Breakthrough experiments in data science: Practical lessons for success

© 2015 IBM Corporation

Breakthrough experiments in data science:

Practical lessons for success

November 2015

© 2015 IBM Corporation2

© 2015 IBM Corporation3 © 2015 IBM Corporation

We asked data scientists and their executive colleagues…


In their own words…

4

“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


Leading organizations are using data science to drive up revenue…

5

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


…and are using data science to improve efficiency and effectiveness

6

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


What can we learn from how forward-thinking enterprises use data science capabilities to extract value from data?

7 © 2015 IBM Corporation


Those already seeing the benefits can offer practical advice to their peers around how to:



Infuse data science into culture: Smarter, faster decision making



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.”


Infuse data science into culture


Design a data science capability: Structure, skills and support



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.”


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.”


Design a data science function


Equip with the right technology: Tools, accessibility and efficiency



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


Showcase your results: Targets, metrics and awareness



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


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?


Backup

22


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

23

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


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:

http://www.ibm.com/ibmcai/cdostudy

http://www-01.ibm.com/common/ssi/cgi-bin/ssialias?subtype=WH&infotype=SA&appname=CHQE_CI_CI_USEN&htmlfid=CIW03090USEN&attachment=CIW03090USEN.PDF

http://ibmcai.com/tag/cdo/

http://www-935.ibm.com/services/c-suite/cdo/

http://www.ibmbigdatahub.com/tag/1578

http://ibm.com/ibmcai/cdostudy

http://ibm.com/ibmcai/cdostudy


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