Artificial intelligence (AI) is transforming the world of business, offering unprecedented opportunities for innovation, efficiency, and competitiveness. However, AI also poses significant challenges and risks, such as ethical dilemmas, social impacts, and legal implications. Therefore, it is essential for businesses to adopt a responsible and ethical approach to AI development and deployment, balancing profitability and social responsibility.

In this blog post, we have outlined some of the key ethical considerations and best practices for implementing ethical AI in business. We have also provided some practical tips and examples on how to apply ethical AI principles in your own business context.

What are ethical considerations in AI?

Ethical considerations in AI refer to the moral and societal implications of creating and using AI systems. They involve evaluating the potential benefits and harms of AI for various stakeholders, such as customers, employees, partners, competitors, regulators, and society at large.

Some of the key ethical considerations in AI include:

• Bias: AI systems can perpetuate and even amplify biases present in the data used to train them, resulting in unfair or discriminatory outcomes for certain groups or individuals. For example, an AI system that evaluates job applicants based on their resumes may favour candidates from certain backgrounds or genders over others.
  
  
  
  
  
• Privacy: AI can collect and analyse vast amounts of personal data, raising concerns about privacy and data protection. For example, an AI system that tracks customer behaviour online may expose sensitive information or preferences that customers may not want to share or use for targeted advertising or marketing.
  
  
  
  
  
• Transparency: AI systems can be complex and opaque, making it difficult to understand how they work or why they make certain decisions. For example, an AI system that recommends products or services to customers may not disclose the criteria or logic behind its recommendations or how it uses customer data.
  
  
  
  
  
• Accountability: AI systems can have significant impacts on people’s lives and livelihoods, making it important to assign responsibility and liability for their actions and outcomes. For example, an AI system that drives a car may cause an accident or injury due to a malfunction or error.
  
  
  
  
  
• Human-Centricity: AI systems should be designed to augment human capabilities and enhance societal well-being, rather than replace or harm humans. For example, an AI system that assists a doctor in diagnosing a patient should respect the doctor’s expertise and autonomy and support the patient’s dignity and consent.

Now it’s time to take a more microscopic approach towards ethical AI aspects. Let’s move on.

Fairness and Bias in AI

Approximately 40 percent of employees have encountered ethical issues related to AI use. Research Institute Capgemini defines ethical issues related to AI as interactions that result in unaccountable, unfair, or biased outcomes. 

Fairness in AI is about ensuring that the AI system provides equal opportunities to all individuals, regardless of their background or characteristics. Bias, on the other hand, refers to the tendency of an AI system to favour certain groups over others. Bias can creep into AI systems through various means, including biassed training data, biassed algorithms, or biassed interpretation of results.

Consider a hiring algorithm that is trained on a dataset where most successful candidates are male. The algorithm might learn to associate success with being male and unfairly disadvantage female candidates. To mitigate such biases, we can use techniques like bias correction and fairness-aware machine learning.

Bias correction involves modifying the training data or the learning algorithm to reduce bias. For instance, we can oversample underrepresented groups in the training data or apply regularisation techniques to prevent the learning algorithm from relying too heavily on certain features.

Fairness-aware machine learning, on the other hand, incorporates fairness constraints into the learning process. For example, we can modify the loss function of the learning algorithm to penalise unfair predictions.

Here’s a Python code snippet demonstrating how to use the fairlearn library to assess and mitigate bias in a machine learning model:

This code trains a logistic regression model with a fairness constraint that ensures demographic parity. The ExponentiatedGradient class implements a reduction approach to fair classification where a classifier is learned that optimises accuracy subject to fairness constraints.

Privacy and Security in AI

Privacy in AI refers to protecting individuals’ personal information from unauthorised access or disclosure. Security in AI involves protecting AI systems from attacks that could compromise their integrity or availability.

One of the biggest privacy concerns in AI is data privacy. With businesses collecting vast amounts of data to train their AI models, it’s crucial to implement measures that protect this data from unauthorised access and ensure that individuals’ privacy is respected.

