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The Role of Predictive Analytics in Cash Flow Forecasting | Improve Financial Forecasting

Posted on by Imarticus Learning

In the dynamic context of today’s business scenario, maintaining a steady flow of cash is essential in sustaining operations and driving further growth. Cash flow forecasting- an integral part of any financial planning becomes that much more accurate and actionable for businesses with the integration of predictive analytics, which enables them to project financial trends, mitigate related risks, and make decisive decisions. This report tries to explore how predictive analytics changes cash flow forecasting, the impact it has on financial forecasting, and why finance professionals need to take a course in financial accounting to navigate this new world of business analytics.

Understanding Predictive Analytics

Predictive analytics refers to the use of historical data, machine learning, and statistical algorithms to predict probable future outcomes. In brief, it helps a firm identify patterns and trends ahead of time to make prompt decisions. In cash flow forecasting, predictive analytics transform static projections into dynamic responses.

It will help organisations to improve their practice in cash flow management, ensuring liquidity and avoiding cash shortfalls by using predictive analytics. Predictive models analyse historical cash flows, market trends, and also other external economic factors with accuracy that cannot be attained in financial forecasting.

Improvement of Cash Flow Forecasting Using Predictive Analytics

  1. More Accuracy

Traditional cash flow forecasting techniques involve manual calculations and static data, which usually are prone to errors. Predictive analytics automates this process, using actual data to make forecasting more reliable. With past pattern analysis and real-time trending, businesses can anticipate spikes and dips in cash flow before they happen.

  1. Proactive Risk Management

One of the greatest benefits of predictive analytics for cash flow management is in the detection of potential risks in finances. For example, a business can anticipate when there will be shortages of cash and prepare contingencies for the same. This will ensure financial stability and resilience in challenging market conditions.

  1. Scenario Analysis

This enables the use of predictive analytics for organisations to carry out scenario analyses and, thereby, know how cash flows will be affected by different factors. Companies will then have a better idea of where they stand in finance and what they need to do.

  1. Business Analytics Integration

This combination of predictive analytics and business analytical tools is meant to enhance cash flow forecastability. Sophisticated dashboards and visualisation enables the financial teams to easily monitor cash flow trends while developing insights and communication skills concerning such findings.

For more information regarding trends and technologies in predictive analytics, read this blog here. 

Intersection between Predictive Analytics, Financial Forecasting

Financial forecasting encompasses a broader scope, focusing on predicting an organisation’s overall financial performance. Within this domain, cash flow forecasting serves as a critical subset. By incorporating predictive analytics, businesses can achieve the following:

  1. Streamlined Budgeting

Predictive models help streamline budgeting processes by providing accurate forecasts of income and expenses. This ensures that resources are allocated efficiently and aligned with the company’s financial goals.

  1. Enhanced Decision-Making

It makes leaders be decision-makers of data predictions. Whether expanding into other markets, infrastructure investment, or optimizing costs in any form of operations, effective financial forecasting lies at the very heart of strategic plans.

  1. Alignment with Financial Accounting Courses

For finance professionals, learning predictive analytics is becoming fundamental. It involves enrolling in a course on financial accounting for those foundational lessons that one has to be equipped with and learn to unleash the powers and potentialities of predictive analytics. Topics covered may also include forecasting techniques, financial models, and advanced data analytics.

Check out the entire video to learn all about our financial accounting program. 

Advantages of Predictive Analytics Over Cash Flow Management

Effective cash flow management is the backbone of financial health for any organization. Predictive analytics has several benefits that enhance this critical function:

  1. Optimized Working Capital

Proper cash inflow and outflow forecasting can optimize working capital for businesses. Predictive models identify excess funds or potential shortages, thereby enhancing investment and financing decisions.

  1. Reduced Reliance on Short-Term Borrowing

With increased transparency into cash flows going forward, the business will reduce its reliance on short-term borrowing. This reduces interest costs and strengthens financial stability.

  1. Facilitating Business Analytics Projects

Predictive analytics is one of the significant components of business analytics strategy today. The use of predictive analytics in cash flow management enables companies to achieve operational efficiency and promote sustainable growth.

How an Accounting Course Increases Predictive Analytics Capabilities

With the importance of predictive analytics in finance, professionals must keep up with the latest techniques. A complete financial accounting course is a must to train an individual in the following aspects:

Understanding financial statements and its implications for forecasting.

  • Development and application of predictive models in real-life situations.
  • Utilisation of business analytics tools for decision-making.

Such courses help a person not only acquire technical expertise but also stand ahead in the job market.

Real-World Use of Predictive Analytics in Financial Forecasting

Many sectors are utilising predictive analytics to transform the way they practice financial forecasting. Let’s examine a few of them:

  1. Retail

Retailers apply predictive analytics in sales forecasting, inventory management, and cash flow planning. Therefore, using predictive analytics enables retailers to forecast seasonal demand and trends within the market, thus ensuring smooth operations.

  1. Manufacturing

In manufacturing, predictive models assist in optimising production schedules, managing supply chains, and forecasting cash requirements. Thus, production targets are met without financial strain.

  1. Healthcare

Healthcare providers use predictive analytics to effectively manage cash flows, particularly in billing and collections. This allows them to utilize resources efficiently and ensure financial stability.

FAQs 

Q1: What is the difference between predictive analytics and traditional forecasting techniques?

Predictive analytics utilises advanced algorithms, historical data, and real-time insights to create more accurate and dynamic forecasts. Traditional methods are static data-based, with calculations done manually, and may not consider sudden market changes.

Q2: Is it possible for small businesses to benefit from predictive analytics in cash flow management?

Absolutely. Predictive analytics tools are scalable and affordable. They are perfect solutions for small businesses. That is because they allow such small enterprises to predict future cash flow problems, increase resources, and make adequate decisions.

