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Business Analyst Jobs: Skills, Roles, and Responsibilities You Need to Succeed

Posted on by Imarticus Learning

Business analysts have significantly raised the importance of modern business in the contemporary business world. Companies are taking their digitalisation to further heights, which requires expert professionals who work within the objectives of business and technology solutions. Business Analyst jobs not only hold quite promising and good salaries but also provide an ability to influence business decisions and strategies to a vast extent. The following blog will reveal essential business analyst skills, roles, responsibilities, and a step-by-step approach to success in this dynamic field.

What is a Business Analyst?

So, what is a Business Analyst? It’s a job that involves determining an organisation’s needs, analysing processes, and then driving improvements into a business built on overall business goals. This includes all sorts of industries, such as finance, IT, healthcare, and retail sectors, to name a few.

A Business Analyst is usually the “go-between” between IT teams and business stakeholders, ensuring that the technological solution delivers on the overall business strategy. They try to find business needs and document requirements, and help companies use data effectively to make decisions.

What does a Business Analyst Do?

A Business Analyst bridges the organisational business needs gap between its IT and technical teams. Their main tasks include:

  • Analysing and understanding how to improve a company’s operations.
  • Identifying problems or areas for improvement.
  • Recommending solutions that will enhance business processes and increase efficiency.

They help organisations make better decisions through data-driven insights and clear communication of issues to stakeholders.

Requirements Gathering:

Gathering and documenting business requirements is a crucial task for any business analyst. This requires working closely with stakeholders to grasp their needs, objectives, and challenges fully.

Data Analysis:

Business Analysts analyse and interpret data to identify trends and inefficiencies. Their job is essential for suggesting improvements that could be made with a better-optimised business operation.

Process Modelling:

BAs create detailed models and diagrams of business processes. These models help identify areas for improvement by streamlining or automating these processes.

Stakeholder Communication:

Business Analysts can act as a bridge between other departments, ensuring that anyone with a vested interest in the project from any individual, whether within the institution or external client, understands both the business needs and the project’s progress from IT people and the management.

Solution Evaluation:

Having analysed the business’s needs, the Business Analyst will evaluate solutions based on the pros and cons of each recommendation to advise which is best suited for adoption. Changing software systems, business processes, or company policies can be a proposition.

Testing and Validation:

After applying a solution, business analysts may test the new system or process to see if it meets the delineated business objectives. This would ensure that the solution works as expected and contributes value to the organisation.

Project Support:

Business Analysts can significantly support project management teams by clearly defining scope, timelines, and deliverables, ensuring that projects always stay on track to meet an organisation’s objectives.

How to Become a Business Analyst?

For anyone interested in getting off to a good start in Business Analyst jobs, here are a couple of steps that they should take:

Degree Educational Background:

A degree is not highly specific. However, most Business Analysts require a background in business or IT-related disciplines. Business administration, computer science, or finance are good places to start.

Certifications:

One should always consider obtaining a Business Analyst certification. Of course, CBAP and PMI-PBA are the famous ones. These have massive employment value.

Practical Experience:

They will get any form of experience. The entry-level project management positions, or data analysis consulting engagements, will give one a good flavour of how any business works and the role of an analyst in processes to improve.

Professional Networking and Mentorship:

Professional networks or mentorship can help freshers enter even the most competitive fields of Business Analyst jobs. There will be great advice, and a leader will guide a person towards job opportunities.

Business Analyst Job Description

A Business Analyst often works with data, provides comprehensive analysis, and collaborates with different teams to ensure that business processes align with the company’s objectives. Important functions include:

Elaboration of Requirements:

Business Analysts collect business requirements from stakeholders through interviews, workshops, and other data-gathering methods.

Data Analysis:

Data analysis should be at the heart of any Business Analyst job description. It enables one to analyse patterns and trends that help businesses make decisions.

Documentation:

A Business Analyst job description would only be complete with sustainable documentation of all processes, decisions, and requirements, ensuring that all aspects are well governed for all stakeholders in a project.

Solution Implementation:

After analysing the data and suggesting a solution, the Business Analyst implements it with minimum pain and the best possible applications of new processes or technology.

Key Business Analyst Skills

There are many success skills in the position of a Business Analyst. Let’s begin with the most important of them:

Analytical Thinking:

A Business Analyst can break down complex processes and data. Analytical thinking will allow him to identify problems, lead to causal factors, and then propose solutions that work. Adequate analytical skills will enable business analysts to analyse data, identify trends, and forecast trends.

Communications Skills:

The communication requirement is very high as the business analyst acts as a liaison between IT teams and the business stakeholders. He needs to explain technical concepts to non-technical audiences. Clarity and concision in communication will help present solutions and the eventual buy-in from the stakeholders.

Problem-Solving Skills:

A business analyst’s main task is solving problems in their organisation. These tasks can involve better efficiency, cost reduction, or increased revenues. Solutions should be innovative and require sound critical thinking.

Technical Competence:

Business Analysts don’t usually write code, but technology experience is essential. You will need working knowledge of some tools like SQL, Excel, or data visualisation tools like Power BI or Tableau. Some project management tools, such as Jira or Trello, are also helpful.

Stakeholder Management:

Some stakeholders end up having variant priorities; Business Analysts have to deal with such, and their duties and responsibilities include handling expectations from such stakeholders and making sure all of them are in good contact with the project’s goals.

Business Analyst Roles and Responsibilities

Business Analyst roles and responsibilities majorly depend on how any given organisation would need to run a project. Generally, some of the typical responsibilities include;

Elicitation of Requirements:

The stakeholders deduce the business needs and translate the mentioned needs into functional and technical requirements.

Data Analytics:

They implemented the use of data for sound judgment and tracking effectiveness change.

Risk Management:

It captures inherent risk in proposed solutions and notes mitigation strategies.

Project coordination:

Business Analysts are neither project managers nor a project team. They are, however, likely to see only success for projects in terms of on-time delivery.

Process Improvement:

Much of the work of a business analyst involves identifying inefficiency in business processes and proposing improvement solutions.

Business Analyst Jobs by Industry

A Business Analyst position is offered in nearly all industries since all businesses operate based on data-driven insights. The most popular business analyst positions are available in the following sectors:

Information Technology and Technology:

Business analysts in this industry primarily work on aligning business needs with the software solution to be applied. They perform cross-level services involving new system implementation, updating old software, and integrating new technologies into ongoing workflows.

Finance:

Business analysts in the financial sector help optimise the banking process. They ensure that an entity streamlines operations but minimises or monitors risks overall. They also make profit-making operations based on data possible.

Health Care:

A healthcare industry business analyst may identify better patient care and operational efficiency. Innovative technologies are in demand in healthcare services, and they need to revamp the entire process of patient record data.

Retail and E-commerce:

An online business will want the business analyst to study customer journeys, sales, and supply chains to create a better user experience in the workspace. This will help with more efficient logistics and increased sales.

Business Analyst Salary

The salary expectations of a business analyst will vary based on an individual’s experience, location, and industry. Here is what you can expect at each career stage:

Entry-Level Business Analyst Salary:

The average starting salary of Business Analysts varies between ₹3,00,000 and ₹5,00,000 per annum. Business Analyst jobs for freshers also accept fresher employees in different business lines.

Middle-Level Business Analyst Salary:

The average salary for a business analyst with 3 to 5 years of experience is between ₹8,00,000 and ₹10,00,000 a year.

Senior Level Business Analyst Salary:

The senior business analyst can garner up to ₹12,00,000 LPA and above, depending on proper specialisation or high-demand areas.

Preparation for a Business Analyst Interview

Preparing for an interview would include the kinds of Business Analyst interview questions someone would most likely encounter. These often test the ability to solve problems, technical skills, and the capability to manage stakeholders. Some of the most typical interview questions asked of a Business Analyst are:

  1. Can you explain when you identified a business problem and how you solved it?
  2. How do you handle conflicting requirements from different stakeholders?
  3. What techniques do you use to gather and analyse business requirements?
  4. How do you ensure that your recommendations align with business goals?
  5. Can you explain the steps involved in creating a process flow diagram?

