The document discusses predictive analytics, including its definition, how it works, types, tools, and benefits. It also explores applications of predictive analytics in various fields like business, finance, fraud detection, and others. Finally, the document outlines challenges and opportunities involved with predictive analytics, such as issues with data quality, technical resources, and gaining user adoption, as well as opportunities through integrations with big data and cloud computing.

1. How relevant is Predictive analytics
today?
An essay presented to the
Department of Information Systems
University of Cape Town
By Mugerwa Steven (MGR******)
in partial fulfilment of the requirements for the
Information and Communication Technologies (INF2010S) 2012
14 September 2012

2. Plagiarism Declaration
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3. Table of Contents
ABSTRACT.................................................................................................................................... 4
INTRODUCTION ........................................................................................................................... 4
I. BACKGROUND ....................................................................................................................................... 4
II. PURPOSE............................................................................................................................................. 4
1. WHAT IS PREDICTIVE ANALYTICS?.......................................................................................... 5
I. DEFINITION .......................................................................................................................................... 5
II. HOW DOES PREDICTIVE ANALYTICS WORK?............................................................................................... 5
III. TYPES OF PREDICTIVE ANALYTICS............................................................................................................ 6
IV. TOOLS ............................................................................................................................................... 6
V. BENEFITS OF PREDICTIVE ANALYTICS ........................................................................................................ 7
2. WHAT ARE THE VARIOUS APPLICATIONS OF PREDICTIVE ANALYTICS?..................................... 7
I. BUSINESS APPLICATIONS ......................................................................................................................... 7
II. FINANCIAL INSTITUTIONS ....................................................................................................................... 8
III. FRAUD AND THREAT ............................................................................................................................. 8
IV. OTHER FIELDS ..................................................................................................................................... 9
3. CHALLENGES AND OPPORTUNITIES INVOLVED WITH PREDICTIVE ANALYTICS.......................... 9
I. CHALLENGES ......................................................................................................................................... 9
II. OPPORTUNITIES .................................................................................................................................10
4. CONCLUSION....................................................................................................................... 11
BIBLIOGRAPHY........................................................................................................................... 12
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4. Abstract
Predictive analytics can be thought of as analytics of the future. It has a common definition,
numerous approaches but has not been exploited to full potential. According to the Gartner
Hype Cycles, Predictive analytics is said to achieve its full potential in the next two year.
(Gartner, 2012)
This paper argues that real-world applications should adopt Predictive analytics in their day to
day process in order to stay relevant, productive and ahead of the competition (in profit making
firms). The paper goes on to draw an analogy between predictive models and data management
and discusses how organizational management can leverage this in order to predict the future
and make informed decisions based on those predictions.
Introduction
I. Background
The 21st century is very reliant to information technology and is no wonder it’s known to many
as the information age. For our continuous existence, data is by far the World’s most valuable
asset. However, data has many forms i.e. data can be raw of which not much can be understood
from it and therefore concise decisions won’t always be made. Data is most valuable to us in a
processed state normally referred to as information which we can make decisions based on it. In
order for data to be able to help us in precise and smart decision making, it has to go through
critical analysis known as “analytics”.
Analytics is the use of data, statistical and quantitative methods and predictive models to allow
organizations and individuals to gain insights into and act on complex issues. Analytics
comprises of various forms today e.g. Big Data, Business Intelligence as well as Predictive
analytics which will be the basis of this essay.
II. Purpose
Predictive analytics is the topic of question because it comprises modern phenomenon in
practice today such as machine learning (an element of artificial intelligence) as well as the use
of past and present data to help in forecasting/predicting the future. The ability to predict the
future through predictive analytics explains how valuable data is. More organizations across
several industries are using Predictive Analytics as it is a transformational technology that
enables more proactive decision making, driving new forms of competitive advantage
Also because analytics and business intelligence is ranked number 1 in the technology priorities
according to the Gartner EXP Worldwide Survey of 2,300 CIOs - Jan 2012 for increasing
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5. enterprise growth. Predictive analytics which is a big part of analytics and business analytics
naturally therefore becomes a business priority. Predictive analytics can also support plenty of
other business priorities such as growth, productivity etc. Business Intelligence has been
regarded a top application and technological development from 2003-2011 (Luftman & Ben-Zvi,
2011) therefore encouraging more entities to adopt Predictive analytics.
