Enabling the confident enterprise
with business analytics
Informed decision making • Business analytics for industries and SMBs • Analytics applied to processes • Essentials to get started
The impact of business analytics on performance and profitability
Business analytics: helping you put an informed foot forward
How organizations make better decisions
Thomas H. Davenport
The art, act and science of knowing
What business analytics means for small and medium businesses
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Business analytics in action
Gail Bamford, David Wallace, Mike Newkirk and Becca Goren
Embedding analytics into processes
Thomas Davenport, Jeanne Harris and Robert Morison
8 essentials of business analytics
The art of the possible: business analytics to
measure corporate sustainability
P2|Performance and Profitability
The impact of business analytics
on performance and profitability
By Jim Goodnight, CEO, SAS
With the rising complexity of global business, gut decisions and hunches no
longer suffice. Successful 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 of
mankind. A good deal of this data can
be converted into useful information and
competitive advantage – by applying the
The answers are out there – in the data
we capture and store.
Right now, that capture and storage
is costing huge amounts of money.
Analytics converts those tremendous
costs into invaluable assets.
Far more than mere reporting or dashboards or scorecards, business analytics
is a discipline that digs deeper into these
vastly larger sets of data to uncover the
most important insights. It can mean “social network analysis” to study behaviors
and relationships on multiple levels to
uncover fraud. It can involve in-database
analytics to optimize retail assortments
or pricing. It can mean analyzing portfolios to manage risk positions.
For example, with the right analytics, retailers can predict how many red sweaters they need in stock and how many
smalls or larges they need based on local
demographics. They can also determine
optimal prices for hundreds of thousands
of products at multiple locations. Pricing
used to be an art. Now, giant retailers can
zero in on the optimal price for all their
SKUs and stores. Banks can determine
the optimal amount of cash to keep in
ATMs. Automakers can predict how
many spare parts they’ll need on hand –
Harrah’s, a global casino operator, uses
analytics to optimize its marketing and
customer loyalty programs. Thanks
largely to its use of analytics, Harrah’s
ranks No. 1 in profits as a percentage
of revenues and has increased its share
of wallet from 36 percent in 1998 to 45
Performance and Profitability|P3
In the Philippines, the Bureau of Internal
Revenue used analytics to recoup $114
million in unpaid value-added taxes, a
400 percent ROI in the first year. In Sweden, they are using analytics to reduce
the number of patients who die from clinical errors. In addition to reducing unnecessary deaths, they expect to save $10
billion in health care costs at the national
level through their analytic efforts.
1-800-FLOWERS.COM changes prices
and offerings on its Web site, sometimes
hourly, because it uses analytics. It also
uses analytic software to target print and
online promotions with greater accuracy.
And it uses analytics to optimize its marketing, shipping, distribution and manufacturing operations. The result: a $50
million reduction in costs last year.
Here’s my advice: Take the time to learn
about analytics. Take the time to discover
how analytics can provide an objective
view of your world, not only as it appears
today but also how it’s likely to appear
tomorrow. I’m not talking about gazing
into a crystal ball. I’m talking about the
capability of competitive organizations to
develop and implement strategies today
that are based on a careful analysis of
their likely outcomes in the future.
And here’s my crystal-ball view: The ability to predict future business trends with
reasonable accuracy will be one of the
crucial competitive advantages of this
new decade. And you won’t be able to
do that without analytics.
Business Analytics Knowledge Exchange
Jim Goodnight has been at SAS’ helm since the
company’s incorporation in 1976, overseeing an
unbroken chain of revenue growth – a feat almost
unheard of in the software industry.
P4|Face Forward with Business Analytics
Business analytics: helping you
put an informed foot forward
By Jim Davis
Most companies today have plenty of
data. Creating intelligence and gleaning real insight from this data is what
continues to elude organizations. Despite years of talk about scorecards and
metrics, gut feelings and experience are
often still the guides for making important, sometimes critical decisions, even
though current research reveals a clear
link between business performance and
the use of business analytics.
Why BI is not enough
Business intelligence provides historical, metric-driven decision making –
and answers questions like, how many
units did we sell, what did customers
buy and for how much? BI is characterized by the creation of simple rules
and alerts and the distribution of known
facts to systems and people. These
decisions have a low transformational
impact on the business.
So what exactly is business analytics
and how can it help? Business analytics
is, simply put, the application of analytical techniques to resolve business
issues. It provides organizations with a
framework for decision making, helping
organizations solve complex business
problems, improve performance, drive
sustainable growth through innovation,
anticipate and plan for change while
managing and balancing risk.
BI is still a highly valuable part of your
overall business analytics environment,
however, offering an excellent general
purpose backbone for ad hoc analysis
and basic operational reporting.
It sounds like a lot, but if you break it
down it’s all about enabling effective
decision making. Organizations make
decisions every day, and these sit on a
continuum from frequent, up to millions
per day to transformative, which occur
less frequently but greatly impact organizational strategy. The need for agile
decision making has never been greater
but unfortunately, IT infrastructure, people and processes are lagging behind.
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 for automatic
reporting, like reporting on transactions
greater than $10,000 to the regulators.
From a more strategic decision perspective, business analytics can help answer
questions such as what new products
should we offer and in what markets?
Or relative to the example, which credit
card transactions are likely to be fraudulent? Business analytics can predict this
with certainty and automatically deny
transactions – while reporting activities
in real time.
Face Forward with Business Analytics|P5
A business analytics
framework is not a
monolithic and costly
approach but rather
provides for incremental
growth to achieve
strategic goals at any
given stage of an
organization’s value chain.
Business analytics allows organizations
to “face forward,” bringing insight to
transformative decisions. It benefits all
aspects of an organization’s value chain,
• Inbound logistics: receiving, storing,
inventory control and transportation
• Operations: including factors such as
packaging, equipment maintenance,
testing and all activities that add value
from the raw material to final product.
• Outbound logistics: the activities re
quired to get the finished products to
market, including warehousing and
• arketing and sales: activities that
lead a buyer to purchase the product,
including channel selection, advertising, promotion, selling, pricing, retail
management and shelf space optimization.
• Service: activities that maintain a
product’s value, including customer
support, repairs, installation, training,
spare parts management and more.
orter, Michael E., Competitive Advantage : Creating
and Sustaining Superior Performance. 1985.
In this way, business analytics drives
innovation and improves an organization’s speed of response to market and
environmental changes. In the credit card
scenario, business analytics can not
only discover the causal factors of fraud,
but also forecast accurately when it will
occur again. The company can then
change business processes accordingly.
In the following report, you’ll hear from
several experts about how business
analytics can be applied to business
problems across all types of organizations,
industries and value chains. Perhaps
then it will become part of your plan to
outthink and out-smart the competition.
