Data Mining for Enterprise SolutionsA Business Perspective on Mining Data for Corporate Intelligence
By:
Lelia Morrill
Retrograde Data Systems
In Conjunction with
The Teradata Data
Mining Lab
Data Warehousing
Data Mining for Enterprise Solutions
EB-3019 > 1103 > PAGE 2 OF 14
Executive Overview . . . . . . . . 2
Marketplace Transitions . . . 3-4
Using Mining for Competitive Advantage . . . . . 4
Multidimensional Answers on a Global Scale . . . . . . . . 4-5
Adaptive Response for Leveraging Web Interactions . . . . . . . . . . . . . 5-6
The Value of Data Mining —Justifying the Investment. . . 6-7
Applying Data Mining to Solve Real-World Business Problems. . . . . . . . 7-8
The Data Mining Process . . 8-9
Where Does Data Mining Fit? . . . . . . . . . . . . 9-10
Data Mining Challenges . 10-13
The Business Analytic Roadmap . . . . . . . . . . . . 13-14
Summary . . . . . . . . . . . . . . . 14
Executive Overview
This paper explores data mining from the business perspective,
focusing on the premise that for a corporation to realize the poten-
tial ROI to be gained from high-value analytics, the business must
play a lead role in defining, validating, and translating results into
corporate profit.
Teradata®, a division of NCR, has evolved data mining from the
realm of raw algorithms to proven high-value analytics that have
had significant impact on corporate revenue generation and cost
savings. The types of problems that are appropriate for applying
mining solutions vary from simple market basket analysis to com-
plex customer insight and prediction. Data mining can be described
as the process of identifying and interpreting intrinsic patterns in
data to solve a business problem. The mining process yields an
analytic model that can be used to gain insight, reduce costs, and
increase profit. It is the key component of the Knowledge Discovery
process that incorporates analyzing, understanding, deploying, and
using mining results. Data mining analysts use and apply the tech-
nology. The business owns and drives the knowledge discovery
process. This teaming is key to exploiting and translating analytic
models into lucrative action. The business steers and guides the
process by determining and prioritizing the problems that can be
addressed through data mining, problems such as customer reten-
tion, marketing effectiveness, fraud detection, and behavioral
segmentation. The business validates results for expected outcome
and develops a strategy for deploying analytic models into relevant
action and successful programs.
Table of Contents
Data Mining for Enterprise Solutions
EB-3019 > 1103 > PAGE 3 OF 14
Marketplace Transitions
Data mining has consistently proven
its effectiveness in select and focused
situations from oil drilling to niche
identification since the mid 1980s. It has
been used for medical diagnosis, genome
analysis, and behavioral profiling. When
historical data exists with established
precedents for observing trends and
patterns over time, data mining can assist,
in any field, across all industries. If data
mining has been used successfully since
the 1980s, why is it just making a corpo-
rate splash? There have been historical
challenges that are at the root of data
mining’s slow emergence into business,
some of which include:
• Lack of standards and business
packaging
• Inability of tools to scale up to the
volumes of data
• Data mining tools struggle toward
industrial level strength when it comes
to real-world data problems (noisy,
missing and faulty corporate data)
• Databases designed for operational
processing cannot scale up to
voluminous analytical processing
• Corporate warehousing and methods
have been slow to evolve
• The business does not trust results
they cannot validate or understand
• Data analysis and mining are typically
niche-oriented processes that exist
outside of business processes
Technological advances in compute power
and speed, advanced data processing and
management techniques, and greater user
sophistication have changed the face of
today’s business. There is heightened
demand for accessing data, generating
knowledge, and solving difficult business
problems. The marketplace is poised,
more than ever, to exploit advanced data
mining for enterprise profitability. Then
why aren’t more companies devoting
budget and resource to getting up and
running with data mining? There are
several reasons:
1. Most tools still work in their own
proprietary environment. The process
of moving vast amounts of data out
of warehouse databases into the tool
environment (and back and forth
during the exploration process)
is cumbersome, time consuming,
and unintuitive.
2. Most databases are not optimized
for analytic processing.
3. The business has not integrated data
mining and knowledge discovery into
their workflow.
