CHAPTER 1.ppt

McGraw-Hill/Irwin Copyright © 2008, The McGraw-Hill Companies, Inc. All rights reserved.
Analytics to Work
Chapter
1

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• Putting analytics to work is about improving
performance in key business domains using data
and analysis.
• For too long, managers have relied on their
intuition or their “golden gut” to make decisions.
WHAT IT MEANS TO PUT ANALYTICS TO
WORK ?

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• For too long, important calls have been based not on
data, but on the experience and unaided judgment of
the decision maker.
• Some times intuitive and experience-based decisions
work out well.
• Socrates said, “The unexamined life isn’t worth
living”.
WHAT IT MEANS TO PUT ANALYTICS TO WORK ?
WORK ?

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• We’d argue that “The unexamined decision isn’t
worth making”.
• Becoming more analytical is not solely the
responsibility of a manager: it’s an essential concern
for the entire organization.
WORK ?

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• It’s no accident that the companies we cite as
outstanding analytical competitors are often also
outstanding performers.
• Analytics aren’t the only way an organization can
succeed, but in most industries there are excellent
illustrations that the analytical path is a viable route
to success.
WORK ?

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• Help manage and steer the business in turbulent
times.
• Analytics give managers tools to understand the
dynamics of their business, including how economic
and marketplace shifts influence business
performance.
BENEFITS OF BEING ANALYTICAL

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• Know what’s really working.
• Rigorous analytical testing can establish whether
your intervention is causing desired changes in your
business, or whether it’s simply the result of random
statistical fluctuations.

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• Leverage previous investments in IT and
information to get
More insight
Faster Execution and
More business value in many business processes
• Cut costs and improve efficiency.
• Optimization techniques can minimize asset
requirements.

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• Predictive models can anticipate market shifts and
enable companies to move quickly to slash costs and
eliminate waste.
• Manage risk.
• Greater regulatory oversight will require more
precise metrics and risk management models.

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• Anticipate changes in market conditions.
• You can detect patterns in the vast amount of
customer and market data coming your way.
• Have a basis for improving decisions over time.
• If you are using clear logic and explicit supporting
data to make a decision, you or someone else can
examine the decision process more easily and try to
improve it.

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What Do We Mean by “Analytical”?
By analytical we mean the use of analysis, data, and
systematic reasoning to make decisions.
• What kind of analysis?
• What kind of data?
• What kind of reasoning?

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• There are no hard-and-fast answers
• We contend that almost any analytical process can
be good if provided in a serious, systematic fashion.
• The most common is statistical analysis, in which
data are used to make inferences about a population
from a sample.

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• Variations of statistical analysis can be used for a
huge variety of decisions – from knowing whether
something that happened in the past was a result of
your intervention, to predicting what may happen in
the future.
• Statistical analysis can be powerful, but it’s often
complex.

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• When done correctly, statistical analysis can be both
simple and valuable.
• The key is to always be thinking about how to
become more analytical and fact based in your
decision making.
• Data gained through observation can also shed light
on a statistical association.

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• We may know that men with young families
purchase both beer and diapers in the grocery store.
• But only systematic observation can reveal which
they buy first, and Whether it makes sense to shelve
them in close proximity or at opposite ends of the
store.

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• Analytics aren’t just a way of looking at a particular
problem, but rather an organizational capability that
can be measured and improved.

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What kind of Questions Can Analytics
Answer?
• Every organization needs to answer some
fundamental questions about its business.
• Taking an analytical approach begins with
anticipating how information will be used to address
common questions.

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Answer?
These questions are organized across two
dimensions:
• Time frame. Are we looking at the past,
present, of future?
• Innovation. Are we working with known
information or gaining new insight?

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Answer?
• The matrix in figure I – I identifies the six
key questions that data and analytics can
address in organizations.
• Insight into the past is gained by statistical
modeling activities, which explain how and
why things happened.

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Answer?
• Insight into the present takes the form of
recommendations about what to do right
now.

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Answer?
For example
What additional product offering might
interest a customer.
Insight into the future comes from
prediction, optimization, and simulation
techniques to create the best possible future
results.

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Key questions addressed by analytics

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Analytics for the Rest of Us
• Analytical competition is a viable strategic choice
for companies in almost every industry.

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Why it’s Time to put Analytics to Work
• Most companies today have massive amounts of
data at their disposal.
• The data may come from transaction-oriented
applications such as ERP (Enterprise Resource
Planning) systems from software vendors such as
SAP and Oracle, scanner data in retail environments,
customer loyalty programs, financial transactions, or
clickstream data from customer Web activity.

