How relevant is Predictive analytics                         today?                  An essay presented to the     Departm...
Plagiarism Declaration1.          I know that plagiarism is wrong. Plagiarism is to use another’s work and pretend that it...
Table of ContentsABSTRACT....................................................................................................
AbstractPredictive analytics can be thought of as analytics of the future. It has a common definition,numerous approaches ...
enterprise growth. Predictive analytics which is a big part of analytics and business analytics  naturally therefore becom...
   A combination of various input models using different perspectives (known an       ensemble model or a Meta model).Pre...
V.      Benefits of Predictive AnalyticsThe biggest contribution Predictive analytics gives the World is the fact that it ...
ERP consists of resource management for a particular business. Businesses use predictiveanalytics in supply chain manageme...
By using IBM SPSS predictive analytics to identify risks and accelerate claims settlement,Santam Insurance boosted custome...
software must be in sync with other systems in place or                                        risk disrupting business op...
Big Data is a term used to describe large and complicated data sets that can’t be worked on usingtraditional database mana...
BibliographyApte, C. V., Hong, S. J., Natarajan, R. R., Pednault, E. D., Tipu, F. A., & Weiss, S. M. (2003). Data-        ...
Shmueli, G., & Koppius , O. (2011). PREDICTIVE ANALYTICS IN INFORMATION SYSTEMS      RESEARCH. MIS Quarterly, 35(3), 553-5...
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How relevant is Predictive Analytics relevant today?

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This is my view on how relevant is Predictive Analytics relevant today. Although its a high level view, it gives great insights to a person who is looking for somewhere to begin. This was an essay for the

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How relevant is Predictive Analytics relevant today?

  1. 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 theInformation and Communication Technologies (INF2010S) 2012 14 September 2012
  2. 2. Plagiarism Declaration1. I know that plagiarism is wrong. Plagiarism is to use another’s work and pretend that it is one’s own.2. I have used the APA convention for citation and referencing. Each contribution to, and quotation in, this essay from the work(s) of other people has been attributed, and has been cited and referenced.3. This essay is my own work.4. I have not allowed, and will not allow, anyone to copy my work with the intention of passing it off as his or her own work.5. I acknowledge that copying someone else’s assignment or essay, or part of it, is wrong, and declare that this is my own work.Signature 2 Mugerwa Steven- MGR******
  3. 3. Table of ContentsABSTRACT.................................................................................................................................... 4INTRODUCTION ........................................................................................................................... 4I. BACKGROUND ....................................................................................................................................... 4II. PURPOSE............................................................................................................................................. 41. WHAT IS PREDICTIVE ANALYTICS?.......................................................................................... 5I. DEFINITION .......................................................................................................................................... 5II. HOW DOES PREDICTIVE ANALYTICS WORK?............................................................................................... 5III. TYPES OF PREDICTIVE ANALYTICS............................................................................................................ 6IV. TOOLS ............................................................................................................................................... 6V. BENEFITS OF PREDICTIVE ANALYTICS ........................................................................................................ 72. WHAT ARE THE VARIOUS APPLICATIONS OF PREDICTIVE ANALYTICS?..................................... 7I. BUSINESS APPLICATIONS ......................................................................................................................... 7II. FINANCIAL INSTITUTIONS ....................................................................................................................... 8III. FRAUD AND THREAT ............................................................................................................................. 8IV. OTHER FIELDS ..................................................................................................................................... 93. CHALLENGES AND OPPORTUNITIES INVOLVED WITH PREDICTIVE ANALYTICS.......................... 9I. CHALLENGES ......................................................................................................................................... 9II. OPPORTUNITIES .................................................................................................................................104. CONCLUSION....................................................................................................................... 11BIBLIOGRAPHY........................................................................................................................... 12 3 Mugerwa Steven- MGR******