A step toward business analytics
Effective decision making requires
a business analytics framework that
incorporates the people, processes,
technology and culture of an organization. This common framework provides
flexibility across the entire range of
analytical decision-making types from
highly managed operational analytics
(such as a setting a simple credit limit)
to discovery-based analytics (such as
credit fraud scenarios or setting dynamic
A business analytics framework is not
a monolithic and costly approach,
but rather provides for incremental
growth to achieve strategic goals at any
given stage of an organization’s value
chain. It offers business-ready analytical
applications with underlying technologies for key services like data management and quality, reporting and
Business Analytics Knowledge Exchange
Credit card fraud management
P6|Face Forward with Business Analytics
Six questions about your company’s information
The modern organization is awash in information – yet, too often, it falls short of the
tools, methods and expertise it needs to derive the greatest value from this untapped
asset. Information about the most important facets of the business – customers, processes,
employees, competitors and more – is gathered but not analyzed, reported but not
understood, guessed about rather than acted upon. But not with business analytics.
Ask these questions of your company and join aggressive competitors by being a smart
1. here should we leverage business analytics? Focus business analytics where you
already compete. The payoff is greatest where you are playing to your strength, not where
you are playing catch-up.
2. hy now? Because the technology is ready. Because competitors are likely exploring
the possibilities of analytical competition, too. And because it’s always risky to delay
capitalizing on a new business capability.
3. hat’s the payoff? Business analytics is all about anticipating the payoff in order to
maximize it. The analytics initiative succeeds when the business capitalizes on an
opportunity that analytics reveals.
4. hat information and technology do we need? Most companies don’t lack for sufficient
data, but instead suffer from a lack of integration and a lack of quality. Without good data,
you simply can’t do good analytics.
5. hat kind of people do we need? You need a variety of talented people: analytical
professionals who design and refine analytical algorithms, and perform data mining;
analytical semiprofessionals who do substantial amounts of modeling and analysis but
are unlikely to develop sophisticated new algorithms or models; analytical amateurs who
need to understand something of the analytical basis for operations and decisions; and
the analytical manager who focuses the work of analytical professionals.
6. hat roles must senior executives play? Committed senior executives provide the passion
and the resources to drive their organizations in an analytical direction. In virtually every
successful firm, senior management sets an analytical strategy and continually pushes
Face Forward with Business Analytics|P7
HSBC: fraud detection that exceeds
With fraud levels surging around the
world, banks are facing greater regulatory scrutiny, as well as risks associated
with damaging publicity from fraud. The
ability to correctly make split-second
decisions on accepting credit card
transactions – before fraud occurs – is
more important than ever.
Using SAS Fraud Management, part of
the SAS Business Analytics Framework,
HSBC prevents, detects and manages
financial crimes by scoring and accepting or rejecting millions of transactions a
day in real time – at the point of sale.
As a result, the global financial services
leader has achieved significantly lower
incidence of fraud across tens of millions of debit and credit card accounts.
“The proof is in our fraud numbers – our
detection rates and our false positives –
which continue to meet our aggressive goals,” said Derek Wylde, Head of
Group Fraud Risk, Global Security and
Fraud Risk for HSBC.
Jim Davis is Senior Vice President and
Chief Marketing Officer for SAS.
How organizations make
The following article is an edited excerpt of an article distributed by the
International Institute for Analytics.
By Thomas H. Davenport
Author and researcher Tom Davenport is the
President’s Distinguished Professor at Babson College.
His newest book is Analytics at Work: Smarter Decisions,
Better Results (with Jeanne Harris and Robert Morison,
from Harvard Business Press).
Relatively few businesses and organizations have given full and proper attention
to one of their most important activities:
making decisions regarding key questions
such as what strategies and business
models to pursue, which products and
services to offer, 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 performance suffers.
However, new analytics, decision automation tools and business intelligence
systems make it possible to make better
use of information in decisions. “Wisdom
of crowds” approaches and technologies
allow larger groups of people to participate meaningfully in decision processes.
Organizations cannot afford to ignore
these new options if they wish to make
the best possible decisions.
Given both negative and positive incentives to get better, one might expect
that organizations would attempt to
improve their decisions — that they
would prioritize them, examine their
current level of effectiveness, investigate
new options for making them better and
implement some of those options. In
my survey and analysis of dozens of
corporations, I found that while they are,
indeed, doing some of these things,
In a survey and analysis
of dozens of corporations,
Davenport found that very
few organizations have
efforts to improve a variety
very few organizations have undertaken
systematic efforts to improve a variety of
decisions. In this excerpt I describe some
of the more frequent approaches used to
intervene in decision processes.
Analytics, testing and data
Infrastructures predicated on analytics
and data were among the most
common decision-making frameworks
among the surveyed firms. Eighty-four
percent of respondents mentioned an
analytical component in their decision
improvement efforts and 66 percent
mentioned efforts to improve data.
The range of analytical techniques
employed was quite broad. Scoring
approaches based on statistical analyses
(usually some form of regression analysis) were common. Other approaches
included optimization, behavior-based
customer targeting, statistical forecasting,
prediction of various phenomena and the
use of text analytics.
Systematic testing was one form of
analysis that was being used somewhat
frequently by companies; 18 percent
mentioned it specifically in interviews.
One key virtue is that it creates a
decision-oriented context from the start.
If a test between two alternative Web
page designs is performed, it is generally 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 of virtually any form of
analytics is high-quality data, so it is not
surprising that data-oriented responses
were also common. Sixty-six percent
of respondents mentioned some issue
involving data. The most common were:
• Having difficulty in accessing data.
• Creating a common data architecture.
• Eliminating duplicate data.
• ntegrating “master data
• chieving “one version of the truth”
in functional or process areas.
• Dealing with too much data.
• Gathering data from channel partners.
• Creating new metrics.
Not surprisingly, many
that they needed to change
business processes to
make better decisions.
Technology support – and overrides
– for decisions
Several firms surveyed mentioned specific analytical software, testing software,
data warehouses and Web analytics/
reporting software. Two other technologies were mentioned frequently:
specialized information display technologies and business rule engines.
Thirty-eight percent of companies in the
study mentioned some use of specialized
information displays such as scorecards
and dashboards. These tools, typically
found in the business intelligence
category, allow decision makers to see
only the information that they need to
make a decision. Several firms mentioned
using specific display approaches not
generally supported by conventional BI
tools, including the “A3” format for
displaying key issues in a particular
business domain. Some companies are
using neuroscience principles to guide
how information is presented and
digested. This may be a bellwether of
future attempts to link information and
Another popular decision technology
involves using business rules to enable
automated or semiautomated decision
processes — sometimes in conjunction
with analytics (e.g., scoring-oriented
applications). Many organizations employ business rules but allow humans
to override the recommended decisions
Changes in business processes
Not surprisingly, many organizations
reported that they needed to change
business processes to make better
decisions. Forty-three percent mentioned process changes of some type.
For instance, some described process
changes around supply chain management in an IT firm, lease processing in
an auto financing firm, financial processes in health insurance or new product
development processes. Several organizations mentioned changes for decisionoriented processes made in the context
of Six Sigma programs.
