White Paper
Data Mining:
The Top 3 Things You Need to Know to
Achieve Business Improvement from Big Data

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What is Big Data?
Forrester defines big data as “techniques and technologies that make handling data at
extreme scale affordable. It's about having the technology and people with the appropriate
analysis skills to allow firms to make sense of huge volumes of data in an affordable manner.”
What's So Special About Big Data?
It is estimated that 90 percent of the data in the world today was created in just the past 24 months.
One might ask, how is this possible? With 2.5 exabytes1 of new data created every day and more
data crossing the internet every second than was on the entire internet 20 years ago, it is easy to
see how this has occurred.
While companies have been using various CRM and automation technologies for many years to
capture and retain traditional business data, these existing technologies were not built to handle the
massive explosion in data that is occurring today. The shift started nearly 10 years ago with
expanding usage of the internet and the introduction of social media. But the pace has accelerated in
the past five years following the introduction of smart phones and digital devices such as tablets and
GPS devices. The continued rise in these technologies is creating a constant increase in complex
data on a daily basis.
The result? Many companies don't know how to get value and insights from the massive amounts of
data they have today. Worse yet, many more are uncertain how to leverage this data glut for
business advantage tomorrow.
In this white paper, we will explore three important things to know about big data and how companies
can achieve major business benefits and improvements through effective data mining of their own
big data.
Insight #1: Big Data is a Management Revolution
Understandably, there is a tremendous amount of interest, along with some confusion, about “big
data”–what it is, how it can help create competitive advantage. If you ask the opinion of thought
leaders at highly regarded academic and corporate institutions such as MIT, McKinsey and IBM, you
will hear largely universal consensus that big data is real and that it is only going to get bigger.
Harvard Business Review's editor in chief recently wrote of big data, “it has the potential to propel
companies to levels of performance we haven't seen in two decades.”2
Imagine the strides companies could make if they had access to all the petabytes and zettabytes of
data they have collected over the years. Not only would it allow for potentially groundbreaking
1
2
An exabyte is 1,000 times the size of a petabyte, which is the equivalent of roughly 20 million filing cabinets.
“Big Data for Skeptics”, Harvard Business Review, October 2012
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customer and market insights, but it would also enable significantly improved real-time decision
making.
Key findings: MIT Sloan School of Management and IBM's first annual
New Intelligent Enterprise Global Executive Study (2011)
 50% of the 3,000 respondents across 30 industries and 100 countries said that
improvement of information and analytics was a top priority in their organization.
 60% said innovating to achieve competitive advantage was a top business challenge.
 60% said their organization had more data than it can effectively use.
 Those deemed as top performing organizations were twice as likely to apply analytics
to activities.
Additional findings among study's “top performing” organizations:
 Analytics are used in the widest possible range of decisions, large and small.
 Twice as likely to use analytics to guide future strategies as lower performers.
 Twice as likely to use insights to guide day-to-day operations.
 Make decisions based on rigorous analysis at more than double the rate of lower
performers.
The results of MIT and IBM's 2011 New Intelligent Enterprise Study showed that organizations “want
analytics to exploit their growing data and computational power to get smart and innovative in ways
they never could before.” Bottom line, the study shows a proven correlation between top
performance and analytics-driven management. This has important implications to every
organization -- whether they are seeking growth, greater efficiency or competitive differentiation.
Smart business leaders see using big data for what it is: a management revolution. Through big
data, managers can know more about their business and use the data to make decisions based on
evidence rather than intuition, resulting in improved decision making and business performance. As
part of this management revolution, leaders must embrace this evidence-based decision making or
be replaced by others who do.3
Reflecting on technology's role with big data, Gartner analyst Mark Beyer recently said, “Because big
data's effects are pervasive, it will evolve (over the next several years) to become a standardized
requirement in leading information architectural practices, forcing older practices and technologies
into early obsolescence.”
3
“Big Data: The Management Revolution”, Harvard Business Review, October 2012
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By 2020, Gartner expects big data features and functionality (such as data mining) will be routinely
expected from traditional enterprise vendors.
“Organizations resisting this technology change will suffer severe economic impacts,” warns Beyer.
Insight #2: To Be Useful, Big Data Must Be Made Smaller
Most CRM and automation technologies introduced in the past two decades are simply not equipped
to handle the volume, velocity and variety of today's big data. This is especially true of unstructured
data from the digital channel (e.g., social networks, online shopping, and digital marketing).
While today's big data is too big for most existing technologies to handle, there are valuable insights
waiting to be unlocked in the massive amounts of operational (e.g., sales, costs), non-operational
(e.g., sales forecasts) and unstructured digital (e.g., search engine marketing) data being generated
today. To get it, companies must choose technologies designed to handle big data and facilitate
effective data mining to chunk data down to a manageable size. Such technology must also keep
data constantly refreshed and synchronized for relevancy as new data comes in every day.
