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Why is Team Training Important in Finance?

Build a highly capable financial workforce

Written by

CFI Team

Published February 11, 2023

Updated June 28, 2023

Although technology is rapidly changing how organizations do business, finance professionals continue to play a critical role. While you should certainly invest in technology, you also need to focus on building an expert financial workforce.

Embracing training for financial professionals is one of the best ways to accomplish that goal. The development of financial skills directly affects your organization’s profitability and, ultimately, its sustainability. Group training, rather than mandated individual training, is a demonstrably better approach to skill development.

Organizational Improvement through Finance Skill Development

You cannot underestimate the role finance can play in improving the efficiency of human resources, sales, marketing, information technology, and other organizational functions. Your finance professionals should be at the core of your operations by working with data from all aspects of your business. Consider it a holistic approach to success.

For example, the sales and marketing departments may prefer a certain marketing strategy because it brings them more prospective customers and healthy commissions. Your finance team should adopt a broader perspective; perhaps, choosing instead to recommend pursuing clients with a higher customer lifetime value and better retention rates.

Your financial department can make significant improvements throughout the entire business by making informed decisions about the supply chain, capital investments, business location, information technology, recruitment, and training.

Financial Skills Training

How finance professionals contribute to the success of an organization may vary depending on the business model you employ and the industry in which your firm operates. One factor stays constant: your employees need the training to improve financial skills.

Many finance specialists possess qualifications from respected institutions. The practical value of their training varies, with some students learning the theory without grasping the practice. A degree or degrees do not ensure that your employees can perform to the best of their ability. That’s why additional training from organizations like CFI is so important. 

Factors in Finance Team Success

When you assemble a finance team, you may look for a solid college or university background. But you also look at other factors and ways to maximize your department’s assets. They include:

Raw talent Educational ROI

Obviously, you will need to invest capital in this additional training. To get your company to literally buy into this mission means you will have to justify the cost by explaining the value. Fortunately, the cold hard facts are on your side.

A randomized control study involving 852 firms sought to determine the impact of finance skill training, along with marketing skill training, on business performance. The study was conducted in South Africa, an emerging market with access to global markets and state-of-the-art technologies. It’s also a market that faces various domestic and international pressures.

The research paper gathered data from a diverse portfolio of businesses of different sizes and in different market conditions. The study found that both marketing and top finance skills improve your team as a whole and result in increased profitability.

Cost reduction strategies

A business can increase its earnings by improving overall sales or by increasing its efficiency and effectiveness by reducing costs and expenses. The researchers found that the group trained in marketing focused on growth, concentrating on higher sales, improvements in inventory, and hiring more employees. This is a valid approach, but not all firms can adopt it.

Some markets are saturated or dominated by large firms. There may be room to expand sales, but it is likely to be limited. That’s why your finance department needs to be trained in price reduction strategy. This path is demonstrably effective in a digital and price-sensitive global marketplace.

A group trained in skills such as data analysis, Excel, and Tableau focuses on efficiency, a strategy that results in significantly lower costs. Several factors influence the success and sustainability of this strategy, which targets the effectiveness of the people within the organization as well as its processes and supply chain management.

Your competitors will have limited access to the details of your new strategy, but the results will be clear on your bottom line. Changes driven by professionals within the firm are often a sustainable path to better business performance — more so than increased marketing efforts.

Train Your Team and Improve Finance Skills

The success or failure of your training push depends in part on the approach you take. You may choose to require the finance professionals within your company to gain a certain qualification by a specified deadline. However, that approach may feel disjointed.

Your team is likely to complete training at a different pace and during different periods, which will limit their ability to collaborate during crucial financial periods. An awkward start may also sour management on this training investment.

A more effective approach is training in teams, which allows an organization to ensure that its employees receive the same quality of training in stable and optimally sized teams. A publication in the Journal of the Human Factors and Ergonomics Society noted that this approach enhances the effectiveness of training interventions.

In addition, the researchers found that this training enhances cognitive outcomes, affective outcomes, collaborative processes, and performance outcomes. Training in this way is simply more effective in many ways.