However, some decision-focused analysts noted that their original goal wasn’t
necessarily to identify and implement
process changes, and that they had to
work with other groups to accomplish
them. As one head of an analyst group
at an IT firm commented, “We didn’t
initially have the franchise to do process
improvement — our thing was analytics.
But it kept coming up on our projects. So
we eventually just made it a part of our
Decision-oriented methods and tools
Several organizations reported that
one aspect of their decision processes
was an overarching, strategic management approach to guide all aspects of
their efforts. Most of these initiatives are
well-known approaches to business
• An insurance company adopted
enterprise risk management.
• he Six Sigma approach to process
quality and decision outcomes was
implemented at a financial payments
firm and a staffing firm.
• financial services firm uses the
“net promoter score” for customer
• n economic decision analysis
approach, popularized and taught
by Stanford’s Engineering School
and the Strategic Decisions Group,
is used by an oil company.
In addition, three responding organizations developed analytically focused
decision processes that have been widely
used in IT systems development, but are
not widely known in the decision-making
or analytics literature. Sometimes called
“agile methods” or “rapid prototyping,”
they involve the creation of a series of
short-term deliverables, and frequent
review of them by the client and stakeholders for the decision. The organizations that use this approach found
that it led to results that better fit the
decision-makers’ requirements, and at
a faster pace.
From my research, it’s clear that
organizations recognize the importance
of improving decisions. Although the
survey was not a random sample,
individuals in 90 percent of organizations surveyed identified some
attempt to improve decisions through
better processes. Second, organizations employ a variety of intervention
types to improve decisions across
analytics, culture and leadership, and
data. The most successful organizations adopted multiple interventions
at once to improve a decision.
As a result, analysts — previously
responsible for data gathering and
analysis — are morphing into consultants
who may be responsible for framing decisions, process redesign, communication
and education programs, and change
management — all in addition to the
traditional analysis functions.
Organizations seeking to implement
decision improvements should become
familiar with these common intervention
types and create ongoing capabilities to
Davenport’s research found the most
common types of decisions improved by
• ricing decisions (consumer goods,
industrial goods, government contracts,
maintenance contracts, etc.).
• ecisions to target consumer segments
(by retailers, insurers, credit card firms).
• erchandising decisions (brands
to buy, quantities and allocations).
• ocation decisions (for bank
branches or where to service industrial
• reatment protocols for health care.
• roduct development for
Order it now – Analytics at Work: Smarter
Decisions, Better Results
• tudent performance in educational
Read the full International Institute for
• valuating marketing approaches
(in both consumer and
Engage with analytic leaders
• iring decisions.
• ehicle routing decisions.
Analysts — previously responsible for data
gathering and analysis — are morphing into
consultants who may be responsible for framing
decisions, process redesign, communication and
education programs, and change management —
all in addition to the traditional analysis functions.
P12|Business Analytics in Action
Business analytics in action
How are key industries deriving value from their business analytics implementations?
By Gail Bamford, David Wallace, Mike Newkirk and Becca Goren
According to the World Health Organization, 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 affect businesses and
Despite these huge investments, health
care quality is uneven and resistant to
changes and improvements. How can
we enhance health care delivery while
controlling those costs? It starts by
carefully measuring and monitoring the
quality of that care – a complex task
perfectly suited for business analytics. Here’s how some forward-thinking
health care institutions are delivering
better quality of care more efficiently.
Maine Medical Center
Named to US News and World Report’s
“America’s Best Hospitals” list for
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:
• ncreased compliance on medication
reconciliation by more than 50 percent
in a nine-month period.
• ramatically reduced the rate of hosD
pital-acquired infections by measuring
where infections originated and what
admission conditions closely correlated with acquired infections.
• mproved government/industry acI
creditation/compliance by incorporating national guidelines into key
• eveloped new methods for caring for
stroke patients while controlling costs.
By taking better care of these patients,
the hospital expects fewer complications, which will reduce costs.
The Karolinska Institute in Sweden
needed a way to examine the effects
of drugs, other treatments and lifestyle
factors on patients with rheumatoid arthritis. Using SAS Business Analytics,
the Institute has deployed a Web-based
patient self-help application and predictive modeling to determine which treatments will be most effective for certain
segments of RA patients.
Business Analytics in Action|P13
In a challenging economic and regulatory climate, bankers must be especially
vigilant. Two key indicators of a bank’s
health are net charge-offs (NCOs) – the
value of loans written off as uncollectable – and nonperforming loans (NPLs)
that are in default or delinquent more
than 90 days.
In the past two years in the US, bank
NCOs have soared by an average of
more than 350 percent across all institutions, with institutions holding assets
of $5 billion or less showing growth of
almost 500 percent. NPLs as a percentage of average loan balances have risen
more than 278 percent at US banks with
$1 billion or more in assets. How can financial institutions improve their collections and protect their bottom line?
Business analytics can provide the insights that institutions need to reduce
both loan writeoffs and the cost of collections activities. First, models created
within a business analytics framework
can identify likely candidates for workouts and loan modifications. Second,
business analytics can optimize collections activities to improve the probability
of success and maximize self-treatment
among debtor segments. It starts with
three basic steps.
• leanse 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.
• nalyze and score. Develop scoring
models to analyze debtor-customer
segment data against objectives, including “maximize profits” or “minimize
writeoffs” or against constraints, such
as loan types, outstanding balances or
• ptimize and execute treatment
strategies. Analytical models help
collections teams understand who is
most likely to respond, which communication channels work best and how
much payment to expect.
Collections optimization driven by
business analytics delivers the results
that institutions need to improve their
A leading Australian financial institution
previously relied on instinct when contacting delinquent customers. Since introducing SAS for collections optimization, it has
achieved a 300 percent ROI in less than six
months. A debt purchasing firm based in
the UK uses SAS to predict debt portfolio
performance. This enables the firm to
make quicker decisions on acquiring new
debt portfolios at the right prices, collect
more from each portfolio and grow revenues by 50 percent annually.
Source: SNL Financial
P14|Business Analytics in Action
Meaningful ROI with
One SAS customer increased company
profitability by accurately predicting product demand and customer behavior – more
than doubling its forecasting accuracy. It
found that for every 1 percent reduction in
forecast variance, it saved $200,000.
Another manufacturer improved two
seemingly competing objectives. It simultaneously reduced inventory by 20 percent,
eliminating millions of dollars of holding
costs, yet improved service levels, which
directly and positively affected customer
From diapers to jet engines and almost
everything in between, manufacturing
expertise is a competitive differentiator for
companies that follow optimal practices
and methodologies to attack inefficiencies
and eliminate waste. Business analytics
is essential in these settings to improve
production and sales planning, enhance
the supply chain, reduce inventory,
streamline logistics and much more.
For example, with demand forecasting,
business analytics can be a key
contributor to a manufacturer’s success.
Better forecasts deliver ROI by:
• Reducing inventories.
• Improving order fulfillment rates.