Q3 Is a financial accounting course requirement for using predictive analytics?

Not a pre-requisite course by any means, taking a financial accounting course can make all the difference in understanding predictive analytics and its application in the finance sector. It provides foundational knowledge to interpret financial data and the application of advanced forecasting techniques.

Conclusion

Predictive analytics is actually changing the way cash flow forecast for businesses is done, an aspect that has never been possible prior to this technology. Tied with business analytics applications and tools in financial forecasting, this technology is a must-have in any organisation looking to stabilise its finances and grow. Understandably, predictive analytics are no longer optional for any finance professional. One can gain this ability to master this most crucial of skills through financial accounting with the potential for further significant contributions toward his or her organisational success.

It’s about future-proofing your career in a rapidly evolving financial landscape, and investing in predictive analytics and related skills is not just about improving cash flow management. It’s your time to embrace this transformation and drive impactful financial decisions.

Predictive analytics: Staying one step ahead of the curve!

Posted on by Imarticus Learning

The power to predict future trends in the modern business world; to optimize operational processes by making informed decision-making means and cashing in on such power is now data itself, which is termed as gold. It is one of the predictive analytics capabilities taking it to the next level for businesses to tap into historical data supporting accurate provisions for forecasting results as well as basing the report on “the trend is growing towards integrating analytics within strategic decision-making processes: this leads to increased influence and responsibilities within organizations.”

This article will describe what Predictive Analytics is, how it can be used, and how senior managers can become data-driven to get ahead in the game.

What is Predictive Analytics?

Predictive Analytics is a technique of data analytics using statistical algorithms and historical data via machine learning models to predict future events. It integrates Predictive Analytics Basics with state-of-the-art technologies like AI and big data for actionable insights.

Features of Predictive Analytics

Data Ingestion: It is a process of gathering any source of structured as well as unstructured data that includes social media, databases, or even IoT devices.

Data Preprocessing or Cleaning: The cleaning of data to remove inconsistencies so that the quality of data is maintained for analytics.

Model Development: Regression analysis, decision trees, and classification algorithms are some inferences algorithms which are applied in predictive models.

Validation and Testing: This is when the model must be validated in relation to accuracy before the product could be released on real world applications.

Nuclei Techniques of Predictive Analytics

Regression Analysis: Nature between variables.

Data Classification Techniques: It is the process of dividing data into existing classes, which is used by most divisions of customers.

Machine Learning for Beginners: Train an algorithm in such a way that each day, it makes better predictions.

Industries Application of Predictive Analytics

Marketing and Customer Insight

Probable choice of customer to campaign.

Optimisation of the budget with predictive return of investment of channels.

Healthcare Analytics

Predictive analytics for efficient output health delivery.

Resource management of a hospital for smooth functions.

Financial Services

To make it easily detect fraud by making anomalies using algorithms.

A good estimation of credit risk depending upon the prediction made with respect to market trends.

Supply Chain Optimization

To predict the demand by maintaining the inventory’s cost at the minimum.

Predict the disruption so that its delivery can be made hassle-free.

Classification Algorithms and Its Application in Predictive Analytics

What are Classification Algorithms?

Classification algorithms refers to the machine learning algorithms that classify the data points based on predefined labels. This forms the back-bone of Predictive Analytics when solving problems regarding fraud detection and predicting churn.

  • Decision Trees: A model in a tree structure where the decision is taken based on some condition
  • Random Forests: An ensemble of decision trees, which results in higher accuracy
  • Logistic Regression: Classifying any binary outcome as yes/no, pass/fail.
  • Support Vector Machines (SVM): Classify the data points by hyperplane.

Applications of Classification Algorithms in Real Life

Online shopping websites use classification for recommendation.

Banks classify loan applications as risky or low risk and use the algorithms.

Machine Learning in Predictive Analytics

Machine Learning for Dummies

Machine learning works automatically with predictions since it is the way models learn from data and hence improve with time. Therefore, a beginner would know that there is something called supervised and unsupervised learning.

Core Concepts in Machine Learning Applied to Predictive Analytics

Supervised Learning: models are trained so that based on labeled information, a prediction is done.

Unsupervised Learning: patterns have to be discovered with no input in the labeled data-for instance, classify customers.

Reinforcement Learning: Algorithms will do try and error and learn about the best actions

Why Machine Learning Applied to Predictive Analytics is Helpful?

Scalability: it can handle an enormous quantity of datasets quite easily.

Accuracy: The prediction model will be improving continuously.

Automation: time-consuming repetitive tasks, without human intervention

Data Classification Techniques

What are Data Classification Techniques?

The way data breaks into categories; hence, it is easy to analyze and interpret.

Naïve Bayes Classifier: Applying probability in data classification

Business Applications

Customer segmentation for effective marketing.

Risk assessment in finance.

Predictive Analytics and Senior Leadership

Why Senior Leaders Need Predictive Analytics?

Informed Decision Making: The predictive analytics would enable a leader to understand future trends for better planning and strategy.

Resource Optimization: Accurate predictions for resource optimization.

Competitive Advantage: Data-driven moves; a step ahead of your competition.

How to learn the skill of predictive analytics
High-level trainings like IIM Calcutta Senior Management Programme in Business Analytics in collaboration with Imarticus Learning better equip leaders to make decisions utilizing predictive analytics.

IIM Calcutta Senior Management Programme in Business Analytics

Blended Analytics Training for Top Executives

It is a 9-month programme for top management and engages all four kinds of analytics, which include descriptive, diagnostic, predictive, and prescriptive.

Practical Learning through Live Projects

The learner does six real-time AI projects on health care, supply chain, marketing, and financial analytics.

Campus learning at IIM Calcutta

A very distinctive 15 days campus immersion over three visits where the student is soaked into engaging with others for the purpose of active participation and critical thinking.

