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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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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Face Forward with Business Analytics|P5www.sas.com/baexchange

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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Face Forward with Business Analytics|P7www.sas.com/baexchange

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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Better Decisions|P9www.sas.com/baexchange

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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P10|Better Decisionswww.sas.com/baexchange

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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Better Decisions|P11www.sas.com/baexchange

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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Business Analytics in Action|P13www.sas.com/baexchange

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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P14|Business Analytics in Actionwww.sas.com/baexchange

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.

[email protected]

David Wallace is a SAS Global Industry Marketing

Manager or Financial [email protected]

Mike Newkirkis a SAS Global Industry MarketingManager or Manuacturing.

[email protected]

Becca Goren is a SAS Global IndustryMarketing Manager or Communications,Media and Entertainment.

[email protected]

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


18/36

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.


19/36

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.


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


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?


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.


20/36

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.


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.


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.


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.


21/36

The New Know|P19www.sas.com/baexchange


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.


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.


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


22/36

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


23/36

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


24/36

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


25/36

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.


26/36

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.


27/36

Analytics at Work|P25www.sas.com/baexchange

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


28/36

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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8 Essentials|P29www.sas.com/baexchange

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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P30|Art o the Possiblewww.sas.com/baexchange

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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Art o the Possible|P31www.sas.com/baexchange

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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P32|Art o the Possiblewww.sas.com/baexchange

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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Art o the Possible|P33www.sas.com/baexchange

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

Documents

BusinessAnalytics Insights