BusinessAnalytics Insights
Transcript of BusinessAnalytics Insights
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businessanalyticsinsights
Informed decision making Business analytics for industries and SMBs Analytics applied to processes Essentials to get started
Brain trustEnabling the confident enterprisewith business analytics
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Editor-in-Chie
Anna Brown
Copy Editors
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Design
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Copyright 2010 SAS Institute Inc., Cary, NC, USA. All rights reserved. Limited copies may be made or internal sta use only. Credit must begiven to the publisher. Otherwise, no part o this publication may be reproduced without prior written permission o the publisher and copyrightowner. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks o SAS Institute Inc. in the USA andother countries. indicates USA registration. Other brand and product names are trademarks o their respective companies. 104447_S50296.0310
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Contents|P1www.sas.com/bareport
The impact o business analytics on perormance and proitabilityJim Goodnight
Business analytics: helping you put an inormed oot orwardJim Davis
How organizations make better decisionsThomas H. Davenport
Business analytics in actionGail Bamford, David Wallace, Mike Newkirk and Becca Goren
The art, act and science o knowingThornton May
What business analytics means or small and medium businessesMatthew Mikell
Embedding analytics into processesThomas Davenport, Jeanne Harris and Robert Morison
8 essentials o business analyticsJim Davis
The art o the possible: business analytics tomeasure corporate sustainabilityAlyssa Farrell
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contents
ACCESS THIS REPORT ONLINE:
www.sas.com/bareport
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P2|Perormance and Proftabilitywww.sas.com/baexchange
The impact o business analyticson perormance and proftabilityBy Jim Goodnight, CEO, SAS
With the rising complexity o global busi-
ness, gut decisions and hunches no
longer suce. Successul responses to
threats and opportunities now depend on
rapid and smart execution. Let me state
it plainly: Business analytics is the key to
achieving these challenging objectives.
Our world generated more data in 2009
than in the previous recorded history o
mankind. A good deal o this data can
be converted into useul inormation and
competitive advantage by applying theright analytics.
The answers are out there in the data
we capture and store.
Right now, that capture and storage
is costing huge amounts o money.
Analytics converts those tremendous
costs into invaluable assets.
Far more than mere reporting or dash-
boards or scorecards, business analyticsis a discipline that digs deeper into these
vastly larger sets o data to uncover the
most important insights. It can mean so-
cial network analysis to study behaviors
and relationships on multiple levels to
uncover raud. It can involve in-database
analytics to optimize retail assortments
or pricing. It can mean analyzing porto-
lios to manage risk positions.
For example, with the right analytics, re-
tailers can predict how many red sweat-
ers they need in stock and how many
smalls or larges they need based on loca
demographics. They can also determine
optimal prices or hundreds o thousands
o products at multiple locations. Pricing
used to be an art. Now, giant retailers can
zero in on the optimal price or all their
SKUs and stores. Banks can determine
the optimal amount o cash to keep in
ATMs. Automakers can predict howmany spare parts theyll need on hand
and when.
Harrahs, a global casino operator, uses
analytics to optimize its marketing and
customer loyalty programs. Thanks
largely to its use o analytics, Harrahs
ranks No. 1 in prots as a percentage
o revenues and has increased its share
o wallet rom 36 percent in 1998 to 45
percent today.
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Perormance and Proftability|P3www.sas.com/baexchange
In the Philippines, the Bureau o Internal
Revenue used analytics to recoup $114
million in unpaid value-added taxes, a
400 percent ROI in the rst year. In Swe-
den, they are using analytics to reducethe number o patients who die rom clini-
cal errors. In addition to reducing unnec-
essary deaths, they expect to save $10
billion in health care costs at the national
level through their analytic eorts.
1-800-FLOWERS.COM changes prices
and oerings on its Web site, sometimes
hourly, because it uses analytics. It also
uses analytic sotware to target print and
online promotions with greater accuracy.
And it uses analytics to optimize its mar-keting, shipping, distribution and manu-
acturing operations. The result: a $50
million reduction in costs last year.
Heres my advice: Take the time to learn
about analytics. Take the time to discover
how analytics can provide an objective
view o your world, not only as it appears
today but also how its likely to appeartomorrow. Im not talking about gazing
into a crystal ball. Im talking about the
capability o competitive organizations to
develop and implement strategies today
that are based on a careul analysis o
their likely outcomes in the uture.
And heres my crystal-ball view: The abil-
ity to predict uture business trends with
reasonable accuracy will be one o the
crucial competitive advantages o this
new decade. And you wont be able todo that without analytics.
Jim Goodnight has been at SAS helm since the
companys incorporation in 1976, overseeing an
unbroken chain o revenue growth a eat almost
unheard o in the sotware industry.
ONLINE
Business Analytics Knowledge Exchange
www.sas.com/baexchange
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P4|Face Forward with Business Analyticswww.sas.com/baexchange
Most companies today have plenty odata. Creating intelligence and glean-
ing real insight rom this data is what
continues to elude organizations. De-
spite years o talk about scorecards and
metrics, gut eelings and experience are
oten still the guides or making impor-
tant, sometimes critical decisions, even
though current research reveals a clear
link between business perormance and
the use o business analytics.
So what exactly is business analyticsand how can it help? Business analytics
is, simply put, the application o ana-
lytical techniques to resolve business
issues. It provides organizations with a
ramework or decision making, helping
organizations solve complex business
problems, improve perormance, drive
sustainable growth through innovation,
anticipate and plan or change while
managing and balancing risk.
It sounds like a lot, but i you break itdown its all about enabling eective
decision making. Organizations make
decisions every day, and these sit on a
continuum rom requent, up to millions
per day to transormative, which occur
less requently but greatly impact orga-
nizational strategy. The need or agile
decision making has never been greater
but unortunately, IT inrastructure, peo-
ple and processes are lagging behind.
Business analytics: helping youput an inormed oot orwardBy Jim Davis
Why BI is not enoughBusiness intelligence provides histori-
cal, metric-driven decision making
and answers questions like, how many
units did we sell, what did customers
buy and or how much? BI is charac-
terized by the creation o simple rules
and alerts and the distribution o known
acts to systems and people. These
decisions have a low transormational
impact on the business.
BI is still a highly valuable part o youoverall business analytics environment
however, oering an excellent genera
purpose backbone or ad hoc analysis
and basic operational reporting.
For example, BI can alert management
on how many credit card transactions
were completed on a given day. It can
also develop a simple rule or automatic
reporting, like reporting on transactions
greater than $10,000 to the regulators.
From a more strategic decision perspec-
tive, business analytics can help answe
questions such as what new products
should we oer and in what markets?
Or relative to the example, which credit
card transactions are likely to be raudu-
lent? Business analytics can predict this
with certainty and automatically deny
transactions while reporting activities
in real time.
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Business analytics allows organizationsto ace orward, bringing insight to
transormative decisions. It benets all
aspects o an organizations value chain,
including:
Inbound logistics: receiving, storing,
inventory control and transportation
scheduling.
Operations: including factors such as
packaging, equipment maintenance,
testing and all activities that add value
rom the raw material to nal product. Outbound logistics: the activities re-
quired to get the nished products to
market, including warehousing and
distribution management.
Marketing and sales: activities that
lead a buyer to purchase the product,
including channel selection, advertis-
ing, promotion, selling, pricing, retail
management and shel space optimi-
zation.
Service: activities that maintain aproducts value, including customer
support, repairs, installation, training,
spare parts management and more.1
In this way, business analytics drivesinnovation and improves an organiza-
tions speed o response to market and
environmental changes. In the credit card
scenario, business analytics can not
only discover the causal actors o raud,
but also orecast accurately when it will
occur again. The company can then
change business processes accordingly.
A step toward business analytics
Eective decision making requires
a business analytics ramework thatincorporates the people, processes,
technology and culture o an organiza-
tion. This common ramework provides
fexibility across the entire range o
analytical decision-making types rom
highly managed operational analytics
(such as a setting a simple credit limit)
to discovery-based analytics (such as
credit raud scenarios or setting dynamic
credit limits).