Business Analyst Certifications for Career Advancement

A Business Analyst certification is a game changer that can change the character of job opportunities and qualify you for better jobs. Some of the most popular ones are:

  • Certified Business Analysis Professional (CBAP): Offered by the International Institute of Business Analysis (IIBA), this certification is ideal for experienced professionals.
  • Entry Certificate in Business Analysis (ECBA): The IIBA also offers this, which is perfect for newcomers to the field.
  • PMI Professional in Business Analysis (PMI-PBA): The Project Management Institute offers this certification, which is valuable for those who work closely with project managers.

The Future of Business Analyst Jobs

In the post-event scenario of such widespread industrial digital transformations, seasoned Business Analysts must continue to be in demand. New business analysts must also acquire new skills in handling technology, such as artificial intelligence, machine learning, and big data. Technocrats who deal with those technologies would draw a higher paycheck and play a more significant role in formulating businesses’ various policies.

Conclusion

Business Analyst jobs dish out a thrilling combination of problem-solving, data analysis, and other organisational developments. Be it staring from the ground zero as a fresher or sitting atop the promotional ladder, some acquaintance with primary Business Analyst skills and responsibilities is necessary for a long-term stay on that career ladder. After suitable educational qualifications, appropriate certifications, and practical exposure, one can grow into a full-time, lucrative career as a Business Analyst.

A Data Science Course equips you with essential skills in data analysis, machine learning, and statistical methods. It opens pathways to high-demand roles across industries. Enhance your career with hands-on projects and expert-led learning.

FAQs

1. How much do Business Analysts get paid?

In India, the salary of a Business Analyst varies significantly based on experience and the industry. Entry-level Business Analysts typically earn between ₹3,00,000 and ₹5,00,000 per annum. As they gain experience, their salaries can rise substantially.

For those with 5-9 years of experience, salaries can reach around ₹8,30,975, while Senior Business Analysts with over 15 years of experience earn upwards of ₹12,00,000 to ₹18,00,000 annually. The average salary across the board is approximately ₹9,70,000 to ₹10,20,000 per year.

2. What are the critical skills needed for a Business Analyst?

The primary skills expected from a Business Analyst include:

  • Analytical Thinking: Ability to interpret data and identify trends.
  • Communication: Strong verbal and written skills to convey ideas clearly to stakeholders.
  • Problem-Solving: Aptitude for addressing issues and proposing practical solutions.
  • Technical Proficiency: Familiarity with tools like SQL and Excel for data analysis.
  • Stakeholder Management: Skills in building relationships and managing expectations among various stakeholders 

3. Should I be certified to become a Business Analyst?

While certification is not mandatory to become a Business Analyst, obtaining credentials such as CBAP (Certified Business Analysis Professional) or PMI-PBA (Professional in Business Analysis) can significantly enhance your job prospects and earning potential. These certifications demonstrate your expertise and commitment to the field, making you more attractive to employers. 

4. What is a Business Analyst?

A Business Analyst is responsible for gathering business requirements, conducting data analysis, documenting processes, and implementing solutions. Their work involves close collaboration with stakeholders to ensure that projects align with business needs while identifying improvement opportunities. They are crucial in helping organisations make informed decisions based on data.

5. How do you get into Business Analyst jobs as a fresher?

For freshers looking to enter Business Analyst jobs, the following steps are recommended:

  • Education: A relevant degree in business or IT is often required.
  • Internships: Gaining practical experience through internships or entry-level roles can be beneficial.
  • Certification: A Business Analyst Certification can enhance employability and demonstrate your commitment to the field.

Focusing on these areas can help freshers improve their chances of landing their first role as Business Analysts.

Career Path and Growth Opportunities for Machine Learning Engineers

Posted on by Imarticus Learning

For as long as man has invented modern technologies, AI has arisen from the reshaping of industries and established itself in academia whenever researchers need machine learning engineers.

Machine learning is at its core, driving innovations in automation, data analytics, and decision-making processes. This has led to abundant career opportunities and a high demand for professionals skilled in machine learning engineering.

In this blog post, we will continue our career discussion concerning machine learning engineers and other growth opportunities, plus important aspects such as the salary of a machine learning engineer, the best roadmap to be a successful machine learning engineer, and, most importantly, the machine learning engineer course, i.e., how to become a good machine learning engineer.

So, who is a machine learning engineer?

Well, for this kind of discussion about a machine learning engineer’s career path and growth prospects, it is important to understand what it means to be one. A machine learning engineer designs, develops, and deploys machine learning models to solve complex problems. Since these models enable machines to do tasks so close to human intelligence, capabilities include speech recognition, predictive analytics, recommendation systems, and image recognition.

Close cooperation is usually performed with data scientists, software engineers, and AI researchers to ensure they have developed scalable, efficient, and accurate models. The machine learning engineer acts mainly as a bridge between the software engineer and the data scientist within most organizations, ensuring the effective flow of theoretical machine learning models into practical, functioning applications.

Becoming a Machine learning engineer is one of the most pursued careers in the tech world. 

Here’s why:

High Demand: The newly erupting artificial intelligence-driven technologies have put a high demand on machine learning engineers across industries, including healthcare, finance, e-commerce, and entertainment.

Exciting projects have emerged for machine learning engineers, from automation and robotics to AI systems and other inventions that contribute directly to technology advancement.

Lucrative Salary: The salary of a Machine learning engineer is by far the most handsome compared to many other tech jobs. That aside, their experience, coupled with the high demand, makes this job very rewarding in compensation.

Growth Opportunities: As AI and ML become more advanced, there are a tremendous number of growth opportunities in leadership roles and specializations for professionals in the field.

Career Roadmap of a Machine Learning Engineer

A career path in this area of machine learning engineering is rewarding. Here is what the whole roadmap would look like:

  • Educational Foundation

In addition, a foundation education from an accredited institution of computer science, mathematics, or any other related field is essential. Ordinarily, one needs to hold a bachelor’s degree; however, a master’s or Ph.D. would have given the individual more opportunities at the advanced levels.

  • Build Strong Programming and Data Skills

Machine learning experts should be programming in Python, R, and Java. Knowledge of the framework is crucial; TensorFlow, PyTorch, and Scikit-learn are some popular ones. Also, knowledge of data manipulation libraries (Pandas, NumPy) and database systems (SQL, NoSQL) is essential.

  • Greater Experience with Machine Learning Algorithms

Knowing the different types of machine learning. There are supervised and unsupervised reinforcement and deep learning. The best way to practice is through projects, internships, and even courses one can enrol in to train as a machine learning engineer.

  • Join the Workforce

Now that you’ve mastered your craft, it is time to close the first engineering job in machine learning. Entry-level jobs are junior positions that are those of a data engineer or junior machine learning engineer. In such a job, you will have tasks like building data pipelines, training models, and testing.

  •  Climb up the Career Ladder

Experience only takes you to advanced levels; here, you will work on more complex model designs, algorithm optimisations, and even leading small teams. The senior engineers lead large-scale projects and mentor junior engineers while continuing to be very tight partners with AI researchers and data scientists.

  • Leadership and Specialisation

With extensive experience, you can move into leadership roles, such as team lead of the AI/ML team or focus on specializing in subdomains such as deep learning, computer vision, or natural language processing. Such positions usually involve a great deal of responsibility, such as making strategic decisions and supervising large projects.

Scope for Career Growth as a Machine Learning Engineer

The scope for career growth in this area is huge because machine learning touches the nook and corner of the tech world. Here are a few areas to grow:

  • Specialisation Areas in AI Subfields

Machine learning engineers may specialize in deep learning, computer vision, NLP, and reinforcement learning. All these areas are evolving well and offer exciting opportunities.

  • Data Science or AI Research

Many in the industry would attest that machine learning engineers who are excited about application transition into data science roles where they could apply their skills in machine learning models to derive actionable insights from data. Alternately, you transition into AI research, focusing on developing new algorithms and models.

  • AI Leadership Roles

Depending on your experience and time, you can lead as an AI Architect, AI Product Manager, or Head of AI. Such roles blend technical and management competencies in most cases of senior jobs. Besides, salaries often increase substantially by leading freelance and consulting positions.

With the rising trend of companies opting for machine learning, the current need is to hire more freelancers and consultants. If you prefer flexibility or the scope of working across various industries, freelancing can provide creative freedom and monetary rewards.