Differential privacy is one such measure. It adds noise to the output of a function to protect an individual’s information. Here’s a Python code snippet using the diffprivlib library to train a differentially private logistic regression model:

This code trains a logistic regression model while ensuring differential privacy. The epsilon parameter controls the amount of noise added – smaller values provide more privacy but may reduce the accuracy of the model.

Security in AI involves protecting AI systems from attacks that could compromise their integrity or availability. One type of attack that has gained attention recently is adversarial attacks, where small perturbations are added to the input data to mislead the AI system.

Adversarial training is a technique used to make AI models more robust against such attacks. It involves training the model on adversarial examples along with the original data. Here’s a Python code snippet using the cleverhans library for adversarial training:

This code generates adversarial examples using the Fast Gradient Sign Method (FGSM) and then uses these examples for training. The epsilon parameter controls the magnitude of perturbations added – larger values produce more noticeable perturbations but may make the attack more successful.

In conclusion, as businesses continue to leverage AI for various applications, it’s crucial that they do so responsibly by considering these ethical aspects – fairness and bias, privacy and security – in their implementations. By doing so, they can not only ensure compliance with regulations but also build trust with their users and contribute positively to society.

How to implement ethical AI in business?

Implementing ethical AI in business requires a holistic approach that integrates ethics into every stage of the AI development and deployment process, from planning and design to testing and monitoring. It also requires a collaborative effort that involves various stakeholders, such as developers, users, managers, customers, partners, regulators, and society at large.

Here are some of the best practices and tips for implementing ethical AI in business:

1) Foster a culture

The first step to implementing ethical AI in business is to foster a culture and mindset of ethical AI among all the stakeholders involved in the AI development and deployment process. This means:

• Raising awareness and education on the ethical implications and challenges of AI, as well as the ethical principles and guidelines that apply to AI use.
  
  
• Encouraging dialogue and debate on the ethical dilemmas and trade-offs that may arise when using AI, as well as the potential solutions and alternatives that may be available.
  
  
• Promoting ethical decision-making and behavior when using AI, such as following ethical codes of conduct, adhering to ethical standards and best practices, and reporting or addressing any ethical issues or concerns that may emerge.
  
  
• Rewarding and recognizing ethical AI performance and outcomes, such as acknowledging and celebrating ethical AI achievements, providing feedback and incentives for ethical AI improvement, and holding accountable and correcting unethical AI actions or results.

2) Define

The second step to implementing ethical AI in business is to define and align your ethical AI vision and goals with your business strategy and values. This means:

• Establishing a clear and compelling vision of what ethical AI means for your business, such as how it supports your mission, vision, values, and purpose, as well as how it benefits your customers, employees, partners, competitors, regulators, and society at large.
  
  
• Setting specific and measurable goals for your ethical AI initiatives, such as what you want to achieve, how you want to achieve it, when you want to achieve it, and how you will measure your progress and success.
  
  
• Aligning your ethical AI vision and goals with your business strategy and values, such as ensuring that they are consistent with your core competencies, competitive advantages, market opportunities, customer needs and expectations, stakeholder interests, and social responsibilities.

3) Assess

The third step to implementing ethical AI in business is to assess and mitigate the ethical risks and impacts of your AI solutions throughout their entire lifecycle. This means:

• Conducting an ethical risk assessment of your AI solutions before, during, and after their development and deployment, such as identifying the potential sources, types, and levels of ethical risks, as well as the potential beneficiaries, victims, and affected parties of your AI solutions.
  
  
• Implementing an ethical risk mitigation plan for your AI solutions before, during, and after their development and deployment, such as applying appropriate methods, tools, and techniques to prevent, reduce, or manage the ethical risks, as well as providing adequate safeguards, remedies, or compensations for the ethical harms or losses that may occur.
  
  
• Monitoring and evaluating the ethical performance and outcomes of your AI solutions before, during, and after their development and deployment, such as collecting and analysing data and feedback on the actual or perceived ethical impacts of your AI solutions, as well as reviewing and improving your ethical risk assessment and mitigation plan accordingly.

4) Design

Design and develop your AI solutions with ethics in mind from the start. This means:

• Applying a human-centric approach to your AI solutions, such as ensuring that they are aligned with human values, rights, and norms, as well as enhancing human capabilities and well-being, rather than replacing or harming humans.
  
