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.
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
Who is a Data Scientist? | How to become a Data Scientist? | Data Science Cou...Edureka!
** Data Scientist Masters' Program: https://www.edureka.co/masters-program/data-scientist-certification **
This Edureka PPT on "Who is a Data Scientist" will help you understand what a data scientist does, their roles and responsibilities, and what the data science profile is all about. You will also get a glimpse of what kind of salary packages and career opportunities the data science domain offers.
Below topics are covered in this PPT:
Who is a Data Scientist?
What is Data Science?
Who can take up Data Science?
How to become a Data Scientist?
Data Scientist Skills
Data Scientist Roles & Responsibilities
Data Scientist Salary
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Data Science Training | Data Science Tutorial | Data Science Certification | ...Edureka!
This Edureka Data Science Training will help you understand what is Data Science and you will learn about different Data Science components and concepts. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. What is Data Science?
2. Job Roles in Data Science
3. Components of Data Science
4. Concepts of Statistics
5. Power of Data Visualization
6. Introduction to Machine Learning using R
7. Supervised & Unsupervised Learning
8. Classification, Clustering & Recommenders
9. Text Mining & Time Series
10. Deep Learning
To take a structured training on Data Science, you can check complete details of our Data Science Certification Training course here: https://goo.gl/OCfxP2
AI-900: Microsoft Azure AI Fundamentals 2021Sean Xie
This deck is designed for the Udemy course:
Ultimate AI-900: Microsoft Azure AI Fundamentals 2021
https://www.udemy.com/course/ultimate-ai-900-microsoft-azure-ai-fundamentals-2021/
Data Science is all about Data or information ,research and decision-making.Data Science refers to how well data match reality.
content: HISTORY,WHAT IS DATA SCIENCE, NEED OF DATA SCIENCE, APPLICATION OF DATA SCIENCE, CHALLENGES OF DATA SCIENCE, DATA SCIENCE CAREER, SKILL REQUIRED FOR DATA SCIENCE, COURSE, JOBS, SALARY,CONCLUSION
Data Warehouse, Data Warehouse Architecture, Data Warehouse Concept, Data Warehouse Modeling, OLAP, OLAP Operations, Data Cube, Data Processing, Data Cleaning, Data Reduction, Data Integration, Data Transformation
Who is a Data Scientist? | How to become a Data Scientist? | Data Science Cou...Edureka!
** Data Scientist Masters' Program: https://www.edureka.co/masters-program/data-scientist-certification **
This Edureka PPT on "Who is a Data Scientist" will help you understand what a data scientist does, their roles and responsibilities, and what the data science profile is all about. You will also get a glimpse of what kind of salary packages and career opportunities the data science domain offers.
Below topics are covered in this PPT:
Who is a Data Scientist?
What is Data Science?
Who can take up Data Science?
How to become a Data Scientist?
Data Scientist Skills
Data Scientist Roles & Responsibilities
Data Scientist Salary
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Data Science Training | Data Science Tutorial | Data Science Certification | ...Edureka!
This Edureka Data Science Training will help you understand what is Data Science and you will learn about different Data Science components and concepts. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. What is Data Science?
2. Job Roles in Data Science
3. Components of Data Science
4. Concepts of Statistics
5. Power of Data Visualization
6. Introduction to Machine Learning using R
7. Supervised & Unsupervised Learning
8. Classification, Clustering & Recommenders
9. Text Mining & Time Series
10. Deep Learning
To take a structured training on Data Science, you can check complete details of our Data Science Certification Training course here: https://goo.gl/OCfxP2
AI-900: Microsoft Azure AI Fundamentals 2021Sean Xie
This deck is designed for the Udemy course:
Ultimate AI-900: Microsoft Azure AI Fundamentals 2021
https://www.udemy.com/course/ultimate-ai-900-microsoft-azure-ai-fundamentals-2021/
Data Science is all about Data or information ,research and decision-making.Data Science refers to how well data match reality.
content: HISTORY,WHAT IS DATA SCIENCE, NEED OF DATA SCIENCE, APPLICATION OF DATA SCIENCE, CHALLENGES OF DATA SCIENCE, DATA SCIENCE CAREER, SKILL REQUIRED FOR DATA SCIENCE, COURSE, JOBS, SALARY,CONCLUSION
Data Warehouse, Data Warehouse Architecture, Data Warehouse Concept, Data Warehouse Modeling, OLAP, OLAP Operations, Data Cube, Data Processing, Data Cleaning, Data Reduction, Data Integration, Data Transformation
Predictive Analytics: Context and Use Cases
Historical context for successful implementation of predictive analytic techniques and examples of implementation of successful use cases.