4. Companies do not support data
mining from the top.
Most tool providers and database vendors
are aware of the chasm between technology,
business, and mining. They are now trying
to reorient tools and databases, originally
Infrastructurefor Intelligence
Validate andUse Intelligence
Warehouse
KnowledgeDeployment
Business
ITMiners
DeployIntelligence
DevelopIntelligence
Figure 1. Enterprise mining is a collaborative effort between businesses, miners, and IT.
Data Mining for Enterprise Solutions
EB-3019 > 1103 > PAGE 4 OF 14
designed for operational processing or
sampled analysis, not high-volume
analytic processing, to handle increasing
data volume and the analytic business
complexity required to deliver answers.
Teradata has naturally met the challenge
with the combination of the Teradata
Database, which is designed for enterprise
level analytic processing; Teradata Ware-
house Miner, in-place data mining; a Data
Mining Lab, a dynamic, low risk environ-
ment where mining experts work with
clients to develop enterprise mining
solutions; and knowledge discovery
training. Teradata Warehouse is the
leading data warehousing technology
available today, and has now extended
its power to mining. This means a faster,
more intuitive process that keeps analysts
and business users closer to their data.
Teradata Warehouse Miner’s capability
for speedy throughput creates a dynamic,
engaging process where business users
see and use intricate, current mining
solutions. Teradata has made enterprise-
level mining achievable.
Using Mining for
Competitive Advantage
Data mining provides insight that renders
corporate knowledge. With executive
commitment, data mining can provide
powerful, predictive capability that leads
to more strategic business and strengthens
corporate positioning. How? By providing
immediate, accurate business answers
across a global spectrum, adaptive
response to critical market trends, agility
in dealing with competitive land mines,
and insight that translates to customer
intimacy. These are characteristics of
successful companies.
The level of corporate intelligence
required to maneuver judiciously and
deftly in today’s marketplace is confound-
ing. For centuries, business has been run
on the assumptions of experienced
management and the information from
simplistic reporting mechanisms, but the
scope of data that must be sifted through
to glean interesting information is becom-
ing exponentially overwhelming. This is
where data mining excels — in analyzing
the volumes of historical data that contain
the truth about what has occurred in
business operations. It helps companies
decipher quantitative, fact-based intelli-
gence that can be extracted from the data,
and assists them in predicting what will
happen in future situations.
Data mining brings high ROI to the
warehouse investment. It augments CRM
applications by inserting intelligence in
the form of scores, predictions, descrip-
tions, profiles, propensities, and value
into customer records. Data mining makes
CRM smarter. It is becoming a major
component of establishing business
direction and strategic positioning in
today’s progressive companies.
Multidimensional Answers
on a Global Scale
Most companies acknowledge the wealth
of information stored in historical data
and have put forth commitment and
effort to building warehouse environ-
ments that store and manage this data.
But many of these labors have gone by
the wayside because business users don’t
necessarily see the value of static data,
because finding answers to complex
TeradataData Mining and
OLAP Assists
CRM
Channel Analysis:What is the best
channel to reach mycustomer base?
Churn Analysis:Which of my customersare my loyal customers,
and who will alwaysswitch suppliers?
Propensity to Buy:Who is most likely
to purchase thistype of product?
Custom Analytics
Customer Value:Which of my
customers aremost profitable?
Fraud Detection:How can I tell if a transaction is fraudulent?
Cross Sell:What other products
is this customerlikely to buy?
Teradata Warehouse
Miner
Figure 2. Data mining makes CRM smarter.
Data Mining for Enterprise Solutions
EB-3019 > 1103 > PAGE 5 OF 14
questions is difficult and time-consuming
when using standard reporting and data
access tools alone. The value of data
mining is the strength and comprehen-
siveness that it brings for extracting
answers to enterprise-level, multidimen-
sional business questions. With good
tools, the right skill sets, and a quality
data environment, tough questions can
be answered and predictive solutions
deployed globally.
