Where Data and Story Meet - Building the Data Storytelling Capability

1 | Where Data and Story Meet
Data is rapidly transforming the way companies are
transacting and engaging with customers. Gone are
the days of not having enough data, now we are being
inundated with too much data and are struggling to
find ways to make sense of it.
Many organizations seem to be responding by
hiring data scientists, an approach advocated by
Benjamin Spiegel in an article in Marketing Land, where
he writes that every marketing department needs a
data scientist because they are the “secret weapon”
in helping to demystify the rising complexity of the
data landscape. I worry this position supports the
stereotype – one that has often let me down – that
every data scientist is also a great data storyteller. After
readingthis,Icouldn’thelpbutwonderwhythe“skillset”
or “mindset” of great data storytelling seemed so
elusive among both data scientists and marketers. I also
wondered if there was a specific role better suited
for data storytelling and whether or not we should be
seeking the necessary qualities in one individual or to
build a team of varied skillsets?
I found a kindred spirit in Aaron Merlob, formerly
an Associate in Biomedical Informatics at Harvard
Medical School. We met when he was an instructor
at Galvanize in San Francisco, where I learned he
and I share concerns about the growing gap in great
data storytelling. We have teamed up to try and shed
some light on the subject in hopes the gap in data
storytelling talent will soon dissipate.
Where Data
& Story Meet –
The Modern Data Scientist & Modern Marketer

As a business leader, especially in the roles of data
science and marketing, your success is heavily reliant
on bridging the gap between data and story. It is
becoming imperative to build and nurture a great data
storytelling capability, so that you and your teams can
make informed decisions to help grow and transform
your business. In this piece, we explore the increasing
demands in skillsets for the modern data scientist
and marketer. Further, we explore the mindset of data
scientists and whether or not that mindset differs
from great data storytellers. We also reveal different
ways to build the data storytelling capability.
Before we explore the requirements of data scientists
and marketers, the mindsets of great data storytellers
and how to best build the capability of data storytelling,
we must first define what we mean by data storytelling.
What is Data Storytelling?
Data storytelling is the act of humanizing data by turning
it into narrative or a story that creates actionable
insights, frequently supported by visualizations.
As humans, we are conditioned to consume
information not in the form of data, but in the form
of stories – even before we can speak as children.
Data storytelling taps into this by integrating data and
story to provide readers, or participants, with a
true understanding of the insights and an actionable
roadmap of what to do next.
LisaMorgancapturedanumberofdifferentperspectives
on who should be the data storyteller and how you
can write a data story in a recent article for Information
Week. II
The varying perspectives were intriguing – and
we especially liked that of contributors Martin Brown, a
GM at FM Outsourcing, and Zoher Karu, a VP at eBay.
Brown said he often has data scientists, business
analysts, and marketers collaborating on a single story.
However, in doing so, he finds there are three versions
of the same story. Brown continues with a description
of what might be the perfect data storyteller:
“[Data storytelling] is definitely an interdisciplinary
activity,” said Karu. “Data scientists are needed to
extract patterns in data, visualization experts are needed
to convey the message in a compelling easy-to-
understand manner, marketing [needs to be included]
to understand the needs of and reach the desired target
audience, business domain experience is necessary to
hone in on the right set of questions, and an editorial
staff is needed to communicate the surrounding text in
a compelling way.”
The InformationWeek article also recommends data
storytelling follow general storytelling rules (finding
the conflict, adding the characters, and calling out the
drama) and should convey, in a creative way, how the
data can help the business. Clearly, this is an art form –
and quite a complex one at that – so let’s see how we
can isolate the traits of great data storytellers in both
data scientists and modern marketers and then profile
the organizational or operational design that best
nurtures a great storytelling environment.
Understanding the Modern Data
Scientist and Modern Marketer
The modern data scientist must not only be a master
in data analysis; but also, must understand the
business at hand, be able to produce actionable
insights, and convey the findings in a way in which all
business functions can benefit. Similarly, the modern
marketer must not only work in a creative capacity and
rely on data to make decisions; but also, must possess
“Ideally, it would be a data
scientist with a flair for
articulate and emotional
evocation. However, I am still
looking for this elusive person.”