  
• Applying a user-centric approach to your AI solutions, such as ensuring that they are relevant, effective, and sustainable, meeting user needs and expectations, solving user problems, and creating user value.
  
  
• Applying a data-centric approach to your AI solutions, such as ensuring that the data used to train, test, and run your AI solutions are accurate, complete, representative, diverse, and unbiased, as well as respecting the data privacy and security of the data owners and subjects.
  
  
• Applying a quality-centric approach to your AI solutions, such as ensuring that they are reliable, robust, safe, secure, and scalable, as well as testing and validating their functionality, performance, and accuracy.

5) Communicate

Explain your AI solutions with transparency and clarity to all the stakeholders involved or affected by them. This means:

• Disclosing the nature, purpose, and scope of your AI solutions, such as what they are, what they do, how they do it, why they do it, where they do it, when they do it, and who they do it for or with.
  
  
• Disclosing the data sources, methods, and techniques used to create, train, test, and run your AI solutions, such as what data are used, how they are collected, processed, and analysed, what algorithms are used, how they are selected, designed, and optimized, and what metrics are used to measure their performance and accuracy.
  
  
• Disclosing the criteria, logic, and rationale behind the decisions and actions of your AI solutions, such as how they make decisions or recommendations, why they make certain decisions or recommendations over others, what factors or variables influence their decisions or recommendations, and what assumptions or limitations underlie their decisions or recommendations.
  
  
• Disclosing the risks, uncertainties, and limitations of your AI solutions, such as what potential errors or failures may occur, how likely or frequent they are, what are the possible consequences or impacts of them, and how they can be prevented or resolved.

6) Engage

Engage and collaborate with diverse and inclusive stakeholders throughout the AI development and deployment process. This means:

• Identifying and involving the relevant stakeholders for your AI solutions, such as customers, employees, partners, competitors, regulators, and society at large, as well as ensuring that they represent a variety of perspectives, backgrounds, experiences, and interests.
  
  
• Soliciting and incorporating feedback and input from the stakeholders for your AI solutions, such as asking for their opinions, preferences, expectations, concerns, or suggestions, as well as listening to their needs, problems, or values.
  
  
• Empowering and enabling the stakeholders for your AI solutions, such as providing them with the necessary information, education, training, tools, or resources to understand, use, benefit from, or control your AI solutions, as well as respecting their autonomy, agency, and consent.
  
  
• Co-creating and co-delivering value with the stakeholders for your AI solutions, such as working together to design, develop, test, deploy, monitor, evaluate, or improve your AI solutions, as well as sharing the benefits, costs, or risks of your AI solutions.

Conclusion

Ethical AI is not only a moral obligation but also a strategic imperative for businesses. By adopting a responsible and ethical approach to AI development and deployment, businesses can build trust and loyalty with customers, enhance reputation and brand image, reduce risks and costs, and innovate and grow.

We hope that this blog post has helped you gain a better understanding of how to leverage AI for good, while avoiding potential pitfalls and harms.

With the ever-increasing pace of the digital landscape, UAE businesses are actively seeking ways to expedite their time-to-market and deliver innovative solutions. One approach that has gained significant traction is Agile Software Development, a dynamic and collaborative methodology that enables organizations to accelerate their time-to-market while maintaining flexibility and adaptability. By embracing Agile Software Development Services, UAE businesses can navigate the rapidly evolving technology landscape, respond swiftly to changing customer demands, and achieve a competitive edge in their respective industries.

In this blog, we will delve deeper into the benefits of Agile Software Development Services for UAE businesses. We will explore how Agile methodologies enable organizations to adapt to market dynamics swiftly, enhance customer satisfaction, foster innovation, and streamline product development cycles. Join us as we uncover the key advantages of Agile and its transformative impact on accelerating time-to-market, helping businesses in the UAE thrive in the digital era.

Introduction to Agile Software Development

In today’s fast-paced digital landscape, businesses in the UAE are continually seeking ways to deliver high-quality software solutions more efficiently. Agile software development has emerged as a popular approach that enables organizations to adapt and respond swiftly to changing requirements and market demands. In this section, we will explore what the agile methodology entails, the benefits it offers for software development, and its core principles and values.

