There are a variety of uses for analytics in sports business. From player development to business development, organizations use analytics to improve the performance of their organization in an effort to sustain their brands and garner revenue. These concepts will be further discussed throughout the course. However, for this assignment, students will identify the variety of career opportunities in sports analytics.

Find at least two careers of interest in the sports business industry that utilize analytics. You can use job search sites like Indeed, LinkedIn, Monster, or others. Careers may also be found directly on websites for organizations like Nike or ESPN. Once you identify two careers, complete a 500-750-word paper that includes the title of each position, as well as the essential functions or job duties for each. Also, include qualification standards like specific education requirements, prior experience, and specific job knowledge or skills. Lastly, include a reflection that captures why the careers you selected are of interest.

Prepare this assignment according to guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required.

Business Analytics
Order Description
Having covered the CRISP-DM methodology at length at the university, you decide to apply it to this project.
You are free to make reasonable assumptions about possible data sources. However, keep in mind that some data may not be allowed to be used in the Netherlands. You need not know the precise laws in the Netherlands or elsewhere, just highlight your legal/ethical concerns if any arise.
(a) Elaborate on the Business Understanding: determine business objectives and possible ways to achieve them. Assess the situation, making assumptions where necessary, and determine data mining goals. [35%]
(b) DiscussthenextstagesofDataUnderstandingandDataPreparation.Howdoesyour plan of these stages look like? Think of additional data sources that might be useful for this problem. Be creative but realistic. Describe all data sources in terms of their expected properties (structured, unstructured, 4Vs). Comment on practical challenges that may arise from using these sources. [30%]
(c) What variable do you expect to use as target? What specific challenges your predictive analytics on detecting fraudulent claims might face using the past data? Why will you need to partition the data for predictive modelling? Will over-sampling be needed? [35%]
Analytics in Practice – Individual Assignment

The assignment is due on 21 January 2016 and will count for 60% of the final module
mark. All work must be submitted via my.wbs. There is an upper limit of 2000 words and
you should aim to express yourself as concisely as possible.
Introduction
The following case is based on a real client project of IBM:
“Zorg en Zekerheid is a medium-sized and independent regional health insurer in the
Netherlands, with more than 460 employees and more than 380,000 policyholders. The
company is committed to providing accessible and affordable healthcare. The majority of
policyholders and healthcare providers submit claims for treatments that have actually
taken place. However, a small number commit fraud — for example by adapting invoices.
There are instances of policyholders who, after returning from vacation, submit invoices
for medical costs made abroad. Further examination shows that the invoiced amount has
been altered and is many times higher than the original amount. There are also instances
of “up-coding” — a form of fraud committed by healthcare providers performing simple
services but claiming for more complex alternatives, which results in higher costs.”
Zorg en Zekerheid needed a more accurate and efficient solution to detect claims fraud.
Assume you have been hired by Zorg en Zekerheid to work on this project — to build a
predictive modelling solution that translates many data elements from a diverse range of
sources into quantitative risk ratings for fraudulent claims.
Tasks
Having covered the CRISP-DM methodology at length at the university, you decide to
apply it to this project.
You are free to make reasonable assumptions about possible data sources. However, keep
in mind that some data may not be allowed to be used in the Netherlands. You need not
know the precise laws in the Netherlands or elsewhere, just highlight your legal/ethical
concerns if any arise.
(a) Elaborate on the Business Understanding: determine business objectives and
possible ways to achieve them. Assess the situation, making assumptions where
necessary, and determine data mining goals. [35%]
(b) Discuss the next stages of Data Understanding and Data Preparation. How does your
plan of these stages look like? Think of additional data sources that might be useful
for this problem. Be creative but realistic. Describe all data sources in terms of their
expected properties (structured, unstructured, 4Vs). Comment on practical
challenges that may arise from using these sources. [30%]
(c) What variable do you expect to use as target? What specific challenges your
predictive analytics on detecting fraudulent claims might face using the past data?
