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Day: March 24, 2023

Certifications: How it can help in a business analytics career in 2023

Posted on by Imarticus Learning

Introduction

Business analytics has emerged as a strong area of growth. Organisations have realised the importance of analytics. As a result, the demand for analytics professionals is on the rise. These professionals are expected to have a deep understanding of data and how it can improve business performance.

The secret to business analytics is to approach it as a strategic initiative. It should not be looked at just as a technical process. Certifications from reputed organisations can further your career path.

What is business analytics certification?

Business analytics certification is a professional certification programme. It works as proof of knowledge of an individual in this field. It also works as a confirmation of the skills. It helps build one’s credibility and career prospects. In addition, it improves earning potential.

A business analytics certification programme covers a wide variety of topics. Various platforms also come up with certifications that hold value in the industry. A certification in this field is important to showcase that an individual has the required skill and knowledge.

How to select the right business analytics certification?

Selecting the right business analytics certification can be challenging. There are a lot of options available. Here are some factors to consider while selecting the right one:

  1. Relevance to career goals

Choose a certification programme that aligns with one’s career goals. Consider the skills one wants to develop. Look for programmes that offer training in various areas. The important bit here is to match the expectation with the training being provided.

  1. Credibility

It is important to consider the credibility and reputation of the programme. Also, take into account the organisation’s reputation. Search for programmes that have the endorsement of companies and trade associations. This will help to make the right choice.

  1. Curriculum and course delivery

Review the curriculum and course delivery methods. Ensure that the programme covers the topics that are needed. Also, consider the course delivery method. Both these factors are important to understand if the option is relevant.

  1. Eligibility

Check the eligibility requirements of the programme. Some programmes may require specific educational qualifications, work experience or technical skills. The eligibility should be checked properly to ensure it is a good fit.

  1. Cost and duration

Consider the cost and duration of the certification programme. Ensure that the programme is affordable and fits within the budget and schedule. The cost of the programme should not be the only criterion for selection. It can be a case where the right option is a bit costly. But, it would add more value as compared to the cheapest one.

  1. Support and resources

Look for programmes that offer support and resources. Support like study materials, online forums and mentorship help a lot. These resources can help prepare for the certification exam and succeed in the career.

To summarise

Business analytics is a booming field. It is expected to have strong demand for professionals in the future. It is important to have the right certifications in place to identify the right opportunities.

Business analytics certifications can help one gain the necessary knowledge and skills. This knowledge can be used effectively in business decisions. These certifications can help to stand out in a competitive job market.

The Postgraduate Program in Data Science and Analytics from Imarticus Learning can be considered for an in-depth learning experience. It is among the most popular business analytics online courses available. This certificate programme in business analytics helps provide basic to advanced knowledge about business analytics and data science concepts.

What is Decision Analysis and Risk Modelling?

Posted on by Imarticus Learning

Analysing choices concerning predefined benchmarks is part of decision analysis, which helps people make wise choices. Risks, trade-offs, and probable outcomes are among the aspects that are taken into account.

On the other hand, risk modelling is an essential process that helps identify and assess potential dangers connected to a specific decision or condition. It comprises seeing possible threats and devising plans to deal with them successfully while utilising various tools and techniques.

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The principles of decision analysis and risk modelling can be better understood by aspiring certified management accountants (CMAs) and finance professionals, as these abilities are essential for making wise company decisions.

Also, these ideas are crucial subjects addressed in the CMA Exam. Thus CMA USA training is an excellent approach to gaining proficiency in these ideas.

What is Decision Analysis?

Decision analysis (DA) is a method of decision-making that entails determining and evaluating each component of choice and then acting with the intention that results in the best outcome.

Decision analysis seeks to guarantee that choices are made with access to all pertinent data and options. For instance, a business can choose between two million-dollar investments, or an individual might use it to select between different retirement savings options.

The foundations of decision analysis can be utilised to handle a wide range of difficulties, from complicated commercial problems to straightforward day-to-day issues.

How Does Decision Analysis Work?

Decision analysis enables businesses to assess and simulate the possible results of various actions to choose the best move. The firm must comprehend many facets of a problem to make an informed decision to be productive.

Understanding the many objectives, outputs, and uncertainties involved in the analysis includes using probabilities to gauge how specific actions will pan out.

Framing the issue to permit additional examination is one of the most crucial elements. As the initial step in decision analysis, framing is developing a framework to assess the problem from several angles. These may consist of success indicators, action plans, and opportunity statements.

A model can be constructed to determine the favorability of various outcomes once the framework has been established.

Influence diagrams and decision trees are two types of models.

What Is Risk Modelling?

Compared to more conventional approaches, like purchasing insurance to protect your organisation. 

Creating practical risk analyses, emphasising how beneficial insurance may be, and adopting a more thorough approach to risk research and solutions are all part of risk modelling.

How Does Risk Modeling Work?

Making accurate assumptions about risk requires using historical data and simulation, extreme value theory (EVT), market risk, and expert elicitation. In addition to making assumptions, modelling calculates potential/maximum risk using economic, statistical, and financial methods.

Some people like categorising modelling into three primary categories: hybrid, qualitative, and quantitative. Comparative modelling relies more on experience and irrational knowledge than quantitative modelling on statistical facts and numerical evidence. The hybrid business model, combining the two, will benefit most companies.

Benefits of Decision Analysis

The benefits of decision analysis include the following:

Systematic and structured approach: Decision analysis is an organised, structured method of decision-making that helps guarantee all pertinent data is considered and analysed consistently and impartially.

Improved decision quality: Decision analysis can assist people and organisations in making better decisions and getting better results by discovering and evaluating all pertinent information and possible consequences.