Agile methodology is an iterative and collaborative approach to software development that emphasizes adaptability, flexibility, and customer satisfaction. Unlike traditional waterfall methods, which follow a linear sequence of phases, agile methodology promotes an incremental and iterative development process. It emphasizes close collaboration between cross-functional teams, frequent feedback, and continuous improvement throughout the software development life cycle.

Benefits of Agile for Software Development:

• Accelerated Time-to-Market: Agile enables shorter release cycles and faster delivery, helping UAE businesses get their software solutions to market quickly.
• Enhanced Flexibility: Agile allows for changing requirements, ensuring solutions align with evolving customer needs and market trends.
• Increased Customer Satisfaction: Involving customers throughout the process improves alignment and satisfaction.
• Improved Product Quality: Regular feedback and testing in agile development lead to early defect detection and higher-quality software.
• Better Risk Management: Agile promotes transparency, visibility, and early risk identification, enabling proactive mitigation.
• Efficient Resource Allocation: Collaborative teams optimize resource utilization, improving productivity and cost-effectiveness.

Agile Principles and Values:

1. Individuals and Interactions over Processes and Tools: Prioritizing effective communication and collaboration within teams.
2. Working Software over Comprehensive Documentation: Emphasizing functional software delivery over excessive documentation.
3. Customer Collaboration over Contract Negotiation: Close collaboration with customers to understand and incorporate their feedback.
4. Responding to Change over Following a Plan: Embracing change and adapting plans to meet evolving requirements.

An Agile software development company empowers UAE businesses to accelerate time-to-market, adapt to change, and deliver high-quality solutions. By embracing the agile methodology’s principles and values, organizations can foster collaboration, customer-centricity, and innovation, leading to growth and success in the dynamic UAE market.

Advantages of Agile for UAE Businesses:

Agile software development services offer numerous advantages for UAE businesses, enabling them to accelerate their time-to-market and stay competitive in a dynamic market. Here are some key benefits of adopting Agile methodologies:

1. Enhancing adaptability and flexibility in a dynamic market:
2. Agile approaches allow businesses to quickly respond and adapt to changes in customer demands, market trends, or project requirements.
3. Regular iterations and incremental development enable businesses to incorporate feedback and make adjustments throughout the development process, ensuring the final product aligns with evolving market needs.
4. Reducing time and cost overruns through iterative development:
5. Agile practices emphasize iterative development cycles, where software is developed in small increments, tested, and refined. This approach minimizes the risk of major project delays or cost overruns.
6. Early and continuous testing in Agile helps identify and address issues promptly, avoiding expensive rework and ensuring efficient use of resources.
7. Increasing customer satisfaction and stakeholder collaboration:
8. Agile methodologies prioritize customer satisfaction by involving stakeholders throughout the development process.
9. Regular feedback loops and constant communication enable businesses to gather insights from customers, align expectations, and deliver solutions that meet their needs.
10. Collaborative teamwork in Agile promotes transparency, trust, and shared accountability, fostering stronger relationships between businesses and their stakeholders.

Agile software development services empower UAE businesses to boost adaptability, minimize time and cost overruns, and enhance customer satisfaction. Through iterative development and stakeholder collaboration, Agile methodologies offer the tools needed to expedite time-to-market and maintain a competitive edge. Embracing Agile practices becomes a strategic advantage in today’s dynamic and fiercely competitive business landscape.

Implementing Agile in UAE Businesses:

To successfully implement agile software development practices in UAE businesses, it is important to follow key steps and address challenges that may arise. Here are some crucial considerations:

Key Steps to Adopting Agile Software Development Practices:

• Educate stakeholders: Create awareness about the benefits of agile methodologies and how they align with business objectives.
• Form cross-functional teams: Build teams comprising members from different disciplines to encourage collaboration and diverse perspectives.
• Prioritize iterative delivery: Break projects into manageable increments for frequent feedback and value-driven releases.
• Foster continuous improvement: Encourage a culture of learning, adaptability, and continuous refinement.