It is worth noting in this new age of remote work and collaboration that training should tap into learning innovations. Finance specialists need to upskill or re-skill while staying current with their traditional finance responsibilities. Scheduling on-site training would probably be costlier and would require more time investment.

Business plans tailored to finance upskilling can include online instruction suitable for all types of firms. These plans can include team training for Wall Street professionals, finance departments in industry-leading firms, and businesses in developing markets.

You will also need to quantify the results to keep this training on your company’s radar. Ideally, measuring this training should include individual and group progress tracking to ensure compliance and skills gap assessments to identify employees’ knowledge. You will be able to identify competency gaps and gain real-time insights into students’ progress, outcomes, and areas for additional training.

Additional Resources

Learning as Part of Organizational Culture

Why Online Training for Finance Teams Makes Sense

Employee Development in Finance

See all team development resources

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Why Basic Biology Is So Important In Biomedical Research

One of the most heartbreaking stories about abysmal experimental design involves amyotrophic lateral sclerosis (ALS), better known as Lou Gehrig’s disease. The search for a treatment for this deadly degenerative disease is rife with studies so poorly designed that they offered nothing more than false hope for people essentially handed a death sentence along with their diagnosis. Tom Murphy was one of them.

Once an imposing figure, Murphy had played football and rugby in college. His six-foot-three frame and barrel chest gave him a solid presence. But his handshake wasn’t the crushing grip you might expect. The first time we met, it was a gentle squeeze. When we met again a year later, we didn’t shake hands at all. Murphy had lost his formerly impressive strength due to ALS.

People around the world donated more than $100 million to fight this deadly ailment during the Ice Bucket Challenge of 2014, but for most people its real-life consequences are an abstraction: something about the degeneration of nerves. For Murphy, a fifty-six-year-old father of three, ALS was a very concrete, slow march toward the day when his nerves could no longer direct his diaphragm to draw air into his lungs. (Physicist Stephen Hawking is the rare exception who has managed to survive for many years despite the diagnosis.)

Murphy, remarkably, was not bitter about this turn of events when he told his story. Nor was he resigned to fading away when he first noticed some unusual muscle twitches in the winter of 2010. He went to his doctor, who, after a brief examination, sent him to a neurologist. Murphy actually ended up seeing three different neurologists before he finally got the diagnosis.

“When the guy said, ‘Sorry to tell you, but you have two to four years. Get your stuff together,’ I thought, ‘Really?’ It was a real curveball. I would never have thought that in a million years.” To prepare for what was likely to come, Murphy and his wife, Keri, sold the family home and bought a modern ranch-style house in Gainesville, Virginia, which Murphy could navigate without having to contend with stairs. He would eventually be getting around on wheels, once the muscle tone in his legs had faded. A giant TV graced the open and airy living room, where Murphy watched sports that he could no longer play himself.

Rigor Mortis by Richard Harris is available now. Basic Books

But Murphy’s doctors also offered at least a sliver of hope. “The first thing they told me is we have a drug trial; would you like to be in it? And of course I thought it sounded pretty good,” Murphy said. People with ALS find their strength declines within a few years, and trials of potential drugs are only available to reasonably strong patients. So most only get one shot at an experimental treatment. In May 2011 he settled on the test of a drug called dexpramixole (or simply “dex”), becoming one of about nine hundred patients enrolled in a multi-million-dollar study. But when the drug company analyzed the data collected, the news was disappointing. Dex was not slowing the progression of symptoms in this group of patients. The trial was a bust.

Murphy was philosophical. There’s no question the disease is a tough one to counteract. Almost everything scientists have tried for ALS has failed (other than one drug with very marginal benefit). So all scientists in the field have gone in knowing the likelihood of failure is high, but they didn’t know exactly why until a nonprofit research center called the ALS Therapy Development Institute (ALS TDI) in Cambridge, Massachusetts, began investigating that question. Researchers there decided to look at the original studies to see what they could learn. They discovered that the original animal studies to test these drugs were deeply flawed. They all used far too few mice, and as a result they all came up with spurious results. Some experiments used as few as four mice in a test group. Sean Scott, then head of the institute, decided to rerun those tests, this time with a valid experimental design involving an adequate number of mice that were handled more appropriately. He discovered that none of those drugs showed any signs of promise in mice. Not one. His 2008 study shocked the field but also opened a path forward. ALS TDI would devote its efforts to doing this basic biology right.