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Why it’s Time to put Analytics to Work
• Companies, governments, and nonprofits, in
sophisticated economies and developing nations
alike.
• Collect and store a lot of data, but they don’t use it
effectively.

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Where Do Analytics Apply?
• Analytics can help to transform just about any part
of your business or organization.
• Many organizations start where they make their
money in customer relationship.
• They use analytics to segment their customers and
identify their best ones.

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• They analyze data to understand customer
behaviors, predict their customers’ wants and needs,
and resources devoted t marketing focus on the most
effective campaigns and channels.
• They price products for maximum profitability at
levels that they know their customers will pay.

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• Finally, they identify the customers at greatest risk
of attrition, and intervene to try to keep them.
• Supply chain and operations is also an area where
analytics are commonly put to work.
• Human resources a traditionally intuitive domain,
increasingly uses analytics in hiring and employee
retention.

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• Just as sports teams analyze data to pick
and keep the best players, firms are using
analytical criteria to select valuable
employees and identify those who are most
likely to depart.
• Analytics can also be applied to the most
numerical of business areas: Finance and
accounting.

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• In banking and insurance, the use of analytics to
issue credit and underwrite insurance policies grows
ever more common and sophisticated.

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When Are Analytics Not Practical?
• There are some times when being analytical just
doesn’t fit the situation.
• When There’s No time. Some decisions must be
made before data can be gathered systematically.
• When There’s No Precedent. If something has
never been done before, it’s hard to get data about it.

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• When History Is Misleading. Even when ample precedents
exist, as the fine print on the stockbroker ads warns, "past
performance is not necessarily indicative of future results”.
• When the Decision Maker Has Considerable Experience.
Sometimes a decision maker has made a particular decision
often enough to have internalized the process of gathering
and analyzing data.

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• If you’re an experienced home appraiser
For example
• You can approximate what a home is worth without
feeding data into an algorithm.

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• When the Variables Can’t Be Measured. Some decisions are
difficult to make analytically because the key variables in the
analysis are hard to measure with rigor.
For example
• while the process of finding a romantic partner of spouse has
been the subject of considerable quantitative analysis( as
employed by firms such as eHarmony)

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• We are not strong believers in the power of analytics
to help you choose a mate.
• Analytics can be start, but cannot replace intuitive
judgments I such domains; you may want to meet
your “match” in person before buying a ring!

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When Analytical Decisions Need Scrutiny
• We are inevitably headed to a more analytical future
• You can’t put all the data genies back in their server
bottles.
• But if we are going to use analytics, we have to do it
well.
• The same process and logic errors that cause people
to err without analytics can creep into analytical
decisions.

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Typical Decision- Making Errors
Logic Errors
• Not asking the right questions
• Making incorrect assumptions and failing to test
them
• Using analytics to justify what you want to do
(gaming or rigging the model/data) instead of letting
the facts guide you to the right answer

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• Failing to take the time to understand all the
alternatives or interpret the data correctly.
Process Errors
• Making careless mistakes (transposed numbers in a
spreadsheet or a mistake in a model)
• Failing to consider analysis and insights in decisions

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• Failing to consider alternatives seriously
• Using incorrect or insufficient decision-making
criteria
• Gathering data or completing analysis too late to be
of any use
• Postponing decisions because you’re always
dissatisfied with the data and analysis you already
have.

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Combining Art and Science
• Analytics will continue to be critical in financial
services and all other industries, so the bet decision
makers will be those who combine the science of
quantitative analysis with the art of sound reasoning.
• Such art comes from experience, conservative
judgment, and the savvy to question and push back
on assumptions that don’t make sense.

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• Art also plays a role in creatively formulating and
solving problems-from data collection to modeling
to imagining how results can best be deployed and
managed.
• Creating targets for analytical activity calls for a
mixture of intuition, strategy and management
frameworks, and experience.

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• To choose the best target, the decision
maker must also have a vision of where a
company and its industry are headed and
what its customers will value in the future.
• Recognizing the limits of analytics is a key
human trait that will not change.

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THE ANALYTICAL DELTA
• WHAT DOES IT TAKE TO PUT ANALYTICS TO
WORK in your business?
• What capabilities and assets do you need in order to
succeed with analytics initiatives?
Group them under the acronym DELTA – the Greek
letter (depicted as ) that signifies “change” in an
equation.

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Together they can change your business equation:
D for accessible, high – quality data
E for an enterprise orientation
L for analytical leadership
T for strategic targets
A for analysts

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Good data is the prerequisite for everything analytical;
it is “clean” in terms of accuracy and format.
• When drawn from several sources, it is integrated
and consistent.
• It is accessible in data warehouses, or else easily
found, filtered, and formatted.

