said, there are instances when
it should be quick, such as when it's in response to a timely event that
requires clarification.
Data analysis isn't about graphics and
visualizations; it's about telling a story. Look at data the way a detective examines a crime scene.
Try to understand what happened and what evidence needs to be collected. The
visualization—it can be a chart, map or single number—will come naturally once
the mystery is solved. The focus is the story.
Data journalism (and analytics in a broader
sense) is a form of creation. There
is so much data and so many data types that only experienced analysts can
separate the wheat from the chaff. Finding the right information and the right
way to display it is like curating an art collection. Ample context and
commentary is often needed to fully appreciate an insight. When visuals are
applied to data, they can enlighten the audience to insights that
they wouldn’t see without charts or graphs. Many interesting patterns and
outliers in the data would remain hidden in the rows and columns of data tables
without the help of data visualizations.
Visualizing data
Good data visualization does a few things. It
stands on its own; if taken out of context, the reader should still be able to
understand what a chart is saying because the visualization tells the story. It
should also be easy to understand. And while too much interaction can distract,
the visualization should incorporate some layered data so the curious can
explore.
Insight
The data and visualizations are chosen by the author and
presented to the reader as a finished product, similar to a printed magazine
article. Conversely, the reader-driven narrative provides ways for the reader
to play with data. It’s all in the details. Think
about your favorite authors. When you read their books, you probably feel like
you’re part of the world they’ve created. You probably feel like you “know”
their characters. Marketers are storytellers, too. And good storytelling is all
about the details..
Finally, when narrative and visuals are merged together, they
can engage or even entertain an audience. When you combine the right
visuals and narrative with the right data, you have a data story that can
influence and drive change. Marketers are responsible for messaging; as
such, they're often the bridge between the data and those who need to learn
something from it, or make decisions based on its analysis.
Excepts #IoT
Actuarial businesses such as insurance have
the benefit of huge amounts of data describing the past behavior of systems
that only slowly change their habits. That seems a long way from the tools and
information other businesses have at their disposal, but IoT will change that.
It’s going to be the one tool that delivers on the promises made - and gussied
up - by the Big Data tribes, because only highly automated intelligent systems
will be able to run the millions of models and spot the optimum outcomes that
will map out future speculative markets with something like the precision with
which insurers understand their historical ones.
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