Overcoming Challenges and Resistance to Change:

• Promote transparency: Communicate the benefits of agile to all stakeholders and address concerns or misconceptions.
• Provide training and support: Offer comprehensive training programs and mentorship to equip employees with agile skills.
• Encourage collaboration: Foster a collaborative environment where individuals feel empowered to contribute and share ideas.
• Celebrate successes: Recognize and celebrate achievements to motivate teams and reinforce the positive impact of agile practices.

By implementing these steps and addressing challenges head-on, businesses in UAE can unlock the full potential of agile software development, achieving faster time-to-market and increased customer satisfaction.

Accelerating Time-to-Market with Agile Practices

In the rapidly evolving business landscape of the UAE, Agile software development has become a catalyst for accelerating time-to-market. By embracing Agile practices, UAE businesses can streamline their development processes, foster collaboration, and deliver high-quality software solutions with exceptional efficiency. Efficient project planning and execution, along with leveraging customer feedback and iteration cycles for faster releases, are two critical aspects that contribute to expediting the time-to-market.

Agile techniques for efficient project planning and execution:

• User Stories and Backlog Prioritization: Breaking down requirements into user stories and prioritizing them based on business value.
• Sprint Planning and Estimation: Collaboratively defining achievable sprint goals and estimating efforts for each user story.
• Daily Stand-ups: Regular team meetings to discuss progress, identify and remove obstacles, and align efforts.
• Continuous Integration and Delivery: Employing automation to continuously integrate and deliver new features, ensuring a steady flow of updates.

Leveraging customer feedback and iteration cycles for faster releases:

• Iterative Development: Delivering software in small increments, allowing for faster feedback and course correction.
• User Acceptance Testing (UAT): Involving end-users in the testing process to validate functionality and gather valuable insights.
• Retrospectives and Continuous Improvement: Reflecting on each sprint to identify areas of improvement and implement changes for subsequent iterations.

By embracing agile methodologies tailored to software development UAE, businesses can respond swiftly to market demands, reduce time-consuming rework, and drive customer-centric innovation, ultimately accelerating their time-to-market.

Agile software development services have proven to be a game-changer for UAE businesses in accelerating their time-to-market. By adopting Agile methodologies, companies can respond swiftly to market demands, adapt to changing requirements, and deliver high-quality software solutions in shorter development cycles. This approach promotes collaboration, transparency, and continuous improvement, ensuring that businesses stay competitive and meet customer expectations effectively.

Agile testing, despite all its benefits, still is quite challenging for testers. The software testing Dubai industry have only recently adopted agile testing methodologies, and their efforts for each project begin with devising strategies to overcome challenges should any arise.

An experienced software development company in Dubai wouldn’t try to tackle these challenges head on, especially if they follow agile practices. An agile ecosystem enhances the role of QA in projects but requires them to have a solid understanding of agile development methodologies and the challenges lying dormant in it.
 

Agile Testing Challenges

 
The core functionality of agile development is to deliver working software frequently, as iterations. Each iteration will have added features and enhancements compared to the preceding ones. These enhancements should be of value to the client. And this is where challenges begin for developers and the QA team.
 

1. Last minute changes or change in requirements

Changing requirements mid-sprint is quite common in an agile environment. As a matter of fact, agile is designed to handle such scenarios without much risks. However, this can still become problematic for developers and testers who have already started working on the enhancements for the next iteration. They would sometimes have to scrap the half-baked iteration completely, affecting the scope of the project or making it harder to meet deadlines.

Solution

In such a scenario, what the testers can do is to document every little detail about the tests that have been run, and which parts of the application haven’t been tested. This would help the project team and the manager make an informed decision on the features that can be added while considering the risks.

2. When the client/product owner is unable to define a feature he/she wants

Quite often, the client or the product owner might get an idea to add a specific feature to the product during development. Not all of them might be able to detail a good set of acceptance criteria so the developers can understand the feature and the context it applies to. Still, they may provide a vague description to the developers, subsequently asking them to build a prototype to get more ideas about the feature and its behavior in the product.

This makes it particularly challenging for the testers as they won’t have an understanding of the feature. It’ll be difficult for them to construct test cases.

Solution

The testing lead would be having the experience to start testing without detailed requirements by considering advanced scenarios so as to test the basic concept. These high level test scenarios can still maintain the context even if there are changes in the details. Over time, they can communicate with the client after submitting test results. Based on the results, it would be easier to get an overview of how the client want the feature to behave.

