1. Predictive Analytics
in Action: Real-World
Examples and Advice
Predictive analytics projects are inherently complex and potentially
costly. But for organizations that get it right, they can pay off in improved
decision making and competitive advantages over business rivals.
An Essential Guide
1 2 3 4Editor’s Note Predictive
Analytics
Programs
Need Open
Organizational
Minds
Recipe
for Predictive
Analytics
Success
Includes
One Part
Storyteller
Surveys Point
to Skills, Training
as Predictive
Analytics
Hurdles
Virtualization
Cloud
ApplicationDevelopment
Networking
StorageArchitecture
DataCenterManagement
BusinessIntellegence/Applications
DisasterRecovery/Compliance
Security

2. 2 Predictive Analytics in Action: Real-World Examples and Advice
Home
Editor’s Note
Predictive
Analytics Programs
Need Open
Organizational
Minds
Recipe for
Predictive Analytics
Success Includes
One Part
Storyteller
Surveys Point to
Skills, Training as
Predictive Analytics
Hurdles
1editor’s note
Modeling the Future:
A Challenging but Rewarding
Proposition
Who wouldn’t want to predict the future, especially when money is at
stake? Alas, businesses can’t just rely on crystal balls, tarot cards and palm
readers—at least if they want to stay in business. But companies can turn to
predictive analytics software to help them peer into the business future—for
example, to predict which customers are likely to be open to cross-selling of-
fers and which ones might not be worth additional sales attention.
But they don’t call it advanced analytics for nothing. If your organization is
looking to deploy and use predictive analytics tools, you’ll need to make sure
that you have the right level of analytics skills in place. Time for an infusion
of data scientists, perhaps? Building predictive models is a complex, time-
consuming process that requires trial-and-error testing in order to get the
algorithms to produce the desired analytical results. And convincing busi-
ness and operational managers to trust what the models are telling them and
adjust their strategies and processes accordingly is another big challenge.
This three-part guide offers practical advice from experienced analytics
professionals and industry consultants on how to successfully manage a pre-
dictive analytics program. The lead story details key steps to take in develop-
ing and implementing a program, starting with ensuring that your company
is open to the possibilities enabled by predictive analytics. Next, we recount
the lessons that one analytics exec has learned about building a predictive
analytics team. And we report on a pair of surveys pointing to a lack of skills
and proper training as predictive analytics inhibitors.
Craig Stedman
Executive Editor, SearchBusinessAnalytics.com

3. 3 Predictive Analytics in Action: Real-World Examples and Advice
Home
Editor’s Note
Predictive
Analytics Programs
Need Open
Organizational
Minds
Recipe for
Predictive Analytics
Success Includes
One Part
Storyteller
Surveys Point to
Skills, Training as
Predictive Analytics
Hurdles
2Best
Practices
Predictive Analytics Programs
Need Open Organizational Minds
Has the current fervor to pounce on every piece of available data for po-
tential analytical uses spawned a world in which information often is col-
lected for its own sake? Sometimes it might seem that way. But in the
ever-expanding universe of “big data,” predictive analytics software is one
technology that can take advantage of the great variety of data accumulated
by an organization as it works to model customer behavior and future busi-
ness scenarios.
And using predictive analytics tools to interpret data is becoming more
important to businesses: The most successful companies and rising-star en-
terprises sedulously employ them to help point the way forward on business
strategies and operations, according to analysts who focus on advanced ana-
lytics technologies. But that doesn’t happen magically, they cautioned; orga-
nizations need to take the right steps to develop effective predictive analytics
programs.
In many industries, getting a leg up on the competition can be more chal-
lenging than ever—especially if companies are set in their ways. The starting
point in embracing predictive analytics should be ensuring that an organiza-
tion has a proper frame of mind about using the technology, the analysts said.
An open, dexterous attitude that’s naturally curious, eager to learn and will-
ing to adapt will produce the best results.
Douglas Laney, an analyst at Gartner Inc. in Stamford, Conn., thinks a pre-
dictive analytics program should begin by questioning historical business
methods while searching far and wide for better ones. Companies “should
not only focus on how things have been done in the past but be open to
big ideas for innovations and transformations,” he said.“This could mean

