Challenges in Resource and Project Management

The main pain point for many technology companies lies in resource allocation: who is working on what and when. This lack of visibility and control can lead to underutilization or overallocation of personnel, affecting team morale and project delivery. Currently, many companies try to manage this using spreadsheets like Excel, but this manual solution is prone to errors and does not scale well with business growth.

Additionally, time tracking and project profitability are aspects that are often overlooked or inefficiently managed. Without an appropriate tool, it is almost impossible to obtain a clear view of the profitability of individual projects or team efficiency.

Some of the main challenges faced by technology companies include:

• Lack of visibility → Impact: There is no precise knowledge of the team’s workload to manage resources and projects efficiently. Time loss in generating and sharing reports for clients.
• Profitability calculation per project: are estimates → Impact: Loss of money, lack of knowledge about which services are best to offer. Lack of knowledge about which projects are more or less profitable.

• Inefficient communication between teams and stakeholders, and expectations of quick delivery → Impact: can lead to misunderstandings, scope changes, lower service quality, potential customer loss.
• Overload → Impact: turnover and its consequences.

Automation and Optimization through Management Tools

The implementation of an appropriate project and resource management tool can fundamentally transform the way a technology company operates. These tools not only automate operational and repetitive tasks but also provide clear visibility of resource allocation, project progress, and profitability. The result is significant time savings, which helps the team focus on their tasks and ensures workflow through automation.

Combining centralized tools in a single platform can provide a holistic view of company performance, allowing leaders to make informed decisions based on real-time data. This data generates greater confidence as the information available is always up-to-date. This visibility improves team performance with real-time visual data.

Artificial intelligence incorporated into management tools helps reduce manual work and accelerate productivity.

“A single source of information for everything: teams linked across the organization on a single centralized work platform.”

Having all the information in one place allows for a better understanding of the work that needs to be done. It provides ease and agility at the collaboration level. An integrated platform like COR provides IT teams with the visibility needed to deliver quality service, regardless of where they are located. It allows team members to collaborate, share knowledge, and receive instant feedback.

Teams can quickly provide excellent service experiences

Companies working with agile software development methodologies seek to deliver small working pieces of software in a short time to increase customer satisfaction. These methodologies use flexible approaches and teamwork to deliver continuous improvements.

Transparent interaction, teamwork, flexibility, and mutual trust are fundamental in the practice of agile methodology. Although it is generally the project leader or product owner who sets the delivery priorities, it is the team that takes the lead in execution, deciding how to organize and break down the work into specific tasks and responsibilities independently.

Teams that adopt the agile methodology are equipped to quickly adapt to market variations or consumer opinions without compromising the complete annual planning. However, in this approach, numbers are not the central element; people are the priority. Establishing relationships with customers and colleagues takes precedence over following strict protocols, and delivering solutions that effectively meet customer needs is more crucial than producing extensive and detailed documentation. An agile team maintains a unified vision and works together to materialize it in the best way they consider. They set their own standards of quality, usability, and integrity. Their own interpretation of “completed” guides the speed at which they progress in their tasks. Although this approach may seem daunting at first, business leaders find that placing trust in an agile team results in greater dedication and an effort to meet or even exceed management expectations.

Complete visibility across the organization allows for better management criteria and brings together teams and tools for coordination that leads to remarkable results.

A management tool provides a centralized, flexible, and collaborative way to deliver the exceptional service that customers expect. Through efficient management, teams can provide exceptional operational and support services.

Success Case: Integration and Analysis

A successful strategy is to move resource management out of Excel and use dedicated tools that integrate with project tracking systems like COR. This allows technology companies to move from manual planning and tracking to a more automated and data-driven approach.

Automating data collection and analysis allows sales and marketing teams to optimize their tasks, focusing on value-generating activities such as prospecting and building customer relationships, rather than data management. Additionally, real-time visibility of schedules and team workload improves resource allocation, leading to better project planning and execution. It also helps teams capture and prioritize projects so they can align their roadmaps and focus on the highest impact opportunities.

The leader can prioritize and analyze the team’s work in context and with visibility at all levels. Developers can focus on their work while the company remains coordinated. From planning to progress visualization, backlog management, and error tracking, COR facilitates seamless collaboration throughout each project’s stages using agile best practices.

RESULTS:

• Increase in operational efficiency and optimization in resource allocation for IT projects.
• Improvement in collaboration and communication between technical and non-technical teams.
• Effective communication and a comprehensive view of project profitability.
• Integration of everything into one platform, avoiding manual work.

Profitability and Success Measurement

Finally, a comprehensive management tool allows for better measurement of profitability and effectiveness. By automatically tracking time and costs associated with each project, companies can quickly identify those that exceed budget or schedule.

Through platforms like COR, powerful analysis and reports are obtained. Valuable performance insights can be visualized, bottlenecks can be identified, and data-driven decisions can be made to continuously improve processes.

Conclusion

Technology companies face unique challenges that require innovative solutions. Adopting comprehensive management tools is not just an operational improvement but a strategic transformation that can lead to greater efficiency, profitability, and success in the current competitive market. By focusing on automation, visibility, and analysis, technology companies can overcome their current challenges and position themselves for future growth.

The post How management tools can solve current challenges in Tech Companies appeared first on COR.

The post Centralized and Intelligent Management for Tech Companies appeared first on COR.

Challenges in Resource and Project Management

The main pain point for many technology companies lies in resource allocation: who is working on what and when. This lack of visibility and control can lead to underutilization or overallocation of personnel, affecting team morale and project delivery. Currently, many companies try to manage this using spreadsheets like Excel, but this manual solution is prone to errors and does not scale well with business growth.

Additionally, time tracking and project profitability are aspects that are often overlooked or inefficiently managed. Without an appropriate tool, it is almost impossible to obtain a clear view of the profitability of individual projects or team efficiency.

Some of the main challenges faced by technology companies include:

• Lack of visibility → Impact: There is no precise knowledge of the team’s workload to manage resources and projects efficiently. Time loss in generating and sharing reports for clients.
• Profitability calculation per project: are estimates → Impact: Loss of money, lack of knowledge about which services are best to offer. Lack of knowledge about which projects are more or less profitable.

• Inefficient communication between teams and stakeholders, and expectations of quick delivery → Impact: can lead to misunderstandings, scope changes, lower service quality, potential customer loss.
• Overload → Impact: turnover and its consequences.

Automation and Optimization through Management Tools

The implementation of an appropriate project and resource management tool can fundamentally transform the way a technology company operates. These tools not only automate operational and repetitive tasks but also provide clear visibility of resource allocation, project progress, and profitability. The result is significant time savings, which helps the team focus on their tasks and ensures workflow through automation.