This essay is setting out to go in detail and explain what predictive analytics is, how predictive
analytics can be applied in various disciplines today, how it works, its opportunities and
challenges as well as its place in the current technological World.
1. What is Predictive Analytics?
I. Definition
Predictive Analytics is a branch of business intelligence that uses data mining and statistics to
make predictions on future happenings. (Ganesh, Reddy, Manikandran, & Krishna, 2011)
Predictive analytics is the branch of data mining (Predictive Analytics is today often referred as
data mining) concerned with forecasting probabilities. It is the use of a combination of machine
learning, statistical analysis, modeling techniques, and database technology, to process data and
uses it to predict future trends and behavioural patterns therefore uncovering problems and
opportunities in an organization.
These techniques are applied to many disciplines, including marketing, healthcare, financial
field like insurance, fraud which will be discussed in more detail. These are usually disciplines in
which there's an abundance of data and a need to forecast the future. Predictive analytics helps
organizations predict with confidence what will happen next so that smarter decisions can be
made and improve objective outcomes.
II. How does Predictive Analytics work?
Predictive analytics include statistical models and other empirical methods that are aimed at
creating empirical predictions (Shmueli & Koppius , 2011)
There are many different algorithms used in Predictive Analytics to try to classify patterns,
trends and behaviours for a particular variable e.g. for customers. Various models are created in
order for Predictive analytics to be possible. These include:
 machine learning,
 statistical analysis
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6.  A combination of various input models using different perspectives (known an
ensemble model or a Meta model).
Predictive models are not perfect, but they are a lot better than just guessing. For example, if we
know that the conversion rate for a promotion is just 3%, it would help to have a good idea of
who those 3% of people are so that we can focus on them first.
The specific algorithm chosen depends on a combination of the intended use of the prediction
e.g. do we need to know why a customer has a certain rank? As well as on how well the
algorithm interacts with the data. No algorithm works best with all data in in all situations.
What most of the algorithms have in common is how the data is presented to create a predictive
investigation whose outcomes can be modelled. Some example algorithms to look at are Logistic
Regression, Visualisation and Neural Networks etc. for situations where the behaviour is
yes/no.
III. Types of Predictive Analytics
 Descriptive models
It is the task of providing a representation of the knowledge discovered without necessarily
modelling a specific outcome. This will be used to categorize or group behaviour in data sets to
describe a pattern but nothing beyond that.
 Predictive models :
However, descriptive analytics is simply not enough. In the society we live in today, it is
imperative that decisions be highly accurate and repeatable. For this, organisations are using
predictive analytics to literally tap into the future and, in doing so, define sound business
decisions and processes. While descriptive analytics lets us know what happened in the past,
predictive analytics focuses on what will happen next.
IV. Tools
Historically Predictive analytics required a specified skill set to do what it does today. But the
introduction of Predictive IT analytics systems like Hewlett-Packard’s Service Health Analyzer,
IBM’s SPSSpowered Tivoli product, Netuitive’s eponymous offering and other systems make this
job much simpler, easier and achieve results quicker.
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7. V. Benefits of Predictive Analytics
The biggest contribution Predictive analytics gives the World is the fact that it can be used in
various industries because of the fact that it works with data to predict the future. Below is a list
of how organizations can benefit from the use of Predictive analytics.
 It helps to manage performance & risk. It can predict issues prior to and solve any
problems such as an outage, degradation in service, or other impacts on business plans
 It helps organizations in advanced planning & scheduling capabilities leveraging
analytics such as capacity planning, capacity management and workload scheduling
 It helps in business optimization. This means a business can constantly adapt to change
within dynamic infrastructures
 It captures meaningful business insights from operational & business data
 It helps identify new business opportunities for profitable growth
 Leveraging service and infrastructure analytics, organizations can optimize operations
and ensure predictable business outcomes.
All in all predictive analytics will be at the forefront to help organizations control costs and
acquire a competitive advantage in their industries.
2. What are the various applications of Predictive Analytics?
Analytics and predictive analytics will be applied across many domains from banking,
insurance, retail, telecom, energy etc. The existence of various analytical software as well as
high levelled skill sets make Predictive analytics possible.