Machine Learning Engineer Salary and Job Market

Machine Learning Engineer Salary in India

The average salary for a Machine Learning Engineer in India is perceived as competitive. Industry reports show that the average range for mid-level positions is between ₹8 lakh and ₹20 lakh annually. Senior experienced professionals can earn as much as ₹50 lakh every year.

Machine Learning Engineer Salary Worldwide

With an average salary of $110,000-$150,000, working as a machine learning engineer in developed countries such as the USA can be even more rewarding for professionals. This value can go further in advance due to the skill demands from the tech hubs of San Francisco and New York.

Jobs as a Machine Learning Engineer

Machine learning engineer jobs abound. From startups to the largest tech companies, including Google, Amazon, and Microsoft, these firms recruit, and a good number are indeed doing this. For example, healthcare, finance, e-commerce, and autonomous driving are some of the biggest areas where jobs continue to grow almost exponentially.

How to be a Machine Learning Engineer?

To know how to become a machine learning engineer, follow these instructions carefully.

  • Educational Background

You must attain a computer science, mathematics, or statistics degree. Your course will heavily rely on a strong background in linear algebra, probability, statistics, and calculus.

  • Course in Machine Learning Engineer

A complete course on machine learning engineering can help you build the desired skills. Look for courses that involve hands-on projects, from data preprocessing to model building and deployment.

  • Practical Experience

Work on projects that would help you build machine-learning models from scratch. An internship or open-source contributions are excellent means to prove your skills.

  • Latest Trends

Update yourself on the latest trends. Machine learning is an increasingly evolving field that requires top-of-the-line tools, frameworks, and algorithms. To stay on top of the latest libraries, tools, and frameworks, read industry blogs, attend webinars, or join online communities.

Roadmap to Becoming a Machine Learning Engineer

A roadmap is a structured learning and growth process. Below are the simplified roadmaps:

Phase 1: It is to lay the foundation in programming, mathematics, and data structures.

Phase 2: Master machine learning algorithms, frameworks, TensorFlow, and PyTorch.

Phase 3: Work on data preprocessing, model evaluation, and deployment.

Phase 4: Work on real-world projects and build a portfolio.

Stage 5: Deep Learning, NLP Reinforcement learning, etc.

Conclusion

Machine learning engineers offer many promising opportunities for personal growth and financial rewards. Whether you are joining or looking to make a career change in this exciting field, the demand for machine learning engineers does not slacken.

Proper training, practical exposure, and a good understanding of algorithms in machine learning will help you fashion a successful and richly rewarding career in this booming industry. If you are looking for a machine learning engineer job, want more knowledge of the machine learning engineer salary scenario, or want to know how to be a machine learning engineer, there is no better time than now to interact with this exciting field.

Navigating Derivatives Trading: Strategies for Managing Risk and Maximising Returns

Posted on by Imarticus Learning

Trading of derivatives is emerging as a powerful tool for investors, allowing them to buy and sell assets strategically for the future. A derivative is a financial contract that derives value for an underlying asset or benchmark decided by two or more parties. 

Derivates have predetermined and fixed expiry dates, offering a compelling alternative to traditional asset acquisition. Derivatives trading can significantly increase gains, making it a popular choice over directly trading underlying assets. 

Investors can control substantial quantities of assets with minimal upfront capital in this form of trading. Let us understand what derivatives trading is and how you can benefit from it. 

What is a derivative? 

A derivative is a financial contract between two or more parties that derives its value from the underlying asset. These assets include indices, stocks, currencies, commodities, exchange rates, or interest rates. 

Investors can profit by speculating on the future value of the underlying asset or its movement—up or down—without buying the asset itself. This is why a derivative is called a ‘derivative’ since its value is derived from the underlying asset. 

What is derivatives trading? 

In this form of trading, investors buy or sell a derivative contract for speculation. Since a derivative contract is ‘deriving’ its value from an underlying market, you can trade on the market’s price movements. 

Derivatives trading is a leveraged form of trading in which you can buy a large number of underlying assets by paying a relatively smaller amount. 

How are derivatives traded? 

Now that you have a brief idea of what a derivative is, you might wonder how it is traded. Derivatives can be traded in two ways – over the counter or on exchange. 

  • Over the counter: In this form of trading, the contract terms are privately negotiated between the parties involved. 
  • On-exchange: You can also trade derivatives through a regulated exchange that offers standardised contracts. Known as exchange-traded products, they offer the advantage of having the exchange as an intermediary. As the exchange guarantees payment, it largely reduces counterparty risk. 

Learn more about derivate trading strategies by enrolling in the IIM investment banking course by Imarticus. 

Types of derivative contracts 

Here are some of the most common derivative contracts used in derivatives trading: 

  • Options are derivative contracts that allow holders to buy or sell an asset at a predetermined price on a future date. 

Features: 

  • Option holders use it to hedge against any potential losses or predict future price movements. 
  • Investors have the freedom to decide whether to execute the trade based on market conditions. 
  • It can be applied to various assets such as commodities, stocks, and currencies. 

Futures: These are standardised agreements between the buyer and seller to purchase or sell an asset at a predetermined price on a date in the future. 

Features: 

  • Future traders use it to protect against price fluctuations, offering asset insurance. 
  • These contracts have a set structure, including expiration date and contract size, making it easy to trade on exchanges. 
  • As it is exchange-traded, futures offer high liquidity and effective price discovery. 

Swaps: These contracts adapt according to parties’ needs by exchanging cash based on various financial tools. 

Features: 

  • Investors use these contacts to manage and optimise their exposure to different financial threats. 
  • It involved swapping cash flow with currency swaps, with interest rate swaps being the most common types. 
  • Parties can tailor swap agreements according to their specific financial circumstances. 

Forwards are customised transactions between two parties, curated according to their specific needs. 

Features: 

  • Parties can craft agreements with unique terms. This offers higher flexibility than different exchange-traded counterparts. 
  • Forwards can be used in various assets like interest rates, commodities, etc. 
  • As forward contracts don’t need the exchange setting, transactions are confidential between parties. 

Risk management strategies in derivatives trading 

As a derivatives trader, you might be aware of the potential risks but should also know how to navigate them. Here are some risk management strategies that will make the process easier for you: 

  • Diversification 

If you have ever invested in the stock market, you know the importance of having a diverse portfolio. Investing in various assets can help achieve higher returns with minimal risk. To mitigate potential risks, you can invest in different asset classes like equities, real estate, fixed income, and commodities. 

  • Hedging 

In this process, investors take an offsetting position in a related asset to reduce the price risk of an existing asset position. This reduces volatility risk in derivatives trading. Hedging can be done in the following ways: 

  • Leveraging derivatives – like forwards or options – to hedge against price fluctuations. This can save investors from huge losses. 
  • Trading on assets like currencies, gold, or government bonds. 
  • Pair trading is when a long position is taken in one stock and a short position is taken in another (provided they are correlated). 

  • Setting limits 

You can set a limit to reduce losses. Investors can save their portfolios from major value declines by selling assets when their prices fall below a certain point. You can seize a position by allocating a specific capital amount to a particular investment based on market conditions and risk tolerance. 

Summing up 

The popularity of derivatives trading is increasing every day due to the high returns it offers. But it does come with its pitfalls. Building an investment plan can help you avoid risks and make more profit. 

If you want to build a career in investment banking, check out the Executive Programme in Investment Banking and Capital Markets by Imarticus in association with IIM Calcutta. Taught by industry experts, the programme teaches capital management and advanced skills that are useful in real-world scenarios. 

Frequently Asked Questions

What are some different risks in derivatives trading? 

Some of the risks associated with trading in derivatives are market risk – the risk of a decline in the value of an underlying asset, credit risk – the risk of loss when the counterparty defaults on the obligations and liquidity risk – the risk when derivates are closed at a price before maturity. 

Who are margin traders? 

A margin in derivatives trading is the starting amount you need to pay the broker. It is a part of the entire value of the investor’s position. This payment feature helps margin traders purchase more stocks than they can afford. 

What are some cons of trading derivatives? 