Combining centralized tools in a single platform can provide a holistic view of company performance, allowing leaders to make informed decisions based on real-time data. This data generates greater confidence as the information available is always up-to-date. This visibility improves team performance with real-time visual data.

Artificial intelligence incorporated into management tools helps reduce manual work and accelerate productivity.

“A single source of information for everything: teams linked across the organization on a single centralized work platform.”

Having all the information in one place allows for a better understanding of the work that needs to be done. It provides ease and agility at the collaboration level. An integrated platform like COR provides IT teams with the visibility needed to deliver quality service, regardless of where they are located. It allows team members to collaborate, share knowledge, and receive instant feedback.

Teams can quickly provide excellent service experiences

Companies working with agile software development methodologies seek to deliver small working pieces of software in a short time to increase customer satisfaction. These methodologies use flexible approaches and teamwork to deliver continuous improvements.

Transparent interaction, teamwork, flexibility, and mutual trust are fundamental in the practice of agile methodology. Although it is generally the project leader or product owner who sets the delivery priorities, it is the team that takes the lead in execution, deciding how to organize and break down the work into specific tasks and responsibilities independently.

Teams that adopt the agile methodology are equipped to quickly adapt to market variations or consumer opinions without compromising the complete annual planning. However, in this approach, numbers are not the central element; people are the priority. Establishing relationships with customers and colleagues takes precedence over following strict protocols, and delivering solutions that effectively meet customer needs is more crucial than producing extensive and detailed documentation. An agile team maintains a unified vision and works together to materialize it in the best way they consider. They set their own standards of quality, usability, and integrity. Their own interpretation of “completed” guides the speed at which they progress in their tasks. Although this approach may seem daunting at first, business leaders find that placing trust in an agile team results in greater dedication and an effort to meet or even exceed management expectations.

Complete visibility across the organization allows for better management criteria and brings together teams and tools for coordination that leads to remarkable results.

A management tool provides a centralized, flexible, and collaborative way to deliver the exceptional service that customers expect. Through efficient management, teams can provide exceptional operational and support services.

Success Case: Integration and Analysis

A successful strategy is to move resource management out of Excel and use dedicated tools that integrate with project tracking systems like COR. This allows technology companies to move from manual planning and tracking to a more automated and data-driven approach.

Automating data collection and analysis allows sales and marketing teams to optimize their tasks, focusing on value-generating activities such as prospecting and building customer relationships, rather than data management. Additionally, real-time visibility of schedules and team workload improves resource allocation, leading to better project planning and execution. It also helps teams capture and prioritize projects so they can align their roadmaps and focus on the highest impact opportunities.

The leader can prioritize and analyze the team’s work in context and with visibility at all levels. Developers can focus on their work while the company remains coordinated. From planning to progress visualization, backlog management, and error tracking, COR facilitates seamless collaboration throughout each project’s stages using agile best practices.

RESULTS:

• Increase in operational efficiency and optimization in resource allocation for IT projects.
• Improvement in collaboration and communication between technical and non-technical teams.
• Effective communication and a comprehensive view of project profitability.
• Integration of everything into one platform, avoiding manual work.

Profitability and Success Measurement

Finally, a comprehensive management tool allows for better measurement of profitability and effectiveness. By automatically tracking time and costs associated with each project, companies can quickly identify those that exceed budget or schedule.

Through platforms like COR, powerful analysis and reports are obtained. Valuable performance insights can be visualized, bottlenecks can be identified, and data-driven decisions can be made to continuously improve processes.

Conclusion

Technology companies face unique challenges that require innovative solutions. Adopting comprehensive management tools is not just an operational improvement but a strategic transformation that can lead to greater efficiency, profitability, and success in the current competitive market. By focusing on automation, visibility, and analysis, technology companies can overcome their current challenges and position themselves for future growth.

The post How management tools can solve current challenges in Tech Companies appeared first on COR.

The post Centralized and Intelligent Management for Tech Companies appeared first on COR.

The post Reinventing Efficiency in Tech Companies with COR: A Comprehensive Solution appeared first on COR.

With an increasing demand for their skills, becoming a software developer is a popular career choice and there are a wealth of books available on the subject. But with so many options out there, it can be hard knowing which books every programmer should read. In this post, we’ve compiled a definitive list of the top 10 books that every software developer should own.

But before we continue, you may be asking yourself: do programmers really need to read books? After all, it’s code, not words. So surely budding developers are better off coding away and practicing different languages, right?

To a certain extent, that’s true. Mastering different languages require lots of practice, not reading. And with a plethora of blog posts and video tutorials available online, it’s tempting to think that computer science books are a thing of the past. But the truth is they offer valuable lessons for software engineers: experience, expertise, and teachings from the most successful developers.

Programming books offer so much more than just explaining different languages. They teach you about the craft and the business: how to be a good programmer, manage clients and deliver successful projects. By reading books, coders learn different methods and tricks to solving complex problems that can help them save time in their day-to-day lives, as well as become more of an expert on the subject. 

So, here’s COR’s list of books every programmer should read at least once.

1. The Pragmatic Programmer: From Journeyman to Master by Andrew Hunt and David Thomas

Why is it a must-read book for learning to code?

The Pragmatic Programmer is a timeless classic that any newbie or experienced programmer can read multiple times and learn something new with each reading. First being published over 20 years ago, the teachings in this book remain relevant today despite advances in technology. This is one of the best books for learning to code as it focuses not just on the technical side of things, but also on the business and career side of computer programming. The authors are experienced programmers and use anecdotes and personal experiences to help explain their concepts.

What topics does it cover?

These are just some of the topics covered in The Pragmatic Programmer:

• Career growth and development as a software developer
• How to write code that codes itself
• How to automate your processes
• Testing and debugging
• The best tools to use
• How to take an idea and build it in code
• How to avoid duplicate knowledge
• How to build coding teams. 

2. Clean Code: A Handbook of Agile Software Craftsmanship by Robert C Martin (aka Uncle Bob)

Why is it a must-read book for learning to code?

Clean Code makes our pick of the top 10 books to learn programming thanks to the way effortlessly explains how to write good code. Programming legend Uncle Bob leans on his decades of experience to teach perfect software craftsmanship. He writes about a variety of different techniques he’s learned over the years and how you can apply them to your coding. 

The book is split into three main parts: principles and theories, case studies and learnings, and “smells” from the case studies. (A bad small means bad or flawed coding)

While this book focuses mainly on Java, the principles and ideas can easily be applied to other programming languages. This is Uncle Bob’s first book on coding and although written in 2009, the content largely remains relevant today.

