Introduction to Business Intelligence and Analytics
Business Intelligence and Analytics:
Session 1: Business Intelligence,
Data Science and Data Mining
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d
s
2
Meta Introduction:
Ove
r
a
l
l
g
o
a
l
s
o
f
th
i
s
c
l
as
s
Data
Warehousing
/
Data
Engineering
H
o
w
t
o
s
t
o
r
e
a
n
d
a
c
c
e
s
s
h
u
g
e
a
m
o
u
n
t
s
o
f
d
a
t
a
?
Data
Mining
/
Data
Science
H
o
w
t
o
d
e
r
i
v
e
k
n
o
w
l
e
d
g
e
a
n
d
p
r
o
f
i
t
a
b
l
e
b
u
s
i
n
e
s
s
a
c
t
i
o
n
o
u
t
o
f
l
a
r
g
e
d
a
t
a
b
a
s
e
s
?
Simulation
H
o
w
t
o
m
o
d
e
l
a
n
d
a
n
a
l
y
s
e
c
o
m
p
l
e
x
r
e
l
a
t
i
o
n
s
h
i
p
s
i
n
o
r
d
e
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t
o
d
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r
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v
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p
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o
f
i
t
a
b
l
e
b
u
s
i
n
e
s
s
a
c
t
i
o
n
?
3
Main
fo
c
u
s
areas
Provost, F.; Fawcett, T.: Data Science for Business; Fundamental Principles of Data Mining
and Data- Analytic Thinking. O‘Reilly, CA 95472, 2013.
Steve Williams
: Business Intelligence Strategy and Big Data Analytics, Morgan Kaufman
Elsevier, 2016
Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn, Guide to
Intelligent Data Analysis, Springer-Verlag London Limited, 2010
Carlo Vecellis, Business Intelligence, John Wiley & Sons, 2009
Eibe Frank, Mark A. Hall, and Ian H. Witten
: The Weka Workbench, M Morgan Kaufman
Elsevier, 2016.
Jason Brownlee, Machine Learning Mastery With Weka, E-Book, 2017
Nikhil Ketkar, Deep Learning with Python, Apress, 2017
François Chollet, Deep Learning with Python, Manning Publications Co., 2018.
4
Main
li
t
e
r
at
u
re
The
15th annual KDnuggets Software Poll
https://www.kdnuggets.com/polls/2014/analytics-data-mining-data-science-
software-used.html
Huge attention from analytics and data mining community and vendors, attracting
over 3,000 voters.
Software
5
The top 10 tools by share of users were
–
RapidMiner, 44.2% share ( 39.2% in 2013)
–
R, 38.5% ( 37.4% in 2013)
–
Excel, 25.8% ( 28.0% in 2013)
–
SQL, 25.3% ( na in 2013)
–
Python, 19.5% ( 13.3% in 2013)
–
Weka, 17.0% ( 14.3% in 2013)
–
KNIME, 15.0% ( 5.9% in 2013)
–
Hadoop, 12.7% ( 9.3% in 2013)
–
SAS base, 10.9% ( 10.7% in 2013)
–
Microsoft SQL Server, 10.5% (7.0% in 2013)
Software
D
e
c
i
s
i
o
n
S
u
p
p
o
r
t
S
y
s
t
e
m
s
i
n
t
h
e
b
r
o
a
d
e
s
t
s
e
n
s
e
c
a
n
b
e
d
e
f
i
n
e
d
a
s
Computer
technology
solutions
that
can
be
used
to
support
complex
decision
making
and
problem
solving.
[Shim
et
al.
2002]
Broad
definition
that
encompasses
many
areas
Application
systems
Mathematical
modeling
Data
driven
modeling
Subjective
modeling
De
c
i
s
io
n
Sup
p
o
r
t
Sys
t
ems
Technological
development
More
powerful
computers,
networks,
algorithms
Collect
data
throughout
the
enterprise
Operations,
manufacturing,
supply-chain
management,
customer
behavior, marketing campaigns, …
Exploit
data
for
competitive
advantage
8
Ubi
q
u
i
ty
o
f
d
a
ta
o
p
p
o
rtu
n
i
ti
es
There
is
no
unique
or
mathematical
definition
of
Business
Intelligence
The
Data
Warehousing
Institute
defines
Business
Intelligence
as…
The
process,
technologies
and
tools
needed
to
turn
data
into
information,
information
into
knowledge
and
knowledge
into
plans
that
drive
profitable
business
action.