Alumni Network and Certificate upon graduation

Certificate issued by IIM Calcutta with access to an excellent network of over 30,000 lifetime of networking among fellow professionals.

Pedagogy for Senior Management

This learning mode encompasses classroom direct-to-device teaching and case studies together with industrial expert guest lecturers for it to be totally complete.

Questions and Answers About Predictive Analytics

What is predictive analytics?

Predictive analytics predicts and enhances the future trends and even improves the operations of which facilitate better decision-making capabilities across the health, marketing, and finance sectors.

What is the role of machine learning in terms of predictive analytics?

This is machine learning-based predictive analytics. Here, it is training data and the model trains in reality of updates to make real-time predictions. Thus, it turns out to be accurate since it’s based on future algorithms, which are advanced.

What are classification algorithms?

Classification algorithms describe the models of machine learning where the data is classified in relation to pre-defined labels. It is used in fraud detection and customer segmentation.

Why use predictive analytics by the senior leaders?

Predictive analytics by senior leaders enables them to take decisions which sound great, optimize resource allocation and obtain a competitive advantage. 

Why Join IIM Calcutta Senior Management Course?

This will equip the senior managers with advanced analytics skills, practical experience in real life through project assignments, and elite brand certification from IIM Calcutta.

Conclusion

Predictive analytics is changing the very face of how businesses work, with insight that can power smarter decisions and innovative strategies. Totally necessary to begin to get familiar with the basics of predictive analytics up to applying classification algorithms and embracing machine learning for a beginner to stay in the game.

This IIM Calcutta Senior Management Course in Business Analytics is, certainly the exclusive opportunity for its seniors to lead changes by driving data. It makes them rich with variety of projects done along with being certified from some of the finest and top universities in this regard, which prepares it so that the leader manages to get the maximum possible achievable value from predictive analytics that situates him or her as the ‘driver at his seat’.

You will build sure futures, lead forward, stay ahead of the curve, and predictiveness.

Predictive Analytics in Financial Risk Management: Building Models with R

Posted on by Imarticus Learning

Financial institution managers must take appropriate measures to manage risk effectively in today’s continuously changing environment. This is essential to avoid complications that could lead to instability and unprofitability. An effective way to do this is by leveraging predictive analytics in finance. Where other uncertainties may leave financial firms in the dark, predictive analytics provides the historical analysis and patterns necessary for success.

One of the most prominent tools for creating those models is R Programming, which is currently popular for risk analysis, statistical analysis and data visualisation. To better understand, let’s explore the importance of predictive modelling techniques in the risk management system of finance. We’ll also navigate how R can be used to build these models and the core skills to perform these tasks, so keep reading!

The Role of Predictive Analytics in Financial Risk Management

Financial risk management is one of financial institutions’ most fundamental operational necessities. Considering its significance across organisations today, predictive analytics has entered this realm, offering solutions backed by solid data. 

Risk management with predictive analytics is improving traditional risk management by converting data into usable information. This analytical approach allows institutions to:

  • Identify patterns in historical data to anticipate future trends
  • Quantify risks such as credit defaults, market volatility and operational hazards
  • Optimise decision-making by preparing for economic shifts and emerging market trends

For instance, a bank can use predictive models to assess the likelihood of customer loan defaults by analysing borrower history and economic indicators. Early detection of such risks empowers businesses to adjust strategies, prevent losses, and comply with regulatory frameworks.

Predictive Modelling Techniques for Risk Management

Effective predictive analytics in finance relies on advanced modelling techniques. Here are some widely used approaches:

  • Linear and Logistic Regression
    • Predict relationships between variables (e.g., predicting credit score changes)
    • Logistic regression models help calculate the probability of default events
  • Time Series Analysis
    • Applied for making market forecasts and future interest rates
    • Uses details of previous performances in the computation of probable performances in certain durations of time
  • Machine Learning Algorithms
    • This comprises decision trees, Random forest and support vector machine
    • Recognises patterns which regular models can overlook, hence aiding in accurate risk assessments
  • Monte Carlo Simulation
    • Assesses the risk capability of varied financial outcomes in rather ambiguous environments
    • Assists institutions in estimating the risk relative to the changing market conditions

For example, banks can use these credit risk models to determine the likelihood of customers defaulting on their loans by analysing their performance and other economic factors. Recognition of such risks in their infancy enables management to modify tactics, minimise risk and maintain legal requirements.

Building Predictive Models with R for Financial Risk Management

R is an indispensable tool in financial risk management. It supports efficient data analysis, predictive modelling and visualisation, enabling professionals to address complex financial challenges. Here’s how R programming for risk analysis is applied in practice:

  • Data Cleaning and Preprocessing
    Raw financial data often contains noise or missing values. R provides libraries like dplyr and tidyr to clean and structure the data for further analysis.
  • Building Models Using R Packages

R supports various packages to build predictive models by:

  • Using forecasts for time-series predictions
  • Leveraging caret for machine learning models like regression and classification
  • Applying glm() function for logistic regression to predict event probabilities

 

  • Visualising Risk Insights
    R’s powerful visualisation tools, such as ggplot2, help transform complex data into insightful charts and graphs, enabling stakeholders to make informed decisions.
  • Scenario Analysis and Simulations
    With tools like riskR and MonteCarlo, financial analysts can simulate scenarios to understand risk exposure and plan accordingly.

By mastering these functionalities, financial professionals gain a competitive edge, making R an invaluable asset for predictive analytics and risk management.

Why Predictive Analytics Skills Are Crucial in Financial Services?

As uncertainty gains ground and markets become more fickle, predictive analytics is no longer a desirable bonus but a necessity. R is one of the key tools for building predictive models, and professionals skilled in these tools and other such techniques are in high demand. From market behaviour prediction to compliance with the law, predictive analytics is the basic component of contemporary credit risk management.