What topics does it cover?

These are just some of the topics covered in Clean Code:

• How to write clean Java
• How to write clearer code
• Agile methodologies
• How to name variables
• The best way to format codes
• How to turn bad code into good code
• How to spot bad smells 

3. Head First Design Patterns: A Brain-Friendly Guide: Building Extensible and Maintainable Object-Oriented Software by Eric Freeman and Elisabeth Robman

Why is it a must-read book for learning to code?

Head First Design Patterns is essential reading if you’re learning to program. Its visually rich content makes it one of the best books to learn how to code: it’s full of diagrams, images, and illustrations that help you memorize different designs in less time. It’s also a fantastic way for programmers to learn from others’ experiences and choose patterns that have been tried and tested. 

What makes this classic book different from others is how it explains the reasoning behind different designs, and not just how to implement them. While it doesn’t cover all possible designs, it explains the most useful patterns for problem-solving – something that can be applied to other areas of programming. The latest edition is updated for Java 8.

What topics does it cover?

Here are just some of the points you’ll learn in Head First Design Patterns:

• How to solve software design problems
• How to create flexible and reusable software
• Object-orientated design principles
• How to design elegant code

4. The Clean Coder: A Code of Conduct for Professional Programmers by Robert C. Martin (Uncle Bob)

Why is it a must-read book for learning to code?

Uncle Bob’s The Clean Coder is the follow-up to his earlier Clean Code and is a great book for all those wanting to be better programmers. This makes our pick of the best coding books as it focuses on the business of coding and the soft skills necessary to succeed in the industry. In this book, you’ll learn how to quote for projects, estimate the time needed and manage tight deadlines. 

Uncle Bob uses his wealth of experience to teach the values all software developers should maintain, as well as rules and codes of conduct to follow. It also explains the best ways to manage your time and clients, and how to avoid burnout. This book is perfect for those who want to level up their careers.

What topics does it cover?

These are some of the things you’ll learn in The Clean Coder:

• Refactoring and testing
• How to be a professional programmer
• What bad coding means for time management
• How to deal with difficult clients
• How to learn new skills and grow

5. The Mythical Man-Month: Essays on Software Engineering by Frederick Brooks Jr.

Why is it a must-read book for learning to code?

The Mythical Man-Month is a top 10 book for learning to code for the way it tackles the issue of effective project management. Originally published in the 90s, Brooks’ book has been one of the most important and influential publications on the subject of software development. He uses his experiences to teach readers that large and complex programming projects are often affected by poor management. He also explains why it’s essential to maintain the integrity of the product throughout the design process. 

The author introduced the concept of Brook’s Law in The Mythical Man-Month, a project management theory. He claims that adding manpower to an already delayed project won’t speed it up, and even risks slowing it down. 

What topics does it cover?

• How to fix errors without creating more
• How to manage a software development project
• How labor should be organized
• The most common mistakes
• How to avoid these mistakes

6. Code Complete: A Practical Handbook of Software Construction by Steve McConnell 

Why is it a must-read book for learning to code?

Steve McConnell’s Code Complete is widely considered one of the most in-depth and detailed books on software development. And at a meaty 960 pages, you can believe it. In this book, Steve McConnell talks about programming from both an artistic and scientific point of view, exploring the whys, and not just the hows, behind coding methodologies. Full of real-world examples, the book provides readers with a practical guide to different programming languages.

For budding programmers, and experienced developers, this book is a must-read for the wealth of information it contains. Although lengthy, readers can dip in and out of its pages as challenges arise in their day to day.

What topics does it cover?

• Simple design concepts for maximum creative output
• How to refactor code
• How to solve common problems
• Producing quality code
• How to create a collaborative coding environment
• Perfect coding craftsmanship

7. Working Effectively with Legacy Code by Michael Feathers

Why is it a must-read book for learning to code?

This book is essential reading for programmers as it skillfully tackles one of the biggest challenges in software development: legacy code. For those newer developers who may not be aware of what legacy code is, here’s an explanation:

Legacy code is coding produced by other developers, using different methodologies, technologies, or programming languages that are no longer supported, or used on the current project. 

Despite the rapid technological and scientific advances of the past few decades, legacy code continues to be huge a challenge for big companies. In his book, Michael Feathers uses his years of coding experience to explain different techniques for refactoring old code without having to start from scratch. This book helps programmers work more efficiently with legacy code and contains a wealth of practical strategies. The book is based on Java, C++, and Csharp programming languages.

What topics does it cover?

Working Effectively with Legacy Code covers the following topics, and more:

• How to identify what legacy code to modify
• The process of refactoring code
• Strategies for working with untested code
• The theories behind software change
• How to test new code

8. Introduction to Algorithms by Thomas H Cormen

Why is it a must-read book for learning to code?

This is a good book for those wanting to understand more about one of the most trending topics in computer science: algorithms. Suitable for all different experience levels, Thomas Cormen’s Introduction to Algorithms is regarded as the most complete book on the subject matter. It covers a vast range of different algorithms, with simple explanations in pseudocode that make it easy to understand for all. Even though the author uses elementary-level examples to help the reader grasp the concepts, the book still manages to cover more complex mathematical reasoning behind algorithms. 

Introduction to Algorithms is full of exercises for readers to practice and is one of the most used textbooks in computer science studies.

What topics does it cover?

• Web trees
• Edge-based flow
• Dynamic programming
• Multithreaded algorithms
• Divide-and-Conquer (also known as recurrence)
• Probabilistic analysis
• Data structures 

9. Refactoring: Improving the Design of Existing Code by Martin Fowler

Why is it a must-read book for learning to code?

In this classic book, Martin Fowler skillfully guides the reader through the complex issue of refactoring. It helps developers understand the topic in greater depth, covering what refactoring is, why it’s important and how to implement it. Readers will learn how to make their coder easier to read, test, and maintain, as well as identify when refactoring a codebase may be necessary. 

Clear and concise, the book contains a wealth of step-by-step, real-world examples that programmers can follow to practice their refactoring skills. In each of his examples, the author describes the logic behind each one so readers can apply it to other problems that need solving. The book mainly uses Javascript examples, but the ideas and theories can easily be applied to other programming languages.

What topics does it cover?

•  How to identify “bad smells” in coding
• The correct structure for your code
• Correct testing for refactoring
• Quick application
• The best tools to use

10. Design Patterns: Elements of Reusable Object-oriented Software by John Vlissides, Erich Gamma, Richard Helm, and Ralph Johnson. 

Why is it a must-read book for learning to code?