Predictive analytics can be applied to more than one industry simply because of its ability to
generate useful predictions that companies can use to make informed decisions. Predictive
analytics uses statistical analysis and predictive modelling in order to make proactive decisions.
This means that entities make decisions prior which is preferred to reactive decision making
which is merely a response to a setback or a change in business operations. Below are the
various ways in which Predictive analytics is applied in the real World.
I. Business Applications
Predictive analytics is revolutionizing the way companies do business today. The greatest
benefit of deployment for any predictive system is reaped when predictive analytics is
integrated into business processes. The most commonly used applications of Predictive
analytics in business are Enterprise Resource Planning (ERP) and Customer Relations
Management (CRM) applications.
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8. ERP consists of resource management for a particular business. Businesses use predictive
analytics in supply chain management to manage stock levels (just-in-time). Revenues can also
be forecasted by looking into past sales data and use a time series analysis. Organizations can
predict the next point or two forward in a series, and then as more real data is gathered,
predictions are made.
Customer relationship management (CRM) systems perform the tasks of monitoring activities,
coordinating resources, and generally keeping your organization on track with its sales
processes. In business, predictive analytics are often used to answer questions about customer
behaviour. For example, companies often want to know whether or not a particular customer is
likely to be interested in a particular offer or whether a new customer will become a long-term
customer given a certain set of premiums and benefits.
Therefore predictive analytics helps business to segment their customers into understandable
groupings as well as calculate metricises such as reorder rates, seasonality by customer type,
targeted marketing, and selling initiatives. This will therefore make marketing strategies much
simpler and cost effective as an organisation now has information about particular customers.
Ultimately, businesses want predictive analytics to suggest how to best target resources for
maximum return. This way it uncovers hidden insights from data so one can create personalized
experiences that will reduce business costs, increase customer loyalty and also identify risks
that could derail entity plans and take timely corrective action (proactive decisions over
reactive).
II. Financial Institutions
Financial institutions have been able to adopt the use of predictive analytics very smoothly into
their infrastructure. Predictive analytics is used by banks, micro-finance, retailers and insurers
to calculate credit scores.
Predictive analytics is used to calculate organisation and individuals credit scoring. A credit
score is a figure processed through tracking of a customer’s credit history, loan application,
earnings in order to predict future creditworthiness of individuals/entities. Lenders i.e. banks,
micro-finance and other specialists use Predictive analytics to determine who qualifies for a
loan as well as which customers will bring in the most revenue. Credit scoring is used
throughout the credit industry in South Africa.
III. Fraud and threat
This is mainly used by Insurance companies and to an extent banks. South African firms have
been able to use Predictive analytics to monitor their business environment, detect suspicious
activity, and control outcomes to minimize loss.
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9. By using IBM SPSS predictive analytics to identify risks and accelerate claims settlement,
Santam Insurance boosted customer service and managed to beat fraud.
"In the first month of using the SPSS solution, we were able to identify patterns that enabled us to
foil a major motor insurance fraud syndicate. Within the first four months, we had saved R17
million on fraudulent claims, and R32 million in total repudiations – so the solution delivered a full
return on investment almost instantly!" - Anesh Govender, Head of Finance, Reporting and
Salvage, Santam Insurance (IBM, 2011)
IV. Other fields
 Predictive analytics is used health care to determine which patients are at risk of
developing particular conditions.
 Predicting crime
 Predictive analytics is already being used in traffic management in identifying and
preventing traffic gridlocks.
 Operational activities to ensure staff, processes and assets are aligned and optimized to
maximize productivity and profitability.
 Applications have also been identified for energy grids, for water management.
 Risk Management
 Educational institutes to predict student grades.
3. Challenges and Opportunities involved with Predictive Analytics
I. Challenges
It is not always easy to incorporate Predictive analytics in any organisation due to various
challenges faced in the workplace. This could consist of both internal and external constraints of
an organization making it a struggle for organizations to find a balance during implementation.
These challenges are compiled in the table below.
Challenge Description
Technical Factors  Data Quality; the aspect of data is very important as it is
the core ingredient for predictive analytics to work. This
means data has to be consistent, readable and accurate.