Reduced uncertainty: By offering a transparent and impartial framework for weighing options and gauging risks, decision analysis can help eliminate uncertainty.

Increased efficiency: Decision analysis can assist people and organisations in making more effective use of their time and resources by dissecting difficult decisions into smaller, more manageable components.

Enhanced communication and collaboration: By giving decision-makers a common language and framework for weighing options and analysing risks, decision analysis can aid in communication and collaboration.

Improved risk management: Decision analysis may assist companies in reducing their exposure to risk and avoiding costly mistakes by identifying potential hazards and creating strategies to mitigate or manage such risks.

Benefits of Risk Modelling

Cost savings: Organisations can design systems to reduce costs and increase return on investment by using risk modelling to identify potential hazards and the expenses that go along with them.

Better resource allocation: Risk modelling can assist organisations in allocating their resources more effectively and efficiently by highlighting the most critical risks and possible outcomes.

Improved compliance: Businesses may develop strategies to comply with relevant laws and regulations using risk modelling to detect potential regulatory and compliance risks.

Better stakeholder engagement: By displaying a transparent and impartial approach to managing risks and making choices, risk modelling can assist companies in engaging stakeholders and fostering trust.

Improved competitive advantage: Organisations can obtain a competitive edge in their sector and experience long-term success by successfully managing risks and making more intelligent decisions.

Enhanced strategic planning: By identifying potential risks and opportunities and evaluating the possible effects of various scenarios, risk modelling can assist businesses in creating more successful strategies.

Difference Between Decision Analysis and Risk Modelling

Decision Analysis Risk Modelling
It is a systematic and organised method of decision-making. It is a statistical and analytical approach to risk assessment.
Evaluates all critical data and outcome possibilities. Stresses locating and calculating potential hazards.
Simplifies difficult decisions into simpler, more logical chunks. Reduces risks to manageable, smaller components.
Recognises and assesses trade-offs between alternatives. Analyses the likelihood and possible consequences of various risks.
Evaluates various factors and viewpoints to choose the best course of action. It recognises potential hazards and creates plans to reduce or control such risks.
Aims for better decision-making and better results. Strives to minimise risk exposure and prevent expensive errors.
Requires interpreting information and making subjective judgements. Utilises statistical analysis and objective data.
It can be used in a variety of situations where decisions need to be made. Utilised mainly concerning risk management.

Conclusion

Decision analysis and risk modelling are critical tools for businesses and finance professionals to make informed decisions in a rapidly changing environment. By utilising these techniques, professionals can mitigate risks, maximise opportunities, and improve their organisations’ bottom line.

If you want to enhance your skills and knowledge, consider taking Imarticus Learning’s Certified Management Accountant (CMA) course. This comprehensive US CMA course covers all aspects of the CMA Exam and provides hands-on training in the latest techniques and best practices in risk modelling and decision analysis. With this CMA USA training, you will gain the skills and knowledge to become a certified management accountant, an in-demand profession in the business and finance industries. 

Enrol now in Imarticus Learning’s CMA USA course and take the first step towards a successful career in management accounting.

Benefits of Data-Driven Decisions in Supply Chain Management

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One of the major attributes driving business success is customer satisfaction. With proper data in hand, companies can work on their supply chain management strategy to cater to customer demands and combat delivery inefficiencies. Supply chain as well as procurement process management produces huge volumes of data. It is important for leaders to adopt the right approach to using valuable data for improved operations. 

Supply chain leaders like the Chief Supply Chain and Operations Officer uses Machine Learning and AI models for improving supply chain operations. Automating a data pipeline is also of great help. A data-driven supply chain provides higher agility and greater productivity, particularly when there are chances of disruptions. Professionals who have completed a supply chain management certification course deal with this subject better. 

Understanding data-driven supply chain management

Data-driven supply chain management includes data usage strategically for better prediction of inventory and production changes, which is closer to real-time. This directly plays a crucial role in quicker decision-making. 

When it comes to a data-driven approach to devising a supply chain management strategy, primarily there is the use of new data sources like AI or ML technologies for making predictions. Data-driven supply chains provide a complete and vivid picture of the entire supply chain performance. 

Benefits of data-driven decisions in supply chain management

As a Chief Supply Chain and Operations Officer, you cannot undermine the importance and benefits of data-driven decisions in supply chain management. Let us look at some of these benefits:

  • Actionable insights

With the use of data-driven decisions within the supply chain, businesses can gain insights into the demands of various products and materials. This helps in getting more accurate forecasts. With access to data in real-time, organisations are in a position to adjust to demand fluctuations or emerging trends. This opens up opportunities in generating more revenue. 

Businesses also get an insight into the logistical capacity requirements thus dealing with inventory undersupply or oversupply issues successfully. Data-driven decisions help in meeting a balance between demand and supply, leading to cost-savings and more importantly, enhanced customer experience. 

  • Improved accountability and end-to-end visibility

When businesses implement data-driven decisions in devising strategies for supply chain management, they have improved accountability of the whole work. Moreover, there is a detailed record of every step in the supply management chain offering end-to-end visibility. 

Transparency in various operations is a vital point of a sustainable supply chain. With real-time data-driven decisions, organisations have complete transparency, which proves to be useful for all stakeholders. Transparency is inclusive of the due diligence measures in supplier appointments, identifying procurement sources as part of procurement process management and the metrics used for calculating carbon emissions. 

  • Improved inventory and logistics management

Inventory and logistics management are integral parts of the supply chain management process. With data-driven decisions, vendors and suppliers have immediate updates if there are any kinds of order backlogs. Along with attending to the backlogs immediately, businesses can devise strategies to adjust the inventory management process.  

The logistics department also benefits from data-driven decisions as they receive real-time information regarding their consignments, different cargo batches and updated delivery status of each consignment. With enhanced operational management, you can track your goods conveniently. The final result is a happy and satisfied customer. 