This presentation introduces big data and explains how to generate actionable insights using analytics techniques. The deck explains general steps involved in a typical analytics project and provides a brief overview of the most commonly used predictive analytics methods and their business applications.
Vijay Adamapure is a Data Science Enthusiast with extensive experience in the field of data mining, predictive modeling and machine learning. He has worked on numerous analytics projects ranging from healthcare, business analytics, renewable energy to IoT.
Vijay presented these slides during the Internet of Everything Meetup event 'Predictive Analytics - An Overview' that took place on Jan. 9, 2015 in Mumbai. To join the Meetup group, register here: http://bit.ly/1A7T0A1
FOUR TYPES OF BUSINESS ANALYTICS TO KNOWBUSINESS ANALYTICSby AJeanmarieColbert3
FOUR TYPES OF BUSINESS ANALYTICS TO KNOW
BUSINESS ANALYTICS
by Anushka Mehta October 13, 2017
For different stages of business analytics huge amount of data is processed at various steps. Depending on the stage of the workflow and the requirement of data analysis, there are four main kinds of analytics – descriptive, diagnostic, predictive and prescriptive. These four types together answer everything a company needs to know- from what’s going on in the company to what solutions to be adopted for optimizing the functions.
The four types of analytics are usually implemented in stages and no one type of analytics is said to be better than the other. They are interrelated and each of these offers a different insight. With data being important to so many diverse sectors- from manufacturing to energy grids, most of the companies rely on one or all of these types of analytics. With the right choice of analytical techniques, big data can deliver richer insights for the companies
Before diving deeper into each of these, let’s define the four types of analytics:
1) Descriptive Analytics:Describing or summarizing the existing data using existing business intelligence tools to better understand what is going on or what has happened.
2) Diagnostic Analytics: Focus on past performance to determine what happened and why. The result of the analysis is often an analytic dashboard.
3) Predictive Analytics:Emphasizes on predicting the possible outcome using statistical models and machine learning techniques.
4) Prescriptive Analytics:It is a type of predictive analytics that is used to recommend one or more course of action on analyzing the data.
Let’s understand these in a bit more depth.
1. Descriptive Analytics
This can be termed as the simplest form of analytics. The mighty size of big data is beyond human comprehension and the first stage hence involves crunching the data into understandable chunks. The purpose of this analytics type is just to summarize the findings and understand what is going on.
Among some frequently used terms, what people call as advanced analytics or business intelligence is basically usage of descriptive statistics (arithmetic operations, mean, median, max, percentage, etc.) on existing data. It is said that 80% of business analytics mainly involves descriptions based on aggregations of past performance. It is an important step to make raw data understandable to investors, shareholders and managers. This way it gets easy to identify and address the areas of strengths and weaknesses such that it can help in strategizing.
The two main techniques involved are data aggregation and data mining stating that this method is purely used for understanding the underlying behavior and not to make any estimations. By mining historical data, companies can analyze the consumer behaviors and engagements with their businesses that could be helpful in targeted marketing, service improvement, etc. The tools used in this phase are MS Excel, MATLAB ...
Running title TRENDS IN COMPUTER INFORMATION SYSTEMS1TRENDS I.docxanhlodge
Running title: TRENDS IN COMPUTER INFORMATION SYSTEMS 1
TRENDS IN COMPUTER INFORMATION SYSTEMS 4
Trends in Computer Information Systems, and the Rise to Business Intelligence
Shad Martin
School for Professional Studies
St. Louis University
ENG 2005 Dr. Rebecca Wood
November 23, 2016
Introduction
Our quest to increase our knowledge of Computer Information Systems has produced a number of benefits to humanity. The innovation humans have discovered in Computer Information Systems has led to new sub-areas of study for students and professionals to continue their progression to master all that Computer Information Systems has to offer. Amy Web of the Harvard Business Review reported 8 Tech Trends to Watch in 2016, She noted, “In order to chart the best way forward, you must understand emerging trends: what they are, what they aren’t, and how they operate. Such trends are more than shiny objects; they’re manifestations of sustained changes within an industry sector, society, or human behavior. Trends are a way of seeing and interpreting our current reality, providing a useful framework to organize our thinking, especially when we’re hunting for the unknown. Fads pass. Trends help us forecast the future” (Harvard Business Review, 2015). In short, Amy’s reference to understanding the emerging trends in Computer Information can provide a framework from which, students, professionals, and scientists to conscientiously create a path towards optimizing their efforts. Ensuring we have a fundamental approach to analyze data will enhance our understanding of this subject further.