Developing such skills and techniques not only helps to become more informed and make the right choices but also opens up the possibility of high-paying jobs. Anyone interested in building vast experience in this field will need to proceed to specialised programs, including financial services courses.

If you are serious about navigating the expanding field of predictive analytics and risk management, consider the Financial Services and Capital Markets Management Program offered by IIM Lucknow and Imarticus Learning. This elaborate course uses the R programming language to impart the latest information on financial markets, forecasting techniques, and risk assessment.

Conclusion

Today, predictive Analytics in finance has proven to be incredibly valuable. From identifying risks in advance to modifying decision-making performance, predictive models allow institutions to overcome uncertainty. Using R programming makes these models’ convenience a notch higher, making it a must-have tool in financial risk management.

Through specialised financial services courses, you can learn about the trends within the sector. The result? Enhanced proficiency in predictive analytics skills that help you fuel organisational success and secure a future in the financial field.

The Future of Predictive Analytics: Trends and Technologies to Watch

Posted on by Imarticus Learning

Predictive analytics is changing how businesses work, giving us insight into the future. As tech moves forward, so does the potential for analytics. This blog looks at the latest trends and tech as they decide the future of predictive analytics and how you make better decisions in every tech-based move. Get started here!

What is Predictive Analytics?

First, let’s define predictive analytics. It involves using historical data, statistical algorithms, and machine learning to predict future outcomes. By analysing patterns from past behaviour, businesses can forecast future events such as sales performance, customer behaviour, and market trends. 

This foresight helps companies get ahead of the competition, optimise operations and make better decisions.

It can draw from a wide range of data sources, from customer transactions to social media activity, making it applicable to many industries, including retail, finance, healthcare, and more. This powerful tool can reduce risk and capitalise on opportunities, all while giving you an edge.

Predictive Analytics: The Future of Business

The future of predictive analytics is even wider. With big data, companies have more data than ever before. Predictive models can become more accurate, sophisticated and granular and give us deeper insight into many business areas.

As businesses become more data-driven, it will go from being a differentiator to a requirement. Companies that get in now will likely stay relevant in a more competitive world.

Trends in Predictive Analytics

This particular analytics is moving fast, driven by tech and data science. Here are some of the key trends.

1. AI and Machine Learning

One of the massive trends in future of predictive analytics  is the increasing integration of artificial intelligence (AI) and machine learning. These technologies allow predictive models to learn from new data over time without human intervention and become more accurate and efficient. As AI improves, this analytics is expected to become more intuitive and dynamic and produce faster and more reliable forecasts.

2. Real-Time Analytics

Previously, predictive models used historical data to predict the future. However, this analytics has improved with the advancement of tech real-time data. 

This trend allows companies to make predictions on the fly as data changes. Businesses can now adjust marketing campaigns, inventory management, and customer service in real-time to become more efficient and effective.

3. Predictive Maintenance in IoT

Another trend is predictive maintenance, especially in industries with IoT devices. These connected devices generate vast amounts of data, which can be used to predict when machines or equipment will fail. This technology can prevent downtime and reduce maintenance costs across manufacturing, logistics and more.

4. Democratisation of Predictive Analytics

As more companies adopt these analytics, we see a trend towards democratisation. User-friendly tools and platforms are emerging that allow non-technical users to use these analytics without writing code. This points towards companies of all sizes accessing and implementing predictive analytics across all levels of the organisation.

Key Technologies for Predictive Analytics

Several technologies are driving its growth and making it more accurate and efficient. With a business analytics course, you can get to the bottom of the process. Let’s look at the key technologies.

1. Cloud Tech

The cloud is a key technology behind the evolution of predictive analytics. Its scalability, storage, and processing power allow companies to run complex models on large datasets. Companies can deploy this technology to the cloud to reduce infrastructure costs and provide it to remote teams so they can collaborate and get results faster.

2. Big Data

So much data is generated daily, and big data platforms are needed to manage and process it all. This type of analytics loves big data, and with big data technology, companies can analyse vast amounts of information quickly and accurately and get insights they couldn’t get before.

3. Machine Learning Frameworks

Companies can use machine learning algorithms to improve their predictive capabilities and build models that learn and adapt to new data. Machine learning frameworks like TensorFlow, PyTorch and Scikit-learn are part of predictive analytics. These frameworks provide the tools to build, train and deploy predictive models. 

4. Natural Language Processing (NLP)

Natural Language Processing (NLP) is becoming increasingly important in this analytics, especially in customer service and marketing industries.

NLP allows machines to understand, interpret, and generate human language and provide deeper insights into customer feedback, social media trends, and online reviews. With predictive models, companies can predict customer needs and sentiments and craft more personalised experiences.

Predictive Analytics in Business

This particular analytics has already changed industries like finance, retail and healthcare. Its ability to forecast and make data-driven decisions is priceless. For example:

  • In finance, these analytics can forecast market trends, assess risk and identify fraud.
  • In healthcare, predictive models can predict patient outcomes while improving treatment plans. It can also predict disease outbreaks.
  • In retail, it can segment customers, predict buying patterns and optimise inventory management.

Conclusion

Interested in building a career in predictive analytics or want to upskill in data analysis? Get the inside scoop on the latest predictive analytics technologies and how to use them in business. With the Senior Management Programme in Business Analytics, you can gain access to the workings of the model. 

Ultimately, you’ll be equipped to succeed in a data-driven world. As data grows, so will its role in business, impacting everything from marketing strategies to operational efficiency.

Frequently Asked Questions

What is predictive analytics, and why is it important?

Predictive analytics uses historical data and algorithms to forecast the future. It’s crucial for helping businesses anticipate trends and improve operations.

How does AI impact predictive analytics?

AI improves this type of analytics by allowing models to learn from new data and get more accurate and faster over time. AI-driven predictive models can adapt to changing data patterns and make more reliable predictions.