  • Better planning capacity

Every business wants to expand and for that proper planning is a prerequisite. Data-driven decisions in supply chain management provide valuable insights into emerging trends and the demand for a product. This proves to be highly useful for future project planning and devising and implementing effective business strategies. 

For instance, in a manufacturing company, real-time data and visibility offer valuable insights into various things including manufacturing inefficiencies, production volumes, various challenges related to raw material sourcing, etc. When businesses get more accurate information from data, they can have better planning. With planning, a business gets a competitive edge over others. 

  • Cost-savings

Data-driven decisions help in saving good amounts of money in supply chain operations. This is done by making sure that the demands for raw materials and related products are fulfilled timely. This is important so that there is no delay in the production plants or in the assembly lines. Supply chain operations can be made cost-effective if the products move on the quickest routes in the shortest time span. 

With necessary insights and transparent visibility from data-driven decisions, supply chain executives are successful in addressing various challenges in the supply chain.  Some of these include boosting profitability, reducing wastage and increasing operational efficiencies. 

  • Enhanced customer experience

Whether it is procurement process management or supply chain management, the main aim of any business is to provide the best customer experience. A Chief Supply Chain and Operations Officer implements data-driven decisions for enhanced customer experience. The majority of customers have a positive and smooth experience, making them happy customers. 

With real-time data, officers and managers monitor and analyse supply chain operations closely. They can work on ways to increase accuracy and minimise waiting times for product deliveries to customers. As a result, customers receive their orders on or before time and in good condition. 

Conclusion

Businesses are realising the value and importance of data and data-driven decisions in supply chain management and procurement process management. Right from making improved data-driven decisions for a particular supply chain management strategy to enhancing end-to-end operations, businesses are leveraging data in the best possible manner. 

If you are interested in making a career in the supply chain industry as a Chief Supply Chain and Operations Officer, a supply chain management certification course will be of great help. You can take up an IIM supply chain management programme and become a leader in the supply chain and operations industry. 

Imarticus Learning in collaboration with IIM Raipur offers an Executive Certificate Programme for Global Chief Supply Chain and Operations Officer. This 10-month programme targets senior supply chain professionals and helps them in acquiring operational, technological, strategic and personal skills for carving a niche in the supply chain industry. The study programme has six modules, covering various critical features of supply chain management and operations. 

What does the future of cryptocurrency look like?

Posted on by Imarticus Learning

The rise of cryptocurrency has completely changed how we think about money and transactions. The financial sector has been drastically altered by the emergence of cryptocurrencies like Bitcoin, Ethereum, and others in previously unthinkable ways. But what lies in store for cryptocurrencies in the future? Will it stay strong, alter the financial environment, or disappear into obscurity?

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The future of cryptocurrencies is a hotly debated subject as the twenty-first century advances. Numerous experts believe that as more people become aware of cryptocurrencies’ potential advantages and utility, their use and acceptance will only increase.  

Here are ten points that shed some light on what the future of cryptocurrency may look like.

  • Increased adoption: Cryptocurrencies are becoming more widely accepted as a means of payment, and this trend is likely to continue. As more people become aware of cryptocurrencies and their benefits, they will likely be more willing to use them in everyday transactions. 
  • Greater regulation: Governments worldwide are starting to take notice of cryptocurrencies and are beginning to create rules to govern their use. While this may be seen as a negative by some, it will likely lead to greater adoption and acceptance of cryptocurrencies by businesses and individuals. 
  • More stable prices: One of the cryptocurrencies’ biggest criticisms is their volatility. However, as more people use cryptocurrencies and become more widely accepted, their costs will likely become more stable. It will also make cryptocurrencies a more viable option for investment and payment. 
  • Increased privacy: Privacy is a significant concern for many people regarding financial transactions. Cryptocurrencies offer a level of privacy that is unmatched by traditional financial institutions. People are becoming more concerned about privacy and will likely begin using cryptocurrencies. 
  • Improved security: Cryptocurrencies are secured by advanced cryptography, which makes them almost impossible to counterfeit or hack. As more people become aware of this level of protection, more people will likely begin using cryptocurrencies. 
  • New use cases: While cryptocurrencies are primarily used for payment, they can be used for other purposes. As new use cases for cryptocurrencies are discovered, their value will also likely increase. 
  • Increased efficiency: Cryptocurrencies offer a level of efficiency unmatched by traditional financial institutions. People can process transactions quickly and inexpensively without the need for intermediaries. As more people become aware of this level of efficiency, more people will likely begin using cryptocurrencies. 
  • Greater accessibility: Cryptocurrencies are accessible to anyone with an internet connection, regardless of where they are. It makes them an ideal option for people who do not have access to traditional financial institutions. As more people become aware of this level of accessibility, more people will likely begin using cryptocurrencies. 
  • Integration with traditional financial institutions: While cryptocurrencies are often seen as a replacement for conventional financial institutions, they also have the potential to be integrated with them. For example, cryptocurrencies could facilitate cross-border payments or provide more secure and efficient transactions. As more traditional financial institutions explore the potential of cryptocurrencies, they will likely become more widely accepted. 
  • Continued innovation: Cryptocurrencies are a relatively new technology, and there is still much innovation. With recent developments, cryptocurrencies will become even more widely accepted and valuable. This innovation could come from new use cases, improved security, or increased efficiency.

The future of cryptocurrency looks bright and promising. While there are still challenges, such as regulatory hurdles and price volatility, the benefits of cryptocurrencies are clear. As more people become aware of these benefits, cryptocurrencies will likely become more widely accepted and used, leading to a more efficient and secure financial system.