Why will you need to partition the data for predictive modelling? Will over-sampling
be needed? [35%]
Let’s build a smarter planet
Zorg en Zekerheid
Health insurer detects insurance fraud with IBM
SPSS predictive analytics software
Overview
Business Challenge
Zorg en Zekerheid needed a more accurate
and efficient solution to detect claims
fraud – including the wrongful practices
of “up-coding” and adapting invoices.
Solution
Zorg en Zekerheid turned to the data
mining solution, IBM® SPSS® Modeler
to detect anomalies regarding claims.
What Makes it Smarter
Modeler easily detects patterns and
trends in structured or unstructured data
using a unique visual interface, supported
by advanced analytics. It has allowed the
firm to more accurately and efficiently
detect fraud better than previously-used
data mining solutions.
The Result
IBM® SPSS® Modeler has taken the
guesswork out of fraud detection by
automatically discovering anomalies that
point to fraud. Fraud investigations that
once took weeks now take merely days.
Zorg en Zekerheid is a medium-sized and independent regional health
insurer in the Netherlands, with more than 460 employees and more
than 380,000 policyholders. The company is committed to providing
accessible and affordable healthcare. The customer’s health is key,
which is demonstrated by high-quality services, short lines of
communication with healthcare providers and its non-profit basis. By
keeping close contact with care providers, such as family practitioners,
physiotherapists and other healthcare specialists, Zorg en Zekerheid is
able to make good arrangements regarding the rates and the criteria
with which health services must comply.
Dectecting fraud from millions of records
The majority of policyholders and healthcare providers submit claims
for treatments that have actually taken place. However, a small number
commit fraud – for example by adapting invoices. There are instances
of policyholders who, after returning from vacation, submit invoices for
medical costs made abroad. Further examination shows that the
invoiced amount has been altered and is many times higher than the
original amount.
There are also instances of “upcoding” – a form of fraud committed by
healthcare providers performing simple services but claiming for more
complex alternatives, which results in higher costs. Through active
anti-fraud measures, Zorg en Zekerheid aims to reduce costs and
ensure that premiums of policyholders remain affordable.
These days, most claims are submitted digitally, straight from the care
provider to the insurance company. There are millions of records, and
the challenge is to quickly identify the records that are fraudulent.
Let’s build a smarter planet
In 1999, Zorg en Zekerheid set up a Special Investigations Unit to
detect and combat fraud. This department of fraud experts – consisting
of four investigators, an analyst and a unit manager – is primarily
engaged in detecting anomalies regarding claims. These deviations are
then investigated to determine possible fraudulent practices. Once
providers engaging in fraudulent practices have been tracked down,
money paid to them can then be recovered.
Previous solutions time consuming, inaccurate
To uncover fraudulent cases, Zorg en Zekerheid was using software that
analyzed data on the basis of pre-defined risk indicators. This required
manually selecting the data on the basis of these indicators and subsequently
determining if fraud was involved. This tended to be a very timeconsuming
process that did not always produce the desired results.
“For the detection of fraud, it is important for us to be able to look into
data without knowing in advance what we are going to find and which
records will be involved,” said Andor de Vries, fraud analyst with Zorg
en Zekerheid. “This is referred to as ‘unsupervised learning’ and
requires a solution capable of analyzing larger quantities of data,
discovering patterns automatically and bringing anomalies to light.”
Looking to increase accuracy and efficiency
After working with the previous software for three years, Zorg en
Zekerheid sought a solution that would produce more accurate results.
The main criterion was that the “chance of being caught” had to be
greater. In other words, the new solution had to be capable of detecting
deviations – and ultimately cases of fraud – more accurately and
efficiently. Zorg en Zekerheid began examining various data mining
solutions, including SAS® and IBM SPSS branded software solutions.
Business Benefits
Rather than manually • selecting data
on the basis of risk indicators, IBM
SPSS predictive analytics software
automatically discovers patterns
and anomalies
• Significantly improved accuracy of
detected deviations
• Previously, the full investigation
process took weeks – now the firm can
track down fraud cases within days
• Financial results of the Special
Investigation Unit have doubled each
year since 2007
Smarter Insurance: Using predictive analytics to better detect fraud
Instrumented IBM SPSS predictive analytics enables Zorg en Zekerheid to easily
detect patterns and trends in structured and unstructured data –
helping the firm to better identify fraud.