  4. 4. AbstractPredictive 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 GartnerHype 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 today process in order to stay relevant, productive and ahead of the competition (in profit makingfirms). The paper goes on to draw an analogy between predictive models and data managementand discusses how organizational management can leverage this in order to predict the futureand make informed decisions based on those predictions.Introduction I. BackgroundThe 21st century is very reliant to information technology and is no wonder it’s known to manyas the information age. For our continuous existence, data is by far the World’s most valuableasset. However, data has many forms i.e. data can be raw of which not much can be understoodfrom it and therefore concise decisions won’t always be made. Data is most valuable to us in aprocessed state normally referred to as information which we can make decisions based on it. Inorder for data to be able to help us in precise and smart decision making, it has to go throughcritical analysis known as “analytics”.Analytics is the use of data, statistical and quantitative methods and predictive models to alloworganizations and individuals to gain insights into and act on complex issues. Analyticscomprises of various forms today e.g. Big Data, Business Intelligence as well as Predictiveanalytics which will be the basis of this essay. II. PurposePredictive analytics is the topic of question because it comprises modern phenomenon inpractice today such as machine learning (an element of artificial intelligence) as well as the useof past and present data to help in forecasting/predicting the future. The ability to predict thefuture through predictive analytics explains how valuable data is. More organizations acrossseveral industries are using Predictive Analytics as it is a transformational technology thatenables more proactive decision making, driving new forms of competitive advantageAlso because analytics and business intelligence is ranked number 1 in the technology prioritiesaccording to the Gartner EXP Worldwide Survey of 2,300 CIOs - Jan 2012 for increasing 4 Mugerwa Steven- MGR******
  5. 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 theres 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 5 Mugerwa Steven- MGR******
  6. 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 weknow that the conversion rate for a promotion is just 3%, it would help to have a good idea ofwho 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 predictione.g. do we need to know why a customer has a certain rank? As well as on how well thealgorithm 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 predictiveinvestigation whose outcomes can be modelled. Some example algorithms to look at are LogisticRegression, Visualisation and Neural Networks etc. for situations where the behaviour isyes/no.III. Types of Predictive Analytics  Descriptive modelsIt is the task of providing a representation of the knowledge discovered without necessarilymodelling a specific outcome. This will be used to categorize or group behaviour in data sets todescribe a pattern but nothing beyond that.  Predictive models :However, descriptive analytics is simply not enough. In the society we live in today, it isimperative that decisions be highly accurate and repeatable. For this, organisations are usingpredictive analytics to literally tap into the future and, in doing so, define sound businessdecisions and processes. While descriptive analytics lets us know what happened in the past,predictive analytics focuses on what will happen next. IV. ToolsHistorically Predictive analytics required a specified skill set to do what it does today. But theintroduction 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 thisjob much simpler, easier and achieve results quicker. 6 Mugerwa Steven- MGR******
  7. 7. V. Benefits of Predictive AnalyticsThe biggest contribution Predictive analytics gives the World is the fact that it can be used invarious industries because of the fact that it works with data to predict the future. Below is a listof 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 andacquire 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 ashigh levelled skill sets make Predictive analytics possible.Predictive analytics can be applied to more than one industry simply because of its ability togenerate useful predictions that companies can use to make informed decisions. Predictiveanalytics 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 makingwhich is merely a response to a setback or a change in business operations. Below are thevarious ways in which Predictive analytics is applied in the real World. I. Business ApplicationsPredictive analytics is revolutionizing the way companies do business today. The greatestbenefit of deployment for any predictive system is reaped when predictive analytics isintegrated into business processes. The most commonly used applications of Predictiveanalytics in business are Enterprise Resource Planning (ERP) and Customer RelationsManagement (CRM) applications. 7 Mugerwa Steven- MGR******