Discover MBA in Fintech training program with Imarticus Learning

Are you looking to learn FinTech Online? A ground-breaking program, the MBA in Fintech offered by KL University provides comprehensive knowledge of the Fintech industry. This cryptocurrency Course offers practical training to address real-world challenges in the Fintech sector and a thorough understanding of how technology transforms the finance industry. 

Course USPs:

  •  Once you have completed the program, you will be prepared to pursue a career in the Fintech sector and choose the profile that best suits your interests. 
  • This Artificial intelligence in Fintech program explores the sector’s fundamentals. It gives students hands-on experience with cutting-edge technologies like API, Blockchain, Cloud Computing, AI, Machine Learning, RPA, IoT, and Big Data. 
  • The course offers a rare chance to gain a profound understanding of the dynamics of technology, which are constantly changing, and how they affect the financial and other sectors.

For further details, contact the Live Chat Support system or visit one of our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Hyderabad, Delhi, Gurgaon, and Ahmedabad.

Why is Noise Removal Important for Datasets?

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Noisy data in datasets impact the prediction of meaningful information. Studies stand evidence that noise in datasets leads to poor prediction results and decreased classification accuracy. Noise impacts algorithms in missing out patterns in any dataset. To be precise, noisy data is equivalent to meaningless data. 

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When you learn data mining, you get to know about data cleaning. Removing noisy data is an integral part of data cleaning as noise hampers data analysis significantly. Improper data collection processes often lead to low-level data errors. Also, irrelevant or partially relevant data objects might hinder data analysis. For enhancing data analysis, all such sources are considered noise.  

In data science training, you will learn the skills of removing noise from datasets. One such method is data visualisation with tableau. Neural networks are also quite efficient in handling noisy data. 

Effective ways of managing and removing noisy data from datasets

You must have heard the term ‘data smoothing’. It implies managing and removing noise from datasets. Let us look at some effective ways of managing and removing noisy data from datasets:

  • Regression

There are innumerable instances where the dataset contains a huge volume of unnecessary data. Regression helps in handling such data and smoothens it to quite an extent. For the purpose of analysis, regression helps in deciding the suitable variable. There are two variables in regression, which are as follows:

  • Linear Regression 

Linear regression deals with finding the best line for fitting between two variables so that one is used for predicting the other. 

  • Multiple Linear Regression

There is the involvement of two or more variables in multiple linear regression. By using regression, you can easily find a mathematical equation for fitting into the data. This helps in smoothing out the noise successfully to quite an extent. 

  • Binning

When you learn data mining, you will surely learn about binning. It is one of the best and most effective ways of handling noisy data in datasets. In binning, you can sort the data. You can then partition this data into bins of equal frequency. You can replace the sorted noisy data with bin boundary, bin mean or bin median methods.

Let us look at the three popular methods of binning for smoothing data:

  • Bin median method for data smoothing

In this data smoothing method, the median value replaces the existing values that are taken in the bin. 

  • Bin mean method for data smoothing

The mean value of the values in the bin replaces the actual value in the bin in this data smoothing process. 

  • Bin boundary method data smoothing

In this data smoothing method, the maximum and minimum values in the bin values are then replaced by the boundary value that is closest.

  • Outlier Analysis

Outliers are detected by clustering. It is evident from the name that close or similar values are organised in clusters or in the same groups. The values which do not fit into the cluster or fall apart are considered outliers or noise. 

However, outliers provide important information and should not be neglected. They are extreme values which deviate from other data observations. They might be indicative of novelty, experimental errors or even measurement variability. 

To be precise, an outlier is considered an observation which diverges from a sample’s overall pattern. Outliers are of different kinds. Some of the most common kinds are as follows:

  • Point outliers

These are single data points, which rest away quite far from the rest of the distribution.  

  • Univariate outliers

These outliers are found when you look at value distributions in a single feature space. 

  • Multivariate outliers

These outliers are found in an n-dimensional space containing n-features. The human brain finds it very difficult to decipher the various distributions in n-dimensional spaces. To understand these outliers, we have to train a model to do the work for us. 

  • Collective outliers

Collective outliers might be subsets of various novelties in data. For instance, it can be a signal indicating the discovery of any new or unique phenomena. 

  • Contextual outliers 

Contextual outliers are strong noises in datasets. Examples to illustrate this include punctuation symbols in text analysis or background noise signals while handling speech recognition. 

  • Clustering 

Clustering is one of the most commonly used ways for noise removal from datasets. In data science training, you will learn how to find outliers and also the skills of grouping data effectively. This way of noise removal is mainly used in unsupervised learning. 

  • Using neural networks

Another effective way of removing noise from datasets is by using neural networks. A neural network is an integral part of Artificial Intelligence (AI) and a subset of Machine Learning, in which computers are taught to process data inspired by the human brain. It is a kind of Machine Learning process known as Deep Learning where interconnected nodes are used in a layered structure for analysing data. 

  • Data visualisation with tableau

Tableau is a data processing programme which creates dynamic charts and graphs for visualising data in a professional, clean and organised manner. While removing noise from datasets, this programme proves to be truly effective. Clear identification of data is possible with data visualisation with tableau. 

Conclusion

Almost all industries are implementing Artificial Intelligence (AI), Machine Learning (ML) and Data Science tools and techniques in their works. All these technologies work with huge volumes of data, using the most valuable ones for improved decision-making and forecasting trends. Noise removal techniques help in removing unimportant and useless data from datasets to make them more valuable. 

If you are looking to make a career in data science, you can enrol for an IIT data science course from IIT Roorkee. You can also go for a Machine Learning certification course in conjunction with a data science programme. 