strategic insight and understanding of that data in
order to activate and communicate it effectively.
In order to be successful in today’s ever-changing
data landscape, the modern data scientist should
ideally possess eight different skills and abilities:
advancedanalytics,businessacumen,communications
& collaboration, creativity, data integration, data
visualization, software development, and system
administration.1
But can one person actually possess
all of those skills and abilities? According to Accenture,
it most definitely is a challenge: “Executives are
struggling to find individuals who possess all eight
data scientist skills and abilities. A data scientist team
can serve as an alternative.”III
Similarly, today’s marketers require a unique
combination of skills, abilities, and mindsets. With the
customerexperienceadrivingforceinnearlyallbusiness
decisions, modern marketers must possess creativity,
technology, analytics, customer voice and corporate
vision capabilities, business acumen, and a thorough
understanding of data as it applies to business goals.
Yet most, even those holding the CMO designation,
do not possess all six traits. According to Forrester’s
2017 Predictions Guide, CEOs will exit at least 30%
of their CMOs for not mustering the blended skillset
needed to drive digital business transformation,
design exceptional personalized experiences, and
propel growth.IV
The Interactive Advertising Bureau’s Data Center of
Excellence recently released a model and whitepaper
that define the key components of the data lifecycle,
using those components as indicators of a company’s
maturity. The study found only about one-third of
marketers feel they have a good grasp of dealing
with data. According to Neil O’Keefe, SVP at the
Direct Marketing Association (DMA), “This is a cry for
help from the marketing community. This is the future
of marketing and the future is now. More marketers
are searching our education to better train existing
staff and DMA hears from hiring managers that more
and more their target hire is a data scientist and not a
marketer.”V
The Modern Data Scientist
The original conception of a data scientist was a
businessperson with a technical skillset. More than
a simple liaison between business and IT, he or she
played on both teams: identifying strategic and tactical
business problems and then being a part of the team
solving them, too.
In an oft-quoted Quora post from 2014, Michael
Hochster at Pandora saw two types of data scientists:
Type A and Type B. The A is for Analysis and the B
is for Building.VI
Type A “analysis-focused” are the deep subject matter
experts, and are highly specialized in getting every last
drop of performance out of data.
Type B “building-focused” are more familiar with
software engineering practices, contribute to big
data systems, online machine learning initiatives, and
generally strive to find their work ‘in production.’ That
said, technical specialization does not allow data
scientists to live up to their original conception as a
business person with a technical skillset: they have
become just another technical contributor.
Historically, analysis- and building-focused data
scientists were enough to accomplish most all data
science requirements; however, we now need data
scientists to understand how the data and systems
actually apply to the business. Burtch Works, a highly
regardeddatasciencerecruitingfirm,writes:“Putsimply,
1
Harris, Jeanne; Shetterley, Nathan; Alter, Allan; and Schnell, Krista. “The Team Solution to the Data Scientist Shortage” Accenture
Institute for High Performance, 2013.

adatascientisthasformidableexperiencewithstatistics
and computer science, as well as the business acumen
to derive actionable insights from data and prescribe
actions based on those insights.”VII
Following the
release of the Burtch Works 2016 Annual Data
Science Salary Survey, Linda Burtch, Founder &
Managing Director of Burtch Works, indicated
“business acumen is the #1 complaint or concern that
I hear from clients.”VIII
Let us now consider a third type of data scientist:
Type C for Consulting. Type C “consulting-focused”
data scientists are part of internal consulting efforts,
building internal decision support tools, advising others
on how to get the most from data, and above all,
helping communicate the value hidden in their internal
and external data to their broader organizations. Riley
Newman at Airbnb wrote about his group as being in
a proactive partnership with the business rather than
engaging in reactive stats-gathering.IX
Meanwhile,
Drew Harry at Twitch pointed out, “The consultation
process is the heart of what a good data scientist
does. They teach their customers how to ask good
questions and how to interpret the results. They don’t
just hand someone numbers.”X
In one form or another, the value Type C data scientists
bring to the table is their ability to act as change agents,
while working in a sea of ambiguity and unknowns,
and who, ultimately, tell stories with data. Therefore,
effective storytelling, or lack thereof, is either the key
accelerant or the key inhibitor of capturing value.
Types of Modern Data Scientists