A business analytics ramework is not
a monolithic and costly approach,but rather provides or incremental
growth to achieve strategic goals at any
given stage o an organizations value
chain. It oers business-ready analytical
applications with underlying technolo-
gies or key services like data man-
agement and quality, reporting and
advanced analytics.
A business analyticsramework is not amonolithic and costly
approach but ratherprovides or incrementalgrowth to achievestrategic goals at anygiven stage o anorganizations value chain.
1 Porter, Michael E., Competitive Advantage : Creating
and Sustaining Superior Performance. 1985.
In the ollowing report, youll hear romseveral experts about how business
analytics can be applied to business
problems across all types o organizations
industries and value chains. Perhaps
then it will become part o your plan to
outthink and out-smart the competition
ONLINE
Business Analytics Knowledge Exchange
www.sas.com/baexchange
Credit card raud management
www.sas.com/ba-cardraud
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P6|Face Forward with Business Analyticswww.sas.com/baexchange
Six questions about your companys inormationThe modern organization is awash in inormation yet, too oten, it alls short o thetools, methods and expertise it needs to derive the greatest value rom this untapped
asset. Inormation about the most important acets o the business customers, processes,employees, competitors and more is gathered but not analyzed, reported but notunderstood, guessed about rather than acted upon. But not with business analytics.
Ask these questions o your company and join aggressive competitors by being a smart
organization.1. Where should we leverage business analytics? Focus business analytics where you
already compete. The payo is greatest where you are playing to your strength, not where
you are playing catch-up.
2. Why now? Because the technology is ready. Because competitors are likely exploring
the possibilities o analytical competition, too. And because its always risky to delaycapitalizing on a new business capability.
3. Whats the payo? Business analytics is all about anticipating the payo in order tomaximize it. The analytics initiative succeeds when the business capitalizes on an
opportunity that analytics reveals.
4. What inormation and technology do we need? Most companies dont lack or sufcient
data, but instead suer rom a lack o integration and a lack o quality. Without good data,you simply cant do good analytics.
5. What kind o people do we need? You need a variety o talented people: analyticalproessionals who design and refne analytical algorithms, and perorm data mining;
analytical semiproessionals who do substantial amounts o modeling and analysis butare unlikely to develop sophisticated new algorithms or models; analytical amateurs whoneed to understand something o the analytical basis or operations and decisions; and
the analytical manager who ocuses the work o analytical proessionals.
6. What roles must senior executives play? Committed senior executives provide the passionand the resources to drive their organizations in an analytical direction. In virtually everysuccessul frm, senior management sets an analytical strategy and continually pushes
it orward.
ONLINE
www.sas.com/ba-sixquestions
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Jim Davis is Senior Vice President and
Chie Marketing Ofcer or SAS.
HSBC: raud detection that exceeds
aggressive goals
With raud levels surging around the
world, banks are acing greater regula-
tory scrutiny, as well as risks associated
with damaging publicity rom raud. The
ability to correctly make split-second
decisions on accepting credit card
transactions beore raud occurs is
more important than ever.Using SAS Fraud Management, part o
the SAS Business Analytics Framework,
HSBC prevents, detects and manages
nancial crimes by scoring and accept-
ing or rejecting millions o transactions a
day in real time at the point o sale.
As a result, the global nancial services
leader has achieved signicantly lower
incidence o raud across tens o mil-
lions o debit and credit card accounts.
The proo is in our raud numbers ourdetection rates and our alse positives
which continue to meet our aggres-
sive goals, said Derek Wylde, Head o
Group Fraud Risk, Global Security and
Fraud Risk or HSBC.
ONLINE
www.sas.com/ba-hsbc
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P8|Better Decisionswww.sas.com/baexchange
Relatively ew businesses and organiza-
tions have given ull and proper attention
to one o their most important activities
making decisions regarding key questions
such as what strategies and business
models to pursue, which products and
services to oer, which customers to
target, what prices to charge and what
employees to hire. Organizations with
poor decision processes and tools
eventually encounter poor outcomes
and perormance suers.
However, new analytics, decision auto-
mation tools and business intelligence
systems make it possible to make better
use o inormation in decisions. Wisdom
o crowds approaches and technologies
allow larger groups o people to partici-
pate meaningully in decision processes
Organizations cannot aord to ignore
these new options i they wish to make
the best possible decisions.
Given both negative and positive incen-
tives to get better, one might expect
that organizations would attempt to
improve their decisions that they
would prioritize them, examine thei
current level o eectiveness, investigate
new options or making them better and
implement some o those options. In
my survey and analysis o dozens o
corporations, I ound that while they are
indeed, doing some o these things
How organizations makebetter decisionsThe ollowing article is an edited excerpt o an article distributed by the
International Institute or Analytics.
By Thomas H. Davenport
Author and researcher Tom Davenport is thePresidents Distinguished Proessor at Babson College.
His newest book isAnalytics at Work: Smarter Decisions,Better Results (with Jeanne Harris and Robert Morison,
rom Harvard Business Press).
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very ew organizations have undertaken
systematic eorts to improve a variety o
decisions. In this excerpt I describe some
o the more requent approaches used to
intervene in decision processes.
Analytics, testing and data
Inrastructures predicated on analytics
and data were among the most
common decision-making rameworks
among the surveyed rms. Eighty-our
percent o respondents mentioned ananalytical component in their decision
improvement eorts and 66 percent
mentioned eorts to improve data.
The range o analytical techniques
employed was quite broad. Scoring
approaches based on statistical analyses
(usually some orm o regression analy-
sis) were common. Other approaches
included optimization, behavior-based
customer targeting, statistical orecasting,
prediction o various phenomena and the
use o text analytics.
Systematic testing was one orm o
analysis that was being used somewhat
requently by companies; 18 percent
mentioned it specically in interviews.
One key virtue is that it creates a
decision-oriented context rom the start.
I a test between two alternative Web
page designs is perormed, it is gen-
erally assumed that a decision to adopt
the winning page will be made. Other
analytical approaches may not have as
clear a path to a decision.
A prerequisite o virtually any orm o
analytics is high-quality data, so it is not
surprising that data-oriented responses
were also common. Sixty-six percento respondents mentioned some issue
involving data. The most common were:
Having difculty in accessing data.
Creating a common data architecture.
Eliminating duplicate data.
Integrating master data
management.
Achieving one version of the truthin unctional or process areas.
Dealing with too much data.
Gathering data from channel partners.
Creating new metrics.
In a survey and analysiso dozens o corporations,Davenport ound that veryew organizations haveundertaken systematiceorts to improve a varietyo decisions.
Not surprisingly, manyorganizations reportedthat they needed to changebusiness processes tomake better decisions.
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Technology support and overrides
or decisions
Several rms surveyed mentioned spe-
cic analytical sotware, testing sotware,
data warehouses and Web analytics/
reporting sotware. Two other tech-
nologies were mentioned requently:
specialized inormation display technolo-
gies and business rule engines.
Thirty-eight percent o companies in the
study mentioned some use o specialized
inormation displays such as scorecards
and dashboards. These tools, typically
ound in the business intelligence
category, allow decision makers to see
only the inormation that they need to
make a decision. Several rms mentioned
using specic display approaches not
generally supported by conventional BI
tools, including the A3 ormat or
displaying key issues in a particularbusiness domain. Some companies are
using neuroscience principles to guide
how inormation is presented and
digested. This may be a bellwether o
uture attempts to link inormation and
decision making.
Another popular decision technology
involves using business rules to enable
automated or semiautomated decision
processes sometimes in conjunction
with analytics (e.g., scoring-orientedapplications). Many organizations em-
ploy business rules but allow humans
to override the recommended decisions
when appropriate.