Scott died of ALS in 2009 at the age of thirty-nine—the disease runs in his family. His successor, Steve Perrin, has carried on as Scott would have, insisting on rigorous animal studies as the institute’s scientists search for anything to help people like Tom Murphy. And they’re not simply taking the basic—and what should have been obvious—step of starting with enough mice in each experiment. Male and female mice develop the disease at somewhat different rates, so if scientists aren’t careful about balancing the sexes in their experiments, they can get spurious results. Another problem is that the ALS trait in these genetically modified mice can change from one generation to the next. The scientists at ALS TDI look at the genetics of every single animal they use in an experiment to make sure that all are identical. “These variables are incredibly important,” Perrin said. Other scientists had often overlooked those pitfalls.

To get robust results, Perrin’s group uses thirty-two animals—and compares them to an untreated group of thirty-two more mice. Academic labs don’t use large numbers of mice in their experiments in part because they cost a lot of money. Perrin said each one of these tests costs $112,000, and it takes nine months to get a result. If you’re testing three dosages of a medication, each requires its own test. Perrin’s institute has shown clearly that cutting corners here can lead to pointless and wasteful experiments. Even so, “we still get some pushback from the academic community that we can’t afford to do an experiment like that,” he said. It’s so expensive that they choose to do the experiments poorly.

It’s not fair to blame the scientists entirely for this failure. The National Institutes of Health (NIH) paid for much of this research, and funding was stretched so thin that scientists said they didn’t get as much as they needed to do their studies. So they made difficult choices. As a result, funders, including the NIH, spent tens of millions of dollars on human trials using these drugs, without first making sure the scientific underpinnings were sound. ALS patients volunteered to test lithium, creatine, thalidomide, celecoxib, ceftriaxone, sodium phenylbutyrate, and the antibiotic minocycline. A clinical trial involving the last one alone, bankrolled by the NIH, cost $20 million. The results: fail, fail, fail, fail, fail, fail, fail. Science administrators had assumed that the academic scientists had all done the legwork carefully. They had not.

Excerpted from RIGOR MORTIS: How Sloppy Science Creates Worthless Cures, Crushes Hope, and Wastes Billions by Richard Harris. 2023. Available from Basic Books, an imprint of Perseus Books, a division of PBG Publishing, LLC, a subsidiary of Hachette Book Group, Inc.

What Is Classification In Machine Learning And Why Is It Important?

blog / Artificial Intelligence and Machine Learning What is Classification in Machine Learning and Why is it Important?

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The world has become data-driven, and artificial intelligence and machine learning are using this data to understand society, predict business outcomes, and drive decision-making and growth. Classification in machine learning is one of the most common and widely used supervised machine learning processes. It helps in categorizing data into different classes and has a broad array of applications, such as email spam detection, medical diagnostic test, fraud detection, image classification, and speech recognition among others.

This guide is a deep dive into classification in machine learning, types of classification tasks, classification algorithms, and learners in classification problems. But before we dig deep, let’s understand some related concepts. 

Overview of Supervised Learning

Supervised learning, or supervised machine learning, is a subcategory of artificial intelligence and machine learning. It employs human supervision to accurately label and train algorithms. Moreover, in this approach, the machine is trained with the help of labeled input and output data to analyze the training data and predict accurate outcomes for new and unseen data. Here, past data is used to train the algorithm to categorize file types, such as images, words, and documents, and then predict outcomes by learning the new data via learning patterns. 

Supervised learning is beneficial for collecting and producing data output and helps solve real-world computation queries. Also, these supervised approaches are valuable in developing business applications. However, training the model can take time, and the process can become even more challenging while classifying big data. 

The supervised machine learning algorithms can be broadly classified into two categories: Regression and classification. 


A regression problem is used to predict the output for real or continuous values. Moreover, this technique aims to map a predictive relationship between dependent and independent variables. 