Imarticus Learning is your one-stop destination when you are seeking a Certificate Programme in Data Science and Machine Learning. Created with iHub DivyaSampark@IIT Roorkee, this programme enables data-driven informed decision-making using various data science skills. With the 5-month course, learn the fundamentals of Machine Learning and data science along with data mining. Acclaimed IIT faculty members conduct the course. Upon completion of the programme, you can make a career as a Data Analyst, Business Analyst, Data Scientist, Data Analytics Consultant, etc. 

Enrol for the course right away!

Why Supply Chain Analytics Matthers in SCM

Posted on by Imarticus Learning

Supply Chain Management (SCM) is a complex and critical process that involves planning, executing and controlling the flow of goods and services from the point of origin to the point of consumption. 

One of the most complex challenges in SCM is reconciling supply and demand, to which sales and operation planning provides a solution. However, successful sales and operation planning implementation demands a deep understanding of the supply chain processes and data. This is where supply chain analytics becomes pivotal, adding insight and innovation to optimise SCM operations. 

This article will attempt to explore the importance of supply chain analytics in SCM. Read on to learn more.

What is Supply Chain Analytics?

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Supply chain analytics is the practice of using data analysis and business intelligence tools to gain insights into the performance of a supply chain. It involves collecting and analysing data from various sources such as suppliers, manufacturers, logistics providers and customers.

By utilising supply chain analytics, companies gain unprecedented visibility into their supply chain operations, allowing them to track inventory levels, monitor supplier performance and avoid potential bottlenecks or disruptions.

Armed with these powerful insights, companies can make data-driven decisions to optimise their supply chain efficiency, reduce costs and provide unparalleled customer service.

What are the Different Types of Supply Chain Analytics?

Supply chain analytics encompasses various analytical methodologies, each with distinct characteristics and analytical approaches. The types of these techniques are as follows:

  • Descriptive Analytics: It is concerned with getting insights into the past events of an organisation’s supply chain operations. It employs techniques such as data visualisation, statistical analysis and trend analysis that can summarise historical data and detect prevailing trends and patterns.
  • Predictive Analytics: This involves harnessing past data to generate predictive models for future occurrences. It is particularly useful for helping businesses anticipate future demand, identify risks, optimise supply chain operations and pinpoint opportunities.
  • Prescriptive Analytics: It offers guidance on the most optimal course of action to achieve predetermined outcomes. It integrates both descriptive and predictive analytics to suggest an action plan for a given scenario.
  • Diagnostic Analytics: It focuses on ascertaining the underlying causes of issues or anomalies in supply chain operations. It uses techniques such as data mining and drill-downs that can identify inefficiencies within a firm’s supply chain processes, allowing for targeted improvements.
  • Real-time Analytics: It enables businesses to obtain instantaneous insights into their supply chain operations. It leverages current data to provide real-time decision-making support for inventory management, logistics, transportation and other real-time supply chain activities.

The Significance of Supply Chain Analytics

The significance of supply chain analytics in SCM cannot be overstated. It provides valuable insights into the complex processes of supply chain operations that are often unpredictable and irregular.

Below are a few reasons why supply chain analytics is important in SCM:

Better Decision Making 

By offering significant insights into supply chain operations, supply chain analytics assists organisations in making educated decisions. These insights may be utilised to optimise supply chain operations and save costs.

Inventory Optimisation 

By giving real-time access to inventory levels, demand trends and supplier performance, supply chain analytics may assist organisations in optimising their inventory levels. This data may be utilised to make better inventory management decisions and lower inventory expenditures.

Enhanced Customer Service 

Organisations may use supply chain analytics to track the delivery of goods and services, monitor customer feedback and respond to consumer concerns as quickly as possible. This enables organisations to enhance customer satisfaction and deliver better customer service over time.

Risk Management

Organisations can detect and manage supply chain risks, supply chain interruptions, supplier performance concerns and demand fluctuation. This is also achieved through supply chain analytics, which gives them real-time visibility into their supply chain management operations.

Conclusion

The world of supply chain analytics is an ever-evolving and complex arena, with businesses striving to gain a competitive edge by harnessing the power of data analytics. It is imperative that organisations recognise the pivotal role of supply chain analytics in driving business success and invest in the technologies and expertise necessary to unlock its full potential.

If you are an aspiring supply chain professional looking to enhance your supply chain management skills, then look no further. Imarticus’s Advanced Certification Program in Digital Supply Chain Management is the ultimate IIT supply chain management course incorporating the latest supply chain analytics concepts. 

This supply chain analytics course provides a comprehensive and practical digital supply chain management approach. You’ll learn how to apply cutting-edge supply chain analytics techniques, design effective supply chain networks and optimise supply chain operations. With expert instruction from industry leaders and real-world case studies, you’ll gain the knowledge and skills required to excel in the dynamic world of supply chain management.

How Do CFOs Manage Capital Allocation While Driving Business Growth?

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Capital allocation refers to the process of allotting and investing the financial resources of a company. The Chief Executive Officer (CEO) and the Chief Financial Officer (CFO) discuss and decide upon the capital allocation policies of a company to boost profitability and efficiency. Capital allocation is crucial for the efficient digital transformation of a company. The CFO plays a pivotal role in strategising capital allocation by focusing on client needs, supply chain management, capital budgeting, and such other factors to drive business growth.

Capital allocation is highly effective for the resolution of supply chain issues. A capital allocation strategy that is in complete alignment with the business goals of a company also helps in evaluating investment options. One of the key approaches to capital allocation is to hold extensive discussions between the CFO and the stakeholders of a company.

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The CFO should furnish the stakeholders with pertinent information related to research and development investment, mergers and acquisitions, capital expenditure policies, and debts incurred by the company during the discussion. The stakeholders may engage in providing guidance and recommendations to the CFO for efficient capital allocation. Read on to gain a deeper insight into how the CFO manages capital allocation by the adoption of different strategies in order to drive growth and development in the company.