Some common disadvantages of derivatives trading are the change in the amount of time until expiration, change in interest rates, or any costs associated with holding the underlying asset. In over-the-counter derivates, counterparty risks can be challenging to predict. 

What is a regulatory risk?

Potential changes in rules and regulations can impact trading activities and financial outcomes. In extreme conditions, these conditions can even destroy the company’s business model. This is why creating a trading strategy plan is crucial.

Common Challenges in AI Model Deployment and How to Overcome Them

Posted on by Imarticus Learning

Deploying an AI model in a business is a complex job. Organisations often face challenges despite following best deployment practices, adapting proven market strategies, and leveraging cutting-edge technologies.

Generally, AI model deployment faces bottlenecks because of abuse, misuse, bias, lack of integrity features like transparency, and integration issues with existing systems. These challenges can be overcome by data training processes, bias mitigation algorithms, powerful integration strategies, and regulated AI guidelines, as guided by the law of the land. 

When deployed in edge devices, the AI model poses challenges, such as limited resources, connectivity, scalability, and security. To get it right, organisations must choose the optimised AI framework for edge devices. Less memory or processing power may be complemented by providing edge cloud services, and security measures like data encryption and access control must be guaranteed by the AI model.   

Common Challenges  

The involvement of several department experts makes the process error-free. Technically, the IT team assesses the infrastructure needs, data scientists should consider the training data set sourcing.

Developers, on the other hand, must estimate investments in other/related software or systems. From the operational point of view, the AI model must consider inputs from key departments like marketing, sales and human resources to reach the desired organisational purpose. 

There are six common AI model deployment challenges starting from the scope preparation of the project to its live functioning. They are as follows – 

  • Data-set-related challenges 

The AI model has its foundation in the training data sets. Data Scientists must ensure the quality and the width of data to determine its accuracy. Some of the common data problems are as follows –

  • Imbalanced data 

Using the right data is essential. A cloth retailer must not use the shoe data for sizing shirts or dresses.

  • Limited data 

AI models with insufficient data will never be able to predict desired data with accuracy.

  • Poor quality data

The quality of data must be assured before creating the AI model.

  • Algorithm-related challenges 

The algorithm is the main structure of the AI model. Developers must prepare and train the algorithms so that they suit the project’s goals. The key challenges are as follows –

  • Choosing the right algorithms 

Having the right algorithms in place is the first step. Algorithms must fulfil the scope and purpose of the project.

  • Overfitting 

When an AI model delivers a specific outcome repeatedly while ignoring other desired results, the situation is known as the overfitting of algorithms.

  • Underfitting 

When the AI model delivers the desired outcome with training data sets but fails in the real-world test, the situation is called underfitting of algorithms.  

  • Hardware and software-related challenges 

While Data Scientists lay the foundation of the AI model and Developers craft the algorithm structure, the IT department must take charge of the software and the hardware challenges of the AI model. The considerations are as follows –

  • Hardware resources 

Processing and reviewing large amounts of data sets in a complex AI model requires huge storage space and server performance. 

  • Software resources 

Specialised software tools and frameworks are sometimes required to get integrated with the existing system to operate the desired AI model.

  • Challenges in hiring skilled talent 

It may be noted that an AI model can operate to its desired capacity provided it is crafted and operated by trained professionals. The challenges faced in hiring skilled talent are as follows – 

  • Search for AI talent. 

Trained or veteran professionals like Data Scientists, Developers and IT professionals must be hired to train AI models to accuracy and expectation.

  • Lack of trained AI professionals 

When an organisation works with less trained people, they never reach their desired goal. They tend to lose both in terms of money spent on research and the time lost. 

  • Challenges in managing AI projects 

Organisational AI projects are expensive and resource-heavy. The management, thus, needs to strike a balance between the finance, technology outsourced and operational schedules. Common challenges in managing AI projects are as follows –

  • Communication gaps 

To implement the AI model, several stakeholders like Data Scientists, Developers and professionals from IT legal and finance departments need to get involved. Gaps in inputs from any of the stakeholders may lead to the loss of time, money and accuracy.

  • Unrealistic expectations 

Users may expect miracles out of an AI model. They must understand the AI model’s purpose, goals, capabilities and limitations.

Guidelines for AI Deployment

Overcoming AI deployment issues is possible by following these guidelines –   

  • AI deployment best practices include the establishment of clear policies for AI model usage.
  • It is required to collaborate with industry peers, institutions and Government bodies to exchange best practices.
  • Access control should be with authorised users only.
  • The AI model should be monitored to ensure misuse and abuse.
  • The AI model is to be audited regularly for compliance with ethical standards.

AI model implementation also involves machine learning processes. Machine learning deployment challenges are an integral part of the AI model system. Dedicated AI training programs for employees can help an organisation reach that level of supremacy and accuracy in AI model implementation. 

Conclusion 

The AI model is the business future. Its predictions help organisations plan effectively for targeted business growth. Its implementation comes with enthusiasm and challenges. 

The creation of an AI model involves several stakeholders and a pivotal leadership to control the process. It has also crafted a lucrative professional career in the field of machine intelligence. 

The Professional Certificate In Product Management with CEC, IIT Roorkee by Imarticus will give the prospective candidates the perfect start at the beginning of their careers.   

Visit the official website of Imarticus for more details.

Frequently Asked Questions

What are the ethical considerations in AI deployment?

The process should be bias-free, end-to-end encrypted for privacy, and be fair and legally accountable. Abuse and misuse of AI models must be checked. Bias mitigation algorithms are to be adapted.

How do organisations tackle the AI talent shortage?   

The remedy to tackle the AI talent shortage is to upskill the existing AI workforce through reputed institutions or consultants. This also serves as a retention policy.

How can a business improve data?

Having good-quality data is the foundation of the AI model. Data must be fetched from reliable resources.

How can context understanding be improved in the AI model?

Context understanding in AI deployment may be improved through transfer learning, domain adaptation, integrating hybrid models and implementing human-in-the-loops systems. 

Understanding Performance Appraisal: Definitions, Types, and Best Practices

Posted on by Imarticus Learning

Performance appraisal is a systematic process of evaluating an employee’s job performance. It is a crucial component of human resource management. Performance appraisals serve as a tool for providing feedback, identifying training needs, determining compensation, and making promotion decisions.

Appraisals are part of performance management, a broader process that includes setting performance expectations, providing feedback, and supporting employee development. To become an expert in other essential strategies and analytics for human resource management, enrol in IIT and Imarticus Learning’s HR analytics course.

Types of Performance Appraisal Methods

Here are some common types of performance appraisal:

  • Graphic Rating Scale: A simple method that uses a rating scale to assess employee performance on various dimensions.
  • Behaviorally Anchored Rating Scale (BARS): A more specific method that uses concrete examples of job behaviours to anchor the rating scale.
  • Forced Choice: A method that requires the rater to choose from a set of predetermined statements about the employee’s performance.
  • Critical Incident Method: A method that focuses on specific examples of an employee’s positive and negative behaviour.
  • 360-Degree Feedback: A comprehensive method that involves feedback from multiple sources, including peers, subordinates, superiors, and sometimes customers.
  • Management by Objectives (MBO): A goal-setting method that involves setting specific, measurable, achievable, relevant, and time-bound (SMART) goals with employees.

Challenges in Performance Appraisal

Here are some challenges we face during performance appraisals:

  • Subjectivity: Performance appraisals can be subjective, leading to biases and inconsistencies.
  • Resistance to Change: Employees may resist feedback, especially if it is negative.
  • Lack of Time: Managers may need help to allocate time for conducting thorough performance appraisals.
  • Political Influences: In some organisations, political factors can influence performance ratings.

Addressing Challenges and Enhancing Effectiveness

To address these challenges and enhance the effectiveness of performance appraisals, organisations can:

  1. Provide Training: Train managers on effective appraisal techniques and communication skills.
  2. Use Technology: Utilise performance management software to streamline the process and ensure consistency.
  3. Foster a Culture of Feedback: Create a culture where feedback is valued and encouraged.
  4. Conduct Regular Reviews: Regularly reviews the performance appraisal process to identify areas for improvement.

Performance Appraisal and Employee Development

Performance appraisals can be a valuable tool for employee development. Organisations can help employees grow and reach their full potential by providing feedback and identifying training needs.