A classification problem is used to identify specific categories of new observations based on one or more independent variables. Also, in this article, we will focus on classification.

Overview of Classification

Classification is a supervised machine learning process of categorizing a given set of input data into classes based on one or more variables. Additionally, a classification problem can be performed on structured and unstructured data to accurately predict whether or not the data will fall into predetermined categories. 

Classification in machine learning can require two or more categories of a given data set. Therefore, it generates a probability score to assign the data into a specific category, such as spam or not spam, yes or no, disease or no disease, red or green, male or female, etc.   

Some Applications of Machine Learning Classification Problems

Image classification 

Fraud detection

Document classification 

Spam filtering  

Facial recognition

Voice recognition 

Medical diagnostic test 

Customer behavior prediction 

Product categorization

Malware classification

Types of Classification Tasks in Machine Learning

Before discussing classification tasks in machine learning, let’s first take a brief look at classification predictive modeling.  

Classification Predictive Modeling

In machine learning, classification is a predictive modeling problem where the class label is anticipated for a specific example of input data. For example, in determining handwriting characters, identifying spam, and so on, the classification requires training data with a large number of datasets of input and output. The most common classification algorithms are binary classification, multi-class classification, multi-label classification, and imbalanced classification, which are described below.

Binary Classification

Binary is a type of problem in classification in machine learning that has only two possible outcomes. For example, yes or no, true or false, spam or not spam, etc. Some common binary classification algorithms are logistic regression, decision trees, simple bayes, and support vector machines. 

Multi-Class Classification

Multi-class is a type of classification problem with more than two outcomes and does not have the concept of normal and abnormal outcomes. Here each outcome is assigned to only one label. For example, classifying images, classifying species, and categorizing faces, among others. Some common multi-class algorithms are choice trees, progressive boosting, nearest k neighbors, and rough forest. 

Multi-Label Classification

Multi-label is a type of classification problem that may have more than one class label assigned to the data. Here the model will have multiple outcomes. For example, a book or a movie can be categorized into multiple genres, or an image can have multiple objects. Some common multi-label algorithms are multi-label decision trees, multi-label gradient boosting, and multi-label random forests. 

Imbalanced Classification What is a Classification Algorithm?

A classification algorithm is a supervised learning technique that uses data training to determine data into different classes. Classification predictive modeling is trained using data or observations, and new observations are categorized into classes or groups. Classification predictive modeling is the task of a mapping function (f) from input variables (x) to discrete output variables (y). In this approach, the algorithm generates a probability score and assigns this score to the input. For example, email service providers use classification to generate probability scores for email identification to determine if the email is in the spam class or not. 

Learners in Classification Problems Lazy Learners

Lazy learners store the training data and wait until a testing dataset appears. The primary aim of lazy learning is to continuously update the dataset with new entries. However, as the data is continually updated, it becomes outdated frequently. Thus, these algorithms take comparatively less time to train and more time to predict. Lazy learning algorithms are beneficial when working with large, changing datasets with a smaller set of queried attributes. Lazy learning is easy to maintain and can be applied to multiple problems. Some examples of lazy learners include local regression, lazy bayesian rules and k-nearest neighbor (KNN) algorithm, instance-based learning, and case-based reasoning.

Eager Learners

Eager learners construct a classification layer before receiving the training and testing the dataset. Before it observes the input queries, eager learning builds an explicit description of the training function based on the training data. Because it is building a classification model, eager learning takes more time to train the dataset and less time to predict as compared to the lazy learning system. Eager learning is required to commit to a single hypothesis that covers the entire instance space. Some examples of eager learners include decision trees, naive Bayes, and artificial neural networks (ANN). 

ALSO READ: How to Become a Machine Learning Engineer and Have a Lucrative Career

To conclude this comprehensive guide, with the evolution of digital technology, classification in machine learning has become a critical asset. The data landscape has made it imperative that we understand how data is being utilized by machine learning and apply the same to work. Classification in machine learning is opening up unparalleled opportunities for many industries and organizations to stay viable and thrive in a dynamic landscape. 