Strategic capital allocation management recommendations for CFOs

Capital allocation is key to the success of a company. It helps in assessing the future of the company. The pandemic, caused by the global outbreak of Coronavirus, has made capital allocation even more important for companies across the world.  Economists, around the globe, recommend the following strategies for effective capital allocation:

1.       Work on Mergers and Acquisitions (M&A)

M&A may sound risky for companies but research reveals that merging with or acquisitioning other companies is actually a prudent move. In fact, business organisations that partake in M&A ventures perform better even during financial crisis periods. Capital allocation to M&A also helps companies in acquiring substantial shareholder returns, especially at the end of a fiscal crisis period.

It is crucial to exercise due diligence before signing the M&A deals. The process of due diligence involves build-or-buy assessment and analysis of the new goods and services to be offered. Due diligence also necessitates a thorough understanding of whether the new goods and services can be purchased externally or created in-house. In a nutshell, capital allocation to M&A does not fail to contribute to business growth despite the risks.

2.       Focus on capital allocation and reallocation whenever there is scope for growth

Take note of shifts in customer preferences, client needs, and such other factors to assess the areas of growth and stagnation. Quit hoarding cash and look out for novel investment opportunities instead. A top-notch way of identifying the areas of latent business growth is to follow the two-step process outlined by Harvard Business Review. In this process, the first step is to focus on the identification of particular goods and services that have the potential for growth.

The second step in this process is to assess whether the potency of growth is for the short term or the long term. This assessment helps in determining the products and services that will have the power to contribute to business growth even during financial crises. Also, take note of the market and industrial specifics for capital allocation or reallocation.

3.       Try to avoid stock buybacks

Do not make stock buybacks a part of the capital allocation scheme. Instead, invest the money in the business for successful capital allocation. An important reason to avoid investing in stock buybacks is to save money for emergencies. Avoid buybacks and invest money in assets that will contribute to the long-term stability and growth of the company.

4.       Make the capital allocation scheme evaluation a continuous process

In view of the rapidly changing markets, it is important to review and modify the strategies of capital allocation and capital budgeting continuously for flourishing business growth. Revisit and analyse the capital allocation scheme continuously to make sure that the scheme is consistent with the changes that prevail in the markets. It has been observed by economists that companies that re-evaluate their capital allocation policies consistently succeed in attaining higher net worth and revenues than companies that stick to the same policies year after year.

5.       Take care of the dividends

If the company is going through a financial crisis then one of the ways in which the company can save money is by decreasing shareholder pay-outs. However, if there is no crisis, then there is no need to slash the dividends. Dividends are often used to provide financial support to the pensioners of the company. Capital allocation to dividends should be done in a way without disrupting the pecuniary future of the company.

Conclusion

Capital allocation involves extensive decision-making with regard to dividends, research and development, mergers, acquisitions, debt management, and other financial aspects of a company. It is the sole objective of capital allocation to elevate the shareholder value and stock returns of a company in the years to come.

Capital allocation is essential for the creation and maintenance of an investment portfolio. If you seek to devote your time to learning about capital allocation then you may enrol in the Chief Financial Officer course offered by Imarticus. In the Chief Financial Officer course, the candidate shall be able to gain a world of financial knowledge. From learning to assess the rapidly evolving market dynamics to taking control of capital budgeting during financial crises, the course covers it all. 

Enrol in the course right away to develop a strong grasp on the concepts of capital allocation in companies.

The Role of Predictive AI in Fintech Solutions

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In the former times, when financial transactions were carried out in brick-and-mortar institutions, bankers had personal connections with the customers. They would help them with their decisions. Although the fact that digitisation has brought the world to our footsteps is a huge advantage, it has also caused a loss of this personal connection.

We are aware of the countless things that artificial intelligence can do. Artificial intelligence is known for its ability to process humongous amounts of data at a time. Businesses are embracing artificial intelligence to meet their goals, achieve growth, and make their services more relevant to the customers’ needs and preferences.

best fintech course in India

In fintech, artificial intelligence (AI) is being used to revert this loss of personal connection. AI helps to process the data and information of the customers and analyse them in order to predict upcoming trends. This way, fintech companies can help customers have a better experience! A fintech certification course can equip you with the relevant knowledge.

This article discusses how predictive AI is making an impact on fintech solutions. Read on to find out. 

How is AI being used by Fintech Companies?

The fintech industry includes a lot of intricacies and is rich in innovation. The fintech industry is resorting to solutions such as artificial intelligence, machine learning, and cloud computing for numerous tasks like decision-making, analysing customer behaviour and purchase trends, gaining valuable insights, increasing efficiency, etc. 

Discussed below are some potential uses of artificial intelligence in the fintech industry. 

  • Artificial intelligence supports better decision-making: Data-driven decisions are more likely to be accurate and error-free. Machines are asked questions, after which they analyse vast volumes of data. They then deliver the most relevant results, which help the leaders in the fintech industry make better decisions. 
  • Elimination of manual effort and scope for error: AI has a technology known as robotic process automation (RPA), which helps to automate the tasks that would otherwise be performed by humans. This not only saves time but also helps to eliminate the chances of human error. 
  • AI can detect dubious activities: AI also helps to secure cashless transactions by providing security of the highest order. There are AI tools that can study the behavioural patterns of the users and detect any sign of fraudulent attempt. 
  • AI chatbots for swift communication: Chatbots are a supreme pillar of artificial intelligence, and the fintech industry has availed its advantage to the fullest. Earlier in the article, we mentioned that the intrusion of technology in the field of finance had caused a loss of personal connection. 