Interconnected Quick fraud detection helps Zorg en Zekerheid streamline its
communication with healthcare providers – helping the insurer to
keep its rates low and customer satisfaction high.
Intelligent IBM SPSS predictive analytics takes the guesswork out of fraud
detection. Fraud investigations that once took weeks now take
merely days.
Let’s build a smarter planet
“After the pilot with IBM SPSS Modeler, we were so enthusiastic about
the results that I thought it was redundant to look elsewhere. We have
worked with the software for two years now and I’ve never lost my
initial enthusiasm. I recommend Modeler to everybody.”
In order to demonstrate the power of IBM SPSS Modeler, a pilot project
was set up in which a model was created to detect deviations in claims.
“We had just solved a fraud case and I gave the data regarding that case
to IBM SPSS consultants to incorporate in the pilot project,” continued
De Vries. “After a quick analysis, they selected five healthcare providers
who were potentially submitting fraudulent claims, from a total of over
a hundred providers. The healthcare provider which had just turned out
to be fraudulent, was also on that list. From that moment on, I was sure
that I was dealing with the right party.”
IBM SPSS Modeler shortens fraud investigations,
saves money
From day one, IBM SPSS Modeler has made a considerable contribution
to Zorg en Zekerheid’s fraud detection approach, and the organization
has made great progress in this regard ever since. Not only has the
accuracy of detected deviations improved significantly, but the process
moves much faster as well.
“While previously the full investigation process might have taken
weeks, we are now able to track down fraud cases within days,”
concluded De Vries. “We typically express the added value of our
department in terms of financial results. By using IBM SPSS Modeler,
these results have doubled each year since 2007. We are obviously very
satisfied with this score.”
Solution Component
Software
• IBM® SPSS® Modeler
“While previously the full
investigation process
might have taken weeks,
we’re now able to track
down fraud cases within
days. We typically express
the added value of our
department in terms of
financial results. By using
IBM SPSS Modeler,
these results have doubled
each year since 2007.”
— Andor de Vries, fraud analyst,
Zorg en Zekerheid
YTC03065USEN-01
About IBM Business Analytics
IBM Business Analytics software delivers complete, consistent and
accurate information that decision-makers trust to improve business
performance. A comprehensive portfolio of business intelligence,
predictive analytics, financial performance and strategy management, and
analytic applications provides clear, immediate and actionable insights
into current performance and the ability to predict future outcomes.
Combined with rich industry solutions, proven practices and professional
services, organizations of every size can drive the highest productivity,
confidently automate decisions and deliver better results.
As part of this portfolio, IBM SPSS Predictive Analytics software helps
organizations predict future events and proactively act upon that insight
to drive better business outcomes. Commercial, government and
academic customers worldwide rely on IBM SPSS technology as a
competitive advantage in attracting, retaining and growing customers,
while reducing fraud and mitigating risk. By incorporating IBM SPSS
software into their daily operations, organizations become predictive
enterprises – able to direct and automate decisions to meet business goals
and achieve measurable competitive advantage. For further information
or to reach a representative visit www.ibm.com/spss.
© Copyright IBM Corporation 2010
IBM Corporation
Route 100
Somers, NY 10589
US Government Users Restricted Rights – Use, duplication of disclosure restricted by
GSA ADP Schedule Contract with IBM Corp.
Produced in the United States of America
May 2010
All Rights Reserved
IBM, the IBM logo, ibm.com, WebSphere, InfoSphere and Cognos are trademarks
or registered trademarks of International Business Machines Corporation in the
United States, other countries, or both. If these and other IBM trademarked terms
are marked on their first occurrence in this information with a trademark symbol
(® or TM), these symbols indicate U.S. registered or common law trademarks owned
by IBM at the time this information was published. Such trademarks may also be
registered or common law trademarks in other countries. A current list of IBM
trademarks is available on the web at “Copyright and trademark information” at
www.ibm.com/legal/copytrade.shtml.