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1. Major analytics applications, those that really
improve performance and competitiveness,
invariably touch multiple parts of the enterprise.
2. If your applications are cross-functional, it doesn’t
make sense to manage your key resources – data,
analysts, and technology – locally.

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3. Without an enterprise perspective, chances are you’ll
have many small analytical initiatives but few, if any,
significant ones.
•Organizations that really capitalize on analytics in
their business decisions, processes, and customer
relationships have a special kind of leadership.

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• Their senior managers are not just committed to the
success of specific analytical projects; they have a
passion for managing by fact.
• Their long – term goal is not just to apply analytics
in useful areas of the business, but to become more
analytical in decision – making styles and methods
across the enterprise.

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• Even very analytically inclined leaders are not going
to write blank checks to fund analytics generally.
• What really gets their attention is the potential return
of employing analytics where it will make a
substantial difference.

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• An analytical target may be strong customer loyalty,
highly efficient supply chain performance, more
precise asset and risk management, or even hiring,
motivating, and managing high-quality people.
• Companies need targets because they cannot be
equally analytical about all aspects of their
businesses, and analytical talent isn’t plentiful
enough to cover all bases.

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• Analysts have two chief functions: they
build and maintain models that help the
business hits analytical targets, and they
bring analytics to the organization at large
by enabling business people to appreciate
and apply them.

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• You need all five elements working together.
• Lack of any one of the DELTA elements can be a
roadblock to success.
A five – stage model of progress
• Stage1: Analytically impaired. The organization
lacks one or several of the prerequisites for serious
analytical work, such as data, analytical skills, or
senior management interest.

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• Stage 2: Localized Analysis. There are pockets of analytical
activity within the organization, but they are not coordinated
or focused on strategic targets.
• Stage 3: Analytical Aspirations. The organization envisions a
more analytical future, has established analytical capabilities,
and has a few significant initiatives under way, but progress
is slow-often because some critical DELTA factor has been
too difficult to implement.

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• Stage 4: Analytical companies. The organization has
the needed human and technological resources,
applies analytics regularly, and realizes benefits
across the business.
• But its strategic focus is not grounded in analytics,
and it hasn’t turned analytics to competitive
advantage.

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• Stage5: Analytical competitors. The organization
routinely uses analytics as a distinctive business
capability.
• It takes an enterprise-wide approach, has committed
and involved leadership, and has achieved large-
scale results.
• It portrays itself both internally and externally as an
analytical competitor.

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DATA
The prerequisite for Everything Analytical
• Structure (what is the nature of the data you have?)
• Uniqueness (how do you exploit data that no one else has?)
• Integration (how do you consolidate it from various sources?)
• Quality (how do you rely on it?)
• Access ( how do you get it?)
• Privacy (how do you guard it?) and
• Governance (how do you pull it all together?).

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DATA
Structure:
•How your data is structured matters because it affects
the types of analyses you can do.
•Data in transaction systems is generally stored in
tables.
•Tables are very good for processing transactions and
for making lists, but less useful for analysis.
•When data is extracted from a database or transaction
system and stored in a warehouse, it frequently is
formatted into “cubes”.

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DATA
Structure:
•Data cubes are collections of prepackaged
multidimensional tables.
•Cubes are useful for reporting and “scaling and dicing
” data.
•Data arrays consist of structured content, such as
numbers in row and columns (a spreadsheet is a
specialized form of array).
•By storing your data in this format, you can use a
particular field or variable for analysis if it is in the
database.

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DATA
Structure:
•Arrays may consist of hundreds or even thousands of
variables.
•Unstructured, nonnumeric data- the “last frontier” for
data analysis- isn’t in the formats or content types that
databases normally contain.
•It can take a variety of forms, and companies are
increasingly interested in analyzing it.

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DATA
You may hypothesize
For example
That the vocal tone of your customers during services calls is a
good predictor of how likely they are to remain customers, so
you would want to capture tat attribute.
Or you may analyze social media – blogs, web pages, and web –
based ratings and comments – to understand consumer
sentiments about your company.

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DATA
Uniqueness
•To get an analytical edge, you must have some unique data.
•For instance, no one else knows what your customers bought
from you-and you can certainly get value from that data.
•A unique strategy requires unique data.
•Data that was once unique and proprietary can become
commoditized too.
•Data gold mines can also potentially come from basic company
operations.

CHAPTER 1.ppt

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CHAPTER 1.ppt