Performance appraisals can also be used to identify high-potential employees and provide them with opportunities for advancement. Organisations can invest in their development and ensure a strong talent pipeline by identifying employees with the skills and potential to take on leadership roles.

Performance Appraisal and Compensation

Performance appraisals are often used to determine compensation increases and bonuses. By linking performance to rewards, organisations can motivate employees to achieve their goals and contribute to the company’s success.

However, it is important to ensure that compensation decisions are based on objective criteria and avoid biases. Other factors, such as market rates and the organisation’s overall financial health, should also be considered.

The Role of Technology in Performance Appraisal

Technology can significantly improve the performance appraisal process by streamlining it and streamlining tasks. Performance management systems and software can automate tasks, collect and store data, and provide analytics.

Additionally, technology can facilitate 360-degree feedback by allowing employees to provide feedback anonymously. This can lead to more honest and candid feedback, which can be valuable for employee development.

Best Practices for Performance Reviews

Performance reviews are a critical component of human resource management, providing an opportunity to assess employee performance, provide feedback, and identify areas for improvement. To ensure that performance reviews are effective and valuable, organisations should adhere to the following best practices:

Clear Expectations

  1. Communicate clearly: Ensure that employees understand their job roles, responsibilities, and performance expectations.

  2. Set SMART goals: Work with employees to establish specific, measurable, achievable, relevant, and time-bound (SMART) goals.

Regular Feedback

  1. Provide ongoing feedback: Offer regular feedback throughout the year rather than waiting for the formal appraisal process.

  2. Use constructive feedback: Focus on providing specific, actionable, and constructive feedback.

Open Communication

  1. Create a safe environment: Foster a culture of open communication where employees feel comfortable sharing their thoughts and concerns.

  2. Encourage two-way communication: Encourage employees to participate actively in the review process and ask questions.

Objectivity

  1. Use objective criteria: Base performance evaluations on objective criteria and evidence rather than personal opinions or biases.

  2. Avoid subjectivity: Minimise the impact of personal biases and preferences on the evaluation process.

Fairness

  1. Treat employees fairly: Ensure that all employees are treated fairly and consistently, regardless of their position or performance level.

  2. Avoid discrimination: Avoid discrimination based on protected characteristics, such as race, gender, age, or disability.

Development Focus

  1. Identify training needs: Use performance reviews to identify areas where employees can benefit from additional training or development.

  2. Support: Offer support and resources to help employees develop their skills and advance their careers.

Continuous Improvement:

  1. Encourage growth: Create a culture of continuous improvement and encourage employees to strive for excellence.

  2. Review the process: Regularly review and update the performance appraisal process to ensure its effectiveness.

Wrapping Up

Performance appraisal is a vital tool for managing employee performance and driving organizational success. By following best practices and addressing potential challenges, organisations can create a performance appraisal system that is fair, effective, and beneficial for both employees and the organisation.

If you wish to have a solid career in human resource management, enrol in the Human Resource Management And Analytics Certification Programme offered by Imarticus Learning in collaboration with IIT Roorkee.

Frequently Asked Questions

What is the purpose of a performance appraisal?

A performance appraisal is a systematic process of evaluating an employee’s job performance against predetermined standards. It serves as a tool for providing feedback, identifying training needs, determining compensation, and making promotion decisions.

What are the different types of performance appraisal methods?

Common methods include graphic rating scales, behaviorally anchored rating scales (BARS), forced choice, critical incident method, 360-degree feedback, and management by objectives (MBO).

What are some best practices for conducting performance appraisals?

Best practices include setting clear expectations, providing regular feedback, using objective criteria, ensuring fairness, focusing on development, and using technology to streamline the process.

What are the challenges in performance appraisal?

Challenges include subjectivity, resistance to change, lack of time, and political influences.

Understanding Safety Inventory: How Supply Uncertainties Affect Stock Levels

Posted on by Imarticus Learning

Safety inventory, also known as buffer or reserve stock, is a crucial inventory management component. It refers to the additional quantity of inventory held to mitigate the risks associated with supply uncertainties. By maintaining a sufficient level of safety inventory, businesses can ensure a continuous supply of materials and avoid stockouts that can lead to disruptions in production, customer dissatisfaction, and lost sales.

A solid supply chain analytics course can help you become an expert in dealing with supply chain issues such as stockouts. Let us learn about safety inventory and stock management strategies.

Understanding Supply Chain Uncertainty

Supply uncertainty refers to the variability in the availability and timing of inventory replenishment. It can arise from various factors, including:

  1. Demand fluctuations: Variations in customer demand can make it difficult to forecast inventory requirements accurately.
  2. Supplier lead times: The time it takes for suppliers to deliver orders can vary, leading to uncertainties in supply.
  3. Transportation delays: Disruptions in transportation networks can cause delays in inventory deliveries.
  4. Supplier disruptions: Supplier bankruptcies, natural disasters, or labour disputes can disrupt the supply chain.

The Role of Safety Inventory

Safety inventory management is a buffer to absorb the impact of supply uncertainties. By maintaining a sufficient level of safety inventory, businesses can:

  • Avoid stockouts: Prevent production or customer service disruptions due to insufficient inventory.
  • Reduce lead time variability: Mitigate the impact of fluctuations in supplier lead times.
  • Protect against demand surges: Meet unexpected increases in customer demand.
  • Manage supplier risks: Mitigate the impact of supplier disruptions.

Determining the Optimal Safety Inventory Level

The optimal level of safety inventory depends on several factors, including:

  • Demand variability: The degree of variation in customer demand.
  • Lead time variability: The variability in supplier lead times.
  • Service level: The desired probability of avoiding stockouts.
  • Inventory holding costs: The costs associated with holding inventory.

Various methods can be used to calculate the optimal safety inventory level, such as:

  • Reorder point method: This method calculates the reorder point based on expected demand and lead time.
  • Standard deviation method: This method uses statistical techniques to calculate the safety stock based on the demand and lead time standard deviation.
  • Simulation modelling: This method involves creating a computer model to simulate different scenarios and evaluate the impact on inventory levels.

Balancing Safety Inventory Costs and Risks

Maintaining a high level of safety inventory can be costly due to the costs associated with holding inventory, such as storage costs, obsolescence costs, and opportunity costs. On the other hand, insufficient safety inventory can lead to stockouts and disruptions in operations.

Therefore, balancing the costs of holding safety inventory and the risks associated with stockouts is essential. By carefully considering the factors that affect supply uncertainty and using appropriate methods to calculate the optimal safety inventory level, businesses can minimise the costs and risks associated with inventory management.

Crucial Considerations

Here are some important considerations we should keep in mind when working on inventory level optimisation:

  • Inventory turnover: The rate at which inventory is sold and replaced. A high inventory turnover indicates efficient inventory management.
  • Obsolete inventory: Inventory that is no longer needed or salable due to changes in demand, technology, or product life cycles.
  • Inventory obsolescence costs: The costs associated with disposing of obsolete inventory.
  • Supply chain visibility: The ability to track and monitor inventory levels and movements throughout the supply chain.

Safety Inventory and Demand Forecasting

Accurate demand forecasting is essential for effective safety inventory management. By accurately predicting future demand, businesses can optimise their safety stock levels and avoid stockouts or excess inventory.

Various forecasting techniques can be used, including:

  • Time series analysis: This involves analysing historical demand data to identify patterns and trends.
  • Causal modelling: This method considers external factors influencing demand, such as economic conditions or marketing campaigns.
  • Judgmental forecasting: This method relies on expert opinions and judgement to predict future demand.

Safety Inventory and Supply Chain Resilience

A robust safety inventory management strategy is essential for building a resilient supply chain. By maintaining sufficient safety stock, businesses can mitigate the impact of disruptions, such as supplier failures, natural disasters, or transportation delays.

A resilient supply chain is more likely to withstand shocks and continue operating effectively in the face of challenges. This can help businesses maintain customer satisfaction, avoid financial losses, and protect their reputation.

Safety Inventory and Inventory Carrying Costs

While safety inventory is essential for avoiding stockouts, it also incurs costs associated with holding inventory. These costs include storage costs, obsolescence costs, and opportunity costs.