In order to leverage big data, companies must make it smaller so current technologies can handle it.
Making big data smaller can be accomplished through data mining.
________________________________________________________________________________
What is Data Mining?
According to Gartner, “data mining is defined as the process of discovering meaningful correlations,
patterns and trends by sifting through large amounts of data stored in repositories. It employs
pattern recognition technologies, as well as statistical and mathematical techniques.” In essence,
data mining allows companies to harness everyday business data to obtain valuable knowledge
and insights that will allow for problem solving and business improvement.
________________________________________________________________________________________________________________________
Data mining uses automated data analysis to prepare big data for segmentation, dialog and
reporting. Because most companies have an incredible amount of data, it is important to begin the
data mining process by defining business objectives. This will help identify and address the specific
data needed for collection and analysis in order to achieve the desired insights. Without a starting
framework, companies might get overwhelmed trying to evaluate all the data in the company's data
warehouse and never achieve any meaningful insight to a business objective or challenge.
Sample business objectives for conducting a data mining exercise might include:
 Explanatory: To explain some observed event or situation
Example: Why have sales increased through Twitter?
 Confirmatory: To confirm a hypothesis
Example: Will a 5 channel touch strategy increase sales by 10% or more?
 Exploratory: To analyze data for new or unexpected relationships
Example: How is Facebook contributing to the company's blog traffic?
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What are Data Warehouses?
Data warehouses are formal
repositories of large volumes of
data that a company collects over a
long period of time. The gathered
data may be specialized and used
by various corporate departments
such as accounting, purchasing
and marketing. Having a
centralized repository of all the
company's data allows for more
manageable user access and
analysis.
What are Data Marts?
Data marts contain a subset of data from the
data warehouse, focused on a single subject.
Data marts are the access layer of the data
warehouse environment where specific
departments or approved team users can
access and utilize specific business data as
needed, without altering the data from the
central data warehouse.
Examples of benefits derived from creating a
data mart:
 Less cluttered than a central data warehouse.
 Easy access to frequently needed data.
 Greater flexibility and agility than a central
data warehouse.
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While some vendors offer technology that bypasses the need to create a data mart, we believe that it
is a necessary and beneficial step in an effective data mining process. By taking the time to create
the logical and physical structure of a data mart focused on a single subject or functional area,
companies are able to track and statistically analyze specific sub-set of data from the central data
warehouse. This approach systematically drives designated appropriate data into a data mart and
ensures the data stays fresh and relevant because only approved, relevant data will be updated in
the data mart as new data comes in.
Easy Access to Relevant Data
Designated data in the data mart is categorized for review and consumption by select business
teams or departments (e.g., marketing, finance). With easy access to relevant content, managers
can ideally identifying hidden patterns in a data set to solve a problem, identify and validate triggers
(e.g., evaluate results when an electronics retailer sends a discount for BlueRay DVD movies to
shopper who purchased a new HDTV), and gain other insights for business benefit. In the case of
sales and marketing, for instance, it delivers objective and actionable customer insights to show how
each customer is interacting with each channel. Such data will shed light on the customer's preferred
marketing content, timing and channels – potentially helping the company stop wasting time, money
and effort on ineffective content or channels.
Data mining has largely been used by traditional enterprises (including leading-edge and Fortune
500 companies and non-profit organizations) in the past decade to achieve tangible business
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benefit. The uses of data mining will continue to expand--regardless of industry or the type of product
or service offering--as analytics technologies are more widely adopted.
Business leaders appreciate the objective nature and the credible insights data mining provides
about their customers' preferences and consumer behaviors because it is based on the
organization's own collected data (e.g., point of purchase, loyalty programs, direct mail, web
analytics, social media chatter).
Data Mining Techniques
There is a wide variety of data mining techniques that can be used to help discover useful
information, depending upon the nature of the data and the insights being sought. Some common
methods include decision trees, regression modeling, clustering. (Editor's note: With so many
different options and benefits, we cannot adequately address the various techniques in the confines
of this white paper. If you would like to learn more, please talk with a member of our
technical/consulting staff for further information.)
Common Data Mining Techniques
 Profiling Populations
 Analysis of business trends
 Target marketing
 Usage Analysis
 Campaign effectiveness
 Product affinity
 Customer Retention and Churn
 Profitability Analysis
 Customer Value Analysis
 Up-Selling
 Conversion Funnel Analysis
 Revenue Attribution
 Promotion and Price Optimization
 Behavioral Segmentation
 Lifecycle Optimization
 Predictive Churn Modeling
It should be noted that data mining technologies
have been in use for years. According to a
study in 2003, for example, only 35% of large
companies surveyed at that time had deployed
data marts and data warehouses for data
mining purposes.4
Fortunately, in recent years, technologies
equipped to handle the volume, velocity and
variety of today's big data have improved
greatly. Also good news, as with most
technologies, prices have come down and are
quite reasonable compared to the original cost
of older CRM and automation technologies in
use today.