Changes in business processes
Not surprisingly, many organizations
reported that they needed to change
business processes to make better
decisions. Forty-three percent men-
tioned process changes o some type.
For instance, some described process
changes around supply chain manage-
ment in an IT rm, lease processing in
an auto nancing rm, nancial process-es in health insurance or new product
development processes. Several organi-
zations mentioned changes or decision-
oriented processes made in the context
o Six Sigma programs.
However, some decision-ocused ana-
lysts noted that their original goal wasnt
necessarily to identiy and implement
process changes, and that they had to
work with other groups to accomplish
them. As one head o an analyst groupat an IT rm commented, We didnt
initially have the ranchise to do process
improvement our thing was analytics.
But it kept coming up on our projects. So
we eventually just made it a part o our
standard approach.
Decision-oriented methods and tools
Several organizations reported that
one aspect o their decision processes
was an overarching, strategic manage-
ment approach to guide all aspects otheir eorts. Most o these initiatives are
well-known approaches to business
and management.
An insurance company adopted
enterprise risk management.
The Six Sigma approach to process
quality and decision outcomes was
implemented at a nancial payments
rm and a stang rm.
A nancial services rm uses the
net promoter score or customer
satisaction decisions.
An economic decision analysis
approach, popularized and taught
by Stanords Engineering School
and the Strategic Decisions Group,
is used by an oil company.
In addition, three responding organiza-
tions developed analytically ocused
decision processes that have been widely
used in IT systems development, but are
not widely known in the decision-makingor analytics literature. Sometimes called
agile methods or rapid prototyping,
they involve the creation o a series o
short-term deliverables, and requent
review o them by the client and stake-
holders or the decision. The organi-
zations that use this approach ound
that it led to results that better t the
decision-makers requirements, and a
a aster pace.
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Analysts previously responsible or datagathering and analysis are morphing intoconsultants who may be responsible or ramingdecisions, process redesign, communication andeducation programs, and change management all in addition to the traditional analysis unctions.
Conclusion
From my research, its clear that
organizations recognize the importance
o improving decisions. Although the
survey was not a random sample,
individuals in 90 percent o organiza-
tions surveyed identied some
attempt to improve decisions through
better processes. Second, organiza-
tions employ a variety o interventiontypes to improve decisions across
analytics, culture and leadership, and
data. The most successul organiza-
tions adopted multiple interventions
at once to improve a decision.
As a result, analysts previously
responsible or data gathering and
analysis are morphing into consultants
who may be responsible or raming deci-
sions, process redesign, communication
and education programs, and changemanagement all in addition to the
traditional analysis unctions.
Organizations seeking to implement
decision improvements should become
amiliar with these common intervention
types and create ongoing capabilities to
deliver them.
ONLINE
Order it now Analytics at Work: SmarterDecisions, Better Results
http://www.analyticsatworkbook.com/
Read the full International Institute forAnalytics researchwww.sas.com/ba-iia
Engage with analytic leadersand researcherswww.iianalytics.com
Analytics improvesdecisionsDavenports research ound the mostcommon types o decisions improved by
analytics include:
Pricing decisions (consumer goods,
industrial goods, government contracts,
maintenance contracts, etc.). Decisions to target consumer segments
(by retailers, insurers, credit card rms).
Merchandising decisions (brands
to buy, quantities and allocations).
Location decisions (for bank
branches or where to service industrial
equipment).
Treatment protocols for health care.
Product development for
pharmaceutical frms.
Student performance in educational
organizations.
Evaluating marketing approaches
(in both consumer and
B2B environments).
Hiring decisions.
Vehicle routing decisions.
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P12|Business Analytics in Actionwww.sas.com/baexchange
Business analytics in actionHow are key industries deriving value rom their business analytics implementations?
By Gail Bamord, David Wallace, Mike Newkirk and Becca Goren
HEALTH CARE
According to the World Health Organi-
zation, global health spending totalled
more than US$4.1 trillion in 2007, with
$639 as the total health expenditure
per person. That number will only grow
in ways that aect businesses and
citizens.
Despite these huge investments, healthcare quality is uneven and resistant to
changes and improvements. How can
we enhance health care delivery while
controlling those costs? It starts by
careully measuring and monitoring the
quality o that care a complex task
perectly suited or business analyt-
ics. Heres how some orward-thinking
health care institutions are delivering
better quality o care more eciently.
Maine Medical CenterNamed to US News and World Reports
Americas Best Hospitals list or
orthopedics, heart care and gynecologic
care, Maine Medical Center uses SAS
Business Analytics to understand key
patient care metrics and sustain a
quality-driven culture. The data-driven
approach has produced excellent results:
Increased compliance on medicatio
reconciliation by more than 50 percen
in a nine-month period.
Dramatically reduced the rate of hos
pital-acquired inections by measurin
where inections originated and wha
admission conditions closely corre
lated with acquired inections.
Improved government/industry ac
creditation/compliance by incorpo
rating national guidelines into ke
metrics.
Developed new methods for caring fo
stroke patients while controlling costs
By taking better care o these patients
the hospital expects ewer complica
tions, which will reduce costs.
Karolinska Institute
The Karolinska Institute in Swede
needed a way to examine the eect
o drugs, other treatments and liestyl
actors on patients with rheumatoid ar
thritis. Using SAS Business Analytics
the Institute has deployed a Web-based
patient sel-help application and predic
tive modeling to determine which treat
ments will be most eective or certai
segments o RA patients.
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BANKING
In a challenging economic and regula-
tory climate, bankers must be especially
vigilant. Two key indicators o a banks
health are net charge-os (NCOs) the
value o loans written o as uncollect-
able and nonperorming loans (NPLs)
that are in deault or delinquent more
than 90 days.
In the past two years in the US, bank
NCOs have soared by an average omore than 350 percent across all insti-
tutions, with institutions holding assets
o $5 billion or less showing growth o
almost 500 percent. NPLs as a percent-
age o average loan balances have risen
more than 278 percent at US banks with
$1 billion or more in assets.1 How can -
nancial institutions improve their collec-
tions and protect their bottom line?
Business analytics can provide the in-
sights that institutions need to reduceboth loan writeos and the cost o col-
lections activities. First, models created
within a business analytics ramework
can identiy likely candidates or work-
outs and loan modications. Second,
business analytics can optimize collec-
tions activities to improve the probability
o success and maximize sel-treatment
among debtor segments. It starts with
three basic steps.
Cleanse and integrate. Cleanse and
standardize third-party credit and
customer data, enrich it (e.g., add
geocoding tags) and integrate it into
a single data store.
Analyze and score. Develop scoring
models to analyze debtor-customer
segment data against objectives, in-
cluding maximize prots or minimize
writeos or against constraints, such
as loan types, outstanding balances ordays delinquent.
Optimize and execute treatment
strategies. Analytical models help
collections teams understand who is
most likely to respond, which commu-
nication channels work best and how
much payment to expect.
Collections optimization driven by
business analytics delivers the results
that institutions need to improve theirprotability.
Optimizing collectionsA leading Australian fnancial institutionpreviously relied on instinct when contact-
ing delinquent customers. Since introduc-ing SAS or collections optimization, it has
achieved a 300 percent ROI in less than sixmonths. A debt purchasing frm based inthe UK uses SAS to predict debt portolio
perormance. This enables the frm tomake quicker decisions on acquiring new
debt portolios at the right prices, collectmore rom each portolio and grow rev-
enues by 50 percent annually.
1 Source: SNL Financial
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MANUFACTURING
From diapers to jet engines and almost
everything in between, manuacturing
expertise is a competitive dierentiator or
companies that ollow optimal practices
and methodologies to attack ineiciencies
and eliminate waste. Business analytics
is essential in these settings to improve
production and sales planning, enhancethe supply chain, reduce inventory,
streamline logistics and much more.
For example, with demand orecasting,
business analytics can be a key
contributor to a manuacturers success.
Better orecasts deliver ROI by:
Reducing inventories.