For example, a global company wanted to
understand product trends across stores on
multiple continents. Data mining helped
them to understand which stores were
doing best and worst. It also helped them
understand which factors, such as location,
weather, demographic profile, season,
product category, size of store, and years
in business, were impacting trends and
revenues. The resultant understanding was
then deployed to the sales team who put
together tailored programs to fit each
store’s needs, to the marketing team who
designed more targeted, relevant cam-
paigns, and to the product development
team that bundled and repackaged for
better customer service. A predictive model
was deployed to help strategic planners
choose optimal sites for new stores. Data
mining is a comprehensive process, but
can provide far reaching answers.
Adaptive Response for
Leveraging Web Interactions
Whether reacting to market trends, or
dealing intelligently with an on-line
customer, data mining helps companies
to respond real-time, and to adapt to the
situation at hand. If an on-line customer is
frustrated and having difficulty, or perhaps
having success and considering serious
product purchase, most companies would
like to intercede to create a satisfying
experience that leads to immediate or
future sales. Companies can use data
mining in this situation to develop
descriptive models that profile customer
behavior, on-line trends and buying
patterns. They can also use data mining
to develop predictive models that score
customers for value, propensity to
respond to ads, propensity to buy (various
products), or to defect. And, they can
develop mining models that can predict
how much a customer will buy, how much
they will spend, how often they will shop,
and how satisfied they are. All of these
models can then be integrated and
deployed online to adaptively respond to
an individual user session. The integrated
suite can be deployed to the customer
service center, which can respond know-
ingly to each customer’s request. Models
can be developed singularly or in concert.
Once web data have been organized and
integrated into a customer data warehouse
environment, the most valuable mining
can occur — Total Customer Management
across all channels. Data mining can be
used to develop a hard hitting set of
high-value analytics, for example:
• How is the business growing across
the web channel?
• How can we make our total product
offering available through the web?
• What are the ramifications to our
standard points of distribution, e.g.,
the Call Center?
Data Warehouse
Historical Data
Intelligence
CustomerTransactions
Meaningful,PersonalCustomer
Interaction
Smarter CRM
OperationalDatabases
Detailed Data
High-ValueAnalytics
Figure 3. Intelligence mined from corporate data is leveraged across the enterprise.
Data Mining for Enterprise Solutions
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The business must participate in, own,
and drive the process, and bring their
acumen to a sound, strategic deployment
strategy. Integrated mining creates the
ability to respond adaptively in real-time,
real-world business.
The Value of Data Mining �
Justifying the Investment
Companies can acquire facts about their
customers through querying the data
warehouse. They can organize customer
data so that CRM applications can take
advantage of managing relationships and
lifecycles. But to get to a point of customer
intimacy, where the customer is under-
stood at an individual level, including
their behavior, trends and preferences, the
analytic power of data mining is required.
Data mining is one of the most lucrative
reasons to build a data warehouse, and
brings far greater value to CRM.
In the 90s, fraud occurring in the insurance
industry accounted for eighty billion dollars
in losses per year across the industry. Using
data mining to offset fraudulent activity
even by 10% meant saving 8 billion dollars
per year. Progressive credit card companies
have understood the value of data mining
and have used models in an operational
mode to circumvent fraud, saving millions
per analytic model per year. Many of those
companies are extending their tried and true
data mining models to web activity, eager to
save several million dollars more per year.
The stories of successful mining, and the
millions that can be earned and saved,
extend across all industries. Manufactur-
ing has used mining to hone capacities,
loads, and processing, saving millions in
lost time and breakdowns. Telecommuni-
cation companies have used mining to
prevent churn, to cross sell, to bundle,
and gain customer insight, saving and
earning millions per year. Banks have
successfully implemented mining for
target marketing, offering new products
and services, and bundling products to
better serve customers’ needs, thereby
saving and earning millions.
It is unknown how effective data mining
will be at any given business until the
data, technology, and culture have been
assessed for readiness and potential
business profit. But what is clear is that
without data mining, companies will not
discover the power of insight and knowl-
edge that exists currently or potentially in
their corporate data. Recognition of the
importance of data warehousing has been
a big step that has gained momentum
over the last several years. Having clean,
reliable, organized data allows for consis-
tency and repeatability in the complex,
compute intensive mining process. With
warehousing as the foundation, data
mining brings the power and intelligence
layer to the warehouse environment, and
ultimately to the business user’s desktop.