Insight #3: Beware of a DIY Approach
While effective big data and data mining technology are readily available today, quite often a
significant challenge is the acquisition, implementation and adoption of this advanced technology.
According to Bill Franks, chief analytics officer of Teradata and author of Taming The Big Data Tidal
Wave, “Today you can find products and solutions for whatever you need to do with big data. The
real problems are getting budget, doing the implementation, getting people up to speed on how to
4
“How Large Corporations Use Data Mining to Create Value”, Management Accounting Quarterly, 2003
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use the tools, getting buy in from various stakeholders, and pushing against a culture averse to
change.”5
Indeed, we have found similar challenges faced by our clients looking to adopt marketing analytics
and marketing automation technologies.
But an even greater challenge facing most companies today is the lack of necessary experience,
best practices and seasoned staff capable of implementing a successful data mining program inhouse. Without these crucial elements, it is highly likely a data mining program conducted internally
without the involvement and guidance of highly qualified and experienced experts will fail. This is
why most companies turn to outside consultants with the necessary technology and proven expertise
for guidance and support.
Let us stress further the importance of securing buy-in by all levels within the organization. Often
there must be organizational transformation in culture (e.g., management must come to view
analytics as essential to problem solving) and staff capabilities (e.g., make sure employees fully
understand and effectively use the methodologies) to achieve data mining success.
According to 40% of respondents in the 2011 IBM and MIT New Intelligent Enterprise Study, the
leading obstacle to widespread analytics adoption is lack of understanding of how to use analytics to
improve the business.
According to Gartner analyst Gareth Herschel, many organizations know they want customer data
mining software as part of their enterprise analytics strategy, but they are uncertain about how to
evaluate and deploy tools.
Our advice to organizations interested to get full benefit from big data is simple: avoid a DIY
approach if you want to save yourself time, money and frustration.
Three Stages of Analytics Maturity
Not sure how to characterize an organization's level of analytics capability? Consider the findings of
a 2011 study by IBM and MIT which identified three levels of analytics capabilities:
1) Aspirational: These organizations are the furthest from achieving their desired analytical goals.
Often they are simply searching for ways to cut costs. They have few of the necessary building
blocks (people, processes or tools) to collect, understand, incorporate or act on analytic insights.
2) Experienced: With some analytical experience, these organizations are looking to go beyond cost
management and want to develop better ways to collect, incorporate and act on analytics so can
begin to optimize their organization.
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Bill Franks work blog on November 13, 2012.
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3) Transformed: These organizations have substantial experience using analytics across a broad
range of functions. They use analytics as a competitive differentiator. Less focused on cutting costs,
they want to increase customer profitability and make smart investments in niche analytics for further
excellence.
IBM and MIT Study 2011: THE THREE STAGES OF ANALYTICS ADOPTION
Three capability levels - Aspirational, Experienced and Transformed - were based on how respondents rated their organization's
analytical prowess
ASPIRATIONAL
Key
obstacles
TRANSFORMED
Use analytics to guide actions
Use analytics to prescribe actions
Financial management and budgeting
All Aspirational functions
Operations and production
Sales and marketing
Strategy/business development
Customer Service
Product research/development
All Aspirational and Experienced
functions
Competitive differentiation through
innovation
Competitive differentiation through
innovation
Revenue growth (primary)
Cost efficiency (secondary)
Lack of understanding how to
leverage
analytics for business value
Lack of understanding how to
leverage
analytics for business value
Executive sponsorship
Culture does not encourage sharing
information
Business
challenges
EXPERIENCED
Use analytics to justify actions
Cost efficiency (primary)
Revenue growth (secondary)
Motive
Functional
proficiency
Skills within line of business
Ownership of data is unclear or
governance is ineffective
Moderate ability to capture,
aggregate
and analyze data
Data
Limited ability to capture, aggregate,
management
analyze or share information and
insights
Limited ability to share information
and
insights
Analytics
in Action
Rarely use rigorous approaches to
make decisions
Limited use of insights to guide future
strategies or day-to-day operations
Some use of rigorous approaches to
make decisions
Growing use of insights to guide
future
strategies, but still limited use of
insights
to guide day-to-day operations
Risk management
Customer Experience
Work force planning/allocation
General management
Brand and market management
Competitive differentiation through
innovation
Revenue growth (primary)
Profitability acquiring/retaining
customers (targeted focus)
Lack of understanding how to
leverage
analytics for business value
Management bandwidth due to
competing priorities
Accessibility of the data
Strong ability to capture, aggregate
and
analyze data
Effective at sharing information and
insights
Most use rigorous approaches to
make
decisions
Almost all use insights to guide
future
strategies, and most use insights to
guide day-to-day operations
Source: SLOANREVIEW.MIT.EDU
Numeric Analytics would like to add its further commentary to the IBM and MIT study's categories
based on our client experience:
Aspirational: These organizations typically are trying to “plug the gap” and train their staff as they
attempt to create a data-driven culture. Quite often, the harsh reality is some key staff will need to be
replaced because they have the wrong background or experience with these technologies.