Design Pattern’s comprehensiveness is the reason why it makes it into our top 10 books for learning to code. No less than 4 authors contributed to this publication, sharing their expertise and wealth of experience to help others master the concept of design patterns. The book starts with an in-depth explanation of what they are and how they can be applied to object-orientated software.

It then goes on to describe some of the most common design problems and different methodologies to solving them. The solutions proposed by the authors may not always be the most obvious, which gives the reader a different perspective on problem-solving. The teachings in this book can be applied to different programming languages, and help programmers maximize their time by using tried and tested theories. Developers can re-use the design patterns in this book and apply them to a wide range of problems, saving them the need to re-learn design concepts. It mainly uses Smalltalk and C++ programming languages. 

What topics does it cover?

• Adapting interfaces of one object to use on another
• How to produce more elegant and flexible software
• How to design object-orientated software

Conclusion

Our list of the top 10 books every programmer should read covers a variety of different subjects within the world of coding. From design patterns to clean coding, these books help budding software developers master their trade and become better programmers. They also help coders understand the craftsmanship behind good coding, as well as the business of software development and how to deliver successful projects. Reading books is essential for coders who want to grow professionally and take their careers to the next level.

The post Our Must Read: Top 10 Books for Developers and Programmers appeared first on COR.

React has a huge array of benefits for developers. Some of them are:

• It is easy to learn.
• It’s also easy to use with your project.
• It’s flexible.
• It has reusable components.
• It is high-performance.
• It’s flexible.
• It improves productivity.
• It offers code stability.
• It offers plenty of tools for the developer.
• It has a vast ecosystem.
• It facilitates strong web development.
• Many of the top companies in the world, including a number of Fortune 500 companies, use React.

When it comes to React projects, there are a few choices of tools to help you, such as Gatsby, Next.js, and Create-React-App.

Here, we’ll take a look at the most popular of these options: Next.js and Create-React-App, exploring the advantages, disadvantages, and use cases and examples for each.

Server-side rendering (SSR) vs. client-side rendering (CSR)

Create-React-App uses client-side rendering (CSR), meaning developers can construct their web projects rendered inside the browser with JavaScript. This means the webpage uses JavaScript to route directly in the browser, rather than having different HTML pages serve the content.

In contrast, Next.js uses server-side rendering (SSR), which allows the application to render the page directly on the server instead of the browser.

What do React vs. Next.js projects look like?

There are a number of advantages and disadvantages to using each of these tools for React/JavaScript projects, which we will outline in more detail below. Essentially, these are tools for front-end projects that facilitate easier and more engaging web development. However the intricacies of each vary, and they require different learning curves, although they are both relatively easy to learn.

Create-React-App 

Create-React-App is ideal for those looking to familiarize themselves with React. With dependencies such as webpack and babel, it streamlines operations and allows developers to leverage core features of the library to build front-end web applications. It also serves as a time-saving tool and allows you to build a single-page application (SPA). 

Advantages of using Create-React-App

It is easy to learn and use

Create-React-App has a low learning curve, and developers can leverage plenty of resources, from tutorials to documentation. 

You will gain flexibility

Developers have the freedom to choose any routing library they want — there are no rules when it comes to Create-React-App.

It is client-side rendered

Client-side rendering has some advantages. For example, you can select any host for your project and deploy your products with ease.

Disadvantages of Create-React-App

There is no SEO support

If you want your web app to have strong SEO, Create-React-App is not the best choice, as a client-side framework.

It’s not very customizable

Customization, too, is quite difficult with CRA. There are no built-in tools available, so you would have to customize the Webpack configuration with third-party tools. 

It makes it difficult to perform out-of-the-box functions

There are certain limitations to CRA, such as the fact that developers would need additional tools, often ones with a steep learning curve, in order to extend its capabilities to allow it to perform out-of-the-box functions.

Next.js

Created by Vercel, Next.js is a framework that enables developers to build single-page applications and performant web apps through server-side rendering. It also offers static-site generations, pre-rendering, excellent functionality, and other features.  Next.js is an extraordinarily popular choice.

Advantages of using Next.js

It’s ultra fast

The speed of Next.js is one of its main advantages. This ultra-fast performance leads to shorter build time.

You can use API routes

Looking to use third-party API? Next.js facilitates this, offering API routes. That way, you can build APIs directly within the application.

It is highly customizable

In contrast to CRA, Next.js is easy to customize. You can add both Babel plugins and Webpack loaders, for example.

It’s deployable

Deployment is simple with Next.js. You can easily deploy your React apps quickly, with no hand-holding.

Disadvantages of using Next.js

There is a lack of flexibility

Developers are only able to use a file router with Next.js. Moreover, you are required to use a Node.js server for dynamic routes. There are also few front pages that are built-in.

There is no state manager built into the framework

Next.js has no built-in state manager. If you need to use one, they you must use another tool to facilitate it.

It’s not ideal for simple apps

While Next.js is a solid choice for more complex web apps and web pages, when you’re working on a fairly simple product, it could make the process unnecesarily complicated.

When to use Next.js

You know the advantages of using Next.js. But when should you actually use it? Major businesses and organizations like Hulu, Netflix, Github, Nike, and Ticketmaster all have it in their stacks. Here are some examples of use cases.

To build a landing page

Landing pages are one standout application of Next.js. It’s also an optimal tool for creating other online collateral used for marketing purposes.

For websites

Multiple types of websites are another use of Next.js. Thanks to ultra-fast loading times, the user experience is greatly improved when developers leverage the framework. Even when the particular device typically has slower load times, Next.js will aid the performance of the website.

When you need strong SEO

Search engine optimization is critical for many businesses, particularly as it pertains to their marketing efforts. Next.js is ideal for facilitating better SEO, due to server-side rendering. This is especially true in contrast to Create-React-App, which offers no built-in support for SEO. So, when you’re looking to drive traffic to your website, Next.js is the better option.

To create eCommerce stores

eCommerce stores demand a variety of features and functions, including high performance and strong SEO. Next.js supports the development of eCommerce stores and webshops, enabling stronger engagement and facilitating traffic to them.

In fact, Next.js has an eCommerce starter kit that enables software developers to create webshops easily and quickly.

For anything that demands excellent performance

Ultimately, anything you build that requires strong performance will more than likely benefit from the aid of Next.js. If this is a priority for your website or application, you should consider the framework for your project.

When to use Create-React-App

Meanwhile, Create-React-App is ideal for a separate set of products and use cases. Facebook, Tesla, Reddit, Airbnb, Netflix, and Dropbox are just some of the huge names that leverage the tool for their projects.

So, when should you use Create-React-App? Here are the main instances.

To create gated applications

If you only want your products to be available to pre-authenticated users, then gated applications are the best choice. These websites and applications don’t require server-side rendering, so Create-React-App and its client-side rendering will more than suffice.