To bridge this gap, financial institutions incorporate the use of chatbots to maintain communication with customers. Chatbots can immediately attend to and address the concerns of the customers, which makes them feel valued and helps to build their trust. 

Predictive analysis is another important task managed by artificial intelligence that deserves notable mention. Predictive analysis can help to anticipate upcoming trends with the help of techniques like data mining, machine learning, etc. Fintech companies use predictive AI to transform their digital operations so that they remain at par with the customer’s needs and the latest market trends.

Using Predictive AI in Fintech 

Predictive analysis uses data, statistics, algorithms, and other techniques to predict the future on the basis of previous data. In the fintech industry, predictive analysis can help to foresee upcoming market trends, understand customer behaviour, and predict investment opportunities. 

Listed below are some of the ways in which predictive AI is contributing to the fintech industry. 

Predictive AI helps to avert the risk of bad loans 

Financial institutions grant loans to generate revenue. However, ever since technology has merged with finance, it has become easy for people to opt for loans over the internet. Predictive AI helps fintech companies make informed decisions by assessing the client’s creditworthiness. 

Personalised recommendations 

Predictive AI helps to improve customer experience by offering personalised recommendations for them. It helps to analyse the suitability of certain plans and financial instruments while taking the different parameters into account. Therefore, the target audience gets to invest in plans that are the most suitable for them. 

Secure transactions

Although the amalgamation of finance and technology has helped to streamline transactions, there is also an increased risk associated with cashless transactions. As mentioned earlier in the article, AI also helps identify incidences of fraudulent activities. With the help of predictive analysis, the data of all fraudulent cases that have taken place in the past can be gathered. 

After analysing the data, the patterns of the dubious activities can be figured out. Based on the data thus gathered, financial institutions can help to secure transactions by building more robust verification processes.

Predictive analysis is essential to carry out different financial services. The use of AI for predictive analysis helps to gather huge volumes of data and analyse it with the help of robust algorithms so that prescriptive solutions can be offered to customers. The right kind of predictive analysis helps fintech companies improve their operation and manage their internal processes easily.

Conclusion

If you are looking forward to transforming your career in the fintech industry by acquiring knowledge about the technologies that are revolutionising the finance industry, a certification course from Imarticus Learning might be right for you. This Imarticus Fintech Certification Course, in partnership with SP Jain School of Global Management, is surely going to give your career a boost!

The course covers the different aspects of Fintech and also equips the learners with knowledge about cloud computing, blockchain, Big Data, artificial intelligence, machine learning, and so on. It also gives the learners access to webinars, networking events, and job boards, which helps them to explore new opportunities. You also get access to SP Jain’s lab, where you can put your theoretical knowledge to practice!

With the community of learned experts, online digital library, and case studies, the scope of learning in this course is endless! After completion of the course, learners will be awarded a certificate from SP Jain School of Global Management and Imarticus Learning. Therefore, get ahead of your competitors with this online certification course in fintech!

Learn blockchain with top certification courses in 2023

Posted on by Imarticus Learning

A blockchain is a form of distributed ledger technology developed as the basis for bitcoin. The technology supports the creation and transfer of any digital asset. 

Blockchain allows users to make transactions directly with each other without an intermediary such as a bank or government agency. On a public ledger, all transactions get logged in blocks. Additionally, you can be sure that your data is unchanged from when it was sent or received.

What is a blockchain?

A blockchain is a decentralized, distributed ledger. It is a digital record of transactions, agreements, or any other form of data that can use to transfer money (i.e., currency). 

Each node maintains its copy of the entire chain, so there’s no single point where an error could occur if something went wrong with one part of it—it would affect every other copy at once instead!

The transparency provided by this system means that anyone can see what happened in any transaction without having access to any personal information; you don’t need ID numbers or passwords here either since everything happens through cryptographic signatures instead.

How does blockchain work?

  • Blockchain is a distributed ledger
  • Blockchain is a decentralized technology
  • Blockchain is a peer-to-peer network
  • Blockchain uses smart contracts to automate transactions and eliminate third parties from the transaction flow

Why is blockchain so popular?

A blockchain system makes it possible to record transactions between two parties. The blockchain consists of multiple computers worldwide, verifying and recording these interactions. 

Because it’s decentralized, anyone can view this information on their computer or smartphone—and once they’ve verified it, they know that no one else has tampered with their copy of the ledger. 

This transparency makes blockchain an excellent tool for keeping track of financial transactions; it also makes it immune to fraud because no one can create fake records without being caught by other users, who will then reject their entries as invalid (since they don’t match up with what everyone else sees).

Who uses blockchain technology?

Blockchain technology gets used in many different industries. Here are some of the most common uses:

  • Financial institutions can use blockchain to make payments more secure and efficient. For example, instead of sending money from one place to another (like when you buy something online), you could send your payment directly from your bank account to theirs—no mediator needed! This would reduce fraud and make transactions faster than they currently are.
  • Developers who work on applications like Bitcoin or Ethereum use open-source software called a “blockchain” because it helps create new cryptocurrencies that run on their networks (e.g., Ethereum). 

Explore Blockchain certification course with Imarticus Learning

By providing practical instruction from business experts, robotic process automation, real-world case studies, blockchain, and cloud computing projects, and crucial connections with fintech leaders and entrepreneurs, the Blockchain certification program enhances students’ understanding of fintech.

Course Benefits For Learners:

  • Fintech has a promising future; students stay up-to-date by attending networking functions, job fairs, or webinars.
  • The students will apply the concepts covered in the online fintech training in their daily jobs.
  • The best fintech courses in India allow students to pursue their ideal career in fintech after graduation.

Best Practices for Securing SaaS Applications

Posted on by Imarticus Learning

SaaS (Software-as-a-Service) is a cloud computing software delivery model that provides access to software on a subscription basis. A SaaS application must not be downloaded or installed, and they are hosted on the cloud and can be accessed over the internet. 