It is important to balance the benefits of safety inventory and the costs associated with holding it. Businesses can minimise these costs by carefully considering the factors that affect supply uncertainty and using appropriate methods to calculate the optimal safety inventory level.

Safety Inventory, Technology and Supply Chain Analytics

Technology can significantly improve safety inventory management. Advanced inventory management systems can help businesses track inventory levels, monitor demand patterns, and optimise safety stock levels.

These systems can also provide real-time visibility into the supply chain, allowing businesses to identify potential disruptions and proactively mitigate their impact. This heightened awareness empowers businesses to mitigate the impact of unforeseen events, ensuring a smoother and more resilient supply chain.

Furthermore, integrating supply chain analytics with inventory management systems unlocks a treasure trove of insights. By analysing vast datasets, businesses can better understand demand patterns, identify trends, and forecast future requirements more accurately. This data-driven approach enables businesses to make informed decisions regarding safety stock levels, reducing the risk of stockouts or excess inventory.

Wrapping Up

Safety inventory plays a critical role in ensuring a reliable and uninterrupted supply of materials. By understanding the factors that affect supply uncertainty and using appropriate methods to calculate the optimal safety inventory level, businesses can minimise the costs and risks associated with inventory management. By effectively managing safety inventory, businesses can improve their overall operational efficiency and customer satisfaction.

Technology acts as a catalyst, driving efficiency and effectiveness in safety inventory management. By leveraging advanced inventory management systems and harnessing the power of data analytics, businesses can optimise their supply chains, minimise costs, and enhance customer satisfaction.

The Advanced Certificate In Supply Chain and Analytics by IIT Roorkee and Imarticus Learning can help you become a master of supply chain management. 

Frequently Asked Questions

What is the difference between safety stock and buffer stock?

Safety stock and buffer stock are synonymous terms for the additional quantity of inventory held to mitigate supply uncertainties.

How is safety inventory calculated?

Various methods, such as the reorder point method, standard deviation method, and simulation modelling, can be used to calculate safety inventory.

What factors affect the optimal level of safety inventory?

The optimal level of safety inventory depends on demand variability, lead time variability, service level, and inventory holding costs.

How can technology improve safety inventory management?

Advanced inventory management systems can help businesses track inventory levels, monitor demand patterns, and optimise safety stock levels. Additionally, technology can provide real-time visibility into the supply chain, allowing businesses to identify potential disruptions and take proactive measures.

Liabilities in Focus: Short-Term vs Long-Term Liabilities on the Balance Sheet

Posted on by Imarticus Learning

Understanding the liabilities on a balance sheet is crucial for grasping the fundamentals of financial management. These financial obligations can make or break a company’s financial health, and distinguishing between short-term liabilities and long-term liabilities is essential for making informed business decisions.

Are you aware that the right balance of these liabilities could significantly influence your company’s cash flow and investment strategies? 

In this post, we will explore the different types of liabilities, their implications on financial management, and how mastering this knowledge can set you on the path to success.

What Are Liabilities?

Liability refers to an amount a financial entity must pay. It represents a value that the entity provides in the future to execute a current obligation resulting from past transactions or events.

Liabilities on a balance sheet are obligations that a company owes to external parties, typically arising from past transactions. They represent future sacrifices of economic benefits. 

On a balance sheet, liabilities are broadly classified into two categories: short-term liabilities & long-term liabilities.

What are Short-Term Liabilities?

Short-term liabilities, often called current liabilities, are obligations a company must settle within one year of its operating cycle. 

These may include:

  • Accounts Payable: Money owed to suppliers for services and goods received.
  • Short-Term Loans: Loans or borrowings due within a year.
  • Accrued Expenses: Expenses incurred yet not paid.

Examples of Short-Term Liabilities

Type of LiabilityDescription
 Accounts Payable
Payments due to suppliers
Short-Term LoansLoans to be repaid within a year
Accrued ExpensesUnpaid expenses incurred

What are Long-Term Liabilities?

In contrast, long-term liabilities on balance sheets are obligations that extend beyond one year. These liabilities often finance a company’s long-term investments and growth strategies. 

Examples of Long-Term Liabilities

A variety of financial obligations fall under the category of long-term liabilities. 

Notable examples include:

  • Bonds Payable: The long-term portion of a bond payable is a long-term liability. Since bonds often have terms spanning several years, most of the amount owed remains long-term.
  • Lease Liabilities: If a lease agreement extends beyond one year, the present value of future lease payments is a long-term liability.
  • Deferred Tax Liabilities: These liabilities arise from tax obligations deferred to future years, classifying them as long-term liabilities.
  • Long-Term Loans: Mortgages, vehicle loans, or financing for machinery and equipment are also considered long-term liabilities, except for any repayments due within the next 12 months.

Examples of Long-Term Liabilities

Type of LiabilityDescription
Long-Term LoansLoans with repayment periods over a year
Bonds PayableDebt issued for long-term financing
Deferred Tax LiabilitiesFuture taxes owed

Key Differences Between Short-Term and Long-Term Liabilities

Understanding the distinction between these two types of liabilities can provide valuable insights into a company’s financial health. 

Here are some key differences:

  • Timeframe: Short-term liabilities are due within one year, whereas long-term liabilities extend beyond one year.
  • Impact on Cash Flow: Short-term liabilities impact immediate cash flow, while long-term liabilities can influence future financial planning.
  • Risk Assessment: High levels of short-term liabilities may indicate liquidity issues, while long-term liabilities can reflect a company’s growth strategy.

Short-Term vs Long-Term Liabilities

Type of Liability   Timeframe  Impact on Cash Flow       
Short-Term          Within 1 Year      Immediate                  
Long-Term           Over 1 Year        Future Planning            

Role of Liabilities in the Balance Sheet Equation 

A balance sheet analysis outlines the company’s assets, liabilities, and equity and demonstrates how assets are financed through a combination of liabilities and equity.

The balance sheet follows the fundamental accounting equation

Assets = Liabilities + Equity

This equation has two primary sections: assets and liabilities plus equity.

Liabilities form one of the key components of this equation, representing the claims that creditors and other external parties have against the company’s assets. They illustrate the financing sourced from creditors and shareholders, which supports the company’s operations and investments.

The Importance of Understanding Liabilities

A solid grasp of the types of liabilities on a balance sheet enhances financial management skills. 

By understanding how short-term and long-term liabilities operate, individuals and businesses can:

  • Assess Financial Health: Identifying liabilities helps evaluate a company’s solvency and liquidity.
  • Make Informed Decisions: Knowledge of liabilities can inform decisions about investments, lending, and business operations.
  • Strategies Financial Planning: Understanding cash flow implications assists in effective budgeting and financial planning.

The Final Words: Unlock Your Potential with the Financial Analysis Prodegree from Imarticus Learning

Understanding liabilities on a balance sheet—specifically the distinction between short-term and long-term liabilities—empowers individuals and businesses alike. This knowledge not only aids in assessing financial health but also enhances strategic decision-making.

If you want to master financial management, consider enrolling in a financial management course. Equip yourself with the knowledge and skills necessary to understand the complexities of finance confidently.

The Financial Analysis Prodegree from Imarticus Learning is in collaboration with KPMG in India. This 120-hour skill-building programme enhances mastery across various functions within core finance, equipping participants with the relevant industry knowledge needed to transform or reignite their careers. 

Gain insights from experts on the ever-evolving finance function, discover how new-age solutions are reshaping the industry, and learn how you can assist in transforming the finance landscape as a future leader.

Key Conflicts in Corporate Governance: Board vs. Shareholders

Posted on by Imarticus Learning

Corporate governance is often seen as a delicate balancing act. Here lies the tension between two crucial players: the Board of Directors and the Shareholders. These factions frequently pull the company in different directions, with the board focusing on long-term strategy while shareholders press for immediate returns. 

But when these forces collide, the fallout can be dramatic, affecting the company’s operations. So, who should have the final say? And what happens when corporate governance issues in companies arise?

In this post, we’ll explore the key conflicts in corporate governance and why resolving them is essential to safeguarding the integrity of modern corporations.

The Role of the Board of Directors and Shareholders: Power Dynamics

A comprehensive definition of corporate governance refers to the systems, mechanisms, and practices that shape companies’ control and direction, influencing decision-making and accountability at all levels.