Improving order fulfillment rates.
Shortening cash-to-cash cycles.
Many manuacturers struggle with
optimally managing and orecasting
their raw materials requirements, work-
in-process (WIP) inventory and inished
goods inventories. Without the right mix o
raw materials, production plans all apart
and customer orders are delayed (or,
worse, canceled). Missing WIP orecasts
similarly leads to ineicient schedules
and a crippling misallocation o inishedstocks not having the right quantities o
the right goods at the right time and in
the right places. While the data is oten
available to prevent, identiy and correct
these imbalances and ineiciencies, it
is usually not integrated, analyzed and
shared across the organization.
Data management technologies can
bring together islands o inormation
such as point-o-sale (POS) data and
historical shipment data. Once that data
is aggregated, business analytics models
and tools can accurately orecast the
demand or products by amily, individua
SKU, geography, customer type, etc.
With a clear and accurate demand
picture, manuacturers can properly
allocate raw materials across plants andregions all optimized by distribution
channel to create complete roll-ups in
master planning schedules.
TELECOMMUNICATIONS
Youve likely experienced it beore your
cell phone loses service one too many
times, so you switch providers. Low
barriers to churning mean providers must
vigilantly and careully invest to maintain
and increase their service quality and
customer satisaction rankings. Aterall, your satisaction keeps them in
business.
Network managers typically receive error
reports and alarms ater a network device
ails. The team addresses the stream o
trouble tickets, but never gets insight into
underlying causes or trends or outages.
The result: long call-resolution times.
With business analytics and approaches
such as predictive ault analysis, networkmanagers can analyze perormance
to pre-empt ailures. They can analyze
trouble tickets and optimize corrective
services, shortening times you are
without coverage.
Strong data management, including
data quality and reporting capabilities
all key underpinnings or business
analytics can help quickly identiy
Meaningul ROI withBusiness AnalyticsOne SAS customer increased company
proftability by accurately predicting prod-uct demand and customer behavior morethan doubling its orecasting accuracy. It
ound that or every 1 percent reduction inorecast variance, it saved $200,000.
Another manuacturer improved two
seemingly competing objectives. It simul-taneously reduced inventory by 20 percent,eliminating millions o dollars o holding
costs, yet improved service levels, whichdirectly and positively aected customer
satisaction.
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Business Analytics in Action|P15www.sas.com/baexchange
service and network issues. Business
analytics helps to:
Identify and remove duplicate trouble
tickets.
Understand faults and performance on
a macro level.
Determine which services have the
highest ault rates.
In addition to analyzing network
perormance, predictive analytics
technologies can help evaluate
demand, aults and systems to improve
resource utilization and quality o service
(QoS). A telco provider can then identiy
when and where network resources
are deployed and quality/perormance
variations over time.
Business analytics allows network and
service managers to better understand
causes and impacts o ailures. They can
prioritize and pre-empt outages, optimize
repairs and mitigate risk with answers to
key questions:
How significant is each factor
inluencing network aults degradation?
Which network faults are tied to a given
trouble ticket?
Which faults are related and what are
their impacts?
Armed with predictive ault analytics,
a telco provider can limit the times you
lose a signal and continually improve
overall service, allowing it to keep your
business.
Gail Bamord is a SAS Global Industry MarketingManager or Public Sector.
David Wallace is a SAS Global Industry Marketing
Manager or Financial [email protected]
Mike Newkirkis a SAS Global Industry MarketingManager or Manuacturing.
Becca Goren is a SAS Global IndustryMarketing Manager or Communications,Media and Entertainment.
One large telco service provider usedSAS to identiy emerging issues (an
average o two weeks prior to ailure)and double the percentage o tickets
resolved within 48 hours.
ONLINE
Get the ull stories on:Maine Medical Centerwww.sas.com/ba-maine
Karolinska Institutewww.sas.com/ba-karolinska
ONLINE
Health care providers keep pace with changewww.sas.com/ba-healthcareprovider
The standard or clinical data analysis and reportin
www.sas.com/ba-pharma
Solutions or better risk management
www.sas.com/ba-banking
Compete in manuacturingwww.sas.com/ba-mg
Invest wisely, communications service providerswww.sas.com/ba-telco
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P16|The New Knowwww.sas.com/baexchange
The Internet makes sel teaching and
lielong learning the rule rather than
the exception. Historians ultimately wilcome to consensus on what to call the
time period between the renzy that was
the dot-com bubble and the period be-
ore society nally enters the data cloud
For want o a better phrase, I call the 20-
year interregnum we currently inhabit
(1995 2015) the Age o Little Inorma-
tion. I come to this label not because the
age exhibits a lack o inormation. Quite
the contrary, it is during this epoch that
inormation previously locked away
in analog orm is becoming widelydigitized. The New Know has changed
our reality along 10 undamental dimen-
sions.
New Know Reality #1:
You will be expected to do
something with inormation.
All this newly digitized inormation has
had, relatively speaking, little impact on
behavior and little impact on organiza-
tional outcomes. We are now exiting ahistorical moment o undermanaged and
only occasionally acted-upon inorma-
tion to an environment requiring much
more active, much more intense, much
more aggressive inormation manage-
ment. You as an executive will be held
much more accountable or your data
management behaviors. You will be
expected to transorm data lead into
knowledge gold via the expeditious
The art, act and science o knowingAn excerpt rom The New Know 1
By Thornton May
1Copyright 2009 by John Wiley & Sons, Inc.
All rights reserved. Reprinted with permission.
Futurist Thornton May positions analysts as heroeso the age we are about to enter in his new book,
The New Know: Innovation Powered by Analytics.
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sensemaking leading to ecacious ac-
tion. In the Age o Little Inormation, we
were data vegetarians. In the New Knowwe will have to become inormation and
knowledge carnivores.
New Know Reality #2:
Therereallyis more to know.
The New Know will be awash with
data. Processing power doubles every
18 months. Storage capacity doubles
every 12 months. Bandwidth through-
put doubles every nine months. There
is more to know. Organizations arehaving trouble keeping up and, sadly,
the act that there are more acts arriving
at a aster rate o speed is not even the
tip o the cognitive iceberg. Like the og
o war, ino warriors speak o the og
o acts (e.g., conusion about what
inormation is to be believed, what inor-
mation sources are credible and what
version o reality is to be acted on). In a
world o multiple sources o inormation
and 24-hour decision making, the very
character o inormation is changing.A act is no longer a act.
The New Know|P17www.sas.com/baexchange
New Know Reality #3:
You will have to know more
about knowing.
One o the major changes dening the
new competitive environment is the
requirement to know more about know-
ing, what experts sometimes reer to as
metacognition. Society is about to
undergo a tectonic shit in how it thinks
about thinking. Driving this cognitive
plate shiting are the RSS eeds, pod-
casts, blogs, old-media headlines and
evening news programs, which are
increasingly lled with images andinstances o current-generation leaders
being asked by dissatised next-
generation voters, customers and
shareholders: What were you
thinking? Looking beneath the surace,
they are really asking: How were you
thinking? Via what processes, using
what data and assisted by what tools did
you arrive at your course o action?
New Know Reality #4:
Brain science and decisionscience are converging.
Scientists do not know how the brain
works yet. But they are sneaking up
on it. Readers may be surprised to learn
that neuroscience has been around
or over 100 years. Neuroscience has
progressed to the point that we at least
know what we do not know.
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P18|The New Knowwww.sas.com/baexchange
To some extent, it is a simple truism
that the brain is involved with all things
that comprise our human existence.
It ollows, loosely, thereore, that
understanding the brain will help us
understand the human condition more
ully. The big news is that the brain pos-
sesses innate qualities that infuence
individual experience and opinions.There are things that can be known
that need to be known by executives
seeking to maximize value rom the
knowledge assets available to the
enterprise.
New Know Reality #5:
The environment is changing
our brain.