SPSS is a trademark of SPSS, Inc., an IBM Company, registered in many
jurisdictions worldwide.
Other company, product or service names may be trademarks or service marks of
others.
Please Recycle
Business Analytics software

Business Analytics
Order Description
Having covered the CRISP-DM methodology at length at the university, you decide to apply it to this project.
You are free to make reasonable assumptions about possible data sources. However, keep in mind that some data may not be allowed to be used in the Netherlands. You need not know the precise laws in the Netherlands or elsewhere, just highlight your legal/ethical concerns if any arise.
(a) Elaborate on the Business Understanding: determine business objectives and possible ways to achieve them. Assess the situation, making assumptions where necessary, and determine data mining goals. [35%]
(b) DiscussthenextstagesofDataUnderstandingandDataPreparation.Howdoesyour plan of these stages look like? Think of additional data sources that might be useful for this problem. Be creative but realistic. Describe all data sources in terms of their expected properties (structured, unstructured, 4Vs). Comment on practical challenges that may arise from using these sources. [30%]
(c) What variable do you expect to use as target? What specific challenges your predictive analytics on detecting fraudulent claims might face using the past data? Why will you need to partition the data for predictive modelling? Will over-sampling be needed? [35%]
Analytics in Practice – Individual Assignment

The assignment is due on 21 January 2016 and will count for 60% of the final module
mark. All work must be submitted via my.wbs. There is an upper limit of 2000 words and
you should aim to express yourself as concisely as possible.
Introduction
The following case is based on a real client project of IBM:
“Zorg en Zekerheid is a medium-sized and independent regional health insurer in the
Netherlands, with more than 460 employees and more than 380,000 policyholders. The
company is committed to providing accessible and affordable healthcare. The majority of
policyholders and healthcare providers submit claims for treatments that have actually
taken place. However, a small number commit fraud — for example by adapting invoices.
There are instances of policyholders who, after returning from vacation, submit invoices
for medical costs made abroad. Further examination shows that the invoiced amount has
been altered and is many times higher than the original amount. There are also instances
of “up-coding” — a form of fraud committed by healthcare providers performing simple
services but claiming for more complex alternatives, which results in higher costs.”
Zorg en Zekerheid needed a more accurate and efficient solution to detect claims fraud.
Assume you have been hired by Zorg en Zekerheid to work on this project — to build a
predictive modelling solution that translates many data elements from a diverse range of
sources into quantitative risk ratings for fraudulent claims.
Tasks
Having covered the CRISP-DM methodology at length at the university, you decide to
apply it to this project.
You are free to make reasonable assumptions about possible data sources. However, keep
in mind that some data may not be allowed to be used in the Netherlands. You need not
know the precise laws in the Netherlands or elsewhere, just highlight your legal/ethical
concerns if any arise.
(a) Elaborate on the Business Understanding: determine business objectives and
possible ways to achieve them. Assess the situation, making assumptions where
necessary, and determine data mining goals. [35%]
(b) Discuss the next stages of Data Understanding and Data Preparation. How does your
plan of these stages look like? Think of additional data sources that might be useful
for this problem. Be creative but realistic. Describe all data sources in terms of their
expected properties (structured, unstructured, 4Vs). Comment on practical
challenges that may arise from using these sources. [30%]
(c) What variable do you expect to use as target? What specific challenges your
predictive analytics on detecting fraudulent claims might face using the past data?
Why will you need to partition the data for predictive modelling? Will over-sampling
be needed? [35%]
Let’s build a smarter planet
Zorg en Zekerheid
Health insurer detects insurance fraud with IBM
SPSS predictive analytics software
Overview
Business Challenge
Zorg en Zekerheid needed a more accurate
and efficient solution to detect claims
fraud – including the wrongful practices
of “up-coding” and adapting invoices.