• Shortening cash-to-cash cycles.
Many manufacturers struggle with
optimally managing and forecasting
their raw materials requirements, workin-process (WIP) inventory and finished
goods inventories. Without the right mix of
raw materials, production plans fall apart
and customer orders are delayed (or,
worse, canceled). Missing WIP forecasts
similarly leads to inefficient schedules
and a crippling misallocation of finished
stocks – not having the right quantities of
the right goods at the right time and in
the right places. While the data is often
available to prevent, identify and correct
these imbalances and inefficiencies, it
is usually not integrated, analyzed and
shared across the organization.
Data management technologies can
bring together islands of information
such as point-of-sale (POS) data and
historical shipment data. Once that data
is aggregated, business analytics models
and tools can accurately forecast the
demand for products by family, individual
SKU, geography, customer type, etc.
With a clear and accurate demand
picture, manufacturers can properly
allocate raw materials across plants and
regions – all optimized by distribution
channel – to create complete roll-ups in
master planning schedules.
You’ve likely experienced it before – your
cell phone loses service one too many
times, so you switch providers. Low
barriers to churning mean providers must
vigilantly and carefully invest to maintain
and increase their service quality and
customer satisfaction rankings. After
all, your satisfaction keeps them in
Network managers typically receive error
reports and alarms after a network device
fails. The team addresses the stream of
trouble tickets, but never gets insight into
underlying causes or trends for outages.
The result: long call-resolution times.
With business analytics and approaches
such as predictive fault analysis, network
managers can analyze performance
to pre-empt failures. They can analyze
trouble tickets and optimize corrective
services, shortening times you are
Strong data management, including
data quality and reporting capabilities –
all key underpinnings for business
analytics – can help quickly identify
Business Analytics in Action|P15
One large telco service provider used
SAS to identify emerging issues (an
average of two weeks prior to failure)
and double the percentage of tickets
resolved within 48 hours.
service and network issues. Business
analytics helps to:
• dentify and remove duplicate trouble
• nderstand faults and performance on
a macro level.
• etermine which services have the
highest fault rates.
In addition to analyzing network
performance, predictive analytics
technologies can help evaluate
demand, faults and systems to improve
resource utilization and quality of service
(QoS). A telco provider can then identify
when and where network resources
are deployed and quality/performance
variations over time.
Business analytics allows network and
service managers to better understand
causes and impacts of failures. They can
prioritize and pre-empt outages, optimize
repairs and mitigate risk with answers to
• ow significant is each factor
influencing network faults degradation?
Gail Bamford is a SAS Global Industry Marketing
Manager for Public Sector.
David Wallace is a SAS Global Industry Marketing
Manager for Financial Services.
Mike Newkirk is a SAS Global Industry Marketing
Manager for Manufacturing.
Becca Goren is a SAS Global Industry
Marketing Manager for Communications,
Media and Entertainment.
Health care providers keep pace with change
• hich network faults are tied to a given
• hich faults are related and what are
Armed with predictive fault analytics,
a telco provider can limit the times you
lose a signal and continually improve
overall service, allowing it to keep your
The standard for clinical data analysis and reporting
Get the full stories on:
Maine Medical Center
Solutions for better risk management
Compete in manufacturing
Invest wisely, communications service providers
P16|The New Know
The art, act and science of knowing
An excerpt from The New Know
By Thornton May
The Internet makes self teaching — and
lifelong learning — the rule rather than
the exception. Historians ultimately will
come to consensus on what to call the
time period between the frenzy that was
the dot-com bubble and the period before society finally enters the data cloud.
For want of a better phrase, I call the 20year interregnum we currently inhabit
(1995 – 2015) the Age of Little Information. I come to this label not because the
age exhibits a lack of information. Quite
the contrary, it is during this epoch that
information — previously locked away
in analog form — is becoming widely
digitized. The New Know has changed
our reality along 10 fundamental dimensions.
Futurist Thornton May positions analysts as heroes
of the age we are about to enter in his new book,
The New Know: Innovation Powered by Analytics.
opyright 2009 by John Wiley Sons, Inc.
All rights reserved. Reprinted with permission.
New Know Reality #1:
You will be expected to do
something with information.
All this newly digitized information has
had, relatively speaking, little impact on
behavior and little impact on organizational outcomes. We are now exiting a
historical moment of undermanaged and
only occasionally acted-upon information to an environment requiring much
more active, much more intense, much
more aggressive information management. You as an executive will be held
much more accountable for your data
management behaviors. You will be
expected to transform “data lead” into
“knowledge gold” via the expeditious
The New Know|P17
sensemaking leading to efficacious action. In the Age of Little Information, we
were data vegetarians. In the New Know
we will have to become information and
New Know Reality #2:
There really is more to know.
The New Know will be awash with
data. Processing power doubles every
18 months. Storage capacity doubles
every 12 months. Bandwidth throughput doubles every nine months. There
is more to know. Organizations are
having trouble keeping up — and, sadly,
the fact that there are more facts arriving
at a faster rate of speed is not even the
tip of the cognitive iceberg. Like the “fog
of war,” info warriors speak of the “fog
of facts” (e.g., confusion about what
information is to be believed, what information sources are credible and what
version of reality is to be acted on). In a
world of multiple sources of information
and 24-hour decision making, the very
character of information is changing.
A “fact” is no longer a “fact.”
New Know Reality #3:
You will have to know more
One of the major changes defining the
new competitive environment is the
requirement to know more about knowing, what experts sometimes refer to as
metacognition. Society is about to
undergo a tectonic shift in how it thinks
about thinking. Driving this cognitive
plate shifting are the RSS feeds, podcasts, blogs, old-media headlines and
evening news programs, which are
increasingly filled with images and
instances of current-generation leaders
being asked by dissatisfied nextgeneration voters, customers and
shareholders: “What were you
thinking?” Looking beneath the surface,
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 of action?”
New Know Reality #4:
Brain science and decision
science 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
for over 100 years. Neuroscience has
progressed to the point that we at least
know what we do not know.
P18|The New Know
Carly Fiorina, former
CEO at Hewlett-Packard,
believes that distilling
truth from overwhelming
amounts of information is
the essence of leadership.
To some extent, it is a simple truism
that the brain is involved with all things
that comprise our human existence.
It follows, loosely, therefore, that
understanding the brain will help us
understand the human condition more
fully. The big news is that the brain possesses innate qualities that influence
individual experience and opinions.
There are things that can be known—
that need to be known by executives
seeking to maximize value from the
knowledge assets available to the
New Know Reality #5:
The environment is changing
The information flood should be viewed
as a permanent macroenvironmental
change. 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 weakening old ones. Because of the current
technological revolution, our brains are
evolving right now — at a speed like
New Know Reality #6:
Information management Is the
essence of leadership.