Data also needs to be stored securely.
 System Architecture; this entails the current systems in
place at a particular workplace or organization. The
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10. software must be in sync with other systems in place or
risk disrupting business operations.
 Resources; this involves the level of infrastructure i.e.
hardware, networks etc. to support predictive analytics.
 Team Skills; this is by far another important aspect as
without professionals, data is of no use to the
organization.
Organisational and  Business Focus; this is the business vision and policies
Management Factors that it follows to attain its objectives. Some organisations
are not entirely in need of Predictive analytics even with
the information it offers individuals.
 Company politics and Management Support; this is
important as management depicts the business
direction. Thus if it adopt Predictive analytics with a
positive view it will definitely succeed. However,
management support in most corporations is sluggish on
adoption of new technologies and therefore leads to a
challenge.
User Participation  Commitment; A resistance to change is usually
experienced by workers in a workplace who don’t want
to undergo training and use new technologies.
 Project Management is difficult as communication about
new technologies is never easy.
These issues in a sense therefore also depict variables that need to be in place for
Predictive analytics to be a success.
II. Opportunities
There is absolutely no question that predictive analytics will be pervasive across a wide range of
applications. It will be everywhere.
Integrations with other technologies such as big data and cloud computing.
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11. Big Data is a term used to describe large and complicated data sets that can’t be worked on using
traditional database management. The big question pertaining to Big Data are "how to extract insights
and value from it as well as being effective about it". The answer is predictive analytics.
Cloud Computing is a set of services that provides computing resources via the Internet. Large
data centers deliver scalable, on-demand resources as a service, eliminating the need for
investments in specific hardware or software, or on organizational data center infrastructure. It
allows for a variety of services, including storage capacity, processing power, and business
applications.
With the power of Predictive analytics and technologies like cloud computing, big and small
organizations could save millions, be more productive and efficient at the same time.
Therefore, Predictive analytics function is not limited to what it can do, but also to what it can
achieve once it is associated with other technologies in an infrastructure.
4. Conclusion
This paper shows my views on how predictive analytics influences the world today as well as
the step process involved in making Predictive analytics possible. The world is heavily reliant
on technologies and the ease brought forward by various tools doesn’t make Predictive
analytics an exception. Although still not widely used in the world, Predictive analytics has
massive potential to change the way we think and leave our lives. It definitely has the potential
to grow rapidly over the following years in order to make predictions and most importantly
stays relevant to our societies.
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12. Bibliography
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intensive analytics for predictive modeling. IBM Journal Of Research &
Development, 47(1), 17.
Baecke, P., & Van Den Poel, D. (2010). IMPROVING PURCHASING BEHAVIOR PREDICTIONS BY
DATA AUGMENTATION WITH SITUATIONAL VARIABLES. International Journal Of
Information Technology & Decision Making, 9(6), 853-872.
doi:10.1142/S0219622010004135
Bradley, P. (2012). Predictive analytics can support the ACO model. Hfm (Healthcare Financial
Management), 66(4), 102-106.
DAVENPORT, T. H., & HARRIS, J. G. (2009). What People Want (and How to Predict It). MIT
Sloan Management Review, 50(2), 23-31.
Ganesh, M. S., Reddy, C. P., Manikandran, N., & Krishna, P. V. (2011). TDPA: Trend Detection and
Predictive Analytics. International Journal on Computer Science & Engineering, 3(3),
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Gartner. (2012, August 16). Press Resources: Gartner. Retrieved September 14, 2012, from
Gartner Web Site: http://www.gartner.com/it/page.jsp?id=2124315
Hair, J. F. (2007). Knowledge creation in marketing: the role of predictive analytics. European,
19(4), 303-315.
IBM. (2011, July). Case Studies:International Business Machines. Retrieved September 13, 2012,
from An International Business Machines Web site: http://www-
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8JJETD?OpenDocument&Site=default&cty=en_us
Luftman, J., & Ben-Zvi, T. (2011). Key Issues for IT Executives. MIS Quarterly Executive, 10(4),
203-212.
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13. Shmueli, G., & Koppius , O. (2011). PREDICTIVE ANALYTICS IN INFORMATION SYSTEMS
RESEARCH. MIS Quarterly, 35(3), 553-572.
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