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Experienced: This group “gets it” and have staffed appropriately for it (e.g., most likely experienced
executives have been brought in). It's highly likely there is still a tendency to make decisions that are
not based on the findings from the company's analytical data.
Transformed: These organizations are “analytics stars” but they are in the minority. They have the
right people with the right experience, along with cutting-edge business titles to reflect their role (e.g.,
Director of Analytics). But while a data-driven culture is in place and decisions are made based on
data, they typically don't have broader use or business cases to draw from. Outside consulting and
process and technology expertise is still needed.
Years ago, Numeric Analytics created its own digital intelligence maturity model based on our
extensive client work and observations. It is interesting to see the similarities between our Digital
Intelligence Maturity Model and the IBM and MIT Study's Three Stages of Analytics Adoption, as
shown below:
As you can see, the major difference between the two is that we have identified two levels of what
the IBM and MIT study identified as “experienced”. We call these levels intermediate and advanced.
Regardless of which stage an organization fits into regardless of which model, without question there
is a need for outside experts who have relevant cross-channel and cross-industry experience to
skillfully guide the way.
To be certain, creating an effective data mart(s) from the central data warehouse can be an arduous
task, but the long-term business benefits and efficiency gains are well worth the initial hard work.
“Organizations that do not include big data analyses as part of their sales strategies will miss
opportunities to recognize demand early and shape it,” according to Gartner analyst Gene Alvarez.
“(Organizations) must access and analyze new sources of data, including mobile customers'
activities and social graphs, to spot trends, predict demand, and provide an appealing customer
experience.”6
6
Information Week, July 31, 2012
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Where to Begin?
Many organizations are uncertain how to begin a data mining project and can be overwhelmed by all
the big data and data mining chatter in the media and by all the different vendors. If your
organization is feeling this way, it is understandable. We are pleased that you are reading this white
paper and hope that it has brought you more insights and clarity on this important topic.
Armed with this valuable information, it's time to proceed. Rest assured that we have “been there,
done that” when it comes to a solid track record of numerous successful data mining projects. When
beginning a data mining project with clients, we begin by assessing their current analytical maturity
level, which provides valuable insights and direction how best to proceed.
Over the years, our data mining clients have told us they are able to drive better business decision
making and have gained valuable insight on what marketing content and channels are working best
to engage target audiences. By leveraging their big data to obtain key insights, they have improved
data-based decision making capability and can solve business problems more quickly than ever
before.
Are you ready to achieve similar business improvements from your big data? Give us a call today.
About Numeric Analytics
At Numeric Analytics, our core competency is built on measurement and analytics. We help
organizations capitalize on data to drive better business decisions, automate their efforts, and
continuously drive measurable improvements through experience and proven optimization
techniques.
Our Analytics and Optimization Team helps organizations move away from poor analytics and
fragmented tactics resulting from the challenge of growing volume and complexity of data. We help
clients move to world-class optimization programs that effectively manage growing data and create
competitive advantage with analytics.
We understand the challenges and the issues surrounding marketing technologies, from
consideration to execution. We provide answers and direction to ensure all the variables are taken
into account so that the best automation solution is selected to drive your organization's integrated
marketing efforts.
With proven consulting expertise and a network of leading technology providers, we have
successfully reworked entire testing programs--redefining the processes, the people and the
platforms used--to create a proven methodology that delivers exponential returns.
Since our founding in 2007, we’ve completed over 600 successful client engagements, including
many Fortune 1000 companies. Over 500 million web pages are tracked through our
implementations. And 65% of our clients re-engage us on other projects. No one else in the market
provides the full spectrum of services that Numeric Analytics offers.
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Numeric Analytics takes pride in our mission to turn “insight into action” for our clients.
If you found this information useful, check out these other recent white papers that
have more information on topics addressed in this document:
 “The Role and Value of Automation in Integrated Marketing Success”
 “Multi-Channel Analytics: The Answer to the 'Big Data' Challenge and Key to
Improved Customer Engagement”
All these documents plus other white papers and case studies can be accessed at
www.numericanalytics.com/experienceWhitePapers.asp or call us at 972-496-7033
to request further information or to schedule a free consultation.
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Visit us at http://www.NumericAnalytics.com
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