The document summarizes key lessons from data science leaders on how to build a successful data science capability within an organization. It provides quotes from various data science professionals on centralizing the core function while distributing support, paying data scientists based on business outcomes, showcasing results through metrics and ROI, and equipping teams with the right tools and accessible data. The document advocates collaborating closely with business and IT partners to infuse a data-driven culture and extract maximum value from data science efforts.

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Breakthrough experiments in data science:
Practical lessons for success
November 2015

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We asked data scientists and their executive colleagues…

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In their own words…
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“When you ask me what the
value of data science is, it's
almost, like explaining the
value of water to a fish.”
– Chief Data Scientist, Media
“We have an asset: it’s the
data. And what you do with
that data dictates whether
you’ll be differentiated in
the future.”
– SVP of Digital & Direct
Business, Retail
“We're going to help the
business go to new
places that it hasn’t yet
even thought of going.”
– Chief Data Scientist,
Biotechnology
“If we didn’t have a data
science capability we would
lose money.”
– Global Director of Data &
Analytics, Manufacturing
“The business realized that,
nowadays, we cannot be
competitive if we are not
data-savvy enough.”
– Senior VP, Banking

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Leading organizations are using data science to drive up revenue…
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Increased business
year over year
Grew top line and
saved on bottom line
Reached expanded
group of customers
Developed a new predictive
analytics platform to offer
preapprovals in real time
Employed modeling
techniques to improve online
promotion targeting and spend
allocation
Assembled location-based
analytics models to better
allocate resources across sites
“With our preapprovals, we
increased our business
40% year over year. And
the losses are about a third
to a quarter of what the
industry is seeing.”
– VP of Credit Risk Analysis &
Econometrics, Banking
“These are opportunities
in the millions, either in
terms of driving the top line,
or just getting smarter in the
online marketing space and
spend allocation.”
– Chief Analytic Officer, Travel
“We’re able to increase
revenue because we were
now reaching people that we
wouldn’t have otherwise
gotten to. And we were able
to minimize the drain from
other competing agents.”
– Lead Data Scientist, Insurance

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…and are using data science to improve efficiency and effectiveness
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Saved millions by
reducing churn
Found new patterns in
holiday shopping
Forecast effects of
weather on sales
Built an online predictive
analytics tool that helped
reduce churn and better direct
resources
Combined trends from
different data sources to
enhance inventory
management
Created a new predictive
model to improve supply chain
management of stores
“Well, certainly the churn
model is a big success –
between $11 and $16 million
dollars in savings per year
has been a lot of proof that
what we do works.”
– Chief Data Scientist, Telecom
“We were able to see that
people were shopping
earlier than ever before. We
needed to have more toys on
the shelf by October;
November was not good
enough.”
– Director Customer Service Systems,
Retail
“I'm able to model the effect
of a hailstorm on a set of
stores that may sell out of
shingles. We’re getting into
other data in order to enhance
some of this bottom line
functionality.”
– Data Scientist & Advanced Analytics
Architect, Retail

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What can we learn from how forward-thinking enterprises use data
science capabilities to extract value from data?
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Those already seeing the benefits can offer practical advice to
their peers around how to:
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Infuse data science into culture: Smarter, faster decision making

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Set expectations
“We had to change the culture and say if you don’t bring data, don’t bother bringing a
topic up. We established very firm criteria up front: show me the research you did
and the data that supports why you believe this to be true.”
– Global Director of Data & Analytics, Manufacturing
Automate decision making
“We’ve tried to integrate some of the business rules into the database and made
some of the decisions for them so they don’t have to do the heavy lifting on it.
Analytics decision management is definitely a key tool.”
– Chief Data Scientist, Telecom
Offer easily consumed data
“Not only have a product but have a product that is easily viewable and consumable
by the business. I think that that’s an absolutely essential thing.”
– Lead Data Scientist, Insurance
Infuse data science into culture

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Design a data science capability: Structure, skills and support