Low-cost communications give rise to
almost toxic levels of spin, hype and
empty rhetoric. Leaders are able to
cut through all the noise. Does your
organization filter its data? Carly Fiorina,
former CEO at Hewlett-Packard,
believes that distilling truth from overwhelming amounts of information is the
essence of leadership. She believes that
all of us are overwhelmed with information, and what sets great leaders apart
is their ability to cut through the clutter
and distinguish the truly important from
the merely interesting.
New Know Reality #7:
A more connected world.
One of the transformational elements
moving society to the New Know is
something analysts at Forrester Research call the “groundswell.” Josh
Bernoff, Vice President at Forrester,
contends: “There’s so much information
flowing out of the groundswell, it’s like
watching a thousand television channels at once. To make sense of it, you
need to apply some technology, boiling down the chatter to a manageable
stream of insights.” The new scarce resource in the next economy will be the
human attention needed to make sense
of information. The question is: How will
we be able to keep up?
The New Know|P19
New Know Reality #8:
Mathematics is now so widely accepted as the arbiter of truth in the modern
world that it has become the backbone
of disciplines ranging from physics (of
course) to economics and sociology.
Backing up a statement with mathematics gives it an aura of validity, even if the
topic has to do with something as mathematically messy as human behavior.
However, many otherwise “normal” executives have a pathological aversion
to math. This is not just unfortunate, it
is dysfunctional. 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 need
to be numerate to do almost anything
(from building bridges to conquering
disease), governments anxiously compare their performance in mathematics
with that of competitor nations.”
New Know Reality #9:
There are significant downsides to
Success requires materially expanding
what you know and adding precision
and efficiency to the processes (analytics) whereby you come to know. Here
is a metaphor to keep in mind as you
think about the New Know. If you are
locked in a room with an elephant, it is
useful 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.
If knowledge is power, then “knowledge about power should be especially
empowering,” says John Murrell, the
very-much-in-the-know editor of Good
Morning Silicon Valley. For instance,
using 15,000 meters, a subset of National Grid Customers will be able to
access their energy — use information
via the Internet, by a thermostat readout, or through text messaging, and use
the data to change their consumption
patterns. Program participants are expected to save 5 percent, or about $70
a year, on their energy bills. Change advocates from all fields of endeavor are
excited about the possibility of putting
new information in front of people in the
hopes of changing behavior.
If you are locked in a
room with an elephant, it
is useful 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
analytics tells you where
that elephant will step.
P20|Business Analytics for SMBs
What business analytics means for
small and medium businesses
An interview with Matthew Mikell, SMB Global Product Marketing Manager
The Wine House discovers
$400,000 in ‘lost’ inventory
Economic times may be tough, but Bill
Knight, owner and President of The Wine
House, is toasting a 100 percent return on
his investment in SAS. The first day its SAS
application was live, the brick-and-mortar
and Internet retailer discovered 1,000
items of wine that hadn’t moved in more
than a year.
“We had a huge sale to blow it out, generating $400,000 in capital in one weekend,”
Knight said, “and just in time, because in
today’s economy, we’d be choking on that
Using SAS, The Wine House has reduced
its aged inventory by 40 percent. “Now I
can get the answers I need and base decisions on facts rather than gut intuition,”
says Knight. “I’ve got less money tied up
in inventory, I know who our best customers are, how to market to them and can
monitor the effectiveness of our marketing.
Our ROI with SAS has been well over 100
percent in less than a year, so my return on
investment has been fantastic.”
When it comes to business analytics,
it sometimes seems like only major enterprises garner the spotlight. That’s
somewhat understandable given the
complexity and scope of their analytical
challenges and the nature of their highprofile brands. But the fact is, far more
small to medium businesses (SMBs) are
poised to implement business analytics
In the US, these companies have
revenues of less than $500 million. In
Europe, the SMB category comprises
companies with a maximum of EUR 450
million (about US$611 million). While in
the Asia Pacific region, SMB often refers
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 for the same
goals to grow their business through
innovation, and need the same sophisticated functionality scaled appropriately
to their processes. In this QA,
Matthew Mikell, SAS Global Product
Marketing Manager, shares his perspectives on what business analytics means
Q. What are some of the unique
challenges that SMBs face with
respect to business analytics?
A: SMBs primarily face the issue of
scale. At SAS we have heard four
general constraints when listening to
organizations that are SMBs:
1) Decision-making style
Transitioning from gut instinct to factbased framework can be difficult in part
because the former approach has likely
served the successful SMB very well.
Most SMBs have Excel experts who
can generate some great static charts
and graphs — and I wouldn’t ever want
to denigrate the value those reports
provide. But there’s so much more value that can be derived from in-depth
analyses. Once SMB executives get
a real glimpse of the insights that are
lurking beneath the surface of their
transaction data, their willingness
to adopt business analytics increases
2) Cash flow
In addition to a shift in decision-making style, cash constraints can pose
very real obstacles for an SMB that
wants to mature in this area. Considering the business analytics framework helps improve margins, retain key
customers and grow share of wallet in their
markets. However, the long-lasting
return on investment far outweighs the
capital required to undergo the transition.
Business Analytics for SMBs|P21
SMB executives – often
owners or people with
lengthy tenures – worry
about letting go of the
information flow and
empowering people to
make decisions that
were previously reserved
3) IT resources and infrastructure
More than 80 percent of SMBs with
about 100 employees have only four
dedicated IT staffers. They’re stretched
thin, and that can make it very difficult
to expand the IT mandate beyond critical business operations into managing
business analytics environments.
4) Business analytics maturity
SMBs must have an appreciation for
the level of skills required to meet overall strategic goals through business
analytics. Research from Aberdeen
Group suggests that SMBs without the
relevant skill sets are poorly positioned
to drive value from an analytical solution. It reports that SMBs using some
sort of analytical applications perform
at a higher level than their competitors
that do not.
The main SMB challenge for moving to
business analytics is the understanding
of its impact on these four critical areas,
and building a capability that is costeffective and remains flexible and easy
Q. Why should SMBs adopt
A: It essentially boils down to competitive pressures. SMBs need to continually innovate. If you’re an SMB that isn’t
constantly seeking to optimize every
possible aspect of the operation, you’re
at a disadvantage. Internally, employees
need these tools to be productive. Otherwise, it’s gut-based decisions, or cutting and pasting from multiple tools.
The truth is what brought you to where
you are typically won’t take you to the
next level. But it’s very difficult, culturally, to walk away from what’s made
you successful. SMB executives – often
owners or people with lengthy tenures –
worry about letting go of the information
flow and empowering people to make
decisions that were previously reserved
Q What’s the best way for SMBs
to tackle the adoption of business
A: Of course, every company differs –
particularly at the SMB size. But we’ve
found that there is a general approach to
the adoption of business analytics. The
first step is to ensure you have sponsorship from company executives. Clearly
lay out the business analytics benefits
and return to the management team.
This transparency is key at the SMB
level as SMB executives are traditionally heavily involved in analyses, reporting and the decision-making process.