4. 4 Predictive Analytics in Action: Real-World Examples and Advice
Home
Editor’s Note
Predictive
Analytics Programs
Need Open
Organizational
Minds
Recipe for
Predictive Analytics
Success Includes
One Part
Storyteller
Surveys Point to
Skills, Training as
Predictive Analytics
Hurdles
2Best
Practices
applying measures effective in other industries to your industry.” Such a
mind-set should extend to the point of embracing approaches that “radically
change the way business processes are done” in an organization, Laney added.
With that in mind, the mentality of the players—particularly the business
managers who are being asked to buy
into the findings of predictive mod-
els—is frequently the key variable that
determines the success or failure of
predictive analytics programs. A per-
spicacious corporate culture champions
objectivity, welcomes new ideas and is
naturally flexible. Conversely, a retro-
grade one resists change and draws heavily on existing biases and subjective
formulas.“Resisting new ways of doing things is the reason most projects
fail,” said John Lucker, head of Deloitte Consulting LLP’s advanced analytics
and modeling practice.
Keep Your Eyes on the Business Prize
The grand plan of a predictive analytics deployment should also begin with a
clear set of business objectives, said Thomas “Tony” Rathburn, a senior con-
sultant at The Modeling Agency LLC, a Pittsburgh-based consulting com-
pany that focuses on data mining and predictive analytics. Then, he added,
a team-oriented strategy is needed to advance those objectives. That is best
constructed through substantive discussions involving program managers,
predictive modelers, data analysts and business representatives.
So critical is the strategy development process that Eric King, president
and founder of The Modeling Agency, recommends retaining “a seasoned
strategic mentor” to help lead the effort and keep it on track.
Once a predictive analytics strategy is in place, it’s time to begin the anal-
ysis process. Laney said “chewy” questions that probe deeply into data will
unearth findings with high operational value. The truly useful ones, he said,
are multifaceted—for example,“How can we grow new customers by 20%
Once a predictive
analytics strategy
is in place, it’s
time to begin the
analysis process.

5. 5 Predictive Analytics in Action: Real-World Examples and Advice
Home
Editor’s Note
Predictive
Analytics Programs
Need Open
Organizational
Minds
Recipe for
Predictive Analytics
Success Includes
One Part
Storyteller
Surveys Point to
Skills, Training as
Predictive Analytics
Hurdles
2Best
Practices
per year for a certain product line without cannibalizing other product lines
given the range of economic forecasts, competitor trends and changing con-
sumer demands?” Run through predictive models, such questions can con-
tribute in a big way to driving new business, according to Laney.
Building Models is a Testing Process
After choosing and deploying the predictive analytics tools that best fit the
job at hand, developing models is the next step. Mike Gualtieri, an analyst at
Forrester Research Inc. in Cambridge, Mass., said analytics algorithms should
be run on 70% of a data set to create an effective predictive model.“Then
you test that model on the remaining
30%,” he said.
Completed models should be regu-
larly tested and enhanced as needed,
and a set of performance metrics
should be put in place for tracking their
accuracy, Gualtieri added—all part of a
process for “continuous monitoring of
the predictive analytics model.”
Moreover, said other analysts, the
entire predictive analytics process requires regular monitoring as business
needs and the nature of the data being collected by an organization change.
Analytics strategies and tactics that worked initially will need to be revisited
and perhaps revised in order to continue achieving optimal results.
The mark of a truly successful predictive analytics program, Lucker said,
is when some of the cost savings or business gains realized from an ongoing
analysis project can be applied to pay for the next one so no new dollars need
to be spent.“Using the value of each project to fund downstream efforts is an
evolutionary approach that comes with a [built-in] return on investment,” he
said. —Roger du Mars
Analytics strategies
and tactics that worked
initially will need to
be revisited and per-
haps revised in order
to continue achieving
optimal results.