  8. 8. ERP consists of resource management for a particular business. Businesses use predictiveanalytics in supply chain management to manage stock levels (just-in-time). Revenues can alsobe forecasted by looking into past sales data and use a time series analysis. Organizations canpredict 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 salesprocesses. In business, predictive analytics are often used to answer questions about customerbehaviour. For example, companies often want to know whether or not a particular customer islikely to be interested in a particular offer or whether a new customer will become a long-termcustomer given a certain set of premiums and benefits.Therefore predictive analytics helps business to segment their customers into understandablegroupings as well as calculate metricises such as reorder rates, seasonality by customer type,targeted marketing, and selling initiatives. This will therefore make marketing strategies muchsimpler and cost effective as an organisation now has information about particular customers.Ultimately, businesses want predictive analytics to suggest how to best target resources formaximum return. This way it uncovers hidden insights from data so one can create personalizedexperiences that will reduce business costs, increase customer loyalty and also identify risksthat could derail entity plans and take timely corrective action (proactive decisions overreactive). II. Financial InstitutionsFinancial institutions have been able to adopt the use of predictive analytics very smoothly intotheir infrastructure. Predictive analytics is used by banks, micro-finance, retailers and insurersto calculate credit scores.Predictive analytics is used to calculate organisation and individuals credit scoring. A creditscore 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 aloan as well as which customers will bring in the most revenue. Credit scoring is usedthroughout the credit industry in South Africa. III. Fraud and threatThis is mainly used by Insurance companies and to an extent banks. South African firms havebeen able to use Predictive analytics to monitor their business environment, detect suspiciousactivity, and control outcomes to minimize loss. 8 Mugerwa Steven- MGR******
  9. 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 tofoil a major motor insurance fraud syndicate. Within the first four months, we had saved R17million on fraudulent claims, and R32 million in total repudiations – so the solution delivered a fullreturn on investment almost instantly!" - Anesh Govender, Head of Finance, Reporting andSalvage, 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. ChallengesIt is not always easy to incorporate Predictive analytics in any organisation due to variouschallenges faced in the workplace. This could consist of both internal and external constraints ofan 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 9 Mugerwa Steven- MGR******
  10. 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. OpportunitiesThere is absolutely no question that predictive analytics will be pervasive across a wide range ofapplications. It will be everywhere.Integrations with other technologies such as big data and cloud computing. 10 Mugerwa Steven- MGR******
  11. 11. Big Data is a term used to describe large and complicated data sets that can’t be worked on usingtraditional database management. The big question pertaining to Big Data are "how to extract insightsand 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. Largedata centers deliver scalable, on-demand resources as a service, eliminating the need forinvestments in specific hardware or software, or on organizational data center infrastructure. Itallows for a variety of services, including storage capacity, processing power, and businessapplications.With the power of Predictive analytics and technologies like cloud computing, big and smallorganizations 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 canachieve once it is associated with other technologies in an infrastructure.4. ConclusionThis paper shows my views on how predictive analytics influences the world today as well asthe step process involved in making Predictive analytics possible. The world is heavily relianton technologies and the ease brought forward by various tools doesn’t make Predictiveanalytics an exception. Although still not widely used in the world, Predictive analytics hasmassive potential to change the way we think and leave our lives. It definitely has the potentialto grow rapidly over the following years in order to make predictions and most importantlystays relevant to our societies. 11 Mugerwa Steven- MGR******
  12. 12. BibliographyApte, C. V., Hong, S. J., Natarajan, R. R., Pednault, E. D., Tipu, F. A., & Weiss, S. M. (2003). Data- 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/S0219622010004135Bradley, 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), 1033-1039.Gartner. (2012, August 16). Press Resources: Gartner. Retrieved September 14, 2012, fromGartner Web Site: http://www.gartner.com/it/page.jsp?id=2124315Hair, 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- 01.ibm.com/software/success/cssdb.nsf/CS/STRD- 8JJETD?OpenDocument&Site=default&cty=en_usLuftman, J., & Ben-Zvi, T. (2011). Key Issues for IT Executives. MIS Quarterly Executive, 10(4), 203-212. 12 Mugerwa Steven- MGR******
  13. 13. Shmueli, G., & Koppius , O. (2011). PREDICTIVE ANALYTICS IN INFORMATION SYSTEMS RESEARCH. MIS Quarterly, 35(3), 553-572. 13 Mugerwa Steven- MGR******

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