The inormation food should be viewed
as a permanent macroenvironmentalchange. Thinking in Darwinian terms,
what adaptive pressures does this
environmental change place on us?
Daily exposure to high technology
computers, smart phones, video games,
search engines stimulates brain cell
alteration and neurotransmitter release,
gradually strengthening new neural
pathways in our brains while weaken-
ing old ones. Because o the current
technological revolution, our brains are
evolving right now at a speed likenever beore.
New Know Reality #6:
Inormation management Is the
essence o leadership.
Low-cost communications give rise to
almost toxic levels o spin, hype and
empty rhetoric. Leaders are able to
cut through all the noise. Does your
organization lter its data? Carly Fiorinaormer CEO at Hewlett-Packard
believes that distilling truth rom over-
whelming amounts o inormation is the
essence o leadership. She believes that
all o us are overwhelmed with inorma-
tion, and what sets great leaders apart
is their ability to cut through the clutte
and distinguish the truly important rom
the merely interesting.
New Know Reality #7:
A more connected world.
One o the transormational elements
moving society to the New Know is
something analysts at Forrester Re-
search call the groundswell. Josh
Berno, Vice President at Forrester
contends: Theres so much inormation
fowing out o the groundswell, its like
watching a thousand television chan-
nels at once. To make sense o it, you
need to apply some technology, boil-
ing down the chatter to a manageablestream o insights. The new scarce re-
source in the next economy will be the
human attention needed to make sense
o inormation. The question is: How wil
we be able to keep up?
Carly Fiorina, ormerCEO at Hewlett-Packard,
believes that distillingtruth rom overwhelming
amounts o inormation isthe essence o leadership.
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The New Know|P19www.sas.com/baexchange
New Know Reality #8:
Math matters.
Mathematics is now so widely accept-
ed as the arbiter o truth in the modern
world that it has become the backbone
o disciplines ranging rom physics (o
course) to economics and sociology.
Backing up a statement with mathemat-ics gives it an aura o validity, even i the
topic has to do with something as math-
ematically messy as human behavior.
However, many otherwise normal ex-
ecutives have a pathological aversion
to math. This is not just unortunate, it
is dysunctional. Some intuition about
numbers, counting and mathematical
ability is basic to almost all animals.
People use math to make decisions
every day. In an age where you needto be numerate to do almost anything
(rom building bridges to conquering
disease), governments anxiously com-
pare their perormance in mathematics
with that o competitor nations.
New Know Reality #9:
There are signifcant downsides to
not knowing.
Success requires materially expanding
what you know and adding precisionand eciency to the processes (analyt-
ics) whereby you come to know. Here
is a metaphor to keep in mind as you
think about the New Know. I you are
locked in a room with an elephant, it is
useul to know where it will step. Every
key process in your enterprise is locked
in a room with an elephant a critical
process, serving a critical customer.
Business analytics tells you where that
elephant will step.
New Know Reality #10:
Knowing can change the world.
I knowledge is power, then knowl-
edge about power should be especially
empowering, says John Murrell, the
very-much-in-the-know editor o Good
Morning Silicon Valley. For instance,
using 15,000 meters, a subset o Na-
tional Grid Customers will be able to
access their energy use inormation
via the Internet, by a thermostat read-
out, or through text messaging, and use
the data to change their consumptionpatterns. Program participants are ex-
pected to save 5 percent, or about $70
a year, on their energy bills. Change ad-
vocates rom all elds o endeavor are
excited about the possibility o putting
new inormation in ront o people in the
hopes o changing behavior.
I you are locked in a
room with an elephant, itis useul to know where
it will step. Every keyprocess in your enterprise
is locked in a room withan elephant a critica
process, serving a criticacustomer. Business
analytics tells you where
that elephant will step
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P20|Business Analytics or SMBswww.sas.com/baexchange
When it comes to business analytics,
it sometimes seems like only major en-
terprises garner the spotlight. Thatssomewhat understandable given the
complexity and scope o their analytical
challenges and the nature o their high-
prole brands. But the act is, ar more
small to medium businesses (SMBs) are
poised to implement business analytics
solutions.
In the US, these companies have
revenues o less than $500 million. In
Europe, the SMB category comprises
companies with a maximum o EUR 450million (about US$611 million). While in
the Asia Pacic region, SMB oten reers
to both employee numbers and revenue,
and range between 200 and 250
employees and $200 million and $500
million in revenue. In many ways, these
businesses are striving or the same
goals to grow their business through
innovation, and need the same sophisti-
cated unctionality scaled appropriately
to their processes. In this Q&A,
Matthew Mikell, SAS Global ProductMarketing Manager, shares his perspec-
tives on what business analytics means
to SMBs.
Q.What are some o the unique
challenges that SMBs ace with
respect to business analytics?
A: SMBs primarily ace the issue o
scale. At SAS we have heard our
general constraints when listening to
organizations that are SMBs:
1) Decision-making style
Transitioning rom gut instinct to act-
based ramework can be dicult in part
because the ormer approach has likely
served the successul SMB very well
Most SMBs have Excel experts who
can generate some great static charts
and graphs and I wouldnt ever want
to denigrate the value those reports
provide. But theres so much more val-
ue that can be derived rom in-depth
analyses. Once SMB executives geta real glimpse o the insights that are
lurking beneath the surace o thei
transaction data, their willingness
to adopt business analytics increases
pretty quickly.
2) Cash fow
In addition to a shit in decision-mak-
ing style, cash constraints can pose
very real obstacles or an SMB that
wants to mature in this area. Consid-
ering the business analytics rame-
work helps improve margins, retain key
customers and grow share o wallet in thei
markets. However, the long-lasting
return on investment ar outweighs the
capital required to undergo the transition.
What business analytics means orsmall and medium businesses
An interview with Matthew Mikell, SMB Global Product Marketing Manager
The Wine House discovers$400,000 in lost inventoryEconomic times may be tough, but Bill
Knight, owner and President o The WineHouse, is toasting a 100 percent return on
his investment in SAS. The frst day its SASapplication was live, the brick-and-mortar
and Internet retailer discovered 1,000items o wine that hadnt moved in more
than a year.
We had a huge sale to blow it out, gener-
ating $400,000 in capital in one weekend,Knight said, and just in time, because in
todays economy, wed be choking on thatinventory.
Using SAS, The Wine House has reduced
its aged inventory by 40 percent. Now I
can get the answers I need and base de-cisions on acts rather than gut intuition,says Knight. Ive got less money tied up
in inventory, I know who our best custom-ers are, how to market to them and can
monitor the eectiveness o our marketing.Our ROI with SAS has been well over 100
percent in less than a year, so my return oninvestment has been antastic.
ONLINE
www.sas.com/ba-winehouse
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Business Analytics or SMBs|P21www.sas.com/baexchange
3) IT resources and inrastructure
More than 80 percent o SMBs with
about 100 employees have only ourdedicated IT staers. Theyre stretched
thin, and that can make it very dicult
to expand the IT mandate beyond criti-
cal business operations into managing
business analytics environments.
4) Business analytics maturity
SMBs must have an appreciation or
the level o skills required to meet over-
all strategic goals through business
analytics. Research rom Aberdeen
Group suggests that SMBs without the
relevant skill sets are poorly positioned
to drive value rom an analytical solu-
tion. It reports that SMBs using some
sort o analytical applications perorm
at a higher level than their competitors
that do not.1
The main SMB challenge or moving to
business analytics is the understanding
o its impact on these our critical areas,
and building a capability that is cost-
eective and remains fexible and easy
to use.
Q.Why should SMBs adopt
business analytics?
A: It essentially boils down to competi-
tive pressures. SMBs need to continu-
ally innovate. I youre an SMB that isnt
constantly seeking to optimize every
possible aspect o the operation, youre
at a disadvantage. Internally, employees
need these tools to be productive. Oth-
erwise, its gut-based decisions, or cut-ting and pasting rom multiple tools.