The revenue gains from the effective
application of high-value analytics have
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Figure 4. The end result of mining is an analytic model trained on business history.
Data Mining for Enterprise Solutions
EB-3019 > 1103 > PAGE 7 OF 14
historically justified both warehousing
and mining investments, and are playing
a larger role in CRM considerations.
Applying Data Mining
to Solve Real-World
Business Problems
Business mining solutions have been
successfully implemented across all
industries at several of the largest compa-
nies worldwide. Because each business
situation is different, and the historical
data captured over the years is far from
standardized or consistent across compa-
nies, it’s virtually impossible to develop a
turnkey mining solution for complicated
enterprise-level questions. Data mining
is a process, and although several of the
components within the process are
automated, there are several phases that
require domain expertise and analytic
shrewdness to sculpt, develop, decipher,
and deploy actionable results. The key
factors to successful mining include
quality data, historical precedence,
experienced analysts, champion business
users, comprehensive tools, and an
integrated, robust environment. The final
result is a valuable analytic model that
allows on-going capability to describe
and predict the business.
Given the right ingredients and a clear
definition of the problem, mining can
be applied to achieve in-depth answers.
Here is a short list of types of business
issues that lend themselves to data
mining solutions:
Customer Segmentation and
Behavioral Profiling
Segmentation is a way to derive homoge-
neous groups based on common traits.
Those attributes in combination provide
insight whereby profiles can be synthe-
sized and segment ids assigned to each
customer. The segments can be further
analyzed and programs derived that are
tailored to the profile of each group,
maximizing response and engagement.
Customer Retention
Patterns of flight are analyzed and factors
leading to churn are discerned. The analytic
model uses identified patterns of flight to
predict risk of current customers allowing
the company to intervene with offers,
programs and enticements that will retain
the customer. Used in conjunction with
customer profitability, lifetime value, and
cross-sell analysis, campaigns can be further
targeted to high-value customers who
exhibit high risk potential and who have
propensity to buy or respond to offers.
Customer Profitability
Customers can be segmented into groups
based on value and profitability. Once
factors of high to low profitability have
been identified, whether behavioral,
demographic, or psychographic, customer
profitability can be predicted by identifying
similar factors in new customers.
Customer Lifetime Value
Patterns of behavior and activity that lead
to high/low value over time are identified
and overlaid onto newer customers. These
are then used to predict the lifetime value
of each customer. Based on findings,
programs can then be tailored to enhance,
maintain, or drop relationships.
Customer Satisfaction
Attributes are identified by business
domain experts that can be used to identify,
understand, and interpret satisfaction.
Metrics, such as revenue per year, increase
per year, number of items purchased, credit
rating, and activity levels, can be used to
segment and score customers. Tailored
programs are then implemented to increase
satisfaction per segment.
Customer Acquisition
Acquiring new customers is far more
intelligent and targeted when done in
conjunction with (or after) profitability,
lifetime value, and/or propensity to
respond analysis. This targets marketing
efforts to the group of prospects that
has the highest potential for value and
loyalty and those who demonstrate high
likelihood of responding.
Targeted Marketing
Segmentation analysis can provide profiles
of demographic and behavioral attributes
as they map to high-value customers. Then
marketing can be focused, as mentioned
above, to targeted segments of high-value,
likely responders with solid potential. This
makes for intelligent, fact-based marketing.
Effective Campaigns
A CRM tool assists companies in managing
customer, marketing, and campaign data.
Data Mining for Enterprise Solutions
EB-3019 > 1103 > PAGE 8 OF 14
Data mining can then be used to gain
customer insight, monitor existing
campaigns, and analyze the effectiveness
of campaigns over time. Attributes
of success can then be applied to new
campaigns and best practices honed
for most profitable results.
Cross Selling
Data mining can be used to discover the
attributes that lead to high propensity to
buy. The model can then be overlaid onto
current customers who can be marketed to
based on high propensity to purchase, thus
enhancing individual portfolios, creating
greater loyalty, and increasing retention.