If you are looking to develop your career in this field, explore the diverse range of online artificial intelligence and machine learning courses offered by Emeritus. These courses are created in collaboration with the top universities across the world to provide you with the most in-depth knowledge and skills. 

Written by Krati Joshi

Write to us at [email protected] 

Why Flexibility In Entertainment Venues Is More Important Than Ever

In 2023, there were 10 Baltimore Ravens home games. To avid sports fans, this number might not be surprising, but when you weigh the revenue opportunities against the amount of money that venue owners invest into a stadium, 10 games per year is just not enough to turn a profit on sports attendance alone.

These days, when I talk to customers in the venue market, they refer to themselves as being in the entertainment and live event business. They can’t pigeonhole themselves into sports entertainment anymore, because the market is small and live games are limited. Plus, venue owners now have to compete with fans watching in their living rooms as the cost of HD TVs continues to drop.

This is the heart of the challenge that modern event venues are facing everywhere: How do you justify the costs (usually hundreds of millions of dollars, or more) while fan attendance is decreasing and the bar for spectacular experiences is constantly rising?

Multifunctional spaces

Below are two trends I’m seeing in the live events market, aimed at capturing a variety of audiences through spectacular (and flexible) technology:

1. Bigger and clearer

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The great thing about digital signage is how customizable and dynamic it can be. If you invest in high-quality displays throughout a venue, the content can be easily changed out for any event. Hosting a concert Friday and a basketball game Sunday? You can completely change the look and feel of a space by simply swapping out the branding and creative elements displayed on wayfinding screens, menu boards and other digital displays throughout the arena. This flexibility is key when designing spaces and specifying technology that will be used for multiple event types.

At Samsung, we’re also seeing that even if customers aren’t ready to implement true HDR in all of their venue displays, they want the technology to be HDR-compatible so they can keep up with the ever-changing standards of image quality. I mentioned that venues’ real competition are personal devices, so live event digital signage needs to provide a clearer picture than what’s already in everyone’s pocket. By choosing HDR-compatible screens, venue owners can invest in high-quality displays now and know that they’re future-proofing their technology.

2. Multiple facilities

Digital signage is just one way to improve venue flexibility. Another approach, one that we’re seeing more these days, is to create multiple facilities within one venue. We’re seeing this demand — rather than having only a large arena that seats 50,000-plus — to accommodate a number of events at the same time.

You might have the stadium itself where you host an NFL or NBA team. Then you might have, in the same venue, a performance space for high-profile concerts, plus an engaging outdoor plaza where community events can take place. With multiple facilities under one roof, your venue can diversify its brand and open itself up to new audiences that might otherwise exclusively associate arenas with sports.

The Chase Center, for instance, holds yoga classes, farmers markets and festivals in Thrive City, the venue’s 11-acre mixed-use complex. The goal of Thrive City is to capture the spirit of the Mission Bay neighborhood and be a gathering space for the community. This is exciting not only for those who have access to a beautiful, privately funded community space, but also for the venue owners who can bring customers into their facilities for longer amounts of time.

Our customers are realizing that it costs a large amount of money to get an attendee to their venue in the first place; we need people to stay on-site longer, after the event ends or before it begins. For instance, we’re creating ways for attendees to have fuller experiences within a venue — like staying at the venue at the conclusion of events to celebrate rather than immediately going off-site to eat or drink. Some venues are even transitioning on-site bars into clubs for post-event entertainment — both sports clubs to watch other events, as well as nightclubs.

With sports seasons called off and concerts postponed in 2023 and 2023, venues that have the flexibility to adapt are going to have an easier time making up for lost revenue. If I had a crystal ball, I would guess there are a lot of organizations figuring out how to double down on other entertainment events next year, especially if their teams aren’t playing right now. In the meantime, venues have a great opportunity to use their outdoor digital displays for public service announcements.

Incorporating state-of-the-art displays, housing multiple facilities under one roof and designating venue grounds as community centers are a few ways Samsung is helping customers get the most out of their investments and increase the reach of venues.

For live events of every kind, there’s a Samsung display that can light up your venue and attract guests — while keeping you on budget. You can discover more ways to redefine your guest experience with digital signage opportunities around every corner in this free, complete guide.