6. 6 Predictive Analytics in Action: Real-World Examples and Advice
Home
Editor’s Note
Predictive
Analytics Programs
Need Open
Organizational
Minds
Recipe for
Predictive Analytics
Success Includes
One Part
Storyteller
Surveys Point to
Skills, Training as
Predictive Analytics
Hurdles
3Team
Building
Recipe for Predictive
Analytics Success Includes
One Part Storyteller
The secret to building a successful predictive analytics team is finding
people with statistical analysis, programming and—perhaps most impor-
tant—storytelling skills, according to one practitioner.
It’s important to find multitalented people because, oftentimes, predictive
analytics teams are rather small, said Jennifer Golec, vice president of strate-
gic analytics at XL Insurance Inc. Multifaceted individuals offer a higher level
of flexibility, she said, and that comes in handy when resources are tight.
Ideally, predictive analytics professionals should be one part programmer,
Golec said, because they’ll be working with a great deal of information and
conducting exploratory analysis. Commercial software can help in these ar-
eas, she explained, but some programming skills will still be helpful for tasks
like manipulating or massaging data
and creating new variables.
Predictive analytics professionals
should also focus on developing sta-
tistical analysis skills because those
are necessary for building multivariate
models, statistical tools that use multi-
ple variables to forecast outcomes.
“The third piece is that you have to be part storyteller. You have to be able
to interpret those results,” Golec said.“[That means] really being able to in-
terpret the insight that you pull from the data. You have to be able to relay
that because if you don’t, you’ll be sitting on this great model and you won’t
be able to implement it.”
Predictive analyt-
ics professionals
should focus on
developing statis-
tical analysis skills.

7. 7 Predictive Analytics in Action: Real-World Examples and Advice
Home
Editor’s Note
Predictive
Analytics Programs
Need Open
Organizational
Minds
Recipe for
Predictive Analytics
Success Includes
One Part
Storyteller
Surveys Point to
Skills, Training as
Predictive Analytics
Hurdles
3Team
Building
More Than Just Crunching Numbers
The popular 2011 film Moneyball—which tells the story of Oakland A’s gen-
eral manager Billy Beane, who used analytics to find undervalued players and
build a great baseball team—might give the mistaken impression that ana-
lytics is all about crunching numbers. But it’s much more than that, accord-
ing to Golec. Organizations must also strive to understand how the results of
predictive models translate to the real business world.
“Sometimes that is the danger with
products like SAS,” Golec said.“They
make it so easy to push the data in and
hit the button and have something
come out. But if you’re not trained to
understand and interpret that output,
you could end up with junk and you
might not know it.”
Golec, who has a doctorate in eco-
nomics from the University of Missouri and previously ran a predictive ana-
lytics program for insurance provider The Hartford, began working for XL
Insurance and its global parent company, XL Group PLC, in October 2011.
Analytics Goal: Ratcheting Back on Risk
One of her first tasks was to find a software vendor that could help the prop-
erty and casualty company build out its fledgling predictive analytics pro-
gram. XL Insurance launched the program to do a better job of avoiding
unnecessary risk and, ultimately, improving its loss ratio.“The loss ratio is
losses over premiums,” Golec said.“The lower it is, the more profitable you
are.”
Golec took a close look at SPSS, which was acquired by IBM in 2011, and
Wolfram Research’s Mathematica tools. But she had worked with software
from SAS Institute Inc. in the past and decided to do so again.
“Half the battle is working with the data, manipulating the data and get-
ting it into a form that allows you to actually do the modeling,” she said.
Organizations must
also strive to under-
stand how the results
of predictive models
translate to the real
business world.

8. 8 Predictive Analytics in Action: Real-World Examples and Advice
Home
Editor’s Note
Predictive
Analytics Programs
Need Open
Organizational
Minds
Recipe for
Predictive Analytics
Success Includes
One Part
Storyteller
Surveys Point to
Skills, Training as
Predictive Analytics
Hurdles
3Team
Building
“SAS allows us to get the data into the shape and form that we want.”
XL Insurance is using several SAS products, including SAS/STAT, a statis-
tical analysis tool; SAS Graph, a visual tool that allows users to present in-
formation in charts and graphs; SAS Enterprise Guide (EG), which makes it
easier to do exploratory analysis of data stores; and JMP, a data visualization
tool.
Ensuring Adoption Central to Implementation Process
The team at XL Insurance is in the process of building predictive models for
risk assessment. The next step, according to Golec, is to implement those
models and closely monitor and measure the results.
Golec said the toughest aspects of the implementation phase will likely re-
volve around change management and, specifically, getting the right people
to adopt predictive analytics findings as part of their usual routines. Making
sure that any workflow or architecture changes are properly documented is
also a major challenge.
Another is “making sure that we’ve come up with how we’re going to
track it and make sure it’s working,” she said.“But I think the big thing in
implementation is just achieving that buy-in and making sure that it’s
used.”—Mark Brunelli