Which industries benefit from predictive analytics?

Predictive analytics benefits all industries: finance (for risk management), retail (for customer behaviour analysis), and healthcare (for patient outcomes and disease trends).

Do I need technical skills to use this type of analytics?

While technical skills are helpful, many modern tools are user-friendly and non-technical users can use predictive analytics technologies. Training such as a business analytics course can help develop the skills.

What Is Predictive Analytics? A Comprehensive Guide to Understanding the Basics

Posted on by Imarticus Learning

In the era of data renaissance and artificial intelligence, predictive analytics is a specialised vertical of data science utilised for extracting future outcomes fairly accurately. Predictive analytics uses historical data, big data mining systems, statistical modelling and machine learning processes.

Organisations use predictive analytics to understand the business risk to face the upcoming challenges more smartly. Predictive analytics can foretell future sales revenue, cash flow and the profit margin.

Besides, predictive analytics also highlights key information regarding project overruns, risks associated with supply chain management, logistics production/execution etc. It also helps to provide a guideline for navigating new business geography. 

Types of Predictive Analytics

Broadly, there are ten predictive analytics techniques. These are as follows –

  • Classification model 

This elementary predictive analytics tool classifies data based on closed-ended queries, whose response may be obtained through’ responses like yes or no. 

  • Forecast model 

This model is also another common model that utilises historical data. Response received to queries in this system is numerical and useful in forecasting sales or revenue estimates.

  • Clustering model 

This model groups data based on the same or similar features. The collective data from different groups is then utilised to find out the overall outcome of the cluster.

Hard clustering is a process in which data is grouped based on the characteristics which completely match the cluster. However, another type of clustering, namely soft clustering, is also applied based on probability theory. In this case, probability or weightage is added to each data to tag its similarity percentage.

  • Outliers model 

This model locates if there is any individual unusual data within a pool of given data. This outlying information may have been generated due to some abnormal or abrupt change in the controlling parameters of business or a case of some potential fraud in financial transactions.

  • Time series model 

This is a predictive analytics tool where historical data over a specific time range is utilised to predict future trends over the same time series i.e. the same months. 

  • Decision tree algorithm 

This predictive analytics model uses graphs plotted based on data obtained from different sources. The purpose of this tool is to identify the different future outcomes based on the different decisions the management undertakes. This compensates for incomplete and missing data and makes it easy for interdepartmental reviews and presentations.    

  • Neural network model 

This model simulates neurons or the human brain through several complex algorithms and provides outcomes from different patterns or cluster data.   

  • General linear model 

It is a statistical tool that can compare two dependent variables over a regression analysis.

  • Gradient boosted model 

In this model, flaws of several decision trees are corrected and ranked. The outcome is a product of several ranked or boosted decision trees.  

  • Prophet model 

This model may be used along with time series and forecast models to achieve a specific or desired outcome in future.  

Predictive Analytics Examples

In today’s world, predictive analytics is a subject that finds application across industries. Below are a few real-world predictive analytics examples for a better understanding of what is predictive analytics. 

  • Insurance sector 

Nowadays, health and all general forms of insurance offerings are guided by predictive analytics. Historical data concerning the percentage of premature claims for customers with similar portfolios are studied. 

This tool not only makes the offer more competitive but also helps craft out a better terms package for the client while keeping the profit margin untouched for the insurance company.

  • Automotive industry 

The neural network model of predictive analytics finds its application in self-driven cars. The car sensors assess and mitigate all safety concerns and challenges a moving vehicle should encounter. Furthermore, historical data can help car dealers or service providers prepare a maintenance schedule for specific car models. 

  • Financial services 

One of the best examples of predictive analytics is its ability to run financial institutions profitably by locating fraudulent activities, identifying potential customers, eliminating loan defaulters and scrutinising other dynamic market scenarios.

Besides the above functions, credit scoring is a major function of financial institutions, and this function is driven by predictive analytics. CIBIL scores for individuals and organisations determine their trustworthiness in securing loans.

  • Healthcare 

In all modern countries, predictive analytics has become a stable cornerstone for the healthcare industry. Historical records of patient data regarding medicine and surgical techniques with the outcomes have become the backbone of future healthcare systems, ailment-wise. These records have also helped create smooth readmission of patients and immediate diagnosis in each case.  

  •  Marketing and retail sector 

Nowadays digital marketing has taken over the age-old traditional marketing practices. Search engines recommend desired products to customers and provide their specifications, prices and past reviews.

Digital marketing techniques target customers based on their recent searches. The retail sector has now become extremely competitive with data-oriented

tailor-made and client-centred products and services.

The target audience may be reached quickly, thereby increasing the sales footprint. Predictive analytics tools also scrutinise client behaviours, purchase power and patterns to improve customer relationships and return on investments.

  • Machines and industry automation 

Predictive analytics also finds its application in this sector. Machines are prone to breakdowns that result in production downtime and sometimes employee safety risks. Historical data on these machines help in preventive maintenance thereby minimising machine failures improving employee safety factors and boosting workforce morale.

  • Energy and utilities 

Oil and gas services manage a serious business. Their management must make informed decisions regarding resource allocation and optimum utilisation. Similarly, based on the actual demand based on weather conditions and available supply, these companies must determine the optimum prices for the energy charges.  

  • Manufacturing and supply chain management 

Product manufacturing is directly linked to the demand and supply ecosystem. Predictive analytics take inputs from historical data to predict accurate market demand over a specific time. 

Demand depends on factors like market trends, weather, consumer behaviour interests, etc. Past data on manufacturing help the organisation eliminate erroneous or age-old processes, thus speeding up production. 

Supply chain and logistics historical data help to speed up and improve the product delivery process to the client, thereby increasing client satisfaction.