Make it clear how business analytics will
resolve a compelling issue or attract and
retain customers, for example.
berdeen Group, 2009, Beyond Spreadsheets:
The Value of BI and Analytics.
P22|Business Analytics for SMBs
The second strategy is to focus on a
particular business process or issue.
Don’t introduce business analytics as
a broad, unfocused utility for general
usage. This will occur naturally as you
solve more focused issues, building up
confidence in fact-based decision making as a core competency. Some of the
typical issues that we see being solved
with business analytics include improving customer data quality for improved
marketing, invoicing or customer service, or improved product pricing and
packaging analysis to drive a higher
Finally, don’t rest on your laurels. Capitalize on your initial success to broaden
deployment to other areas of the organization. Those adoptions move faster
once you can point to a successful track
record in another area.
Q. What’s the difference between
business analytics for large enterprises vs. SMBs?
Q. Can you share some examples
of how SMBs have been able to
capitalize on business analytics?
A: In a nutshell, it’s about scale. Deployment and support strategies will have a
different nature. What’s more interesting
to me, however, is the important commonality: functionality. Business analytics in SMBs is not about presenting a
subset of functionality but rather surfacing the right functionality for the problem
at hand, and opening up to more as the
business requires it. Despite their size,
SMBs face similar challenges to make
better and more informed decisions to
continue innovating in their markets. It is
therefore essential to provide a rich set
of features and a very high level of technology usability.
A: Sure. We’ve worked with an energytrading company that enables staff to
predict what today’s electricity and
gas purchases will sell for months later
when consumers buy. Business analytics
supplies that intelligence to traders in a
cleaner, faster and more accurate way.
A collection agency uses SAS Business
Analytics to analyze bad-debt portfolios
before acquiring those assets. This is a
quantum leap forward from its previous
model, which was simply buying any
debt assets for as little as possible and
hoping to collect successfully.
A player in the secondary-ticket market
uses SAS to develop a deeper understanding of the needs of its thousands
of customers. By segmenting them and
catering to psychographics, the company
can optimize how frequently it contacts
the customers and improves loyalty.
Software for SMBs
Some of the typical issues
that we see being solved
with business analytics
customer data quality
for improved marketing,
invoicing or customer
service, or improved
product pricing and packaging analysis to drive a
higher market share.
Matthew Mikell leads Global Product Marketing for SMB markets and software-as-a-service
(SaaS) offerings at SAS, supporting strategic
planning, messaging and product offerings
through direct and indirect channels.
Analytics at Work|P23
Embedding analytics into processes
In 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 infusing analytics into business processes.
We see examples of analytics at work
within core processes in a variety of
business areas. Statistical analysis has
been a feature of supply chain and logistics management for decades, starting with the techniques of statistical
process control (SPC) and total quality
Real-time analytics are helping guide
call center workers in their interactions
with customers. And analytics are well
established in the engineering and simulation sides of product design.
Among business support functions,
analytics are essential to many facets
of finance, common in the management
of technology operations, and relatively new to human resources (though
of enormous potential there). In corporate development, key decisions —
for example, regarding mergers and
acquisitions—may benefit greatly from
analytics, but few companies take a
process approach to such activities.
Reprinted by permission of Harvard Business Press. Excerpted
from Analytics at Work: Smarter Decisions, Better Results by
Thomas Davenport, Jeanne Harris and Robert Morison.
All rights reserved.
Consider the example of UPS to whet
your appetite for embedding analytics
in your core business processes. As a
logistics company, UPS lives and
breathes the “traveling salesman
problem”—how to reach a variable
series of destinations most efficiently
with the right delivery capacity, and often
in designated time windows, every day.
The solutions naturally demand very
sophisticated and industrialized analytics: for capacity planning of aircraft
and truck fleets, for routing packages
through its distribution network, and for
scheduling and routing delivery trucks.
For a company this steeped in analytical applications, the frontier is moving
closer to real-time, dynamic adjustments.
For example, UPS is experimenting with
algorithms to adjust the order of deliveries as conditions (e.g., road closures,
extraordinary customer need) change.
Making processes analytical
The effects of analytics on the operations of a process can be profound,
and over time you may want to reengineer the overall business process and
revamp its information systems to
capitalize on the potential for analytics-based improvement. But you can
start embedding analytics without a
major overhaul. For processes that rely
extensively on enterprise systems, it
may be possible to simply start taking
advantage of the analytical capabilities
that are already included in the software. However, many process analytics
initiatives will require tools, techniques,
and working relationships that are likely
to be new and unfamiliar at first. We
have found that implementing analyticsenabled processes requires applying
four major perspectives.
P24|Analytics at Work
The effects of analytics
on the operations of a
process can be profound,
and over time you may
want to reengineer the
overall business process
... but you can start
without a major overhaul.
The first is process implementation.
Occasionally a business may create
a new analytically enabled process
or rebuild a process from scratch, but
most often you are adding capability to
and altering an existing process. Especially given the iterative nature of many
analytical applications, it’s essential to
measure baseline process performance
first and to run the enhanced process
in parallel to the original (perhaps as a
pilot or test) in order to refine the new
process and measure its performance
and value. In some cases, process
simulation can yield insights about how
the process might perform even before
Next, organizations should consider
model implementation. Much of the
distinctive work of process analytics
centers on designing, developing and
iteratively refining statistical algorithms
and descriptive or predictive models
or rule-based systems. If you are going to industrialize important decision
processes, it is important that the rules,
assumptions and algorithms in your
model are correct. Analytical projects
generally require different tools and
development methodologies from
those employed in more traditional systems development. And, of course, this
work is performed 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 of systems and technologies supporting the business process.
In building these interfaces, it helps to
employ process-oriented technologies,
including capabilities of ERP systems,
workflow and document management
systems. And integrating and testing
the new systems and interfaces is critical given analytics’ reliance on a broad
range of quality data and the fact that
analytics-based decisions may dramatically change process flow.
Human implementation is the fourth
perspective. Often the greatest implementation challenge, especially when
analytics is new to the process and the
people performing it, is on the human
side. Only people can tell if an embedded application is resulting in good
decisions, so be sure to involve them in
developing, managing and monitoring the assumptions and results of any
embedded model. Another important
factor is developing the right mix of
automated and human decision making
and enabling process performers to
trust and use their new analytical information and sometimes tools.
Analytics at Work|P25
Embedding analytics into processes starts with
a robust analytical architecture that provides an
accurate, timely, standardized, integrated, secure
and reliable information management environment.
SAS and Accenture:
Making business analytics
work for you
SAS and Accenture have joined the
forces of their best and brightest to help
more organizations reap the benefits of
an analytic approach. The new Accenture
SAS Analytics Group combines Accenture’s domain and industry experience
with SAS’ analytic strengths to provide
the services (best business practices,
proof of concepts), technology (both
industry and cross-industry offerings)
and support (competency centers, certification programs) to help companies
reach their competitive potential – more
efficiently and cost-effectively.