9. 9 Predictive Analytics in Action: Real-World Examples and Advice
Home
Editor’s Note
Predictive
Analytics Programs
Need Open
Organizational
Minds
Recipe for
Predictive Analytics
Success Includes
One Part
Storyteller
Surveys Point to
Skills, Training as
Predictive Analytics
Hurdles
4Challenges
Surveys Point to Skills, Training
as Predictive Analytics Hurdles
Businesses recognize the potential of predictive analytics, yet there’s
a large gap between those who see it as important and those who actu-
ally use the technology, according to a pair of surveys conducted by Ventana
Research.
The market research and consulting company, based in San Ramon, Calif.,
conducted an initial study in early 2011 which found that only 13% of the re-
sponding organizations were using predictive analytics. But 80% indicated
it was important or very important, said David Menninger, who was infor-
mation technology research director at Ventana when he was interviewed for
this story; he has since taken a job with a technology vendor.
The reason for that gap? While most businesses consider predictive analyt-
ics important, those that struggle with it lack both the skills and the training
required to be successful with the technology, Menninger discovered in a fol-
low-up study.“Organizations are least mature in the people aspect,”he said.
That conclusion was drawn from the results of a three-month survey of
198 respondents measured against Ventana’s predictive analytics maturity
model, which was used to rate the survey responses across the categories of
process, information, technology and people.
The survey revealed that self-service predictive analytics, or end users cre-
ating and deploying their own analyses, has not been widely deployed, de-
spite a wave of easier-to-use predictive analytics tools coming to market.
Analytics Skills Not a Common Trait
In fact, almost half of the respondents questioned whether users have the
background to produce their own analyses. For the nonbelievers, Menninger

10. 10 Predictive Analytics in Action: Real-World Examples and Advice
Home
Editor’s Note
Predictive
Analytics Programs
Need Open
Organizational
Minds
Recipe for
Predictive Analytics
Success Includes
One Part
Storyteller
Surveys Point to
Skills, Training as
Predictive Analytics
Hurdles
4Challenges
said it came down to two reasons: 83% reported users didn’t have enough
skills, and 58% reported users didn’t understand the mathematics involved.
“[Predictive analytics] requires the specialist skill set—the data scientist,
the statistician, the data mining experts—to be successful,” he said.
Instead of relying on users, 63% of respondents reported their organiza-
tion had a specialized team for predictive analytics or that the task fell to
the business intelligence (BI) and data warehousing (DW) team. But even
then, Menninger’s research indicated that how satisfied respondents are with
the way predictive analytics is used in their organizations (two-thirds said
they’re satisfied) depends, in part, on
who does the work.
The highest levels of satisfaction,
70%, came from respondents who
worked for organizations that employed
specialists such as data scientists to
produce the predictive analytics find-
ings. The lowest levels of satisfaction,
59%, came from respondents whose
BI and DW teams were in charge of
the work.“I think it’s common for organizations to think this will naturally
fall out of the BI and DW team,” Menninger said.“But what this tells me is
that this is not a generalized skill of BI and DW teams.”
Support Lacking for Predictive Analytics Users
Many organizations are also not doing a great job providing the ongoing sup-
port needed to successfully maintain a strong predictive analytics program,
Menninger said.
According to the survey results, businesses are most successful at provid-
ing concept and technique training (44% of respondents felt this was ade-
quate) and have the most trouble delivering help desk support (24% reported
this was adequate). More than a third of respondents, 42%, also found prod-
uct training to be adequate.
Many organizations
are not doing a great
job providing the ongo-
ing support needed to
successfully maintain
a strong predictive
analytics program.