The Modern Marketer
The role of the marketer has not been an easy one
to universally define due to varying and growing
complexities of the function. There are a number of
category iterations of the marketer, and those have often
been described in context of either being left-brained
or right-brained. Left brained are those marketers who
focus more on the “logic” or analytics and tracking
side of marketing. Whereas, right brained marketers
are those who focus more on the “creative” or content
and visual side of marketing. Because we believe the
modernmarketermustusebothsidesofthebrain,weare
going to build on the concept of different “parts” of the
modern marketer, as defined by Salesforce in a blog
post: Part Artist and Part Scientist.
Part A or the “scientific” part uses data to improve
marketing accuracy and effectiveness. This part of a
marketer is heavily focused on performance tracking,
operations, historical and predictive analytics, and how
marketing impacts the bottom line. The scientific part
of a marketer is well-organized and meticulous.
Part B or the “artistic” part connects a company’s
product or brand to the customer on an emotional
level. This part of a marketer tends to be more skilled
at written content, creative and visual assets, social
collaboration tools, and digital marketing. The artistic
part of a marketer is the forever customer advocate.
Let us now consider a third part of a modern marketer:
Part C or the “consultative” part. The consultative
part of a marketer bridges the two mindsets of
artistic and scientific to move the business forward.
It fuels intellectual curiosity and enables strategic
problem solving. The consultative part looks beyond the
established practices for ways to improve the business
and discovers trends in market and customer behaviors
to help support the decision making process. It also
enables the marketer to know the right questions
to ask and gives them the persistence to never give
up until finding a strategic solution. Never being
satisfied with the status quo propels the consultative
part of a marketer to find ways to use both creative
development and data to achieve the most desirable
returns.

The Modern Marketer Brain
Co
nsultati
ve
Scient
ific
Artis
tic

Identifying the Characteristics of
Great Data Storytellers
To better understand the required skill sets and/or
mindset of great data storytellers, we decided to focus
on the data scientist function as our target subject,
since it is this role that currently carries the bulk of data
and insights responsibilities.
We teamed up with Talent Analytics, Corp., an
organization that specializes in understanding how
to hire and retain top talent by creating customized
benchmarks to help companies predict how candidates
will do in their jobs, pre-hire. These benchmarks
are mostly built around the soft skills and mindsets
of candidates, of which companies find difficult to
both articulate and assess during the hiring process.
Our goal of the Talent Analytics partnership was to
explore whether or not we could determine if great
data storytellers are governed by a mindset or skill set.
Talent Analytics, Corp. and the International Institute of
Analytics conducted a study on Analytics Professionals
in order to see if the “mindset” of the data scientist
differed from the mindset of other professionals. The
study revealed an undeniable “raw talent mindset” of
the Analytics Professional.
“Our goal with this groundbreaking Study was to use
quantitative analysis to learn more about the mindset
of Analytics Professionals / Data Scientists rather
than qualitative discussions and guessing,” said Greta
Roberts, CEO and co-founder of Talent Analytics,
Corp. This Study focused on practical outcomes that
could be used by businesses, rather than for purely
academic interests. A clear mindset did emerge. This
led to the creation of a valuable Industry Benchmark for
hiring, deploying and utilizing Analytics Professionals.”
Here are some highlights from the study:
• Analytics Professionals have a cognitive
“attitude” and will search for deeper knowledge
about everything.
• They are driven to be creative and will want to
create not only solutions, but also elegant
solutions (i.e., the code could be more elegant,
or there might be a better graph to visualize the
solution).
• They will thrive in a job culture that values
different approaches and out-of-the-box thinking.
• They have a strong desire to “do things the right
way,” and will encourage others to do the same
• They will be comfortable speaking to defend
what they believe to be right, even in the face
of controversy.
• They have an extremely high sense of quality,
standards, and detail orientation, often
evaluating others by these same traits. They
are highly conscientious and will provide
careful follow-through on detailed projects and
complex assignments.
• They tend to be somewhat restrained and
reticent in showing emotions, and may be less
verbal at team or organizational meetings unless
asked for input, or if the topic is one of high
importance.
• They may take calculated, educated risks – only
after a thoughtful analysis of facts, data, and
potential outcomes. They persuade others on the
team by careful attention to detail, and through
facts, data, and logic, not emotion.
• They appreciate security in projects, systems,
and job culture.
We then looked at 30 data scientists, classified as
great data storytellers, to determine whether their
mindsets differed from the Analytics Professional
benchmark.
From this comparison, we found that data scientists
who had been classified as great data storytellers
are indistinguishable on “mindset dimensions” from
the general population of Analytics Professionals.
Therefore,whenlookingatgreatdatastorytellingamong
data scientists, we believe it is a teachable skillset.
Why? Because the Analytics Professional mindset is
already predisposed to intellectual curiosity, creativity,