  • Stock trading markets 

Predictive analytics is a very crucial tool when it comes to stock trading. Investing in IPOs and stocks is based on historical data.

  • Human resources 

The human resource team in an organisation often uses predictive analytics to determine highly productive processes. They also use predictive analytics to analyse the skill requirements in human resources for future business activities. 

Besides the above examples, predictive analytics has its footprint virtually everywhere. Even mere typing on the mobile or computer system is supported by a predictive text. Predictive analytics have gained immense importance today and have spiralled as a lucrative career opportunity. 

Students are encouraged to pursue a holistic data science course from a good institution. Read about data Scientists and the possible career opportunities to learn more.

Benefits of Predictive Modelling

Today an organisation invests a lot of money in predictive analytics programs to gain the below-mentioned benefits –

  • Data security 

Every organisation must be concerned with security first. Automation in collaboration with predictive analytics takes care of the security issues by flagging unusual and suspicious behaviours in network systems. 

  • Reduction of risk 

Nowadays, companies consider risk as an opportunity. Thus, mitigation of risk is important and not aversion. Predictive analytics, with the input of historical data, has the capability of risk reduction.

  • Operational efficiency 

Efficient work processes result in shorter production cycles and hence, better profitability.

  • Improved decision making 

Last but not least, nobody can deny that an organisation succeed or fails only based on the key decisions made. Nowadays, all key business calls like expansion, merger auction etc. are made based on the inputs from predictive analytics.   

Conclusion

Predictive analytics is the future and goal of artificial intelligence. It combines with machine learning to deliver the desired results. The objective of predictive analytics is to forecast future events. The process eliminates past operational errors and suggests a more pragmatic solution in several business sectors. 

Imarticus Learning’s Postgraduate Program In Data Science and Analytics can help prospective candidates get lucrative opportunities in this domain. The duration of this data science and data analytics course is 6 months.

FAQs

  • What is the predictive model in data mining?

The purpose of applying a predictive model in data mining is to extrapolate the missing data with the help of other available data in the group. The process involves the imposition of statistical models and machine learning algorithms to determine the pattern and relationship of missing data with those available in the system. 

  • How is data collected for predictive analytics?

Data may be available over various platforms like industry databases, social media platforms and the historical data of the firm planning to conduct the predictive analytics process.  

  • How accurate is the predictive analytics process? 

Subjective expert opinion is an outcome of experience and may vary from one individual to another based on the extent of exposure received. However, predictive analytics is data-driven and forecasts accurate outcomes, provided that no large-scale disruptive events or exceptions come in between.

  • Is predictive analytics a part of AI (Artificial Intelligence)? 

Predictive analytics is a core attribute of artificial intelligence.

Data analytics courses: 7 skills everyone needs to know

Posted on by Imarticus Learning

Data analytics courses: 7 skills everyone needs to know

Today, it is not an exaggeration to say that the world runs on data. The world creates 2,500,000,000,000,000,000 bytes (two and a half quintillion bytes) of data every day! Data analytics has become the jargon of the modern business, and talented data analysts are needed now more than ever before. As a result, more and more students are opting for a Data Analytics course as it stands for a lucrative job offer. 

best data analytics courseA Data Analytics Certification helps individuals to disrupt, innovate and drive their organizations’ strategies in a better manner today.

There are a variety of Tableau Course and  Data Analytics Courses online as job openings call from every industry of the market, from manufacturing and supply chain to banking, healthcare, and retail.

Here are the 7 skills that everyone needs to know if they plan to pursue Data Analytics Certification Courses to become an in-demand data professional:

Machine Learning

Machine learning has become the most important arm and leg of Data Science. As an extension of Artificial Intelligence (AI), it helps one in finding patterns in big data sets and build on algorithms to improve accuracy over time. Having a general understanding of machine learning-related tools and concepts will give you an edge over competitors. Some of the courses available on the Imarticus platform are Postgraduate Program In Data Analytics And Machine Learning And Certification In Artificial Intelligence And Machine Learning.

Structured Query Language (SQL)

To communicate with the databases, one needs to know the Structured Query Language (SQL). It allows the data analyst to modify data structures, and organize and update data. By exploring data, one learns how to analyze data as well in time. SQL has a good hold in companies everywhere because of its functionality and maintained effectiveness. Hence, it is considered one of the most important skills needed to land a data analyst job.

Data Visualization

A course in Data Analytics should equip an individual to gather insights and string the information together in a story format so that business owners can make better decisions. One can build data models and create business intelligence reports and use visual representations in a better way. The ability to visualize data empowers a data analyst to gain a better understanding of a company’s situation, deliver useful insights, and make better company decisions.

R or Python – Statistical Planning

Being able to write programs in R or Python can help one visualize as well as analyze data sets better. Although R is regarded better for analytics, Python is considered easier to learn of the two. Both languages fascinate businesses because they can handle complex or large quantities of data. Learning a language goes a long way in building a program that will collect, collate and analyze data.

Modern Statistical Thinking

Collecting, analyzing. and representing data are all parts of statistics. A strong foundation in probability and statistics is an important data analyst skill. Honing skills to develop modern statistical thinking will help an individual identify trends and patterns, avoid biases, fallacies, and logical errors in the analysis and produce accurate and trustworthy results.

Data Management

Managing data is of utmost importance, be it in any vertical of industry, as it interacts with different stakeholders over time. Storing data and information in such a way that it is cost-friendly and secure, helps data analysts to develop another skill set altogether. One can dive deep into how data works in the cloud and physical environments.

Presentation skills

For data to make sense, data analysts must perfect their presentation skills. Data visualization and presentation skills go hand-in-hand. After all, based on historical data, analysts can forecast future trends. Gain the job-ready skills for an entry-level data analyst role so that one can leverage technical skills to build a great career in the field of data analytics.

Things to Take Away!