Accenture SAS Analytics Group
All four perspectives must mesh: process flow and decisions are enabled or
controlled by analytical models, other
information systems interface with the
models and provide clean data feeds,
and people perform the process better
with the help of embedded analytics. If
you lack clear business goals, specifications or momentum, be prepared to
demo or pilot the concept, to work with
stakeholders to define targets and set
ambitions, and to make the business
case for investing in prerequisite assets,
often starting with data.
IT’s role in embedding analytics into
Technology is an integral part of most
business processes today. So the best
route to embedding analytics into processes is often through the technologies and applications that employees
routinely use to do their jobs. Embedding analytics into processes starts
with a robust analytical architecture that
provides an accurate, timely, standardized, integrated, secure and reliable
information management environment.
Scorecards and applications that monitor 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:
1. Automated decision applications.
These sense online data or conditions,
apply codified knowledge or logic, and
make decisions — all with minimal
human intervention. Technology is best
suited to automate decisions that must
be made frequently and rapidly, using
any kind of information (data, text,
images) that is available electronically.
The knowledge and decision criteria
used in these systems need to be highly
The factors that must be taken into
account (the business problem’s
dimensions, conditions and decision
factors) must be clearly understood and
not subject to rapid obsolescence. The
conditions are ripe for automating the
decision when experts can readily
codify the decision rules, a production
system automates the surrounding
process and high-quality data exists in
electronic form. Business activities that
benefit from automated decisionmaking applications include fraud
detection, solution configuration, yield
optimization, recommendation/realtime offers, dynamic forecasting and
operational control (like monitoring and
P26|Analytics at Work
2. Business applications for operational and tactical decision making.
Analytical managers rely on analytical
applications (whether custom developed or from third parties) that are
integrated directly into Web applications or enterprise systems for tasks
such as supply chain optimization,
sales forecasting and advertising
effectiveness/planning. Recommendation, planning and “what-if”
applications can incorporate near
real-time information and multiple
models to dynamically optimize a
solution while factoring in conflicting
goals like profitability and customer
satisfaction. Analytical business
applications are best suited to welldefined, periodic tasks in which most
of the information needed is predictable and available electronically.
Since the data, knowledge and decision criteria are typically less defined
and/or more fluid than those of a fully
automated application, they require
industry and functional expertise.
3. Information workflow, project
management, collaboration and
personal productivity tools. Most
information work is done through
personal productivity tools like
Microsoft Office. As vendors increase the analytical quotient of their
collaboration and productivity tools,
analytics become more accessible
to analytical amateurs throughout
the enterprise. One consumer products company found that its elaborate
modeling tool was ignored by nearly
everyone until the findings were distilled
into a monthly deck of ten PowerPoint
slides and e-mailed directly to the sales
force. As platform vendors align their
products to work together more seamlessly, a manager needn’t know that his
Excel spreadsheet is using the company’s
ERP system to prepare his forecast.
These tools and applications work best
for less structured information with less
defined decision criteria.
To address the growing need to embed
analytics into processes, both specialty
applications vendors and the major
platform vendors are building more
analytical functionality directly into their
tools and applications.
Software companies are building
more industry-specific, process-driven
applications. Major platform providers
like Oracle are embedding analytics
into their products by building statistical
functions directly into their enterprise
data warehouse products. ERP vendors,
which are including more sophisticated
analytical features, remain a powerful
way to integrate industry best practices
into business processes. And Microsoft,
Oracle, SAP and SAS continue to quietly embed more sophisticated analytics
and business intelligence capabilities
into their applications and tools.
8 essentials of business analytics
Find out what business analytics can do for you –
and how to get started
By Jim Davis
Leading banks use business analytics
to predict and prevent credit fraud,
saving millions. Retailers use business
analytics to predict the best location for
stores and how to stock them. Pharmaceutical firms use it to get life-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
But these advanced business applications tell only part of the story. What’s
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
fact-based decisions – which helps
them anticipate and solve complex
business problems throughout the
organization. By embracing an analytical
approach, these companies identify
their most profitable customers,
accelerate product innovation, optimize
supply chains and pricing, and identify
the true drivers of financial performance.
And you can too. Get started with
business analytics by taking these eight
1. mprove the flow and flexibility
High-quality data must be integrated and
accessible across your organization. It
should also be structured in a flexible
way that allows your analysts to discover new insights and provide leaders the
information they need to adjust strategies quickly. Strengthening and flexing
the data backbone of your enterprise
will pay off when you need to change
business processes quickly in response
to market shifts, regulatory or stakeholder demands.
2. Get the right technology in place.
Take an enterprise approach to data
management and analytics to effect
better decisions. Remove disconnected
silos of data, technology or expertise.
Your technology portfolio should
• ptimized data stores to support
core business processes and
• ata integration and data quality
• nalytical software with the means
to effectively deploy, explore and
share results in a meaningful way.
• ntegrated analytical applications
designed to solve defined issues
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 doesn’t 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 analytics
work, analysts must also be able to
communicate effectively with leaders
and link analytics to key decisions and
the bottom line.
4. Demand fact-based decisions.
An analytical company makes a wide
range of decisions. Some are ad hoc;
some are automated; some are transformative. The common thread? Evidence
backs them all. Managers encourage
asking the right questions of the data to
get maximum insight. How results are
deployed is also important – through
operation systems such as customer
relationship management applications or real-time fraud applications
to interactive dashboards, data movies,
in databases – wherever needed to
ensure decision makers have the
information they need when they
need it (and in the way they can best
5. Keep the process transparent.
communication and accountability; it
is key to successful business analytics
projects. The value delivered from an
By embracing an
analytical approach, these
companies identify their
most profitable customers,
innovation, optimize supply
chains and pricing, and
identify the true drivers of
investment in business analytics must
be visible and measureable. Who the
analysts are and what they’re seeking
to accomplish should be clearly communicated to the business, as should
8. Revise your strategies – often.
Your competitors will often duplicate
your analytical initiatives. Staying ahead
requires continuous review of strategy
and development of new skills and
Top five benefits of
6. evelop an analytical center
Create a centralized team approach –
an analytical center of excellence (ACE)
– which promotes the use of analytics
and associated best practices. Your
implementation of an ACE will depend
on your organization’s maturity and
requirements, but the most effective
implementations address all elements
of the organization’s analytic infrastructure: people, process, technology
and culture to support the business’
strategy and operations.
Get started now.
Find important questions that need
answering and problems that need to
be solved. Answer these questions,
solve these problems and create value for the organization. By creating
small wins in any business, function or
department, over time your company
will become an analytical competitor.
1. mproving the decision-making
7. Transform the culture.
A strong analytical culture has executive
sponsorship and encourages creativity.
Experimentation should be seen as part
of learning, and employees should be
given permission to fail as they learn
from trying new things.
Jim Davis is Senior Vice President and
Chief Marketing Officer for SAS.
When Computerworld asked 215 IT and
business professionals to name the key
benefits of business analytics software,
they received a wide range of responses.