Channel Management
Data mining assists companies in better
understanding which channels are most
effective for various segments of cus-
tomers. Patterns of activity are analyzed
to understand trends of usage. Once
understood, offers, programs, services,
and products can be introduced to the
customer in an engaging, responsive
dialogue. Channels can be enhanced to
better serve customers.
Sales Forecasting
Mining can be used to understand sales
trends, to predict revenue, and to estab-
lish business drivers leading to increased
sales. Profiles of sales representative
behavior can be synthesized to profile
best sales practices.
Fraud Detection
Data mining has been used to help
companies identify attributes that can and
do potentially lead to fraud. If data have
been captured over time, patterns can be
used to predict fraud on an on-going
basis. If data have not been captured and
organized through time, data mining can
be used to investigate suspicious charac-
teristics of operational data. For example,
in the case of medical fraud, if attributes
such as number of visits, number of
claims, amount of charges, or number
of procedures, is off average and predicted
values, investigations can be initiated with
the justification of analysis insight to
support and direct.
E-Business and Web Mining
Integrate any or all of the above data
mining models and deploy onto web
servers for adaptively responding to
customer clicks and observed behavior.
Integrate web data with customer ware-
house data and use the high-value
analytics of data mining to gain a total
customer view, behavioral patterns, and
insight across all channels.
The Data Mining Process
Data mining is a process — it is not a
shrinkwrapped package. To be successful,
actionable, and profitable, it must be a
collaboration driven by the business,
developed by mining analysts, and
supported by IT.
To be efficient and repeatable, an environ-
ment designed for industrial strength
warehousing and analytic processing is
necessary. Teradata has hit the mark,
developing the right skill sets, tools,
and architecture for ensuring successful
mining, and has incorporated the practi-
cal knowledge, learned through years of
mining experience, into the Teradata Data
Mining Method. This methodology,
outlined below, has been at the core of
successful data mining project implemen-
tations at some of the world’s largest
companies.
1. Develop Project Scope and Plan —
checkpoints throughout the process.
2. Identify and clarify the business
problem or question to be solved —
this gives the project focus.
3. Determine and prepare the technology
and architecture or environment for
mining — this ensures processing
efficiency and effectiveness.
4. Select, analyze and prepare the data
for the mining process — data mining
hinges on this step — knowing the
data intimately so that it can be used
precisely and intelligently to attain
relevant business results.
5. Develop analytic models — choose
best methods and algorithms, make
final variable selections, iterate to
best model.
Data Mining for Enterprise Solutions
EB-3019 > 1103 > PAGE 9 OF 14
6. Deploy results — compare results
to expected outcomes, validate with
domain experts, test analytic models
for accuracy, deploy to the business
in whatever form is most suitable
(can be incorporated into applications,
databases, warehouse processes or as
standalone code).
7. Transfer knowledge between analysts
and business users throughout the
discovery process.
Business users play a key role in identify-
ing the business question(s) that will be
answered, verifying potential business
factors and data sources, determining
the validity of the analytic models, and
developing deployment strategies for
using the analytic model for business
answers. For example, deciding how
to best deploy a customer value model.
Should it be invoked for scoring in the
database once a customer has been a
patron for six months? Or should it be
manually invoked by territory analysts
who can use it to assess clients per region
and develop targeted programs? Or
perhaps it should be invoked in batch
mode, providing weekly reports to the
right business analysts. These are the types
of considerations that business users will
work through as they formulate strategies
for deploying their predictive models into
useful decision workflow.
The end result of the mining process is
an analytic model that can be used for
understanding the past and predicting
the future. Analytic models can be
deployed into the decision environment
for on-going predictive capability. The
deployment strategy is designed by the
business user and implemented by the
IT organization. Bringing the process
full circle to completion ensures that the
value and ROI of data mining are realized.
Where Does Data Mining Fit?
Corporations discovered the value of
data in the eighties and the importance
of customer centric focus in the nineties.
Businesses continue running operational
systems to manage capture and modifica-
tion of business data, and the processes
are expanding daily with web interac-
tions. How does data mining fit into
all of this? Data mining processing uses
warehouse data as input. It crunches
through historical data finding patterns
and developing rules about business.