What Is Project Quality Management? Why Is It Important?

blog / Project Management What is Project Quality Management? How Does it Boost Customer Satisfaction?

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For anyone facing challenges in meeting project specifications or delivering quality projects, look no further than auto major Toyota for inspiration. Right from the development stage to the production line, the Japanese company uses a systematic approach to ensure every aspect of its projects meets the highest standard. Essentially, this has helped it gain an unassailable reputation in the automotive industry for delivering reliable and high-quality vehicles. You too can achieve similar results by following Toyota’s Project Quality Management (PQM) mantra. Let’s dig deeper into this management approach.

Project Quality Management

It is an integrated framework of any organization’s total quality management process. The management of quality comprises processes required to deliver a project on time while ensuring that the demands of the stakeholders, including customers, are met. Moreover, it is the ability to manage a product and deliver output in conformity with the requirements of the users while maximizing profits for the company.

Project quality management combines two frameworks: quality management and project management. For instance, let’s take the example of a multimedia tech company that is working on a new high-quality software application. Now, to ensure that it meets customer expectations and delivers value, there are two aspects that the project management team needs to look into. First, it has to ensure that there are no functionality defects in the software (this is referred to as quality management). Next, it must ensure that the new software is equipped with the latest features that are in demand in the market so that the company can generate more revenue (this is referred to as project management). 

ALSO READ: What is Project Management and How to Become a Successful PM

Importance of Project Quality Management Better Decision Making

PQM provides relevant data to an organization and enables it to make strategic decisions by identifying quality requirements. This helps generate revenue.

Enhanced Management

PQM promotes a culture of continuous improvement, leading to increased job satisfaction and a sense of accomplishment among the team members. It facilitates the seamless implementation of management processes.

Enhances Brand Reputation

PQM can help an organization establish a reputation for delivering high-quality products and services, leading to increased business opportunities and a stronger brand.

Project Quality Management Processes Quality Management Planning

It sets quality standards and objectives for the project and determines how these standards will be met. In addition, it includes identifying the processes, tools, and techniques to manage and monitor project quality.

Perform Quality Assurance

PQM ensures that the project is executed by the quality plan. Additionally, this includes activities such as regular quality audits and inspections, as well as continuous monitoring of project performance.

Control Quality

It monitors and controls the quality of project outputs, including products, services, and deliverables. Moreover, this is done by testing, inspections, and verifying the project requirements have been met.

Key Elements of Project Quality Management

Customer Satisfaction:

The primary goal of PQM is to ensure that a project meets or exceeds customer requirements and expectations, with customer satisfaction being the best way to assess this. Thus, customer satisfaction is a core element of quality management.

Prevention Over Inspection:

Inspection-based quality control focuses on detecting and correcting defects after they have occurred. In essence, this approach is reactive and can be costly and time-consuming. PQM enables organizations to take proactive measures to prevent defects, improve quality, and achieve better project outcomes.

Continuous Improvement

: In PQM, continuous improvement is viewed as an essential component of achieving high levels of customer satisfaction. Moreover, increasing project efficiency and reducing the cost of quality are also considered vital.

Benefits of a Project Quality Management Plan

A project quality management plan offers the following benefits throughout the PQM lifecycle:

Improved Customer Satisfaction

In a PQM plan, customers’ needs and expectations are clearly defined. Moreover, the organization focuses on achieving these needs. This increases customer satisfaction and a better overall perception of the project and the organization.

Enhanced Project Performance Better Resource Utilization

PQM provides a framework for monitoring and controlling the use of resources. Thus, it results in more efficient use of time, money, and human resources.

Increased Stakeholder Confidence Project Quality Management Tools and Techniques

There are different types of tools and techniques to aid this process.