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Centralize the core with distributed support
“We have different verticals assigned to different areas of the business within one
central data organization. This works pretty well because then all of the data
scientists are together sharing one kind of core research philosophy and then using
that in their specific areas.”
– Lead Data Scientist, Insurance
Pay based on partner’s success
“We succeed when our business partners succeed so I even remunerate my people
based on their partner's successes, not just their contribution.”
– Chief Data Scientist, Media
Collaborate with IT
“Setting up data scientists alone will get you part of the way there but getting the
software engineers and technical people to help you implement is absolutely
essential.”
– Lead Data Scientist, Insurance
Design a data science function

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Equip with the right technology: Tools, accessibility and efficiency

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Consolidate data sources
“It’s much simpler because we have a single data source instead of having it spread
out over multiple data sources all over the company. Instead of having to go out and
request access from 16 different people, you can now get all the access you need.”
– Senior Director of Data Sciences, Insurance
Build data cleansing into tools
“Our warehouse team put together anomaly detection in the warehouse to actually
watch the data. We would find date columns with six different date formats, or there
would be six months worth of missing data. It's really a hygiene issue.”
– Principal Data Scientist, Media & Entertainment
Invest in cloud-based solutions
“I'm a big believer in moving a lot of this stuff to the cloud, and then in house, let the
people focus on their core competencies, which is data analytics, not the
maintaining and junk that goes along with the infrastructure.”
– Director Customer Service Systems, Retail
Equip with the right technology

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Showcase your results: Targets, metrics and awareness

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Focus on high ROI problems first
“Senior management is looking for dollar savings or revenue acquisition improvement,
those types of things. So, you have to pick the projects with the best ROI first.”
– Chief Data Scientist, Telecommunications
Establish control groups to measure value
“We're running experiments. We've got control stores, and we've got test stores.
We're looking at the difference between the two across the variety of different things
we're trying in the world of data science.”
– Data Scientist & Advanced Analytics Architect, Retail
Drive awareness through internal campaigns
“We set up kiosks and roadshows to market what we do. We'll set up a couple of
tables in the cafeteria of core buildings and have videos of the types of projects
we've worked on, especially ones that have some jazzy output.”
– Chief Data Scientist, Biotechnology
Showcase your results

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As you establish your own capability…
 How committed are your senior
leaders to basing decisions on data,
not intuition?
 Can the business easily understand
and act on your data science
insights?
 Do you have one centralized
capability? with all the skills you
need?
 How well do you collaborate with
the business? with IT?
 Have you enabled your
capability with the right tools?
 How accessible and trusted
is your data?
 How effectively are you driving
awareness and adoption?
 Do you have metrics in place
to prove value?

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Backup
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For participant companies, big data and
analytics are a significant area of focus
and investment relative to other
business imperatives
Industries include banking, education,
retail, wholesale, telecommunications,
manufacturing, insurance, healthcare,
pharmaceuticals, travel, finance,
biotechnology and media &
entertainment
Our research highlights practical advice from data leaders for
integrating data science capabilities within your organization
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Qualitative study
22 in-depth phone interviews with US-
based business leaders and data
scientists of companies that have been
successful at integrating a data science
capability within their firms

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Learn more about leading data and analytics
24
Data Leaders: Re-imagining the business of data
The transformative power of data and
analytics is being harnessed by
organizations to make smarter, quicker
and more analytically-informed
decisions. At the helm of this
transformation is the Chief Data Officer
– a strategic leader who employs data
and analytics to create tangible
business value.
The IBM Center for Applied Insights
spoke in-depth with executives to learn
how CDOs are making a difference
within organizations.
www.ibm.com/ibmcai/cdostudy
Download the study
(403KB)
 IBMCAI Blog – CDO
THINKLEADERS
CDO strategies for success
in a new era of big data and
advanced analytics
 Big Data & Analytics
Hub for CDOs
Discover more:

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December 2014
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