Once the analysis is run, business users
validate the output. The intelligence from
the analysis is then incorporated back
into the warehouse in the form of scores,
predictions, forecasts, and descriptions.
Decision applications that access ware-
house data, including CRM, will also
have access to the mined intelligence.
Data mining works hand in hand with
warehousing. The two technologies and
processes complement each other and
offer mutual benefit. A warehouse that
will be used for mining should be
designed with architectural considera-
tions for eventual mining. This also
propagates inclusion of the right players
up-front — a combination of business,
miners, and IT — ensuring that business
needs are met and technical environment
is accounted for.
Build
DevelopAnalytic Model
Feedback Loop
Use�Deploy toTest & Deploy
Test model can be deployed as:� Code� Database triggers� Called module� One-time report
Reports
OLAP
DW
OperationalDatabases
DSS
� Data mining ! process
Figure 5. Deploy analytic models into the enterprise for high-value business usage.
Data Mining for Enterprise Solutions
EB-3019 > 1103 > PAGE 10 OF 14
The final result of mining, that is scores,
predictions, and descriptions, can be
propagated through the business commu-
nity in a variety of ways. One way to make
the data accessible in an ad hoc manner is
by integrating it into warehouse data and
making it available through multi-
dimensional data access tools.
Data mining is not pushbutton and has,
therefore, been very difficult to package
and shrinkwrap as a standalone applica-
tion or with other business applications
such as CRM. Although data mining is an
intensive business process that takes time,
with expertise, a framework based on
experience can be used for faster develop-
ment. Once analytic models are developed,
decision applications, including CRM, can
take advantage of the intelligence generated
by the mining process.
Data Mining Challenges
What are the ingredients of successful
mining? The right people, an integrated,
technological environment, good tools,
and sound business commitment. There
are difficult challenges within each of
these components.
Developing Data Mining Skills
One of the biggest challenges to creating
mining as an internal corporate service is
developing the skill sets. A skilled analyst
will have expertise in statistics, machine
learning algorithms, business analysis,
and technology. Because data mining is
a relatively new field, skilled data mining
analysts are difficult to find. However, this
should not deter companies from moving
forward with data mining. There are many
avenues for developing skills internally,
including hiring data mining consultants
who develop data mining capability with
the objective of transferring knowledge.
This, along with a comprehensive training
program, can build a core competency in
a methodical and confident manner.
Business users must also be trained,
starting with the specifics of the data in
their warehouse. Knowing what is there
and how to navigate it brings a more
savvy business user to the data mining
project. Business users should then be
Technical Components
TOOLS
SYSTEMS
, Load, Access, OLAP, Mining
, WEB, RDBMS, Metadata,
Transform => dtl/aggr warehouse> Access & Presentation
Operational Warehouse
DW INFRASTRUCTURE
Predictive AppAnalyticServer
Warehouse Data Model
Location Customer Market
ProductSalesFinancial
Analytic Business Models
Location Customer Market
Product Sales
Financial
Branch Retention Market Customer
Loss Usage Content
Marketing Workstation
Figure 6. Integrated, business-driven architecture.
Data Mining for Enterprise Solutions
EB-3019 > 1103 > PAGE 11 OF 14
trained in the data mining process and
finally, in how to deploy and use data
mining results to greatest advantage.
Data warehouse practitioners from the IT
community must be trained in the techni-
cal and functional aspects of the mining
process and tools so that they can support
analysts in preparing the environment,
accessing the data, and deploying results
into databases and applications. If IT is
trained up front, they will understand
how to augment their warehouse business
discovery processes with questions that
will bring mining into the warehouse
for up-front value in sync with phased
implementation.
The Right Technological Environment
The right technological environment for
data mining has a foundation of good
quality data. In today’s terms, that means
data warehousing. Data mining can occur
without a warehouse in place, but the
problems with gathering and cleansing the
data can seem insurmountable. Also, once
the process has been completed for one
model, the cycle has to start again from
scratch for each subsequent model. There
is no repeatability. The data warehouse
provides a natural environment for
efficient, on-going data mining. It also
becomes the repository for data mining
results. This includes the information
gleaned from historical data about how
the business has been running and why,
the resultant analytical models, and finally,
the scores, predictions, and intelligence
that come from the data mining endeavor.