In this paper I will expound on three of the top trends used to provide insight into the data produced from the advancements in Computer Information Systems. These trends or methods are taking place in my workplace within a financial institution, and in many other industries. It is important to note this paper does not provide an inclusive list of all methodologies that exist. Individuals can now leverage analytics to synthesize insights from data to identify emerging risk, manage operational risks, identify trends, improve compliance, and customer satisfaction. Data in and by itself is not always useful. Regardless of the data source, trained professional must understand the best approach to structure the data to make it more useful. In this paper, I will touch on three popular methodology trends occurring in Computer Information Systems. Students and professionals who work with large data would benefit from having a solid understanding of the fundamental principles of Business Intelligence as data scientific approach and when to use these methodologies.
The rise of Business Intelligence
Computer Information Systems allow many companies to gather and generate large amounts of data on their customers, business activities, potential merger targets, and risks found in their organization. These large sets of data have given rise to vari.
Why Data Science is Getting Popular in 2023?kavyagaur3
Data science employs mathematics, statistics, advanced programming techniques, analytics and artificial intelligence (AI) to uncover insights that drive business value for their organisation. Then, this information can be used for strategic planning and decision-making.
Data has flooded in massive amounts as a result of digitization. Businesses are making their utmost efforts to take advantage of every opportunity to increase their businesses. This makes the best opportunity for individuals who want to pursue Data Science. The first step is to get the best data science training.
Predicting user behavior using data profiling and hidden Markov modelIJECEIAES
Mental health disorders affect many aspects of patient’s lives, including emotions, cognition, and especially behaviors. E-health technology helps to collect information wealth in a non-invasive manner, which represents a promising opportunity to construct health behavior markers. Combining such user behavior data can provide a more comprehensive and contextual view than questionnaire data. Due to behavioral data, we can train machine learning models to understand the data pattern and also use prediction algorithms to know the next state of a person’s behavior. The remaining challenges for this issue are how to apply mathematical formulations to textual datasets and find metadata that aids to identify the person’s life pattern and also predict the next state of his comportment. The main idea of this work is to use a hidden Markov model (HMM) to predict user behavior from social media applications by analyzing and detecting states and symbols from the user behavior dataset. To achieve this goal, we need to analyze and detect the states and symbols from the user behavior dataset, then convert the textual data to mathematical and numerical matrices. Finally, apply the HMM model to predict the hidden user behavior states. We tested our program and identified that the log-likelihood was higher and better when the model fits the data. In any case, the results of the study indicated that the program was suitable for the purpose and yielded valuable data.
Foresight; The Springboard to maintaining pace and achieving successChiomaChigozieOkwum
Foresight is a veritable ingredient for maintaining competitive advantage in a highly competitive world. The presentation showcases the strengths in deploying foresight for assured growth.
How are machine learning and artificial intelligence revolutionizing insurance?
This presentation explains it briefly, including current trends and effects on the business.
Going Mobile With Enterprise Applications - A study on user behavior and perceptions.
This paper presents findings from three research studies carried out to understand the user behavior and explore the value in using mobile devices for accessing enterprise products.
The focus is essentially on the expectations of the end-‐users, namely, information technology (IT) administrators. In this case, we were exploring how the users of enterprise products might want to leverage mobile technology to access their everyday tasks and information, and therefore identify potential opportunities and challenges for extending their user experience to such devices.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
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Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
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Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
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Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
How relevant is Predictive Analytics relevant today?
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
1. 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. 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
3
Mugerwa Steven- MGR******
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
4
Mugerwa Steven- MGR******
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
5
Mugerwa Steven- MGR******
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.
6
Mugerwa Steven- MGR******
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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