Business
intelligence
encompasses
data
warehousing,
business
analytics
tools,
and
content/knowledge
management.
http://www.tdwi.org/
Busi
n
ess
In
t
e
l
lig
e
nc
e
:
De
f
init
i
o
n
(
1/2)
9
Increased
profitability
Distinguish
between
profitable
and
non-profitable
customers
Decreased
costs
Lower
operational
costs,
improve
logistics
management
Improved
Customer-Relationship-Management
Analysis
of
aggregated
customer
information
to
provide
better
customer
service,
increase
customer
loyalty
Decreased
risk
Apply
Business
Intelligence
methods
to
credit
data
can
improve
credit
risk
estimation
Ben
e
fi
t
s
o
f
Busi
n
ess
In
t
e
l
lig
e
nce
(
1/2)
10
Business
Intelligence
can
help
improve
businesses in
a
variety
of
fields:
•
Customer
analysis
customer
profiling
•
Behavior
analysis
fraud
detection,
shopping
trends,
web
activity,
social
network
analysis
•
Human
capital
productivity
analysis
•
Business
productivity
analysis
defect
analysis,
capacity
planning
and
optimization,
risk
management
•
Sales
channel
analysis
•
Supply
chain
analysis
supply
and
vendor
management,
shipping,
distribution
analysis
11
Ben
e
fi
t
s
o
f
Busi
n
ess
In
t
e
l
lig
e
nce
(
2
/
2
)
Marketing
Online
ad
v
ertising
R
ec
o
mmenda
t
ion
s
for
c
r
o
s
s
-
sell
i
ng
C
ustomer
re
lations
h
i
p
management
F
i
na
n
ce
C
r
edi
t
s
c
o
r
in
g
an
d
trading
Fraud
dete
ction
Workfo
r
ce
management
R
e
tail
Wal-Mart,
A
mazon
etc.
Some
mo
r
e
e
x
am
ples
12
Many
cellphone
companies
have
major
problems
with
customer
retention
Cellphone
market
is
saturated
Customer
churn
is
expensive
for
companies
Keep
your
customers
by
predicting
who
should
get
a
retention
offer
Exam
pl
e
2
:
Predi
cti
n
g
cus
t
om
e
r
c
h
urn
13
D
a
t
a
s
c
i
e
n
c
e
:
a
s
e
t
o
f
f
u
n
d
a
m
e
n
t
a
l
p
r
i
n
c
i
p
l
e
s
t
h
a
t
g
u
i
d
e
t
h
e
e
x
t
r
a
c
t
i
o
n
o
f
k
n
o
w
l
e
d
g
e
f
r
o
m
d
a
t
a
D
a
t
a
m
i
n
i
n
g
:
e
x
t
r
a
c
t
i
o
n
o
f
k
n
o
w
l
e
d
g
e
f
r
o
m
d
a
t
a
v
i
a
t
o
o
l
s
/
t
e
c
h
n
o
l
o
g
i
e
s
t
h
a
t
i
n
c
o
r
p
o
r
a
t
e
t
h
e
p
r
i
n
c
i
p
l
e
s
In
this
class,
we
do
both!
14
Data
s
c
i
e
nce
v
s.
d
ata
min
i
ng
Data
Dri
v
e
n
De
c
i
s
io
n-
ma
k
in
g
(DDD)
DDD:
practice
of
making
decisions
based
on
the
analysis
of
data
(rather
than
intuition)
Type-1 decision: “discover”
something
new
in
your
data
Wal-Mart/Target
example
Type-2
decision:
repeat
decisions
at
massive
scale
(automatic
decision
making)
Customer
churn
example
15
CONCLUSION
•
Success in today’s data-oriented business environment
requires being able to think about how these
fundamental concepts apply to particular business
problems—to think data analytically.
•
An understanding of these fundamental concepts is
important not only for data scientists themselves, but for
any one working with data scientists, employing data
scientists, investing in data-heavy ventures, or directing
the application of analytics in an organization.