and passion for problem solving – characteristics
required in data science.
If you already have a data scientist on your team, then
it is very likely you can mold them into a great data
storyteller via proper support and skills development.
If you do not already have a data scientist on your
team and are looking to hire one, we recommend you
use the Analytical Professional benchmark created by
Talent Analytics, Corp. to guide your search.
Developing and Nurturing
an Environment for Great
Data Storytelling
We interviewed John Miller, principal of Camus Group,
to find additional ways to develop the skill set of great
data storytelling. An expert on the topic, he built the
visual literacy program and curriculum at one of the
top global consulting firms and now helps companies
bring together the power of data products, design
thinking, and management consulting to help
clients understand the most important issue to solve,
Miller states,
“The responsibility of
data storytelling should not
be put on one individual, but
rather spread across a team
made up of individuals, who as
a whole, possess the diversity
of skills and abilities required
for great data storytelling,”
“For example, on a project to visualize the future of
healthcare, the primary author was a management
consultant who worked with a team to storyboard the
narrative, the visual designer inspired the team with
sketches of subsequent data visualization, and the
data scientist and data analys debated the validity of
the insights and conclusion of the analytic model with
the healthcare industry experts. It is very important to
encourage the diversity of skills and perspectives on
the final outcome.”
We also interviewed Hunter Whitney, a design strategy
consultant, instructor, and author of the book Data
Insights. He believes effective data storytelling
increasingly requires an emerging mindset and
skillset that draws on principles from a range of
fields including design, statistics, psychology,
neuroscience and journalism. “People from many
different backgrounds can build up these skills and
knowledge,” Whitney says.
Whitney began his career as a journalist and thinks
there are many important practices from that profession
that directly relate to data storytelling. He says, “Good
journalists need to sift through a lot of data and think
critically to find the heart of the story. They must
also assess the accuracy of what they are reporting
and distill it into forms that are concise, accurate, and
truly engaging to audiences.” Whitney believes different
members of any team will inevitably have different
strengths, but that all would benefit from a shared
baseline understanding of what’s involved in the full
process of identifying and communicating a data story.
He is currently working on an online educational
program with UC Davis Extension and Coursera that
focuses on data visualization and storytelling with
Tableau. “The program emphasizes the underlying
skills and abilities needed to draw insights from data,
and how to communicate those insights in an effective
and compelling way.”
Building teams capable of data storytelling is as easy
as ABC: Activation Between Collaborators. Don’t rely
on the ‘unicorns’ in data science, marketing, or other
business functions who might have all of the analytical,
building, creative, scientific, and consultative skill
requirements. After all, how many unicorns have you
seen in your lifetime? Instead, bring together people
with diverse perspectives and varying technical skills
who exhibit a natural curiosity and passion for solving
problems and the know-how of change management.