3. Giving early feedback to developers

Testing is more of an activity than a phase in an agile environment i.e. testers will have to begin either from the beginning of the development or a bit earlier before the development starts. The testers should make sure that any backlogs are elaborated during the grooming sessions. This requires proper collaboration with the client to gain insights so as to help develop the appropriate acceptance criteria.

This also means testers should provide early feedback to developers. It can be challenging due to the fact that testers are required to ensure two things.

• The new feature works as specified in the acceptance criteria
• The new code hasn’t affected existing functionalities in any way

Solution

Ensure that each story discussed during the grooming sessions has adequate acceptance criteria. The whole QA team has to understand the context of a story (required addition of features and enhancements) before the development itself begins.

It’s a wise approach to create tests, both manual and automated, beforehand so that when the feature is added, testing can begin right away. Developers can be asked to make the development transparent by continuous deployment to testing environments so that testers won’t have to wait their turn. This also gives testers an insight on what the team is trying to achieve.

4. Ensuring functionality across multiple browsers and devices

This mostly pertains to website testing. Websites today generally tend to have a front-end and back-end. The front-end would mostly be based on CSS and JavaScript and both could behave differently depending on the browser or the device.

One of the biggest challenges testers will have to overcome is when they are required to ensure that the website being developed functions as per expectations across all major web browsers and mobile devices.

Solution

Manually doing this would take significant effort and time. This is where automation comes to play. Automated tests can be run in parallel on multiple servers using tools like Selenium Grid. Combining it with an agile-friendly container service like Docker would make things far easier for testers.

Conclusion

 
Though Agile considerably reduces risks and challenges, it’s mostly never easy for testers as their role in the ecosystem is as important as a developer’s. But there are workarounds to almost all of these challenges. However, it requires strategic planning and dedicated efforts. If the testers lack the technical skills, things can get even more difficult. In such cases, outsourcing to a reliable IT solutions company would be a better idea.

New business and tech trends keep coming out every year. But not many project management trends go noticed. However, there are a few exceptions. They start by creating a buzz, and some of them eventually become an accepted standard. As proper project management is vital when it comes to software development, every manager should be aware of those trends that show great potential.

That said, here are a few project management trends that came out on top this year, and will possibly have a much more prominent role next year.
 

Agile will cross over to other fields

 
Software development companies have been practicing Agile development for a long time now, and it’s become quite common nowadays. Apparently, agile is steadily climbing the ladder in parallel. It’s expected that Agile will soon cross over to other fields.

Even marketers use Agile in innovative ways. In addition, UI/UX professionals and graphics designers also use Agile to deliver without compromising quality. It’s particularly noticeable in the finance sector. Finance professionals often use Scrum, and editors use Kanban systems to organize content.

Evidently, Agile methodology is not restricted to the IT domain anymore.
 

Collaboration tools will be adopted more

 
Collaboration tools are vital for coordination between the teams involved in a project and the customer, especially in an Agile ecosystem. Using such tools considerably reduces the delay in projects and ensure proper communication among everyone involved. They can be particularly useful for conferences with customers, and between the teams to achieve a goal specific to the project at hand.

2018 would be seeing more use of collaboration software apps over traditional project management methodologies i.e. apps like Slack and Yammer will grow beyond where they are now. However, collaboration tools are not necessarily designed for project management. Yet they’ve found their place among various businesses.

As the project managers are also responsible for ensuring collaboration for each project, they will have to find ways to effectively adopt collaboration tools in the coming years considering the fact that companies have started showing more interest in such tools.
 

Project portfolio management is advancing

 
Project portfolio management (PPM) software is now a key component when it comes to creating enterprise products. This new product development (NPD) trend resulted in the creation of brands including GenSight, Sopheon etc. all with the use of PPM solutions. The close association between PPM and NPD will only become tighter with the rise of IoT and widespread adoption of Agile.

The present day developments require developers to come up with improved products for their customers, and this can’t be done with conventional PPM solutions. The NPD PPM will most certainly make a mark here, and will likely establish its presence on other fields as well.
 