The Board of Directors is pivotal in steering the company. It is responsible for long-term planning, making key decisions, and ensuring regulatory compliance. Directors are the custodians of the company’s future, overseeing management and making critical choices for all stakeholders.

On the other hand, shareholders hold equity in the company, making them direct stakeholders with vested interests in the firm’s profitability. By their ownership, shareholders possess shareholder rights that grant them a voice, particularly in major corporate decisions like mergers or senior leadership appointments.

Key Conflicts in Corporate Governance: Board vs. Shareholders

The struggle between Board of Directors vs. shareholders is most evident in several key areas:

  • Executive Compensation

A recurring source of tension is executive compensation. The board often approves large compensation packages for senior executives, justifying them as necessary to retain top talent. However, shareholders who bear the financial consequences may view these packages as excessive, especially when they see diminishing investment returns. 

  • Strategic Decision-Making

Shareholders expect the company to grow their wealth, often demanding aggressive business strategies like increased dividends, share buybacks, or expansion into new markets. 

However, the board may choose more conservative, long-term strategies. When the board disregards shareholder demands for more immediate results, corporate governance conflicts flare up, potentially impacting share prices.

  • Mergers and Acquisitions

When a company decides to merge or acquire another, the Board of Directors generally initiates the decision. But what if shareholders believe that the merger won’t benefit them or is being done for board members’ interests? 

A classic example occurred when Vodafone’s shareholders rebelled against the company’s costly acquisition plans, expressing concerns over how the deal would dilute their holdings.

Types of Conflict at the Board Level

Conflicts of interest at the board level include four main tiers. Each tier presents unique challenges and requires careful handling by the board and its chairperson.

Tier 1 Conflict: Personal Interests vs. Company

This occurs when a board member’s interests clash with the company’s objectives. In this situation, a director may engage in unethical actions, such as insider trading, mismanagement of funds, or prioritising their gain over the company’s well-being. Handling this conflict is straightforward: the chair can investigate and, if necessary, remove the director to protect the company’s interests.

Tier 2 Conflict: Loyalty Compromise

Certain dominant board members may manipulate or pressure others, compromising loyalty and decision-making. Some directors may lose their independence and objectivity, agreeing with these dominant figures out of fear or peer pressure. Another form of this conflict is “groupthink,” where directors form alliances based on friendship and make decisions that benefit their group rather than the company. This type of conflict is harder to manage, requiring each board member to remain impartial and independent.

Tier 3 Conflict: Shareholders vs. Company Interests

Shareholders appoint directors to act in their best interests, but once on the board, directors must balance their duties to the company and the shareholders. Disagreements can occur between directors and shareholders, directors themselves, or different stakeholder groups. It is the chairperson’s responsibility to ensure that all conflicts get resolved fairly, balancing the interests of all parties.

Tier 4 Conflict: Company vs. Society

This conflict happens when a company’s pursuit of profits comes at the expense of society. Examples include tax evasion, environmental pollution, or poor treatment of employees. To address this, the chairperson must establish social and ethics committees that oversee the company’s actions, ensuring it operates responsibly and contributes positively to society.

How do Shareholders Influence Corporate Governance?

Despite often being at odds with the board, shareholders possess considerable tools to make their voices heard. 

The most common include:

  • Voting rights: Shareholders vote on key corporate decisions during annual general meetings.
  • Litigation: Shareholders can initiate legal actions if they believe the board is acting against their interests.
  • Activist investors: Large institutional shareholders or hedge funds can apply public pressure on the board to change strategies.

Solutions to Board vs. Shareholder Conflicts

Companies are now looking at more transparent governance structures to manage these conflicts. 

The following strategies have proven effective:

  • Transparent Communication: Regular, honest dialogue between the board and shareholders helps to ensure both sides align well.
  • Balanced Governance Structures: Many companies are now creating governance structures that give shareholders a seat at the table, such as adding shareholder representatives to the board.

These reforms aim to minimise the chances of corporate governance disputes, helping companies to operate more harmoniously.

The Final Words: Master the CFA Course with Imarticus Learning

The key conflict between the Board of Directors and Shareholders remains a defining issue in corporate governance. The board’s fiduciary duties often conflict with shareholders’ desire for financial returns. 

Yet, it is possible to navigate these tensions effectively with better communication, governance structures, and alignment of interests. As corporate governance issues evolve, companies must remain vigilant in balancing these competing interests to ensure long-term success.

With Imarticus Learning, you can gain the skills needed to excel in the highly competitive investment management industry. As one of the leading learning partners in India approved by the CFA Institute, Imarticus Learning helps you succeed in clearing the CFA course and advancing your career.

Ready to take the next step toward your CFA qualification? 

Enrol with Imarticus Learning today and set yourself up for success in the investment management world!

Mastering Internal Controls: Key Skills Every CMA USA Student Must Learn

Posted on by Imarticus Learning

As businesses increasingly face scrutiny and regulatory pressures, the demand for professionals who can successfully implement and manage these controls has never been higher. 

But what exactly are these internal controls, and why should they matter to you as a CMA student?

Every Certified Management Accountant (CMA USA) student must grasp these principles to ensure organisational integrity and financial accountability. This post delves into the key skills for CMA to excel in their careers and safeguard future organisations.

What Are Internal Controls?

Internal control refers to a systematic process to ensure that an organisation achieves its objectives related to operational effectiveness, reliable financial reporting, and compliance with relevant laws, regulations, and policies. 

This broad concept encompasses a range of mechanisms that safeguard against risks, allowing businesses to function smoothly and maintain integrity. 

Internal controls are the processes & procedures that organisations use to manage their operations, ensure accurate financial reporting, and comply with laws and regulations. 

Accounting internal controls safeguard assets, enhance operational efficiency, and promote reliability in financial reporting. A robust internal control framework protects an organisation from fraud and fosters a culture of transparency and accountability.

What Are the Two Types of Internal Controls?

Accounting internal controls consist of two types: preventive and detective measures. 

  • Preventative controls are proactive actions designed to stop errors or fraud before they occur. These include practices such as proper documentation, authorisation processes, and segregation of duties. 
  • Detective controls focus on identifying and resolving issues after they have occurred. They are crucial for spotting problems like quality control, fraud prevention, and regulatory compliance.

Key Skills Every CMA USA Student Must Learn

When exploring key skills for CMA, focusing on developing analytical proficiency, strategic planning, and risk management is essential to succeed in the dynamic world of finance and management accounting.

  • Analytical Skills

Analytical skills are fundamental for CMA students as they enable the assessment of internal control systems. By examining financial data and operational processes, students can identify weaknesses and recommend improvements. Familiarity with analytical tools such as data analytics software can significantly enhance these skills.

Example: A CMA candidate might use data analysis to detect anomalies in financial statements, prompting a deeper investigation into potential fraud.

  • Attention to Detail

An eye for detail is vital when managing internal controls. Minor oversights can lead to significant repercussions, from financial losses to regulatory penalties. CMA students must learn to scrutinise processes and documentation meticulously.

  • Communication Skills

Effective communication is crucial in implementing and monitoring internal controls. CMA students must be able to articulate their findings and recommendations clearly to various stakeholders, including management and auditors.

Tips for Effective Communication:

  • Use clear, concise language.
  • Tailor your message to your audience.
  • Encourage feedback and discussion.
  • Risk Assessment and Management

Understanding risk is central to internal controls. CMA students must learn to identify potential risks, assess their impact, and develop mitigation strategies. This involves analysing internal and external factors affecting the organisation’s operations.

Approaches to Identify Risks:

  • Conducting SWOT analysis (Strengths, Weaknesses, Opportunities, Threats).
  • Engaging in scenario planning.
  • Technological Proficiency

In an increasingly digital world, CMA students must be adept with technology. Familiarity with software such as Enterprise Resource Planning (ERP) systems, data analytics tools, and compliance software is vital for effective internal control management.

Overview of Tools:

  • SAP: Used to manage business operations and customer relations.
  • Oracle: Provides integrated applications to manage the business and drive innovation.