Putting in time and effort and enrolling at a great center can help one to achieve great heights and propel one to succeed. To explore data analytics courses that can help one pursue a lucrative career in this field and assist in upskilling as well, join us now! Contact us through chat support, or drive to our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon. 

Predictions For The Future Of Big Data

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While a lot of experts believe that there’s some great stuff in store for the future of big data, it is also true that technology will be greatly advancing throughout 2017. This is why there are a number of complex facets of big data, which are increasing by the day. Various attributes of big data, such as artificial intelligence and cloud computing, are believed to have a huge impact on big data analytics. There are a number of factors that exhibit the potential to change or more likely determine the direction at which big data is moving. For instance, there will soon be a number of customers who would replace the businesses, in demanding various amounts of data, to look for the cheapest hotels and understanding climate issues and similar concepts. There is a very acceptable idea today, of a reality where it would be the customers, the common man, if you may, who would be demanding personalized, tailored artificial intelligence technology, to suit their particular needs and demands.
While these seem like mere examples, with a tinge of realism, there are absolute chances of these becoming a reality very soon.
Ten years ago, all the data that was ever generated and accumulated, made up the highest denominations of storage space, which was namely Gigabytes or Terra-bytes, but the recent few years have made an explosion of data, into what is known as exabytes; this term roughly refers to billions of Gigabytes of data. This is where we derive the term ‘big data’ from, it is to denote the humongous amounts of data that has been generated, all over the world, in such a short amount of time. Regardless of whatever happens in other aspects of this field, one thing we can be absolutely certain of. That is, that data will be continuously growing, which means that soon there will come a time, when we will be talking about Zettabytes, which roughly amounts for a trillion Gigabytes.
Artificial Intelligence began its advent, as just a buzzword which was continuously used by sci-fi movie enthusiast and was mainly used to refer to technology only seen in sci-fi movies and the likes. Today, this term is no longer reserved for those, who are obsessed with technological gizmos, or those involved in science. It has very well become a part of our everyday lives, through various examples, like Google’s Allo, Microsoft’s Cortana and Apple’s Siri. There are absolute indicators that AI has full potential of transforming, from something nice to have to very essential technology to have. There are so many changes and futuristic developments that big data can make today, as well as in the future.
One of the biggest prediction is the fact that big data can result in various advanced applications for fields of national security, customer behavior tracking, weather forecasting, HR, sports, health and so on.

One prediction is definitely going to happen, which is that big data will have a better, smarter and a huge impacting role to play in the future.

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What’s Machine Learning All About?
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Why Artificial Intelligence is Invaluable for Weather Forecasting and Disaster Prediction

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For most people, weather forecasts are simply indicators of whether they need to carry an umbrella or throw on a coat when they go outside. However, for many industries and types of individuals, weather changes and patterns have a direct impact on their lives and livelihoods.

Agriculture, for example, benefits from accurate weather forecasting because farmers can make better planting and harvesting decisions. For governments, weather forecasts factor in their budget plans and disaster relief fund allotments. Businesses that rely on clear weather (or rough weather) depend on weather forecasts to drive several of their operational processes.

From all this, it is easy to gather that accurate weather and disaster forecasting carries much more weight than we think. Artificial intelligence augments the accuracy and reliability of weather forecasting, especially given that so many details fluctuate every day and with every geographical location. It is a great fit, given the volume of data is nigh impossible to sift through with manual labor alone.

In short, the future of artificial intelligence will also see its increasing use in the weather and natural disaster forecasting domains. Here are  a few more reasons why:

  • Managing several sources of weather data

There are currently more than one thousand weather satellites orbiting the  Earth, each sending back weather data dumps to various collection points. These data dumps are a mix of information about temperatures, cloud patterns, winds, and pollution levels. Then there are thousands of government and private weather stations around the world, each conducting their own real-time research on weather and climate.  It is nearly impossible to sift through all this data manually, but AI algorithms can do it in a matter of hours.

  • Sifting through multiple data categories

Suffice to say that the amount of data generated from satellites and personal weather stations is too much to fathom, and impossible for humans to sift through. However, Artificial Intelligence training can be applied to segregate and classify data from dumps, as well as to pull out key insights for analysis. This is a preliminary process in the weather prediction model, wherein AI segregates data based on indicators, flags significant shifts or patterns, and keeps data classified such that predictions are made as accurately and as scientifically as possible.

  • Preparing for potential disasters

Beyond real-time predictions, AI is also used to identify patterns and prepare for natural disasters in advance, off the back of previous circumstances. It may also split this data between geographies, allowing disaster management teams to evaluate which areas will be hit the hardest and prepare for that. This data is also invaluable for civil engineering teams, architectural firms, and city planning teams who need to take weather into account when mapping out residential and commercial areas.

  • Sending out warnings

Apart from predicting natural disasters, AI can also be leveraged to send out warnings to potential danger zones. This is invaluable when it comes to saving human and animal lives and generally preparing areas for the worse. Warnings can be sent out through media alerts, push notifications, and citizen broadcasts; whatever the method of delivery, AI is vital to sending such notices out in time and to the right people to curb panic and facilitate seamless planning.

Artificial Intelligence Training for Weather Forecasting

Weather forecasting teams and companies need skilled AI scientists and engineers to apply theory to practice in real-time. They need AI professionals who can create automated setups to free human minds for higher-order thinking; they also need pros who are fast on their feet and adept at creative problem-solving.

Using AI for weather forecasting is a whole new ball game – one on which many lives depend.

Boost your Career and Secure a Job During the Pandemic by Availing our Great Fightback Offers today!

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Imarticus Learning introduces the ‘Great Fight Back Offer’ where you can sign up for selected courses and avail of good discounts for a limited period from 26th April-30th April 2021. Sign up today and avail of up to 30% Flat Off on placement-oriented, future-skill professional training programs.