Solution
Zorg en Zekerheid turned to the data
mining solution, IBM® SPSS® Modeler
to detect anomalies regarding claims.
What Makes it Smarter
Modeler easily detects patterns and
trends in structured or unstructured data
using a unique visual interface, supported
by advanced analytics. It has allowed the
firm to more accurately and efficiently
detect fraud better than previously-used
data mining solutions.
The Result
IBM® SPSS® Modeler has taken the
guesswork out of fraud detection by
automatically discovering anomalies that
point to fraud. Fraud investigations that
once took weeks now take merely days.
Zorg en Zekerheid is a medium-sized and independent regional health
insurer in the Netherlands, with more than 460 employees and more
than 380,000 policyholders. The company is committed to providing
accessible and affordable healthcare. The customer’s health is key,
which is demonstrated by high-quality services, short lines of
communication with healthcare providers and its non-profit basis. By
keeping close contact with care providers, such as family practitioners,
physiotherapists and other healthcare specialists, Zorg en Zekerheid is
able to make good arrangements regarding the rates and the criteria
with which health services must comply.
Dectecting fraud from millions of records
The majority of policyholders and healthcare providers submit claims
for treatments that have actually taken place. However, a small number
commit fraud – for example by adapting invoices. There are instances
of policyholders who, after returning from vacation, submit invoices for
medical costs made abroad. Further examination shows that the
invoiced amount has been altered and is many times higher than the
original amount.
There are also instances of “upcoding” – a form of fraud committed by
healthcare providers performing simple services but claiming for more
complex alternatives, which results in higher costs. Through active
anti-fraud measures, Zorg en Zekerheid aims to reduce costs and
ensure that premiums of policyholders remain affordable.
These days, most claims are submitted digitally, straight from the care
provider to the insurance company. There are millions of records, and
the challenge is to quickly identify the records that are fraudulent.
Let’s build a smarter planet
In 1999, Zorg en Zekerheid set up a Special Investigations Unit to
detect and combat fraud. This department of fraud experts – consisting
of four investigators, an analyst and a unit manager – is primarily
engaged in detecting anomalies regarding claims. These deviations are
then investigated to determine possible fraudulent practices. Once
providers engaging in fraudulent practices have been tracked down,
money paid to them can then be recovered.
Previous solutions time consuming, inaccurate
To uncover fraudulent cases, Zorg en Zekerheid was using software that
analyzed data on the basis of pre-defined risk indicators. This required
manually selecting the data on the basis of these indicators and subsequently
determining if fraud was involved. This tended to be a very timeconsuming
process that did not always produce the desired results.
“For the detection of fraud, it is important for us to be able to look into
data without knowing in advance what we are going to find and which
records will be involved,” said Andor de Vries, fraud analyst with Zorg
en Zekerheid. “This is referred to as ‘unsupervised learning’ and
requires a solution capable of analyzing larger quantities of data,
discovering patterns automatically and bringing anomalies to light.”
Looking to increase accuracy and efficiency
After working with the previous software for three years, Zorg en
Zekerheid sought a solution that would produce more accurate results.
The main criterion was that the “chance of being caught” had to be
greater. In other words, the new solution had to be capable of detecting
deviations – and ultimately cases of fraud – more accurately and
efficiently. Zorg en Zekerheid began examining various data mining
solutions, including SAS® and IBM SPSS branded software solutions.
Business Benefits
Rather than manually • selecting data
on the basis of risk indicators, IBM
SPSS predictive analytics software
automatically discovers patterns
and anomalies
• Significantly improved accuracy of
detected deviations
• Previously, the full investigation
process took weeks – now the firm can
track down fraud cases within days
• Financial results of the Special
Investigation Unit have doubled each
year since 2007
Smarter Insurance: Using predictive analytics to better detect fraud
Instrumented IBM SPSS predictive analytics enables Zorg en Zekerheid to easily
detect patterns and trends in structured and unstructured data –
helping the firm to better identify fraud.
Interconnected Quick fraud detection helps Zorg en Zekerheid streamline its
communication with healthcare providers – helping the insurer to
keep its rates low and customer satisfaction high.