Essential skills for mastering internal controls in CMA USA:

SkillImportanceTips for Development
Analytical SkillsIdentify weaknesses in controls.Enrol in data analysis courses.
Attention to DetailPrevent financial inaccuracies.Practice meticulous documentation.
Communication SkillsFacilitate clear reporting.Engage in public speaking workshops.
Risk AssessmentProactively manage potential threats.Conduct regular risk assessments.
Technological ProficiencyLeverage software for effective controlsStay updated with latest tools.

Why Are Internal Controls Crucial for CMA Students?

The need for effective internal controls has become a pressing issue for organisations worldwide. CMAs with the skills to develop and monitor these controls are invaluable assets to their companies.

How to Develop These Skills?

To master financial controls for CMA USA, students should consider joining a specialised CMA USA certification programme focusing on risk management, financial analysis, and internal auditing. Additionally, participating in internships and real-world projects can provide practical experience that complements academic learning.

Among the most useful CMA USA exam preparation tips is to combine theoretical knowledge with hands-on experience, ensuring a well-rounded understanding of key topics such as internal controls. 

Continuous professional development is also essential in this ever-evolving field. Attending industry seminars and networking opportunities expose students to emerging trends and best practices in internal control management.

The Final Words: Excel in Accounting and Finance with Imarticus Learning

Mastering internal controls is crucial for every CMA USA Certification student aspiring to succeed in the dynamic world of finance. By developing key skills such as analytical thinking, attention to detail, effective communication, risk management, and technological proficiency, students can enhance their employability and contribute significantly to their future organisations.

Imarticus Learning helps you excel in the accounting and finance world by providing the tools and support needed to thrive in your CMA USA certification journey. Becoming a CMA opens doors to working with Fortune 500 companies and pursuing prestigious global careers in management and accounting. 

Join Imarticus Learning and secure your future with top placements, guaranteed interviews, and dedicated support!

Valuation Methods: How to Value a Company’s Future Cash Flows

Posted on by Imarticus Learning

Valuing a company is an analytical process that involves assessing its future potential and financial health.

One of the most fundamental approaches to valuation is based on the concept of future cash flows. This method recognises that a company’s true value lies in its ability to generate cash in the future. We also have the relative valuation method and hybrid company valuation techniques available.

In this article, we learn about these essential valuation methods. If you wish to learn how to carry out these valuation techniques in detail, you can enrol in a solid financial analysis course.

Discounted Cash Flow Analysis

The Discounted Cash Flow (DCF) method is the most widely used valuation technique.In this method, we project a company’s future cash flows and then discount them back to their present value using a discount rate. The discount rate reflects the risk associated with the company’s future cash flows.

Steps Involved in DCF Analysis

  1. Projecting Free Cash Flows: Free cash flow is the cash generated by a company’s operations after accounting for capital expenditures. Analysts project future free cash flows based on the company’s historical performance, industry trends, and economic forecasts.
  2. Determining the Discount Rate: The discount rate is the rate of return that investors require to compensate for the risk associated with the company’s future cash flows. We generally calculate this using the Weighted Average Cost of Capital (WACC), which considers the cost of equity and debt financing.
  3. Discounting Cash Flows: The projected free cash flows are discounted back to their present value using the discount rate. This process involves dividing the future cash flows by (1 + discount rate)^n, where n is the number of periods in the future.
  4. Terminal Value: The terminal value represents the value of the company’s cash flows beyond the projection period. It is often calculated using a terminal growth rate or a multiple of the company’s terminal EBITDA.
  5. Calculating Enterprise Value: The enterprise value is the sum of the present value of the projected free cash flows and the terminal value.   

Key Components in DCF Analysis:

  • Accuracy of Projections: The accuracy of the DCF valuation depends heavily on the accuracy of the projected free cash flows and the discount rate.
  • Terminal Value Assumptions: The choice of terminal growth rate or multiple can significantly impact the valuation.
  • Risk Assessment: The discount rate should reflect the company’s specific risk profile, including industry risk, competitive risk, and financial risk.

Relative Valuation Methods

Relative valuation methods compare a company’s valuation to similar companies or industry benchmarks. These methods are:

  1.  Price-to-Earnings (P/E) Ratio: This ratio is the most common relative valuation metric. It allows us to compare a company’s stock price to its earnings per share.
  2. Price-to-Book (P/B) Ratio: The P/B ratio compares a company’s stock price to its book value per share.
  3. Price-to-Sales (P/S) Ratio: The P/S ratio compares a company’s stock price to its sales per share.
  4. Enterprise Value-to-EBITDA (EV/EBITDA) Ratio: This ratio allows us to compare a firm’s EV (or enterprise value) to its earnings before taxes, depreciation, interest, and amortisation.   

Key Components in Relative Valuation

  • Comparability: The companies used for comparison should be similar in size, industry, and business model.
  • Market Conditions: Overall market conditions, such as economic growth or market sentiment, can influence relative valuation.
  • Company-Specific Factors: Growth prospects, competitive advantage, and management quality can influence a company’s valuation.

Hybrid Valuation Methods

Hybrid valuation methods combine elements of DCF and relative valuation to provide a more comprehensive assessment. These methods are:

  1.  Comparable Companies Analysis with Adjustments: This method involves comparing a company to similar companies using relative valuation metrics, but adjusting the multiples based on differences in financial performance, growth prospects, or risk.
  2. Sum-of-the-Parts Valuation: This method is used for diversified companies with distinct business segments. It involves valuing each segment separately and then summing the individual values to arrive at the total company value.

Additional Tips and Tricks for Valuation

While the valuation methods discussed above provide a solid foundation, there are several additional tips and tricks that can enhance your valuation analysis:

  1. Sensitivity Analysis: Conduct sensitivity analysis to assess how changes in key assumptions, such as the discount rate, terminal growth rate, or revenue projections, can impact the valuation. This helps identify the most critical factors driving the valuation and assess the range of potential outcomes.
  2. Scenario Analysis: Develop multiple scenarios based on different economic conditions, industry trends, or company-specific factors. This allows you to evaluate the valuation under various potential future outcomes.
  3. Consider Qualitative Factors: While quantitative factors are essential for valuation, qualitative factors such as management quality, competitive advantage, and industry trends should also be considered. These factors can significantly impact a company’s future prospects and valuation.
  4. Use Multiple Valuation Methods: Applying multiple valuation methods can provide a more comprehensive and robust assessment. By comparing the results from different methods, you can identify potential inconsistencies and gain a better understanding of the company’s value.
  5. Stay Updated with Industry Trends: Keep up-to-date with industry trends, regulatory changes, and economic developments that could impact the company’s valuation. This will help you adjust your valuation assumptions accordingly.
  6. Seek Expert Advice: If you need clarification on valuation techniques or need assistance with complex valuations, consider consulting with a valuation expert or financial advisor.
  7. Use Valuation Software: There are various valuation software tools available that can automate many of the calculations and processes involved in valuation. These tools can save time and improve accuracy.

Wrapping Up

Valuing a company’s future cash flows is a complex task that requires a thorough understanding of valuation methodologies. Both DCF and relative valuation methods have their strengths and weaknesses, and the most appropriate method will depend on the specific circumstances of the company being valued.

By combining these essential valuation methods and considering the key factors we discussed in this article, you can make informed judgments about a company’s intrinsic value. If you wish to become a solid financial analyst, enrol in the Postgraduate Financial Analysis Program by Imarticus Learning.

Frequently Asked Questions

What is the difference between intrinsic value and market value?

Intrinsic value is the perceived worth of a company based on its fundamental factors, while market value is the price at which the company’s stock is currently trading in the market.

How do you calculate the weighted average cost of capital (WACC)?

WACC is calculated by multiplying the cost of equity by the percentage of equity financing and adding it to the cost of debt multiplied by the percentage of debt financing, adjusted for tax savings from interest deductions.

What is the role of comparables analysis in valuation?

Comparables analysis involves comparing a company’s valuation metrics to similar companies in the same industry. This helps determine whether the company is overvalued or undervalued relative to its peers.

What are the valuation multiples commonly used in relative valuation?

Common valuation multiples include price-to-earnings (P/E), price-to-book (P/B), price-to-sales (P/S), and enterprise value-to-EBITDA (EV/EBITDA).

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