For web applications

Likewise, web applications don’t typically require server-side rendering. Usually, they have high performance without SSR. So, if you’re building web applications, Create-React-App will help you cut costs will maintaining a high level of service.

For single-page apps

Single-page applications function inside the browser and do not require reloading the page while someone is using it. Gmail is one example. Development is fairly simple, more so than that with multiple-page applications.

Create-React-App facilitates building single-page applications. While Next.js can also support development of these products, CRM is more commonly the option developers go with for this purpose.

When the developer is relatively new to React

When a developer is just starting out with React, Create-React-App will allow you to learn the ropes quickly, providing a means of attain familiarity with the framework.

The bottom line

When you’re choosing a React framework for your JS project, there are several options. Create-React-App and Next.js have emerged as the two top contenders for building strong React applications.

If you’re in search of tools for learning how to become more familiar with React, Create-React-App, and/or Next.js, there is an extensive community to help support you, give you advice on the best approaches, and answer questions. There are also plenty of courses, tutorials, books, websites, guides, documentation, and other resources available to help you navigate these tools, although for the most part, the learning curve is not too steep — and the payoff is well worth the investment.

The post Is Next.js the Same as React.js? appeared first on COR.

The informational technological advances we see go extremely fast, and that’s without taking into account the many other fields these advances affect. The evolution happening in the IT industry is accelerated and exponential, since a discovery can be utilized in multiple other sectors.

The COVID-19 pandemic has increased the importance IT trends have in our daily lives, and the many changes it has brought to our way of work, the applications and possibilities are enormous. IT professionals have a tough road ahead, since they need to continuously learn, unlearn, and rethink what goes on with new, top technologies in real-time.

Keeping up with current tech trends also means keeping up with needed skills for the future, and sharpening tools for new job opportunities. 

Artificial Intelligence (AI)

AI has been the icon of a series of digital revolutions, the major exponent of developing technologies with real life applications in almost every market, field of work, and industries. In the past decade it has been a subject of constant buzz, and it continues to be one, since we can harness its power into a growing number of uses.

Some of the most common uses involve image and speech recognition, phone’s personal assistants, transportation apps, financial forecasting, and the use of algorithm systems and models. AI can help us navigate the huge quantities of information we produce every day, by helping us analyze, categorize and make sense of big data.

AI will be further used in the evaluation of our digital interactions, gathering and reaching connections and insights we couldn’t get without the help and support of it. Think about the current applications in the healthcare industry. Algorithms can help understand the ups and downs of demand for services like hospitals, first care centers, and clinics. With information such as this, these healthcare providers can anticipate possible requirements, manage their resources accordingly, and allow for smart, data driven decision making.

E-Commerce services and providers, like Amazon, can better understand the underlying customer behaviour, analyze the patterns, and optimize their supply chain. This can help improve client engagement, personalized shopping experiences, and optimizing web designs in order to increase revenue and profitability. Or even digital services like LinkedIn use the algorithms to improve their matches between employee/employer, using and sharing data gathered in their own social media platform.

Just take a look at Markets and Markets’ report on the AI market. The current spending on research and development of AI, machine learning, and cognitive systems is over $57 billion. Markets and Markets forecast the AI field will grow into a $190 billion industry by 2025. And this is only natural, taking into account the reach these emerging technologies have over every sector of the market. This means new job openings and the creation of new positions that will be needed in the development, programming, analysis and maintenance sectors in the IT field. And the salaries these positions currently have are very competitive, even some of the highest in the current  job market. 

Machine Learning

Machine learning is a subset of Artificial Intelligence, and what we mentioned before rings true to this practice as well. Machine learning is the underlying tech used in speech recognition, traffic prediction, spam and malware filtering, product recommendations, machine translations and more. The ability to learn and improve the system through its constant analysis is an incredible thing. 

This technology is also being used in many industries, since it helps businesses employ a better customer experience, and it facilitates the process of solving clients’ questions at a great pace. As well as being used in dynamic pricing. We can rest assured that machine learning is here to stay and it seems it will only grow into a bigger industry. 

Robotic Process Automation (RPA)

RPA is another subset of AI technologies, it basically amounts to the use of software models to automate simple, mundane and repetitive tasks. Many people are afraid of the implications these advances mean to the employment market, and they are not completely off. As much as we like technology trends, they can have a negative side, and not mentioning it is neglecting reality. 

Even Bloomberg and Forbes recognise some of the risks, with the first creating an interactive test in order to see if your job could be automated. McKinsey & Co go as far as saying that AI and RPA will terminate the need for 77,000,000 jobs in the next 20 years. 

HR managers, auditors, credit analysts, sales clerks, maybe even truck drivers could be at higher risks. While other professionals like teachers, lawyers, physicians and surgeons, financial analysts and IT managers are the least likely to be automated. We mention this, not to be alarmist or fall under false panic, but to recognize RPA has real and concrete effects in the employment rates of certain markets.

While some jobs could be at risk of falling into oblivion, this is not only a product of RPA, but it is a direct consequence of the changing landscape of job opportunities. The current workforce needs to understand the changing game, and be on the lookout in order to evaluate how to take advantage of the new technology trends to their benefit. 

The same occurrence has manifested across our history, whenever a new development simplified a task, lowering the need for human jobs. Like the appearance of direct telephone communications, transforming the need for phone operators. Or even the horse and carriage industry with the appearance of the automobile.

Automation as a possible road to better job opportunities. 

 Robotic Process Automation can also be a positive force in the job hunting market. The reach this technology can give us is limited to certain types of tasks, like interpreting applications, following and processing transactions, even data analytics, sending and responding to mass emails, creating spreadsheets and reports, etc. These are tasks that are repetitive and take a lot of time to complete by employees, but don’t require the complex problem solving ability natural to us.

RPA is creating new job opportunities in the field as well, like AI engineers, RPA managers and architects. And there’s a whole side of things that is usually glossed over. The liberation from mundane tasks allows for the opening of new jobs analyzing the share data extracted from these technologies. 

It’s not only data scientists who will play a role in this. But traditional professionals can take on more profitable projects, and give their expertise by counseling, evaluating and creating new perspectives in business processes. AI, as much as it has been evolving, still can’t fabricate complex and all encompassing business strategies or problem solving functionalities.

A case study developing right now is the Accounting industry. While they can lose some part of their project opportunities to taxing and auditing softwares, they can re-invent themselves and use these technology trends to their benefit. 

Edge computing 

With the rise of cloud computing as the mainstream technology in use today, some operations have found certain difficulties with implementing it in certain markets or distant geographic areas. And this has only increased with the normalization of remote work. It is only logical that another form of data gathering emerged with the rise of public clouds.