The truth is what brought you to where
you are typically wont take you to the
next level. But its very dicult, cultur-
ally, to walk away rom whats made
you successul. SMB executives oten
owners or people with lengthy tenures
worry about letting go o the inormation
fow and empowering people to make
decisions that were previously reserved
or executives.
QWhats the best way or SMBs
to tackle the adoption o business
analytics?
A: O course, every company diers
particularly at the SMB size. But weve
ound that there is a general approach to
the adoption o business analytics. The
rst step is to ensure you have sponsor-
ship rom company executives. Clearly
lay out the business analytics benets
and return to the management team.
This transparency is key at the SMB
level as SMB executives are tradition-
ally heavily involved in analyses, report-
ing and the decision-making process.
Make it clear how business analytics will
resolve a compelling issue or attract and
retain customers, or example.
1 Aberdeen Group, 2009, Beyond Spreadsheets:
The Value of BI and Analytics.
SMB executives otenowners or people with
lengthy tenures worryabout letting go o theinormation fow and
empowering people tomake decisions that
were previously reservedor executives
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The second strategy is to ocus on a
particular business process or issue.
Dont introduce business analytics asa broad, unocused utility or general
usage. This will occur naturally as you
solve more ocused issues, building up
condence in act-based decision mak-
ing as a core competency. Some o the
typical issues that we see being solved
with business analytics include improv-
ing customer data quality or improved
marketing, invoicing or customer ser-
vice, or improved product pricing and
packaging analysis to drive a higher
market share.
Finally, dont rest on your laurels. Capi-
talize on your initial success to broaden
deployment to other areas o the orga-
nization. Those adoptions move aster
once you can point to a successul track
record in another area.
Q.Whats the dierence between
business analytics or large enter-
prises vs. SMBs?
A: In a nutshell, its about scale. Deploy-
ment and support strategies will have a
dierent nature. Whats more interesting
to me, however, is the important com-
monality: unctionality. Business analyt-
ics in SMBs is not about presenting a
subset o unctionality but rather surac-
ing the right unctionality or the problem
at hand, and opening up to more as the
business requires it. Despite their size,
SMBs ace similar challenges to makebetter and more inormed decisions to
continue innovating in their markets. It is
thereore essential to provide a rich set
o eatures and a very high level o tech-
nology usability.
Matthew Mikell leads Global Product Market-
ing or SMB markets and sotware-as-a-service
(SaaS) offerings at SAS, supporting strategic
planning, messaging and product oerings
through direct and indirect channels.
P22|Business Analytics or SMBswww.sas.com/baexchange
Some o the typical issuesthat we see being solvedwith business analyticsinclude improving
customer data qualityor improved marketing,invoicing or customerservice, or improvedproduct pricing and pack-aging analysis to drive ahigher market share.
Q.Can you share some examples
o how SMBs have been able to
capitalize on business analytics?
A: Sure. Weve worked with an energy-
trading company that enables sta to
predict what todays electricity and
gas purchases will sell or months later
when consumers buy. Business analytics
supplies that intelligence to traders in a
cleaner, aster and more accurate way.
A collection agency uses SAS Business
Analytics to analyze bad-debt portolios
beore acquiring those assets. This is aquantum leap orward rom its previous
model, which was simply buying any
debt assets or as little as possible and
hoping to collect successully.
A player in the secondary-ticket marke
uses SAS to develop a deeper under-
standing o the needs o its thousands
o customers. By segmenting them and
catering to psychographics, the company
can optimize how requently it contacts
the customers and improves loyalty.
ONLINE
Sotware or SMBswww.sas.com/ba-smb
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Analytics at Work|P23www.sas.com/baexchange
We see examples o analytics at workwithin core processes in a variety o
business areas. Statistical analysis has
been a eature o supply chain and lo-
gistics management or decades, start-
ing with the techniques o statistical
process control (SPC) and total quality
management (TQM).
Real-time analytics are helping guide
call center workers in their interactions
with customers. And analytics are well
established in the engineering and sim-ulation sides o product design.
Among business support unctions,
analytics are essential to many acets
o nance, common in the management
o technology operations, and rela-
tively new to human resources (though
o enormous potential there). In cor-
porate development, key decisions
or example, regarding mergers and
acquisitionsmay benet greatly rom
analytics, but ew companies take aprocess approach to such activities.
Consider the example o UPS to whet
your appetite or embedding analytics
in your core business processes. As a
logistics company, UPS lives and
breathes the traveling salesman
problemhow to reach a variable
series o destinations most eciently
with the right delivery capacity, and oten
in designated time windows, every day.
The solutions naturally demand verysophisticated and industrialized ana-
lytics: or capacity planning o aircrat
and truck feets, or routing packages
through its distribution network, and o
scheduling and routing delivery trucks
For a company this steeped in analyti-
cal applications, the rontier is moving
closer to real-time, dynamic adjustments
For example, UPS is experimenting with
algorithms to adjust the order o deliv-
eries as conditions (e.g., road closures
extraordinary customer need) change.
Making processes analytical
The eects o analytics on the opera-
tions o a process can be proound
and over time you may want to reengi-
neer the overall business process and
revamp its inormation systems to
capitalize on the potential or analyt-
ics-based improvement. But you can
start embedding analytics without a
major overhaul. For processes that rely
extensively on enterprise systems, itmay be possible to simply start taking
advantage o the analytical capabilities
that are already included in the sot-
ware. However, many process analytics
initiatives will require tools, techniques
and working relationships that are likely
to be new and unamiliar at rst. We
have ound that implementing analytics-
enabled processes requires applying
our major perspectives.
Embedding analytics into processesIn their latest book,Analytics at Work: Smarter Decisions, Better Results,Thomas Davenport, Jeanne Harris and Robert Morison show how companies
apply analytics in their daily operations. This excerpt, Embedded Analytics in Action,
explores what to consider when inusing analytics into business processes.
Reprinted by permission of Harvard Business Press. Excerpted
fromAnalytics at Work: Smarter Decisions, Better Resultsby
Thomas Davenport, Jeanne Harris and Robert Morison.
All rights reserved.
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P24|Analytics at Workwww.sas.com/baexchange
The rst is process implementation.
Occasionally a business may create
a new analytically enabled process
or rebuild a process rom scratch, but
most oten you are adding capability to
and altering an existing process. Espe-
cially given the iterative nature o many
analytical applications, its essential to
measure baseline process perormance
rst and to run the enhanced process
in parallel to the original (perhaps as a
pilot or test) in order to rene the newprocess and measure its perormance
and value. In some cases, process
simulation can yield insights about how
the process might perorm even beore
implementation.
Next, organizations should consider
model implementation. Much o the
distinctive work o process analytics
centers on designing, developing and
iteratively rening statistical algorithms
and descriptive or predictive modelsor rule-based systems. I you are go-
ing to industrialize important decision
processes, it is important that the rules,
assumptions and algorithms in your
model are correct. Analytical projects
generally require dierent tools and
development methodologies rom
those employed in more traditional sys-
tems development. And, o course, this
work is perormed by business analysts
and programmers with special skills in
statistical methods and modeling.
Third is systems implementation. The
analytical system must be incorporated
into the set o systems and technolo-
gies supporting the business process
In building these interaces, it helps to
employ process-oriented technologies
including capabilities o ERP systems
workfow and document management
systems. And integrating and testing
the new systems and interaces is criti-
cal given analytics reliance on a broad
range o quality data and the act thatanalytics-based decisions may dramat-
ically change process fow.
Human implementation is the ourth
perspective. Oten the greatest imple-
mentation challenge, especially when
analytics is new to the process and the
people perorming it, is on the human
side. Only people can tell i an embed-
ded application is resulting in good
decisions, so be sure to involve them in
developing, managing and monitor-ing the assumptions and results o any
embedded model. Another important
actor is developing the right mix o
automated and human decision making
and enabling process perormers to
trust and use their new analytical inor-
mation and sometimes tools.