Project Quality Management (PQM) Tools

Cost of Quality (COQ) Analysis:

It calculates the cost of poor quality, including the cost of prevention, appraisal, and internal and external failure

Statistical Process Control (SPC):

This is a set of statistical methods used to monitor and control a process to ensure it is operating within defined limits

Control Charts:

This is a statistical tool used to monitor a process to determine if it is in control, and identifies any special cause variations

Pareto Charts:

These are graphical representations showing which factors are contributing the most to a problem or issue


: It represents a process or workflow, used for identifying potential bottlenecks or areas for improvement

Project Quality Management Techniques

Continuous Improvement:

This is a systematic approach that identifies and implements improvements in a process or product

Quality Audits

: This is an independent review of a process or product to assess its compliance with quality standards

Design of Experiments (DOE):

DOE is a statistical method used to determine the effect of different variables on a process or product

Lean Methodology:

This is a management approach that seeks to optimize the flow of work by eliminating waste and improving efficiency

Phases of Project Quality Management

There are three dimensions or levels of project quality management. The first involves meeting the specified requirements and is called quality control. Then, the next is quality management; which focuses on working beyond the specified requirements. Finally, the last level is known as total quality management. In essence, it focuses on constant improvement to enhance customer satisfaction. 

The following are the five essential phases of project quality management:

Project Quality Initiation

: The project quality management framework starts with identifying a potential project and seeking authorization to initiate it. Here, a project manager is appointed who selects the core team and identifies potential risks involved in the project. 

Project Quality Planning

: Now, all stakeholders are informed about the project and their approval and inputs are received. The project management team starts preparing an action plan to complete the project. This step requires market research to understand customer requirements and gather data on ongoing market trends. In addition, the team identifies customer satisfaction standards and suppliers.

Project Quality Assurance

: In the next stage, the project management team focuses on improving the processes to provide quality deliverables. It involves communicating with external stakeholders to overcome obstacles. The team gathers data, identifies the root cause of defects, and conducts quality audits 

Project Quality Control:

In the quality control phase, the project management team analyzes customer satisfaction levels and focuses on improving processes. This stage mainly involves running various tests and correcting errors to ensure the best quality.

Project Quality Closure:

Lastly, the team delivers the final project to the customers, who accept the project and are satisfied. In fact, this phase also involves providing support and training to the customers, assessing the overall project to improve the processes, and acknowledging and rewarding various members of the project management team.

A Career in Project Quality Management

You will need to know the following if you are planning to build a career in project quality management:

Quality management methodologies, such as Six Sigma or Lean

Quality tools and techniques, such as statistical process control and root cause analysis

Quality standards and regulations, such as ISO 9001 or CMMI

Project management methodologies, such as Agile or Waterfall

Quality metrics and measurement techniques

Quality management software and systems

Risk management and mitigation strategies

Due to intense competition in the market and rapidly changing market conditions, project quality management has become an integral part of the project management framework. Hence, at Emeritus, we have online project management courses that can help you acquire such relevant skills to accelerate your career.

Write to us at [email protected]

What Is Generative Ai And Why Is It Important?

Definition: What is Generative AI?

As the name suggests, Generative AI means a type of AI technology that can generate new content based on the data it has been trained on. It can generate texts, images, audio, videos, and synthetic data. Generative AI can produce a wide range of outputs based on user input or what we call “prompts“. Generative AI is basically a subfield of machine learning that can create new data from a given dataset.

If the model has been trained on large volumes of text, it can produce new combinations of natural-sounding texts. The larger the data, the better will be the output. If the dataset has been cleaned prior to training, you are likely to get a nuanced response.

OpenAI Playground

Similarly, if you have trained a model with a large corpus of images with image tagging, captions, and lots of visual examples, the AI model can learn from these examples and perform image classification and generation. This sophisticated system of AI programmed to learn from examples is called a neural network.

At present, GPT models have gotten popular after the release of GPT-4/3.5 (ChatGPT), PaLM 2 (Google Bard), GPT-3 (DALL – E), LLaMA (Meta), Stable Diffusion, and others. All of these user-friendly AI interfaces are built on the Transformer architecture. So in this explainer, we are going to mainly focus on Generative AI and GPT (Generative Pretrained Transformer).

What Are the Different Types of Generative AI Models?

Amongst all the Generative AI models, GPT is favored by many, but let’s start with GAN (Generative Adversarial Network). In this architecture, two parallel networks are trained, of which one is used to generate content (called generator) and the other one evaluates the generated content (called discriminator).