Edge computing, in simple terms, is the process of gathering, storing, processing and analysing data close to the client’s location, where the data is being generated instead of overseas servers. 

Big data has been the new normal for a few years now, and the sheer amount of data a company has to deal with is only rising, and sometimes cloud computing can’t keep up. Being for the regular technical shortcomings it presents, the limited storage space, or the impossibilities in connection some remote working areas have. Edge computing was developed to help with these issues, especially latency.

The name comes after the fact that it is located “on the edge”, close to where the computing needs appear. This technology acts as mini datacenters, facilitating the processing of time -sensitive data in unconnected locations. Or locations with unreliable internet connectivity. 

Imagine an oil extracting operation in the middle of the sea.They need to gather huge amounts of data, and they need a reliable structure in order to safekeep their valuable information. As well as keeping an eye for any sort of problem in the process they keep, it is an industry in which any problem could be, not only costly, but a danger to its environment.

On a radically different note, some gaming companies are looking to install local edge computing solutions in order to minimize latency, or “lag” in online gaming. 

Another implementation of edge computing is presented by the rise of the Internet Of Thing (IoT), in order to connect the multiple smart devices in use. And this is not only a possibility for smart homes, but also in the healthcare industry. Hospitals have multiple monitoring devices, and these are usually not interconnected, or require the storage of large amounts of data in 3 party clouds. And this can be very complicated due to the lax cybersecurity some providers offer. A local edge system on site could solve this issue. 

Cybersecurity 

On that last note, we should take on the cybersecurity challenges we face today. Especially the rising trend in ransomware attacks. 

One of the main threats cybersecurity experts face today is ransomware attacks, at this point it is almost an industry, the numbers are frankly staggering. Ransomware is a malicious software that infiltrates a computer, a network, or an IT system, in order to encrypt, limit, or restrict access to vital data, until a ransom is paid.  

With the pandemic and the fast, on the go, implementation of remote work , there were many doors left unlocked, and hackers took advantage of this situation. The rose 148%!  But these fallible entryways are older than the pandemic, there are many ways for a hacker to infiltrate a system, the multiple devices carry some of the blame, but the most worrying aspect amounts to common folk ignoring the dangers and the possible protection they could employ. 

And it’s not really their fault, is it? You shouldn’t have to be a cybersecurity engineer in order to lock the door to your devices. But the fact remains that the digital revolution brought multiple tech advances that the regular user doesn’t comprehend, even large corporations and state offices are vulnerable to these attacks. 

The payments can be debilitating, the average ransomware payment in 2021 increased by 82% year over year to $570,000. The highest payment to date was $40M from an insurance company this year. Many victims of these attacks are easy prey specifically because they can’t afford cybersecurity,  like small businesses. Other victims fall due to breaches by mail campaigns, a single employee can suffice for a hacker group to get inside the system.

One way to stop this is to train your employees on the basis of cybersecurity. With only simple instructions and guides, lowbrow attacks can be stopped, since one of the most common ransomware tactics is email phishing. Investing in security can save millions of dollars from being stolen each year.

Blockchain

Blockchain has been one of the new developments in cybersecurity technology. The first implementations are linked to cryptocurrency, like Bitcoin, and proliferated around 2009, with big developments around 2014. This technology is very sophisticated, it works consisting of blocks with timestamped transactions, each unit of blocks is linked to the previous ones, creating a chain of cryptography. They manage two types of records, transaction processes, and blocks. These transactions are the actions implemented, with timestamps, tied together in a block. Since each string of information is impossible to predict, any outside tampering is detected almost instantly. 

The growth of cryptocurrencies for emerging markets had an incredible influence around the world, with some countries even using blockchain technology for their national currency. Even helping charity projects help individuals without bank accounts.

Another important implementation of blockchain security is in fraud-proofing systems for transactions. There’s a lot of interest in these advances, and the possibilities of using it outside the scope of digital currencies.

And this is the important aspect of digital and IT trends. The possibilities for re-inventiation are endless. With data and knowledge about your own internal operations as a business you can reach unimaginable goals, until now. The work done by the IT industry is not only important, but what currently makes the world go round. At COR we take pride in helping professional service businesses keep a profitable margin, take good care of their employees, and optimize their fees, in order to keep doing amazing work. If you want to know how we can help you, take a step in the right direction, and request a demo today.

The post IT Tech Trends in 2021 appeared first on COR.

In the decade plus since that time, LegalTech has proven itself as a necessary field, providing pivotal technology solutions to the people who need them the most.

Of course, crises in the legal field and beyond didn’t end with the economic downturn of 2008. In light of the pandemic, LegalTech has even broader implications and potential impact, helping legal teams resolve issues and conduct practices remotely, more efficiently.

What is LegalTech?

Legal technology, in its simplest terms, is an umbrella term for the software and other technology solutions used to support and streamline legal services at law firms and related businesses and organizations in the sector. The tools assist professionals and clients with undertaking many different types of legal operations. 

LegalTech utilizes new technologies and processes, such as blockchain, artificial intelligence, automation, cloud technology, big data, and more, all toward the goal of making the law industry and related industries more accessible, efficient, profitable, and collaborative. 

LegalTech Areas

The are numerous use cases and sectors within LegalTech, which encompasses a vast array of services, functionalities, and specialties. Some of the areas to which these tools, technologies, and practices are most frequently applied include:

• Accounting and billing
• Analysis
• Client services
• Communication, such as via legal chatbots
• Contract management and smart contracts
• Document management, centralization, and storage
• Document review
• E-discovery
• General management systems
• Template creation
• Workflow facilitation

How can LegalTech help your business? 

So, how, exactly, can LegalTech apply to businesses in the real world? Here are some examples of what it can do.

It can streamline workflows

Aided by automation, LegalTech tools can streamline a wide variety of processes, from legal research to creating templates for clients to document review to billing. This is facilitated in a much less time-consuming way than it would be manually, and it will cut down on the labor and expense needed. 

Artificial intelligence and machine learning algorithms can help you identify critical information in a variety of practices and procedures. Ultimately, law firms benefit from these automated systems in multiple respects.

It provide business intelligence

Legal technology companies, like any business in practically every sector imaginable, must stay informed about how they are performing as an organization. LegalTech offers transparency and means of providing insights in real time through resource management software, reports, and dashboards, which offer critical information and analytics pertaining to profitability, operations, costs, budgets, and much more. 

This information can be applied to improve a variety of operations and better understand the impact their measures are having.