•
Understanding the process and the stages helps to
structure our data-analytic thinking, and to make it more
systematic and therefore less prone to errors and
omissions.
Provost, F.; Fawcett, T.: Data Science for Business; Fundamental Principles of Data Mining
and Data- Analytic Thinking. O‘Reilly, CA 95472, 2013.
Steve Williams
: Business Intelligence Strategy and Big Data Analytics, Morgan Kaufman
Elsevier, 2016
Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn, Guide to
Intelligent Data Analysis, Springer-Verlag London Limited, 2010
Carlo Vecellis, Business Intelligence, John Wiley & Sons, 2009
Eibe Frank, Mark A. Hall, and Ian H. Witten
: The Weka Workbench, M Morgan Kaufman
Elsevier, 2016.
Jason Brownlee, Machine Learning Mastery With Weka, E-Book, 2017
Nikhil Ketkar, Deep Learning with Python, Apress, 2017
François Chollet, Deep Learning with Python, Manning Publications Co., 2018.
Sharda, R., Delen, D., Turban, E., (2018). Business intelligence, Analytics, and Data
Science: A Managerial Perspective, 4th Edition, Pearson.
17
L
i
t
e
r
at
u
re
Explore the fundamentals of Business Intelligence, Data Science, and Data Mining in Session 1. Understand how to solve business problems using data analytics, modeling principles, and practical implementation methods. Delve into data warehousing, data engineering, data mining, data science, simulation, and decision support systems. Discover key literature and popular software tools in the field.
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Presentation Transcript
Business Intelligence and Analytics: Session 1: Business Intelligence, Data Science and Data Mining
Meta Introduction: Overall goals of this class Knowhowtosolvebusinessproblems bydata-analyticthinking Haveanoverviewaboutprinciplesofhowtomodelandhowtosolve businessproblemsinanon-rigorousmanner Knowseveraltoolsandwaysofhowtopracticallyimplementsolution methods 2
Main focus areas DataWarehousing/DataEngineering Howtostoreandaccesshugeamountsofdata? DataMining/DataScience Howtoderiveknowledgeandprofitablebusinessactionoutoflarge databases? Simulation Howtomodelandanalysecomplexrelationshipsinordertoderive profitablebusinessaction? 3
Main literature Provost, F.; Fawcett, T.: Data Science for Business; Fundamental Principles of Data Mining and Data- Analytic Thinking. O Reilly, CA 95472, 2013. Steve Williams: Business Intelligence Strategy and Big Data Analytics, Morgan Kaufman Elsevier, 2016 Michael R. Berthold, Christian Borgelt, Frank H ppner, Frank Klawonn, Guide to Intelligent Data Analysis, Springer-Verlag London Limited, 2010 Carlo Vecellis, Business Intelligence, John Wiley & Sons, 2009 Eibe Frank, Mark A. Hall, and Ian H. Witten : The Weka Workbench, M Morgan Kaufman Elsevier, 2016. Jason Brownlee, Machine Learning Mastery With Weka, E-Book, 2017 Nikhil Ketkar, Deep Learning with Python, Apress, 2017 Fran ois Chollet, Deep Learning with Python, Manning Publications Co., 2018. 4
Software The 15th annual KDnuggets Software Poll https://www.kdnuggets.com/polls/2014/analytics-data-mining-data-science- software-used.html Huge attention from analytics and data mining community and vendors, attracting over 3,000 voters. 5
Software The top 10 tools by share of users were RapidMiner, 44.2% share ( 39.2% in 2013) R, 38.5% ( 37.4% in 2013) Excel, 25.8% ( 28.0% in 2013) SQL, 25.3% ( na in 2013) Python, 19.5% ( 13.3% in 2013) Weka, 17.0% ( 14.3% in 2013) KNIME, 15.0% ( 5.9% in 2013) Hadoop, 12.7% ( 9.3% in 2013) SAS base, 10.9% ( 10.7% in 2013) Microsoft SQL Server, 10.5% (7.0% in 2013)
Decision Support Systems DecisionSupportSystemsinthebroadestsense canbedefinedas Computertechnologysolutionsthatcanbeusedtosupportcomplexdecision makingandproblemsolving.[Shimetal.2002] Broaddefinitionthatencompassesmanyareas Applicationsystems Mathematicalmodeling Datadrivenmodeling Subjectivemodeling