 Covid-19 has changed the ways we lived. We are living amidst new normalcy, and we hope it fades out soon. Before we can unmask and return to normal, safety and health has to be a priority for all. The latter priorities being livelihood!

Yes, patterns of the industries have shifted, and employment and recruitment processes have changed. The parameters and eligibility criteria are moving to different alignment rapidly!

Does it put a question in the mind of what to do to advance and keep up the pace?

Well, the answer is simple to update and upgrade your skills. Those looking for an advance in their respective careers can look for new-age career programs. While those falling short of opportunities can consider shifting their career into a different industry.

Learn Data AnalyticsA practical approach and expert advice can work wonders for any task as it lays down a strategic path to be followed.

Upgrade for New Age Career with Imarticus

Imarticus Learning has transformed many careers by enabling aspirants to acquire in-demand skills using an ‘Industry-First approach.’ The idea is to prepare a talent pipeline for the jobs of tomorrow!

With the growing commercial and technological advancements in the global markets, Finance, Analytics, Technology, and Marketing are the industries that tend to secure the top-most position in terms of job opportunities.

These industries are rapidly becoming one of the most lucrative careers in the business world. Finance and Analytics Courses play a significant role in data evaluation for businesses to streamline decision-making processes. Technology simply helps to implement the strategy coming in through management.

‘The Great Fight Back Offer’

Why let Covid ruin your career when you can upgrade and become a skilled new-age professional! With the online learning mode of Imarticus, you get the facility to learn at home, thus, remaining safe and ensure productivity in the spare time.

Register today and empower your career prospects most affordably!

How Predictive Analytics Help Troubleshoot Network Issues?

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Ten years ago, if a person had suggested a predictive model to prevent a network failure occurring due to a planned breach, people would have not believed him. Today, that has become a reality thanks to predictive analytic tools and different technologies including big data and statistical modeling.

In simple words, a predictive system looks for irregularities or patterns in data and identifies issues in a network or a server before they transform into bigger problems. This piece of information can then be used to troubleshoot it. An example would be a network outage due to failure in the power supply that can be predicted by looking at the irregularities in the flow of power supply in the days before the outage occurred. The possibilities are immense and that is why it looks so promising.

To make this clearer, let’s understand the basics first.

Analysis of Network Behavior

The basic premise of using predictive analytics to troubleshoot network issues is to let it analyze the behavior of the network. For example, analyzing the flow of data in a communications line can help it understand if any loopholes in it could create a possible entrance for a data breach.

This information can then be used as a preventive measure; a defensive mechanism can be laid out even before the breach is attempted, thereby safeguarding the data available in the line. This not only helps in the security but also in network management and policy setting. To know what is happening inside a server network and monitoring it is real convenience for network managers. It halves their daily maintenance work.

Additionally, analytics can also give out trends and insights to organizations. If a certain type of communication mechanism is known for overloading, companies can avoid creating similar structures and instead opt for better versions or entirely different infrastructures. This information can then be utilized during infrastructure development, especially when it comes to the development of server rooms for IT organizations where data breach and upper thresholds need to be monitored by the second.

Predictive Analytics in Action

Experts suggest that such technologies should be put to use in those sectors where issues can cause discomfort to a whole crowd of people. They are referring to healthcare and other emerging sectors like power distribution and aviation traffic management. Network management in these sectors will help increase safety and security and minimize issues/accidents.

Healthcare systems actually need this technology because it can help hospitals better care for their patients who require 24×7 technical support and are continuously connected to the hospital’s server.

Other than looking at the historical data provided by the network, other parameters like weather conditions are also taken into account. There can be a possibility that a thunderstorm could switch off a hospital’s network because of a power supply failure. If the effect of weather on the network can be predicted, then alternatives can be put in place just in time. Although such alternatives are already in place for emergencies, what such models will help in is better implementation and preparation.

A popular example is the use of predictive analytics in emergency services is how General Electric Power uses AI to manage its power grids in the US. According to this example, the predictive model helps the company get rid of the scope of manual errors in its system. It says that simple errors made at the service provider level can sometimes cause outages in the whole sector. This can be avoided if the data entry is taken online and passes through a filter that is connected to such a model.

Any mismatch in the data as compared to what is expected of it will trigger an alert and the response teams can quickly get in action. This is already being executed by GE Power even as it finds ways to make the entire grid system automated. This does not necessarily mean the absence of service engineers, but just the absence of potential errors that they are sometimes bound to make.

All of this is possible only because of the presence and availability of historical data. Without it, the predictive analytics models cannot compare the new tasks. This is one of the challenges that new sectors face as they do not have sufficient data to work on.

Some Challenges in this Field

Predictive analytics don’t fare well for environments that are rapidly changing. This means that environments where the relationship between two actions is quick, the model finds it difficult to grasp it and thereby ignores it and moves to the next action. This can pose a problem because it can lead to incorrect prediction, or worse, even dangerous predictions.

Availability of data, as noted above, is another hurdle but not something to be worried about. For sectors like healthcare, power, and retail manufacturing, there is abundant data. The challenge then is to source and save it properly which can be used to create the models.

Experts also point to the lack of implementation on the part of engineers. Scientists are continuously toiling to create predictive models that help in error detection but on-field engineers and workers are not supporting the system by providing or utilizing data. This can be a field engineer working on a local transformer for GE Power or a systems engineer at the grids network office not willing to listen to the model’s alerts. This shows that there is also a need for awareness among workers and engineers. This is definitely a radical change in how things work but embracing them is the only way to make it serve us better.

Predictive analytics, despite its active use in detecting and troubleshooting network issues, is still at a nascent stage in the global scale. While some countries and corporations are innovating in the field with ample help from scientific organizations, the practice will only strengthen when it comes to the mainstream. And that might take some more time.