It facilitates project and case management

Legal cases, for all intents and purposes, are projects that require project management tools just like anything else. Legal technology companies offer solutions for legal practice management, including time-tracking tools, boards that provide overviews of projects, and more. You can assign responsibilities, prioritize tasks, communicate, and set deadlines through this type of software, improving and giving more oversight to your workflow.

Provide a Repository for Storing Information

Law practices handle an abundance of sensitive information every day. These firms need to have secure repositories for centralizing and storing this data — tools that are easily accessible for information retrieval. 

Enable Remote Work

Even before the pandemic, many businesses, include those in the legal industry, were leveraging tools for remote work, as they increasingly allowed employees to work outside of the office. Today, LegalTech technologies, particularly cloud-based tools, are the response to the need for platforms that facilitate the ability to work from home. 

Improve the Client Experience

Of course, at the end of the day, law practices seek to provide a high-quality client experience. Legal technology is the answer to this. Clients have greater transparency and ability to manage their information and documents, view bills, track updates, and even find lawyers and legal professionals to help them handle their cases. 

One example is legal chatbots, which can manage rote tasks that don’t require the intervention of a human specialist. They can answer questions and address increasingly intricate matters, freeing up time for humans to conduct more specialized tasks.

What are lawyers looking for in these solutions?

Strong Cybersecurity

Of course, without strong cybersecurity, these powerful tools are for naught. Legal professionals demand safe technologies to aid them in their practices because they are so frequently dealing with highly sensitive information that requires privacy. 

Efficiency

Efficiency is the goal of many a legal professional. These individuals are extremely busy and need tools and technologies that help them streamline complicated workflows. Many LegalTech tools automate the more time-consuming processes and offer even more powerful insights that they wouldn’t have gleaned through manual labor. 

Moreover, they can completely transform processes that previously required intervention, such as billing.

Client Independence

Legal professionals are also seeking tools that will allow them to afford clients some independence when dealing with critical matters and cases. While the attorney will always be available to provide the client with guidance, there are numerous products that can aid them, helping clients understand the complexities of the issues involved in their cases, manage key documents and information, and have their questions answered in real time.

Strong Collaboration

Tools that facilitate collaboration, between colleagues, law professionals, and clients, are critical for the legal landscape. LegalTech offers solutions for individuals to work together in real time.

Digital Transformation

Perhaps digital transformation isn’t the first thing many people think of when it comes to law firms. However, legal software is just the tip of the iceberg when it comes to bringing the legal industry into the current age. There are numerous software solutions available to help streamline operations, simplify processes, and ultimately add greater value to legal businesses and organizations of all types.

Increasingly, legal professionals are looking for greater transparency and stronger solutions for handling their caseloads and meeting the needs of their clients — and digital transformation will help facilitate this, through cost-effective, cutting-edge solutions.

Barriers to Tech Solutions

Lack of Information Available

One problem with adopting LegalTech is that many legal professionals simply aren’t aware that the solutions exist or what they are capable of doing. Or, if they are, they may not understand factors like the cost involved, the benefits of the tools, what it takes to deploy them, and how they relate to the legal industry in particular.

Deficiency in Skills and Qualifications for Using LegalTech

Some businesses may be aware that the tools exists and they benefits they offer but aren’t familiar with how to use them most effectively.

That’s why increasingly, law firms and related organizations are turning to legal technology companies or even in-house specialists to help them adopt these technologies at their practices. 

Cybersecurity Holes and Concerns

Cybersecurity is a huge concern, what with the uptick in criminal activities like ransomware attacks, data breaches, and hacking. It’s natural for law firms to be concerned when leveraging new technology.

It’s important for law practices to ask plenty of questions and thoroughly review the measures legal technology companies will take when ensuring that their platforms — and entire businesses — are secure.

Lack of Buy-In

Even after being educated about and understanding the benefits and solutions the umbrella field of LegalTech provides, some legal professionals and companies may still be hesitant about adopting these tools. The field of law has been around for centuries, so some of the resistance to “new trends,” if you will, could come from a reluctance to interfere with processes and procedures that have seemingly work thus far.

While it’s a fair argument, it’s still important to recognize that legal technology won’t replace humans in the field — too much of the practice depends on human intelligence, knowledge, and intuition, which even artificial intelligence can’t broach. Instead, it will enable these professionals to do their jobs even better, providing tools that equip them with powerful advantages.

Conclusion

LegalTech is set to completely overhaul the law industry, offering unprecedented solutions during unprecedented circumstances like the pandemic. With proper implementation and maintenance, this rapidly growing field is set to facilitate digital transformation throughout the field.

The post IT Solutions: LegalTech appeared first on COR.

• Product owner
• Project manager
• UX / UI designers
• Business analyst
• Software developers
• Team lead / Tech lead
• Scrum master

We’ll go into more detail about what each role involves later in the post. 

How Do You Build a Good Software Development Team?

Team structure, talent and communication are just some of the most important factors to consider when building a high-performing software development team. But how do you make sure it’s effective and results-driven? In this section, we’ll look at some top tips for building the perfect team.

• Cover all roles and skills. When you think of software development, you think of programmers and engineers coding away in a whole range of different languages. But the truth is there’s a lot more required if you want to build an effective project team. You need business analysts, technical support and people leaders. Each role has its own specific responsibilities and skills necessary that are essential to a project’s success. Without covering all roles, the team risks delays, poor quality and unsatisfied clients. 

• Communicate goals and KPIs. As with any effective team, it’s essential for each member of the project to know what’s expected of them. Goals should be SMART: Specific, measurable, achievable, realistic and timed. Setting team and individual KPIs are also effective ways of driving peak performance in your team. When team members know their goals and targets,  they’re more likely to own and achieve them. And those who meet or exceed their goals should be recognized and rewarded to keep motivation levels high.

• Hire diverse talent. In the age of remote working and virtual workspaces, it’s easier than ever to recruit a diverse range of professionals. By searching for talent from a range of different backgrounds, you’ll help bring different ideas, perspectives and values to your team. Your diverse colleagues could help you solve problems you were unable to figure out. 

• Make information available. For the optimal delivery of your project, you need to make sure your team members have all the tools they need to access information. Making data available means greater transparency in the progress of your project. It allows team members to see where progress is being made and where improvements may be necessary. This will help you to optimize your operation and deliver your project more efficiently and to a higher standard.

• Automate your processes. Automation means your team members will have more time to spend on billable activities, and that means they can advance quicker with the project and move swiftly onto the next. Programmers should spend time coding, not invoicing. Analysts should be deep in the data, not manually tracking their time. You should equip your team with tools that automates all those time-consuming admin tasks. This will also help look after your team members’ well-being as you’ll be freeing up their time.