11. 11 Predictive Analytics in Action: Real-World Examples and Advice
Home
Editor’s Note
Predictive
Analytics Programs
Need Open
Organizational
Minds
Recipe for
Predictive Analytics
Success Includes
One Part
Storyteller
Surveys Point to
Skills, Training as
Predictive Analytics
Hurdles
4Challenges
Menninger said concept, technique and product training may drive a stron-
ger sense of satisfaction because they require “specialized knowledge” over
the broader needs—and the skills—required by something like a help desk.
“I think it relates back to necessary skills,” he said.“How do you have peo-
ple on the help desk supporting a more
complicated topic? The help desk re-
sources would need to have specialized
training and skills to be able to provide
meaningful support.”
Yet respondents indicated that, in
addition to concept and technique
training, the most effective type of support was brought about by help desk
resources. Organizations that provided either support feature adequately had
an 89% satisfaction rating on average, according to the survey results.
“I suspect that organizations probably think first about doing product
training and less about this generalized set of skills and help desk resources,”
Menninger said.
While the level of satisfaction in a predictive analytics program may wax
and wane based on training, Menninger said the root of that issue is most
likely derived from what he considers to be an even bigger problem—a lack of
skills.
“The skills issue is significant,” he said.“It appears to have been preventing
organizations in the past from either choosing to tackle predictive analytics
or [being able] to tackle it successfully.”
Menninger said predictive analytics requires a deeper kind of knowledge.
“It’s unrealistic today to expect the technology to deliver self-service capa-
bilities,” he said.“[But] if you have the right skills, the technology is available
to be successful with predictive analytics.”—Nicole Laskowski
The most effective
type of support was
brought about by
help desk resources.

12. 12 Predictive Analytics in Action: Real-World Examples and Advice
Home
Editor’s Note
Predictive
Analytics Programs
Need Open
Organizational
Minds
Recipe for
Predictive Analytics
Success Includes
One Part
Storyteller
Surveys Point to
Skills, Training as
Predictive Analytics
Hurdles
about
the
authors
Roger du Mars is a freelance writer
based in Redmond, Wash. He has writ-
ten for Time, USA Today and The Bos-
ton Globe, and he was the Seoul, South
Korea, bureau chief of Asiaweek and the
South China Morning Post. Email him at
rogerdeandumars@yahoo.com.
Mark Brunelli is news director for
SearchBusinessAnalytics.com and the
other websites in TechTarget Inc.’s
Business Applications and Architecture
Media Group. He oversees coverage of
topics such as business intelligence and
analytics, data management, customer
relationship management and business
applications. Email him at mbrunelli@
techtarget.com and follow him on
Twitter: @Brunola88.
Nicole Laskowski is the news editor
for SearchBusinessAnalytics.com. She
covers business intelligence, analytics
and data visualization technologies
and trends. Email her at nlaskowski
@techtarget.com and follow her on
Twitter: @TT_Nicole.
Predictive Analytics in Action: Real-World
Examples and Advice is a
SearchBusinessAnalytics.com
e-publication.
Barney Beal
Senior Executive Editor
Jason Sparapani
Managing Editor, E-Publications
Nicole Laskowski
News Editor
Craig Stedman
Executive Editor
Linda Koury
Director of Online Design
Mike Bolduc
Publisher
mbolduc@techtarget.com
Ed Laplante
Director of Sales
elaplante@techtarget.com
TechTarget
275 Grove Street, Newton, MA 02466
www.techtarget.com
© 2012 TechTarget Inc. No part of this publication
may be transmitted or reproduced in any form or
by any means without written permission from the
publisher. TechTarget reprints are available through
The YGS Group.
About TechTarget: TechTarget publishes media
for information technology professionals. More than
100 focused websites enable quick access to a deep
store of news, advice and analysis about the tech-
nologies, products and processes crucial to your job.
Our live and virtual events give you direct access to
independent expert commentary and advice. At IT
Knowledge Exchange, our social community, you
can get advice and share solutions with peers and
experts.