Although it would seem that data mining
should be the next logical step after
developing a warehouse, in fact, it contin-
ues to be a great difficulty for most
companies that attempt it. This is because
relational database management systems
(RDBMS) were originally designed for
operational processing of high-speed
transactions. Adding, deleting, or modify-
ing records is an entirely different process
than analyzing volumes of historical data.
The persistent challenges include:
• Inability to scale up to the new data
volumes being generated by historical
and web transactions
• Inability of database vendors to
efficiently take advantage of parallel
processing capability
• Inability of mining tools to go directly
against the data source (data ware-
house databases), creating awkward
and cumbersome movement of data
in and out of environments
Teradata Database was originally developed
for high-speed, analytic processing. Mining
an enterprise-level warehouse is now
achievable through Teradata Warehouse’s
parallel, scalable capability. The detailed data
sitting static in too many warehouses can be
Data Warehouse Data
Data Warehouse Data
Mined Intelligence
Name Addr �#Prods Tot$ #Yrs
Name Addr �#Prods Tot$ #Yrs
Prop to buy Prod X, Y, Z, LTV,Prof�ty score, Churn score, Cluster id�
Figure 7. Customer records are augmented with mined intelligence and deployed through OLAP tool.
Data Mining for Enterprise Solutions
EB-3019 > 1103 > PAGE 12 OF 14
brought to life for detailed transaction level
mining. The Teradata solution takes data
mining to a new level — one where enter-
prise-level mining solutions are a reality.
The Right Tools
When developing a data mining practice,
mining analysts typically team up with IT
to determine which tools work best within
the technical architecture. Tools are
usually chosen on the basis of:
Comprehensiveness
Is there a variety of statistical and
machine learning algorithms, for example,
Factor Analysis, Decision Tree, Linear
Regression, Logistical Regression, Rule
Induction, Neural Net, or Clustering?
Different modeling techniques work
for various problems.
Data Manipulation
Can the tool work with data directly in
the source database or must the data be
moved in and out of the tool environment
for derivation, transformation, modeling,
and testing?
Functionality
Is there surrounding functionality that
encases the mining engine, allowing users
to set parameters, easily read output,
understand the validity of the model,
change settings, choose different variables,
create new variables on the fly, and create
and save work flows?
Metadata
Is there easy access to information about
the development and use of the analytic
models? Is the model building informa-
tion integrated with warehouse data
information?
Tools have become more user-friendly,
sophisticated, and industrial strength
compared to ten years ago when mining
was making its first emergence into
corporate scenarios. But most still fall
short in terms of scalability and integra-
tion into RDBMS environments for
in-place mining of detailed data. Teradata
Warehouse Miner’s in-place mining
diminishes the problem of data move-
ment. The data is at the source, and
6.!IT! Deploy models & resulting
intelligence into databases, applications, job-streams
5.!Business! Verify results, confirm expected
outcomes, test model, develop deployment strategy, use on-going
4.!Mining Experts! Prepare data, experiment ! with modeling approaches
(algorithms methods), build analytic models, test, validate
3.!IT! Gather data, define subsets,
develop access routines, prepare technical architecture
1.!Business Users! Initiate project, clarify issue
define business parameters
2.!Mining Experts! Data pre-analysis, sculpt
project with business, clarify expected outcomes
7.!Mining Expert! Monitor, validate, hone,
refresh models over time
Figure 8. Mining process division of labor.
Data Mining for Enterprise Solutions
EB-3019 > 1103 > PAGE 13 OF 14
iterating through exploration, data
selection, and preparation is part of
an integrated, intuitive process. Teradata
Warehouse Miner reduces processing
times by orders of magnitude.
A comprehensive data mining solution
that gets customers up and running,
starting with education, proof of concept
projects, tool evaluation, and a learning
environment for developing new skill sets,
is necessary to experience the benefit.
Management commitment is necessary for
exploiting data as the corporate asset that
it is, and for realizing and acting on the
potential knowledge that is locked within.