Intelligent IBM SPSS predictive analytics takes the guesswork out of fraud
detection. Fraud investigations that once took weeks now take
merely days.
Let’s build a smarter planet
“After the pilot with IBM SPSS Modeler, we were so enthusiastic about
the results that I thought it was redundant to look elsewhere. We have
worked with the software for two years now and I’ve never lost my
initial enthusiasm. I recommend Modeler to everybody.”
In order to demonstrate the power of IBM SPSS Modeler, a pilot project
was set up in which a model was created to detect deviations in claims.
“We had just solved a fraud case and I gave the data regarding that case
to IBM SPSS consultants to incorporate in the pilot project,” continued
De Vries. “After a quick analysis, they selected five healthcare providers
who were potentially submitting fraudulent claims, from a total of over
a hundred providers. The healthcare provider which had just turned out
to be fraudulent, was also on that list. From that moment on, I was sure
that I was dealing with the right party.”
IBM SPSS Modeler shortens fraud investigations,
saves money
From day one, IBM SPSS Modeler has made a considerable contribution
to Zorg en Zekerheid’s fraud detection approach, and the organization
has made great progress in this regard ever since. Not only has the
accuracy of detected deviations improved significantly, but the process
moves much faster as well.
“While previously the full investigation process might have taken
weeks, we are now able to track down fraud cases within days,”
concluded De Vries. “We typically express the added value of our
department in terms of financial results. By using IBM SPSS Modeler,
these results have doubled each year since 2007. We are obviously very
satisfied with this score.”
Solution Component
Software
• IBM® SPSS® Modeler
“While previously the full
investigation process
might have taken weeks,
we’re now able to track
down fraud cases within
days. We typically express
the added value of our
department in terms of
financial results. By using
IBM SPSS Modeler,
these results have doubled
each year since 2007.”
— Andor de Vries, fraud analyst,
Zorg en Zekerheid
YTC03065USEN-01
About IBM Business Analytics
IBM Business Analytics software delivers complete, consistent and
accurate information that decision-makers trust to improve business
performance. A comprehensive portfolio of business intelligence,
predictive analytics, financial performance and strategy management, and
analytic applications provides clear, immediate and actionable insights
into current performance and the ability to predict future outcomes.
Combined with rich industry solutions, proven practices and professional
services, organizations of every size can drive the highest productivity,
confidently automate decisions and deliver better results.
As part of this portfolio, IBM SPSS Predictive Analytics software helps
organizations predict future events and proactively act upon that insight
to drive better business outcomes. Commercial, government and
academic customers worldwide rely on IBM SPSS technology as a
competitive advantage in attracting, retaining and growing customers,
while reducing fraud and mitigating risk. By incorporating IBM SPSS
software into their daily operations, organizations become predictive
enterprises – able to direct and automate decisions to meet business goals
and achieve measurable competitive advantage. For further information
or to reach a representative visit www.ibm.com/spss.
© Copyright IBM Corporation 2010
IBM Corporation
Route 100
Somers, NY 10589
US Government Users Restricted Rights – Use, duplication of disclosure restricted by
GSA ADP Schedule Contract with IBM Corp.
Produced in the United States of America
May 2010
All Rights Reserved
IBM, the IBM logo, ibm.com, WebSphere, InfoSphere and Cognos are trademarks
or registered trademarks of International Business Machines Corporation in the
United States, other countries, or both. If these and other IBM trademarked terms
are marked on their first occurrence in this information with a trademark symbol
(® or TM), these symbols indicate U.S. registered or common law trademarks owned
by IBM at the time this information was published. Such trademarks may also be
registered or common law trademarks in other countries. A current list of IBM
trademarks is available on the web at “Copyright and trademark information” at
www.ibm.com/legal/copytrade.shtml.
SPSS is a trademark of SPSS, Inc., an IBM Company, registered in many
jurisdictions worldwide.
Other company, product or service names may be trademarks or service marks of
others.
Please Recycle
Business Analytics software