Certifications may lose their importance

 
PMP and PRINCE2 are two of the most popular project management certifications, and both have been existing for years. But recently, the certifications have been criticized and their values questioned.

Many seem to argue that the accompanying cost is not worth it, adding that the exam content have also not been updated even though business requirements keep changing. Companies now value the skill of managing projects more than certifications. SMBs have started giving less attention to certifications and more to find people who can manage projects efficiently. This could likely mean that certifications may lose their importance, and PMPs won’t be required to have the certifications to prove their worth.
 

Big data is changing things for the better

 
One of the most popular technology trends that’s impacting project management is big data. Essentially comprising of all the data generated by software tools, big data analysis can reveal valuable insights including but not limited to logged risks, the failure rate of the team, and the members who are less productive.

However, to utilize big data for the betterment of the team and to improve project management, the manager will have to make use of effective analysis tools like for example Apache Hadoop, Skytree Server etc. This part of the problem is already being taken care of, which is why we will be provided with better ways to leverage big data next year.
 

Conclusion

 
This article only lists a few of the popular trends that announced their presence this year. The bottom line is that project management is all set to undergo a radical transformation in the coming times, and the best bet is to prepare for it beforehand and work with the best tools 2018 will present us with.

Prevalent project failure is quite common in the project management industry, and the causes of failure are many – from overly optimistic deadlines to poor management. There are things that a project manager can and cannot control. Outside factors might influence the success of a software development project, and the manager may not be able to do much about that. However, it’s possible to mitigate the effects of such factors on the project.

Basically, apt project management can give the project the best chance for success. And apt project management requires streamlining the management processes. This is where proper project management methodology helps. It essentially makes it easier for project managers to design and implement their projects.

Before understanding how and where a project management methodology should be used, the managers need to be aware of two things.

• Each methodology has their own pros and cons.
• There is no one-size-fits-all. One of the biggest mistakes a project manager can make is to apply the same methodology to every project.

This is why it’s a wise idea to always assess the benefits of a particular methodology against the project’s objectives.

Here are 5 of the most popular project management methodologies that are flexible enough to adapt to projects and the project team.
 

Waterfall Model

 
A traditional project management methodology, this model is about a structure that follows things in a sequential manner, somewhat resembling a waterfall. The ‘flow’ begins with the concepts and inception of the project to its completion. There is a catch though. Once set at the get-go, the project requirements bear no alteration in most cases.

The model is ideal for large software projects when predictability and planning are vital for the projects’ success.
 

Agile Management Methodology

 
You could say that Agile is basically the opposite of Waterfall due to the fact that Agile is all about adaptability and flexibility while waterfall is sequential and predictable. Agile adapts to the feedback from the project team members or from the client, and follows the best course of action for the project’s success.

As such, Agile development is therefore the most effective when the project’s progress relies on input from clients or management. This flexibility makes it a great option for fast-paced project development.
 

PRiSM

 
A sustainable methodology that incorporates a value-maximization model, PRiSM acts as an efficient project structure that considers even environmental factors with a focus on total asset lifecycle. However, it’s unique because of the fact that it demands accreditations. The methodology is mostly used for large scale projects within real estate or construction.
 

Critical Chain Project Management

 
The main merit of this methodology is its focus on rectifying resources, by interpreting a project as a core set of elements so as to create a project timeline. The next phase is ensuring that the critical chain has enough resources within the timeline. In addition, the remaining resources are split up and allocated to various other tasks enabling them to be executed in parallel. The model also keeps enough resources in reserve in case reallocation is necessary.

These should be enough to assert that critical chain project management is the right choice for resource-driven projects.
 

PRINCE2

 
Advertised as a government-endorsed project management methodology, PRINCE2 is widely popular across both public and private sector in the UK. Like PRiSM, PRINCE2 also requires certification, but offers a plethora of courses in return that boosts the project team’s experience while providing the required organization for projects.

PRINCE2 is quite sophisticated and requires the team to thoroughly follow the determined plan and processes involved for the project to be successful. Though process-oriented, the methodology has a broad approach that can help build strong project management skills which essentially makes it useful for almost all kinds of projects of different scales.