DO
• Be open and build a data storytelling team with
people who embody a diverse mix of hard and
soft skills.
• Empower the team with corporate support and
training to further develop business judgment and
consultative skills.
• Collaborate and team with key business units to
determine which questions are the “right” questions
the team should be asking and answering in their
data story. I love this quote from The Hobbit, as
also shown in Whitney’s book: “There is nothing
like looking, if you want to find something…You
certainly usually find something, if you look, but it
is not always quite the something you were after.”
• Integrate all participating team members
into the line of business. Your teams will be
unsuccessful if you assume each contributing
function should stay in their “box” of job
responsibilities and do not allow them to fully
comprehend the intricacies of your business.
Show your data scientists and marketers your
real business problems in actual context and
illuminate your vision for a better future.
• Make sure the data story is applicable to
your business challenge, is relatable to how the
company can grow its business, and is simple
enough for every business unit to understand
and apply.
Conclusion
Great data storytelling is not one size fits all. It requires
an open mind in how one might structure the
team responsibilities, and it also requires on-going
engagement to support the data storytelling capability.
Instead of trying to find the “magic bullet” or “unicorn”
of the great data storytellers, it is recommended to
build a team of diverse individuals who can bring a
variety of interest, skill, and perspective to the table.
Companies should support a teaming atmosphere
where data scientists, marketers, and professionals
alike are immersed in the business line, so that they
are better positioned to know what questions to ask to
move the company forward.
When comparing which is more crucial to data
storytelling success, mindset or skill set, we have
determined that the answer is a whole lot of both. There
is no question great data storytelling requires a wide
range of skill sets; but also, it requires a great deal
of intellectual curiosity, healthy skepticism, passion
of discovery for growth opportunities, and change
management.
Contrary to a perception that qualities of a mindset
cannot be taught, scientists are learning that people
have more capacity for lifelong learning and brain
development than ever thought possible. In her book
Mindset, Carol Dweck compares what she calls
the Fixed Mindset versus the Growth mindset. Fixed
being the mindset that I cannot do what has been asked
of me; whereas, the Growth mindset would be that I
cannot do what has been asked of me yet.
In her book, Dweck writes,
“Although people may differ
in every which way – in their
initial talents and aptitudes,
interests, or temperaments
– everyone can change and
grow through application and
experience.”

Your company and business needs will determine how
best to construct your data storytelling team; however,
your mindset as a corporate leader may be the most
crucial component to successful data storytelling. Are
you willing to lead and support a culture of diversified
and design thinking that might in fact disrupt the way in
which you are currently doing business? We hope so.
About the authors
Randa McMinn is a marketing and customer insights
leader with 18 years of experience in customer
acquisition, engagement and revenue growth for retail,
business services, and real estate organizations. She is
knownforherabilitytoturncrucialinsightsintobusiness
ideas that result in improved organizational and
financial performance. Currently, Randa is consulting
for organizations in technology and business services
with emphasis in start-up, brand development, GTM
strategy, restructuring and change management. In
other full-time roles, she managed B2B, B2C and
B2B2C teams and initiatives that improved enterprise
brand frameworks, drove transformational change, and
advanced technologies through experimental design
and testing.
Aaron Merlob is a data science and engineering
leader with 10 years of experience consulting with
startups and SMBs. At ABUV Media, Aaron leads
a software, reporting and analytics team, a marketing
operation team, and works on culture and leadership
development. In other full-time roles, he has managed
big data teams and projects, owned an analytics
product roadmap, and held a faculty appointment at
Harvard.
i
Spiegel, Benjamin. “Why Every Marketing Department Needs a Data Scientist” Marketing Land, October 22, 2015.
1
Morgan, Lisa. “Data Storytelling: What It Is, Why It Matters” InformationWeek, May 30, 2016.
iii
Harris, Jeanne; Shetterley, Nathan; Alter, Allan; and Schnell, Krista. “The Team Solution to the Data Scientist Shortage”
Accenture Institute for High Performance, 2013.
iv
Condon, Cliff. “2017 Predictions: Dynamics That Will Shape The Future In The Age Of The Customer” Forrester,
October 28, 2016.
v
Kirkpatrick, David. “Why Data Remains a Marketing Challenge – and How to Tame It” Marketing DIVE, May 31, 2016.
vi
Hochster, Michael. “What is Data Science” Quora, 2014.
vii
Burtch, Linda. “The Burtch Works Study Salaries of Data Scientists” Burtch Works Executive Recruiting, April 2015.
viii
Burtch, Linda. “The Burtch Works Study 2016: Updated Salaries of Data Scientists” YouTube April 28, 2016.
ix
Newman, Riley. “How We Scaled Data Science to All Sides of Airbnb Over Five Years of Hypergrowth” VentureBeat,
June 30, 2015.
x
Bierly, Melissa. “How Does the Data Science Team Work at Twitch?” Mode, April 5, 2016.
xixi
Wesson, Matt. “The Modern Marketer: Part Artist, Part Scientist” Salesforce Blog, April 03, 2013.
xii
Roberts, Greta; Roberts, Pasha. “Benchmarking Analytical Talent” Talent Analytics Report, December 2012.

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