Ubiquity of data opportunities Technologicaldevelopment Morepowerfulcomputers,networks,algorithms Collectdatathroughouttheenterprise Operations,manufacturing,supply-chainmanagement, customer behavior, marketing campaigns, Exploitdataforcompetitiveadvantage 8
Business Intelligence: Definition (1/2) Thereisnouniqueormathematicaldefinitionof BusinessIntelligence TheDataWarehousingInstitutedefinesBusiness Intelligenceas Theprocess,technologiesandtoolsneeded toturndataintoinformation, informationintoknowledgeand knowledgeintoplansthatdriveprofitablebusinessaction. Businessintelligenceencompassesdatawarehousing,businessanalytics tools,andcontent/knowledgemanagement. http://www.tdwi.org/ 9
Benefits of Business Intelligence (1/2) Increasedprofitability Distinguishbetweenprofitableandnon-profitable customers Decreasedcosts Loweroperationalcosts,improvelogisticsmanagement ImprovedCustomer-Relationship-Management Analysisofaggregatedcustomerinformationtoprovidebettercustomer service,increasecustomerloyalty Decreasedrisk ApplyBusinessIntelligencemethodstocreditdatacanimprovecreditrisk estimation 10
Benefits of Business Intelligence (2/2) BusinessIntelligencecanhelpimprove businesses inavarietyoffields: Customeranalysis customerprofiling Behavioranalysis frauddetection,shoppingtrends,webactivity,socialnetworkanalysis Humancapitalproductivityanalysis Businessproductivityanalysis defectanalysis,capacityplanningandoptimization,risk management Saleschannelanalysis Supplychainanalysis supplyandvendormanagement,shipping,distributionanalysis 11
Some more examples Marketing Online advertising Recommendations for cross-selling Customer relationship management Finance Credit scoring and trading Fraud detection Workforce management Retail Wal-Mart, Amazon etc. 12
Example 2: Predicting customer churn Manycellphonecompanieshavemajorproblems withcustomerretention Cellphonemarketissaturated Customerchurnisexpensiveforcompanies Keepyourcustomersbypredictingwhoshouldgetaretentionoffer 13
Data science vs. data mining Datascience:asetoffundamentalprinciplesthat guidethe extractionofknowledgefromdata Datamining:extractionofknowledgefromdatavia tools/ technologiesthatincorporatetheprinciples Inthisclass,wedoboth! 14
Data Driven Decision- making (DDD) DDD:practiceofmakingdecisions basedontheanalysisofdata(rather thanintuition) Type-1 decision: discover somethingnewinyourdata Wal-Mart/Targetexample Type-2decision:repeatdecisionsat massivescale(automaticdecision making) Customerchurnexample 15
CONCLUSION Success in today s data-oriented business environment requires being able to think about how these fundamental concepts apply to particular business problems to think data analytically. An understanding of these fundamental concepts is important not only for data scientists themselves, but for any one working with data scientists, employing data scientists, investing in data-heavy ventures, or directing the application of analytics in an organization. Understanding the process and the stages helps to structure our data-analytic thinking, and to make it more systematic and therefore less prone to errors and omissions.
Literature Provost, F.; Fawcett, T.: Data Science for Business; Fundamental Principles of Data Mining and Data- Analytic Thinking. O Reilly, CA 95472, 2013. Steve Williams: Business Intelligence Strategy and Big Data Analytics, Morgan Kaufman Elsevier, 2016 Michael R. Berthold, Christian Borgelt, Frank H ppner, Frank Klawonn, Guide to Intelligent Data Analysis, Springer-Verlag London Limited, 2010 Carlo Vecellis, Business Intelligence, John Wiley & Sons, 2009 Eibe Frank, Mark A. Hall, and Ian H. Witten : The Weka Workbench, M Morgan Kaufman Elsevier, 2016. Jason Brownlee, Machine Learning Mastery With Weka, E-Book, 2017 Nikhil Ketkar, Deep Learning with Python, Apress, 2017 Fran ois Chollet, Deep Learning with Python, Manning Publications Co., 2018. Sharda, R., Delen, D., Turban, E., (2018). Business intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson. 17