The eects o analyticson the operations o aprocess can be proound,and over time you maywant to reengineer theoverall business process... but you can startembedding analyticswithout a major overhaul.
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All our perspectives must mesh: pro-
cess fow and decisions are enabled or
controlled by analytical models, other
inormation systems interace with the
models and provide clean data eeds,
and people perorm the process better
with the help o embedded analytics. I
you lack clear business goals, speci-
cations or momentum, be prepared to
demo or pilot the concept, to work with
stakeholders to dene targets and set
ambitions, and to make the businesscase or investing in prerequisite assets,
oten starting with data.
ITs role in embedding analytics into
business processes
Technology is an integral part o most
business processes today. So the best
route to embedding analytics into pro-
cesses is oten through the technolo-
gies and applications that employees
routinely use to do their jobs. Embed-
ding analytics into processes starts
with a robust analytical architecture that
provides an accurate, timely, standard-
ized, integrated, secure and reliable
inormation management environment.
Scorecards and applications that moni-
tor and alert based on predetermined
thresholds are the norm these days,
but too many remain as standalone
applications. An industrial-strength
IT architecture makes it vastly easier
to weave analytics into ongoing work
processes in three ways:
Embedding analytics into processes starts witha robust analytical architecture that provides anaccurate, timely, standardized, integrated, secureand reliable inormation management environment.
SAS and Accenture:Making business analyticswork or youSAS and Accenture have joined theorces o their best and brightest to help
more organizations reap the benefts oan analytic approach. The new Accenture
SAS Analytics Group combines Accen-tures domain and industry experience
with SAS analytic strengths to providethe services (best business practices,
proof of concepts), technology (both
industry and cross-industry oerings)and support (competency centers, cer-
tifcation programs) to help companiesreach their competitive potential more
efciently and cost-eectively.
1. Automated decision applications
These sense online data or conditions
apply codied knowledge or logic, and
make decisions all with minima
human intervention. Technology is bes
suited to automate decisions that mus
be made requently and rapidly, using
any kind o inormation (data, text
images) that is available electronically
The knowledge and decision criteria
used in these systems need to be highly
structured.
The actors that must be taken into
account (the business problems
dimensions, conditions and decision
actors) must be clearly understood and
not subject to rapid obsolescence. The
conditions are ripe or automating the
decision when experts can readily
codiy the decision rules, a production
system automates the surrounding
process and high-quality data exists in
electronic orm. Business activities thatbenet rom automated decision-
making applications include raud
detection, solution conguration, yield
optimization, recommendation/real-
time oers, dynamic orecasting and
operational control (like monitoring and
adjusting temperature).
ONLINE
Accenture SAS Analytics Group
www.sas.com//ba-partner
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P26|Analytics at Workwww.sas.com/baexchange
2. Business applications or opera-tional and tactical decision making.
Analytical managers rely on analytical
applications (whether custom devel-
oped or rom third parties) that are
integrated directly into Web applica-
tions or enterprise systems or tasks
such as supply chain optimization,
sales orecasting and advertising
eectiveness/planning. Recom-
mendation, planning and what-i
applications can incorporate near
real-time inormation and multiplemodels to dynamically optimize a
solution while actoring in conficting
goals like protability and customer
satisaction. Analytical business
applications are best suited to well-
dened, periodic tasks in which most
o the inormation needed is predict-
able and available electronically.
Since the data, knowledge and deci-
sion criteria are typically less dened
and/or more fuid than those o a ully
automated application, they requireindustry and unctional expertise.
3. Inormation workfow, project
management, collaboration and
personal productivity tools. Most
inormation work is done through
personal productivity tools like
Microsot Oce. As vendors in-
crease the analytical quotient o their
collaboration and productivity tools,
analytics become more accessible
to analytical amateurs throughout
the enterprise. One consumer prod-ucts company ound that its elaborate
modeling tool was ignored by nearly
everyone until the ndings were distilled
into a monthly deck o ten PowerPoin
slides and e-mailed directly to the sales
orce. As platorm vendors align thei
products to work together more seam-
lessly, a manager neednt know that his
Excel spreadsheet is using the companys
ERP system to prepare his orecast
These tools and applications work best
or less structured inormation with lessdened decision criteria.
To address the growing need to embed
analytics into processes, both specialty
applications vendors and the majo
platorm vendors are building more
analytical unctionality directly into thei
tools and applications.
Sotware companies are building
more industry-specic, process-driven
applications. Major platorm providerslike Oracle are embedding analytics
into their products by building statistica
unctions directly into their enterprise
data warehouse products. ERP vendors
which are including more sophisticated
analytical eatures, remain a poweru
way to integrate industry best practices
into business processes. And Microsot
Oracle, SAP and SAS continue to quiet-
ly embed more sophisticated analytics
and business intelligence capabilities
into their applications and tools.
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8 essentials o business analyticsFind out what business analytics can do or you
and how to get started
By Jim Davis
Leading banks use business analytics
to predict and prevent credit raud,
saving millions. Retailers use business
analytics to predict the best location or
stores and how to stock them. Pharma-
ceutical rms use it to get lie-saving
drugs to market more quickly. Even
sports teams are getting in on the action
using business analytics to determine
both game strategy and optimal ticket
prices.
But these advanced business applica-
tions tell only part o the story. Whats
going on inside these market-leading
companies that sets them apart?
They have committed to deploying their
people, technologies and business
processes in new ways. They have
committed to a culture that is based on
act-based decisions which helps
them anticipate and solve complex
business problems throughout the
organization. By embracing an analytica
approach, these companies identiy
their most protable customers
accelerate product innovation, optimize
supply chains and pricing, and identiy
the true drivers o nancial perormance.
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And you can too. Get started with
business analytics by taking these eight
essential actions:
1. Improve the fow and fexibility
o data.
High-quality data must be integrated and
accessible across your organization. It
should also be structured in a fexible
way that allows your analysts to discov-
er new insights and provide leaders the
inormation they need to adjust strate-
gies quickly. Strengthening and fexing
the data backbone o your enterprise
will pay o when you need to change
business processes quickly in response
to market shits, regulatory or stake-
holder demands.
2. Get the right technology in place.
Take an enterprise approach to data
management and analytics to eect
better decisions. Remove disconnected
silos o data, technology or expertise.
Your technology portolio should
include:
Optimized data stores to support
core business processes and
discovery.
Data integration and data quality
sotware.
Analytical sotware with the meansto eectively deploy, explore and
share results in a meaningul way.
Integrated analytical applications
designed to solve dened issues
quickly.
When selecting technologies, consider
risk-to-value: Can the technology
be applied to help reduce costs and
increase revenue? And getting the right
technology in place doesnt have to
mean a complete overhaul.
3. Develop the talent you need.
Develop or recruit analytic thinkers
who seek and explore the right data
to make discoveries. To make analyticswork, analysts must also be able to
communicate eectively with leaders
and link analytics to key decisions and
the bottom line.
4. Demand act-based decisions.
An analytical company makes a wide
range o decisions. Some are ad hoc;
some are automated; some are trans-
ormative. The common thread? Evidence
backs them all. Managers encourage
asking the right questions o the data toget maximum insight. How results are
deployed is also important through
operation systems such as customer
relationship management applica-
tions or real-time raud applications
to interactive dashboards, data movies,
in databases wherever needed to
ensure decision makers have the
inormation they need when they
need it (and in the way they can best
consume it).
5. Keep the process transparent.
Transparency implies openness,
communication and accountability; it
is key to successul business analytics
projects. The value delivered rom an
By embracing ananalytical approach, these
companies identiy theirmost protable customers
accelerate productinnovation, optimize supply
chains and pricing, andidentiy the true drivers o
nancial perormance
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investment in business analytics mustbe visible and measureable. Who the
analysts are and what theyre seeking
to accomplish should be clearly com-
municated to the business, as should
their ndings.