There are several benefits of using SaaS applications, some of which are accessibility, agility, scalability, constant updates, and manageable payment options. However, any SaaS development company has to remain cautious about the security of the applications thus developed. 

SaaS applications deal with massive amounts of data that might be sensitive. The fact that SaaS applications are easily accessible across all devices also raises the security question. This article discusses some of the best practices of SaaS security which application developers can adopt to ensure network security, manage sensitive data and keep the risk of cyber-security threats at bay! 

Why Use SaaS

The use of cloud computing technology makes SaaS applications more flexible. There are plenty of reasons why SaaS is gaining popularity among modern organisations. Some of the benefits of using SaaS for application development are listed below:

  • Cloud accessibility, easy accessibility: One can access a SaaS application from any device; it only needs a stable internet connection. This is ideally suited for companies following a work-from-home or hybrid work model.
  • Cost-effective: SaaS vendors have a subscription-based pricing model. This means one does not have to pay for installation, licences, or infrastructure management. You only pay for as much as you are availing. 
  • Easy to deploy: Unlike conventional on-premises software, SaaS applications do not require any installation and configuration. Businesses can roll out the applications almost immediately. 
  • Scalability: This is another significant benefit offered by SaaS. With SaaS, one can quickly increase the storage or add more services without paying extra for the upgrade. This is ideally suited for growing businesses as they can incorporate the new features as and when required. 
  • Automatic upgradation: In SaaS, the vendors or providers constantly make updates. These updates are thus made available to the customers.

However, despite all these advantages, there are still some risks associated with using SaaS applications, for which developers need to be equipped with knowledge about the best practices of SaaS security.  

Challenges to SaaS Security

With greater development, new challenges abound. As mentioned earlier, security remains one of the significant concerns of SaaS applications. Although SaaS allows individuals and companies to share data outside the company’s boundaries, it has resulted in a greater risk of blunders like data exfiltration and phishing attacks. 

SaaS applications can be accessed remotely. Stakeholders within and outside an organisation can access the files and data. Although this is an advantage, this can also stand as a loophole. 

Anyone with malicious intent can take advantage of this. Some companies deal with users’ sensitive personal information such as bank details, health records, etc. Such data are always exposed to the risk of being breached and tampered with. 

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When you use a SaaS product, you do not have any control over the data management and storage patterns. Therefore, be sure that your provider informs you about the data storage pattern, security measures, recovery procedure, and time in case data is lost. 

SaaS is an excellent tool for developing complex applications; however, the more complexity, the greater the chances of misconfiguration. Misconfigurations can expose SaaS products to the risk of cyber-attacks. Also, accessing SaaS applications from public WiFi or unsecured internet sources increases the risk of virus and malware attacks. 

Therefore, following the best SaaS security practices is crucial to eliminate the risk of attacks and also improve overall security. An IIT Roorkee cybersecurity course will teach you more about the fundamentals of network security, risks associated with cloud computing, cloud security, ethical hacking, and many more!

Best Practices to Secure your SaaS Applications 

Although a daunting task, ensuring SaaS security is one of the top-most priorities of IT professionals. Below are some of the best SaaS security practices that will help you secure your applications. 

User Access

If everyone gets admin-level access, it is sure to lead to security breaches. In order to identify any risk, it is important to constantly monitor the failed login attempts, the device and location from which the login attempt was made, and also the successful logins. 

Also, accesses should be granted for a defined period and renewed as and when required. Organisations also fall victim to internal threats posed by individuals with malicious intent. Not only this but access should also be granted based on the individuals’ roles. 

Enhanced Authentication of the Logins 

To ensure a robust security system, login procedures should be modified, and a multi-factor authentication system should be enabled. In this kind of security system, after users enter the credentials required for login, they are asked to enter an OTP or a code that is shared on both the client’s and the server’s device screen. 

This helps to make sure that access is being granted to the right person. 

Secure the Data 

Data, both in motion and at rest, should be encrypted. As mentioned above, in the case of SaaS applications, where the data is stored is entirely at the providers’ discretion. Encryption helps to safeguard the data from hackers and maintains confidentiality!

Providers should also have a strict policy on data deletion. The data no longer in need should be deleted according to the terms of the agreement. 

Developers can also incorporate real-time monitoring techniques to secure the SaaS applications. This will provide better visibility and control and help protect the data against vulnerabilities such as SQL Injection, XSS, Cross-Site Request Forgery, etc. 

Cloud Access Security Broker

If you are dissatisfied with the level of security your cloud computing service provider is giving to your SaaS application, you can incorporate the use of cloud access security broker (CASB) options. It is a tool that serves as an extra layer of security. It sits between the cloud applications and the service users and examines how data is being used. 

CASB helps identify the security system’s loopholes and safeguards the data against threats. 

Companies are adopting SaaS cloud computing technologies for application development at a rapid pace. Aligning with these security practices can help companies eliminate the risk of cyber-attacks and secure their applications!

Conclusion 

HubSpot, Adobe, and Google are some of the many renowned organisations that are offering SaaS applications. It has been estimated that 85% of companies will be using SaaS for application development by 2025. 

This also calls for a demand for professionals who are skilled in the aspects of providing security to SaaS applications. Imarticus Learning offers learners an excellent opportunity to pursue an IIT Roorkee cybersecurity course that will teach you the fundamentals of network security, cloud security, ethical hacking, application security, etc. 

Upon successful completion of the course, you will receive a certificate that will be co-branded by IIT Roorkee. This IIT Roorkee cybersecurity course and the knowledge you acquire from it will be an asset to your professional credentials and will help you step up in your career by leaps and bounds!