BusinessUsers
Analysts / MinersWarehouseAdministrator
Reports,AdHoc Queries
Data Analysis,Statistics
Analytic Models/Scores, Propensities,
Predictions, Descriptions
DataWarehouse
MiningResults
OLAP Teradata WarehouseMiner Stats
Teradata WarehouseMiner & Mining Partner Tools
Figure 9. Knowledge generation, management and deployment within the warehouse.
Business Analytic Roadmap
Phased solution development and
implementation
Developing an Analytic Roadmap brings
into focus those issues and problems that
can be solved by mining the data ware-
house, and prominently engages the
business community to drive and own
the knowledge discovery process. Bringing
these issues to the forefront at the onset
of warehouse development creates the
possibility of realizing immediate high
return value from the warehouse imple-
mentation. It has been proven that users
who receive up front value are more likely
to support and use the warehouse to
business advantage. A successful ware-
house implementation is not just a
technological feat — it must also be a
cultural achievement that engages the
business community in an interactive,
high-quality data environment where
they can ask questions, find answers,
and synthesize results into knowledge and
business action. An Analytic Roadmap
will give guidance and direction to
developing such an environment.
A mining architect conducts an intensive
discovery process that identifies business
issues, problems, and questions that are
decision oriented and complex, and which
can be clarified and/or solved using data
mining processes. The types of questions
asked might include:
• Questions that are supported by
existing historical data (data that have
been identified as candidate or current
subject areas in the data warehouse).
• Questions that will provide high ROI
if addressed through data mining
and/or analytical reporting (OLAP).
• Questions that the business has
identified as urgent.
• Questions that do not have data
available to support the solution,
but if augmented by third-party data,
could be solved (requires further gap
analysis project).
Data Mining for Enterprise Solutions
Teradata.com
EB-3019 > 1103 > PAGE 14 OF 14
Value of the Analytic Roadmap
Developing an enterprise analytic frame-
work is similar to developing an enterprise
data model. It brings everyone to a consis-
tent understanding of data mining, provides
education in relevant, everyday business
terms, and builds knowledge of the mining
process by breaking down the necessary
steps to get from abstract ideas to imple-
mented solutions. Important outcomes
are the sense of business ownership that
is developed and an understanding of how
to map mining projects to warehouse
implementations to successfully exploit
the warehouse for greater ROI.
Summary
There have been many challenges on
the road to making data mining viable.
Teradata Warehouse has effectively
overcome these obstacles by providing:
• ability to deal with volumes of data
• in-place mining
• integration between mining and
warehousing
• the ability to scale up with processing
and data demands
• the ability to score directly in the
database, making the results immediately
available to the business community
Teradata Warehouse mining transforms
what is otherwise an ad hoc, desktop
process into a profitable enterprise
capability. The promise of translating
high-value analytics into business solu-
tions has been realized in the Teradata
Warehouse mining products and services.
This paper was developed by Lelia Morrill
of Retrograde Data Systems and the
Teradata Data Mining Lab. For more
information, please contact Mike Rote,
Director of Teradata Data Mining for
Teradata, a division of NCR.
Loyalty
Satisfaction
Propensityto Buy
Profitability LifetimeValue
Propensityto Churn
Customer
Profitability
Satisfaction
Retention
Forecasting LifetimeValue
Loss
Financial
TargetMarketing
Mkt BasketAnalysis
Cross-SellStrategies
CampaignEffectiveness
Life CycleSequence
BestCampaign
Marketing
ChannelAnalysis
BestPractices
SalesForecasting
PartnerProfiling Bundling
RepProfiling
Sales
InventoryAnalysis
ShipmentAnalysis
ShipperProfiling
WarehouseOptimization
MaintenanceForecasts
TimelineOptimization
Equipment
Supply/Demand
New ProductProjections
Price PointAnalysis
ProductOptimization
LifecycleAnalysis
ProductBundling
Product
Figure 10. The Analytic Roadmap provides a framework for Enterprise Knowledge Management.
Teradata and NCR are registered trademarks of NCR Corporation. NCR continually enhances products as new technologies and components become available. NCR, therefore, reserves the right to change specifications without prior notice. All features, functions and operations described herein may not be marketed in all parts of the world. Consult your Teradata representative or Teradata.com for the latest information.
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