The five most popular were:
2. peeding up the decision-making
3. etter alignment of resources with
4. Realizing cost efficiencies.
5. esponding to user needs for
availability of data on a timely basis.
Defining business analytics white paper:
P30|Art of the Possible
The art of the possible: business analytics
to measure corporate sustainability
By Alyssa Farrell
In the abstract, business analytics
presents a range of powerful options
to uncover meaningful insights that
promote action. And that promise is
compelling to virtually any organization. But the case becomes even more
persuasive when we consider how
it can be applied to one of the fastest-emerging issues in corporations
today: sustainability and the corporate
“environmental footprint.” Today,
companies are seeking to strengthen the so-called “triple bottom line”
that conceptually expands the
traditional financial framework to
encompass rigorous reporting on the
organization’s performance on sustainability issues such as the carbon
footprint, community development,
occupational safety and dozens of
Art of the Possible|P31
In a report from the Economist Intelligence Unit,
researchers report that the top three motivations for
sustainability initiatives are brand enhancement,
revenue growth and cost savings – in other words,
outcomes that have a direct impact on profitability.
Environmental protection only placed fourth on the list,
amply demonstrating that pragmatism and not altruism
is the dominant motivator.
Three planning challenges
Unfortunately, significant barriers have
impeded decisive corporate action. In
the first MIT Sloan Management Review
Business of Sustainability Survey,
researchers articulated three major
roadblocks. The first is a basic lack of
information upon which to base
sustainability efforts and decisions.
Despite the high profile for sustainability, managers often find themselves
forced to speculate about drivers of
sustainable performance and lack a
deep understanding of issues that are
relevant for their industry. Accessing,
interacting with and analyzing the
fundamental data about energy, water
and waste is a nonnegotiable premise
for effective sustainability.
Second, companies often have
conflicting definitions of precisely what
sustainability means to their
organizations. This makes it extremely
challenging to develop a meaningful
business case for sustainable investments and presents an often
insurmountable barrier to the effective
cross-functional collaboration that is
necessary for success.
Third, without that business case based
on accepted definitions, companies
struggle with precisely how to measure
the ROI of sustainability efforts. What’s
more, tangible and intangible costs and
benefits abound in the sustainability
discipline – but they can be especially
challenging to forecast because the
goals for greenhouse gas emissions
reductions established by governments are often in 10- and 20-year time
horizons, far exceeding the typical oneto three-year payback period.
Traditional reporting and analysis can
often fall short when attempting to predict future impacts of sustainability investments. Business analytics plays a
critical role by enabling the organization
to balance today’s ROI objectives with
longer planning horizons.
These challenges are not uncommon for emerging business issues.
Sustainability is a new discipline for most
organizations, one where there isn’t
a generation of tested and proven
models to call upon and modify. As a
result, many organizations forego the
effort to model the intangible benefits that may result from sustainable
practices. Or, they minimize important
externalities such as environmental or societal costs and benefits – all
of which can become tangible with
Business analytics at
work: gaining energy
efficiency at Poste
The art of the possible is already in
practice at leading organizations today.
The Poste Italiane Group uses software
from SAS to analyze energy efficiency
in more than 250 facilities, including
those with the highest energy consumption – such as data processing
centers, executive centers and the
largest branches. Their analysis has
identified best practices that led to an
immediate reduction in energy
consumption and a 7 percent reduction
in CO2 emissions. Future developments
involve correcting operation and
maintenance behaviors for the systems
and indirectly for the buildings.
P32|Art of the Possible
Sustainability has remained a top
priority with SAS precisely because
of its potential to deliver tremendous
business value. It’s not just the right
thing to do; it’s the smart thing to do.
In addition to employee engagement
practices, from health care to expanded
job opportunities, SAS has made great
progress in reducing its environmental
footprint. For example, a 1-megawatt
solar array is providing clean, renewable
energy to the public energy grid for the
Several construction projects at
SAS offices around the world utilize
principles. Notably, SAS is pursuing
Leadership in Energy and Environmental Design (LEED) certification for
a new conference facility and a new
cloud computing facility located at its
For more information on SAS
and sustainability, check out the
Corporate Social Responsibility Report:
The ROI matters
Despite these challenges, creating the
strongest possible business case is
an essential mandate for today’s sustainability directors. That’s because
although few observers fail to see the
importance of efforts to reduce carbon
output and minimize environmental impact, these benefits are highly unlikely
to achieve primacy in profit-driven enterprises. In a report from the Economist Intelligence Unit, researchers report that the top three motivations for
sustainability initiatives are brand enhancement, revenue growth and cost
savings – in other words, outcomes that
have a direct impact on profitability.
Environmental protection only placed
fourth on the list, amply demonstrating
that pragmatism and not altruism is the
However, while the pro forma income
statement in the analysis is paramount,
the attention organizations are paying
to sustainability matters is definitely
not merely pro forma. The actions,
when implemented, are far-reaching
and transformational. 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 the
US Army reports that 80 percent of
its construction meets Leadership in
Energy and Environmental Design
(LEED) standards, reducing its energy
costs by 8 percent.
Delivering green analytics
Transformational organizations require
a combination of descriptive and
predictive insight – the ability to track
meaningful green indicators, validate
strategies and costs before investing,
identify causal relationships and forecast outcomes. And in these areas,
business analytics can make the difference. Such a business analytics framework can empower the organization to:
• Measure sustainability activities
using accepted methodologies and
• eport on environmental perforR
mance to shareholders and regulators.
• mprove sustainability metrics using
analytical techniques such as optimization, forecasting and data mining to
deliver metrics that matter.
• educe resource usage by accurateR
ly forecasting resource requirements
needed to reach desired outcomes
for a department or enterprise.
Art of the Possible|P33
With business analytics, we start to see
the “art of the possible” with respect
to sustainability. You can measure
emissions and resource consumption
throughout a value chain or product
life cycle. You can ensure regulatory
compliance. And you can build green
strategies with predicted ROI. You
can determine which conservation
efforts or greenhouse-gas reduction
trategies will have the greatest impact
– physically and financially. And you
can identify ways to profit from
environmentally respectful goods and
Undoubtedly, embracing sustainability initiatives will lead to meaningful —
sometimes profound — changes to
processes and culture. This transformation can be an exciting opportunity
to innovate and redefine, to explore
new business models and markets.
By providing the right information and
insights, business analytics can be a
key enabler of strategic sustainability
Accessing, interacting with and analyzing
the fundamental data about energy, water
and waste is a nonnegotiable premise for
Read the full white papers:
The Business of Sustainability: What it Means to
Managers Now. MIT Sloan Management Review.
Management Magnified: Sustainability and
Corporate Growth. Economist Intelligence Unit, 2009
Measure and improve performance with
Alyssa A. Farrell, Manager of Sustainability and
Performance Management Solutions, has responsibility for SAS’ sustainability solutions. Farrell
works with customers to understand best practices and solutions for managing their business
with environmental responsibility in mind.