6. Develop an analytical center
o excellence.
Create a centralized team approach
an analytical center o excellence (ACE)
which promotes the use o analytics
and associated best practices. Yourimplementation o an ACE will depend
on your organizations maturity and
requirements, but the most eective
implementations address all elements
o the organizations analytic inra-
structure: people, process, technology
and culture to support the business
strategy and operations.
7. Transorm the culture.
A strong analytical culture has executive
sponsorship and encourages creativity.Experimentation should be seen as part
o learning, and employees should be
given permission to ail as they learn
rom trying new things.
8. Revise your strategies oten.Your competitors will oten duplicate
your analytical initiatives. Staying ahead
requires continuous review o strategy
and development o new skills and
capabilities.
Get started now.
Find important questions that need
answering and problems that need to
be solved. Answer these questions,
solve these problems and create val-
ue or the organization. By creatingsmall wins in any business, unction or
department, over time your company
will become an analytical competitor.
Top fve benefts obusiness analyticsWhen Computerworld asked 215 IT andbusiness proessionals to name the key
benefts o business analytics sotware,they received a wide range o responses.
The fve most popular were:
1. Improving the decision-making
process.
2. Speeding up the decision-making
process.
3. Better alignment o resources with
strategies.
4. Realizing cost efciencies.
5. Responding to user needs oravailability o data on a timely basis.
ONLINE
Defning business analytics white paper:
www.sas.com/ba-defningba
Jim Davis is Senior Vice President and
Chie Marketing Ofcer or SAS.
ONLINE
www.sas.com/ba-benefts
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In the abstract, business analytics
presents a range o powerul options
to uncover meaningul insights that
promote action. And that promise is
compelling to virtually any organiza-
tion. But the case becomes even more
persuasive when we consider how
it can be applied to one o the ast-
est-emerging issues in corporations
today: sustainability and the corporateenvironmental ootprint. Today
companies are seeking to strength-
en the so-called triple bottom line
that conceptually expands the
traditional nancial ramework to
encompass rigorous reporting on the
organizations perormance on sus-
tainability issues such as the carbon
ootprint, community development
occupational saety and dozens o
other metrics.
The art o the possible: business analytics
to measure corporate sustainabilityBy Alyssa Farrell
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the ROI o sustainability eorts. Whatsmore, tangible and intangible costs and
benets abound in the sustainability
discipline but they can be especially
challenging to orecast because the
goals or greenhouse gas emissions
reductions established by govern-
ments are oten in 10- and 20-year time
horizons, ar exceeding the typical one-
to three-year payback period.
Traditional reporting and analysis can
oten all short when attempting to pre-dict uture impacts o sustainability in-
vestments. Business analytics plays a
critical role by enabling the organization
to balance todays ROI objectives with
longer planning horizons.
These challenges are not uncom-
mon or emerging business issues.
Sustainability is a new discipline or most
organizations, one where there isnt
a generation o tested and proven
models to call upon and modiy. As aresult, many organizations orego the
eort to model the intangible ben-
ets that may result rom sustainable
practices. Or, they minimize important
externalities such as environmen-
tal or societal costs and benets all
o which can become tangible with
business analytics.
Three planning challengesUnortunately, signicant barriers have
impeded decisive corporate action. In
the rst MITSloan Management Review
Business o Sustainability Survey,
researchers articulated three major
roadblocks. The rst is a basic lack o
inormation upon which to base
sustainability eorts and decisions.
Despite the high prole or sustainabili-
ty, managers oten nd themselves
orced to speculate about drivers o
sustainable perormance and lack adeep understanding o issues that are
relevant or their industry. Accessing,
interacting with and analyzing the
undamental data about energy, water
and waste is a nonnegotiable premise
or eective sustainability.
Second, companies oten have
conficting denitions o precisely what
sustainability means to their
organizations. This makes it extremely
challenging to develop a meaningulbusiness case or sustainable invest-
ments and presents an oten
insurmountable barrier to the eective
cross-unctional collaboration that is
necessary or success.
Third, without that business case based
on accepted denitions, companies
struggle with precisely how to measure
In a report rom the Economist Intelligence Unit,
researchers report that the top three motivations orsustainability initiatives are brand enhancement,revenue growth and cost savings in other words,outcomes that have a direct impact on protability.Environmental protection only placed ourth on the list,amply demonstrating that pragmatism and not altruismis the dominant motivator.
Business analytics atwork: gaining energyefciency at PosteItaliane Group
The art o the possible is already inpractice at leading organizations today.The Poste Italiane Group uses sotware
rom SAS to analyze energy efciencyin more than 250 acilities, including
those with the highest energy con-sumption such as data processing
centers, executive centers and thelargest branches. Their analysis hasidentifed best practices that led to an
immediate reduction in energyconsumption and a 7 percent reduction
in CO2 emissions. Future developmentsinvolve correcting operation and
maintenance behaviors or the systemsand indirectly or the buildings.
ONLINE
www.sas.com/ba-poste
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The ROI matters
Despite these challenges, creating the
strongest possible business case is
an essential mandate or todays sus-
tainability directors. Thats because
although ew observers ail to see the
importance o eorts to reduce carbon
output and minimize environmental im-
pact, these benets are highly unlikely
to achieve primacy in prot-driven en-
terprises. In a report rom the Econo-
mist Intelligence Unit, researchers re-
port that the top three motivations or
sustainability initiatives are brand en-
hancement, revenue growth and cost
savings in other words, outcomes that
have a direct impact on protability.
Environmental protection only placed
ourth on the list, amply demonstrating
that pragmatism and not altruism is the
dominant motivator.
However, while the pro orma income
statement in the analysis is paramount,
the attention organizations are paying
to sustainability matters is denitely
not merely pro orma. The actions,
when implemented, are ar-reaching
and transormational. For example, GE
announced that its Ecoimagination
program to reduce environmental
impact generated a $17 billion revenue
stream and reduced costs by more
than $100 million since 2005. And theUS Army reports that 80 percent o
its construction meets Leadership in
Energy and Environmental Design
(LEED) standards, reducing its energy
costs by 8 percent.
Delivering green analytics
Transormational organizations require
a combination o descriptive and
predictive insight the ability to track
meaningul green indicators, validate
strategies and costs beore investing
identiy causal relationships and ore-
cast outcomes. And in these areas
business analytics can make the dier-
ence. Such a business analytics rame-
work can empower the organization to:
Measure sustainability activities
using accepted methodologies and
protocols.
Report on environmental perfor-
mance to shareholders and regulators
Improve sustainability metrics using
analytical techniques such as optimi-
zation, orecasting and data mining to
deliver metrics that matter.
Reduce resource usage by accurate-
ly orecasting resource requirements
needed to reach desired outcomes
or a department or enterprise.
SAS andcorporate sustainability
Sustainability has remained a top
priority with SAS precisely becauseo its potential to deliver tremendousbusiness value. Its not just the right
thing to do; its the smart thing to do.
In addition to employee engagementpractices, rom health care to expanded
job opportunities, SAS has made greatprogress in reducing its environmental
ootprint. For example, a 1-megawattsolar array is providing clean, renewable
energy to the public energy grid or thelocal utility.
Several construction projects atSAS ofces around the world utilize
low-environmental-impact design
principles. Notably, SAS is pursuingLeadership in Energy and Environ-
mental Design (LEED) certication for
a new conerence acility and a new
cloud computing acility located at its
global headquarters.
ONLINE
For more information on SAS
and sustainability, check out the
Corporate Social Responsibility Report:
www.sas.com/ba-csr
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With business analytics, we start to see
the art o the possible with respect
to sustainability. You can measure
emissions and resource consumption
throughout a value chain or product
lie cycle. You can ensure regulatory
compliance. And you can build green
strategies with predicted ROI. You
can determine which conservation
eorts or greenhouse-gas reduction
strategies will have the greatest impact
physically and nancially. And you
can