Basically, the aim is to pit two neural networks against each other to produce results that mirror real data. GAN-based models have been mostly used for image-generation tasks.

GAN (Generative Adversarial Network) / Source: Google

Next up, we have the Variational Autoencoder (VAE), which involves the process of encoding, learning, decoding, and generating content. For example, if you have an image of a dog, it describes the scene like color, size, ears, and more, and then learns what kind of characteristics a dog has. After that, it recreates a rough image using key points giving a simplified image. Finally, it generates the final image after adding more variety and nuances.

What Is a Generative Pretrained Transformer (GPT) Model

Google subsequently released the BERT model (Bidirectional Encoder Representations from Transformers) in 2023 implementing the Transformer architecture. At the same time, OpenAI released its first GPT-1 model based on the Transformer architecture.

Source: Marxav /

So what was the key ingredient in the Transformer architecture that made it a favorite for Generative AI? As the paper is rightly titled, it introduced self-attention, which was missing in earlier neural network architectures. What this means is that it basically predicts the next word in a sentence using a method called Transformer. It pays close attention to neighboring words to understand the context and establish a relationship between words.

Through this process, the Transformer develops a reasonable understanding of the language and uses this knowledge to predict the next word reliably. This whole process is called the Attention mechanism. That said, keep in mind that LLMs are contemptuously called Stochastic Parrots (Bender, Gebru, et al., 2023) because the model is simply mimicking random words based on probabilistic decisions and patterns it has learned. It does not determine the next word based on logic and does not have any genuine understanding of the text.

How Google and OpenAI Approach Generative AI?

Both Google and OpenAI are using Transformer-based models in Google Bard and ChatGPT, respectively. However, there are some key differences in the approach. Google’s latest PaLM 2 model uses a bidirectional encoder (self-attention mechanism and a feed-forward neural network), which means it weighs in all surrounding words. It essentially tries to understand the context of the sentence and then generates all words at once. Google’s approach is to essentially predict the missing words in a given context.

Google Bard

In contrast, OpenAI’s ChatGPT leverages the Transformer architecture to predict the next word in a sequence – from left to right. It’s a unidirectional model designed to generate coherent sentences. It continues the prediction until it has generated a complete sentence or a paragraph. Perhaps, that’s the reason Google Bard is able to generate texts much faster than ChatGPT. Nevertheless, both models rely on the Transformer architecture at their core to offer Generative AI frontends.

Applications of Generative AI

We all know that Generative AI has a huge application not just for text, but also for images, videos, audio generation, and much more. AI chatbots like ChatGPT, Google Bard, Bing Chat, etc. leverage Generative AI. It can also be used for autocomplete, text summarization, virtual assistant, translation, etc. To generate music, we have seen examples like Google MusicLM and recently Meta released MusicGen for music generation.


Apart from that, from DALL-E 2 to Stable Diffusion, all use Generative AI to create realistic images from text descriptions. In video generation too, Runway’s Gen-1, StyleGAN 2, and BigGAN models rely on Generative Adversarial Networks to generate lifelike videos. Further, Generative AI has applications in 3D model generations and some of the popular models are DeepFashion and ShapeNet.

Limitations of Generative AI

While Generative AI has immense capabilities, it’s not without any failings. First off, it requires a large corpus of data to train a model. For many small startups, high-quality data might not be readily available. We have already seen companies such as Reddit, Stack Overflow, and Twitter closing access to their data or charging high fees for the access. Recently, The Internet Archive reported that its website had become inaccessible for an hour because some AI startup started hammering its website for training data.

Apart from that, Generative AI models have also been heavily criticized for lack of control and bias. AI models trained on skewed data from the internet can overrepresent a section of the community. We have seen how AI photo generators mostly render images in lighter skin tones. Then, there is a huge issue of deepfake video and image generation using Generative AI models. As earlier stated, Generative AI models do not understand the meaning or impact of their words and usually mimic output based on the data it has been trained on.

It’s highly likely that despite best efforts and alignment, misinformation, deepfake generation, jailbreaking, and sophisticated phishing attempts using its persuasive natural language capability, companies will have a hard time taming Generative AI’s limitations.

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