Check out this post here to learn more about running an effective IT team.

Product Owner

What do they do?

As the name suggests, the product owner is responsible for all things relating to the product. They listen to briefs and design a product that creates value and meets the client’s requirements. They’re involved at all stages of the project and adapt to any changes. They have to fully understand business needs and are familiar with market trends. As the ones who most understand the final product, they’re essential to any software development team.

What skills do they bring to the team? 

The best product owners have a wide range of skills. They need to be creative and have lots of ideas to help them devise a product that matches the client’s brief. They also need to be analytical, as their role involves data-driven decision-making.

Who do they most interact with in the team?

Product owners interact mostly with business analysts and clients. They also work with quality assurance engineers and testers to ensure a high-standard final product.

Project Manager

What do they do?

The project manager is in charge of the development, organization, and delivery of a project. They break it down into modular parts and design the workflow. They distribute tasks amongst the team and are responsible for contracts, budgets and timeframes. Mitigating risks and managing unforeseen circumstances or delays are project management responsibilities. They ensure things run to plan and authorize any changes in delivery.  Without the project manager, there’d be no plan or structure to deliver the work.

What skills do they bring to the team?

Project managers need strong leadership and organizational skills as they are the ones who coordinate different team members. They also need to be effective at communication as they connect all different parts of the project, as well as liaising with the client

Who do they most interact with in the team? 

As the ones overseeing all parts of the project from start to finish, they interact with all members of the team, as well as the client and stakeholders. That said, the business analyst interacts more with the client, while the project manager focuses more on the team.

UX and UI Designers

What do they do?

UX means user experience, so the UX designer’s main responsibility is ensuring a smooth and user-focussed outcome. Their role is to imagine themselves as the end-user interacting with the product. They make it easy to use and focus on all aspects of the experience: usability, functionality and performance. 

UI means user interface which means the UI designer’s role focuses specifically on software and how it looks and feels to the user. They need to make it intuitive and straightforward. 

UX/UI designers take the client and product brief and use wireframes to develop the idea. They’re a key part of any software development team as they’re the ones who most champion the users’ needs. There’s a lot of overlap in these two roles, and depending on the size of the project they could be condensed into one.

What skills do they bring to the team?

UX and UI designers need to have a creative flair and strong design focus. They need to be analytical and think outside of the box. 

Who do they most interact with in the team?

Unless merged into one role on smaller projects, UX and UI designers mostly interact with each other. They also work closely with the programmers to make sure their designs can be brought to life through code.

Business Analyst

What do they do?

The business analyst is responsible for delivering the client’s commercial needs. That means they listen to the client’s business goals and help them define clear objectives. Then, they take these objectives and convert them into viable software solutions. They’re involved from the very beginning of the process, and connect the client’s needs with the realities of the project. They conduct market research, check out competitors and define target audiences. Creating documentation and testing solutions are also within their remit. 

What skills do they bring to the team?

They need strong number skills, as they interpret data to back up their decisions. They need critical thinking and creativity as they “translate” what the client needs into a deliverable solution.

Who do they most interact with in the team?

The business analyst works closely with the product owner and project manager.

Software developers

What do they do?

No software development project is complete without your developers. They’re the ones that actually create the final product by coding in a range of different programming languages. Depending on experience level, teams may have junior, mid and senior developers. In larger projects, you’ll often find different experience levels within the software development teams. Junior programmers can support the rest of the team by helping with simpler tasks, freeing up more senior developers to tackle complex coding.

Software developers aren’t just grouped together according to their experience level, but also to their skillset and specialization. These are known as front-end, back-end and full-stack developers.

Front-end developers create everything the end-user sees and interacts with. They focus on the usability and functionality of the software product. Whereas back-end developers are responsible for the processes and functionality behind what the user sees.

Let’s take app development to help us understand, and we’ll use the example of an e-commerce app. Front-end developers code how the app looks and how users shop. While back-end developers code everything that takes place behind the scenes, like inventory, product uploads, and categorizing.

Full-stack developers have broader, but less in-depth, knowledge of front and back-end programming languages. 

What skills do they bring to the team?

Software developers need to be curious as they test different codes when creating the software product. They need a strong eye for detail to spot any broken coding and have a solid knowledge of different programming languages.

Who do they most interact with in the team?

In the software development process, developers work closely with the UX and UI designers. 

Developers shouldn’t be confused with software architects, which is a similar but different role. Software architects design the internal structure of a code and are responsible for technical support throughout the software development process. And software engineers are in charge of connecting the client’s needs with technical solutions. 

Team lead and tech lead

What do they do?

Often confused, team lead and tech lead are two related but different roles. The team lead is in charge of the overall performance, motivation, and organization of their team. A developer often assumes the role of team lead. They make sure team members work well together and are on track to deliver their software solutions on time. They’re sometimes referred to as engineer managers or architects and are also responsible for the development and learning of their team members.

The tech lead is responsible for overseeing any technical or hardware requirements not relating to the software development process itself. They provide technical direction and integrate external hardware. 

What skills do they bring to the team?

A team lead will of course need strong leadership and communication skills.

Who do they most interact with in the team?

As the ones in charge, team leads interact with all members of the IT project.

Scrum master 

What do they do?

The scrum master’s role is to ensure the team follows agile methodologies and frameworks. Agile methodologies as a concept can be described as a project management mindset that focuses on collaboration and value creation. As detailed in the Agile Manifesto, its core values are:

• Individuals and interactions over processes and tools
• Working software over comprehensive documentation
• Customer collaboration over contract negotiation
• Responding to change over following a plan

Scrum can be described as a framework within agile methodologies. The name comes from the sport Rugby, where players group together in a scrum to advance up the field, score and win the game. In project management, scrum allows for quick development as the work is carried out in small parts called sprints. 

The scrum master is responsible for how the framework is followed in the team. Their role involves removing obstacles, creating a productive environment and helping the team work well together. Although technically a leader, they hold no authority over other team members. 

What skills do they bring to the team?

Scrum masters need strong leadership, coaching and organizational skills. They also need to have a project management background and an in-depth understanding of agile software development and methodologies.

Who do they most interact with in the team?

As the ones responsible for ensuring the use of agile methodologies in the team, the scrum master interacts with all team members.

Conclusion

As well as developers, a successful software development team requires a project manager, business analysts, UX/UI designers, team leads and more. A scrum master is necessary to ensure agile software development in a team following agile methodologies. And to build a high-performing team, you need a clear structure, collaboration and communication. It helps as well to hire talent from diverse backgrounds to help bring fresh ideas and new perspectives.

The post Fundamental Roles on a Software Development Team appeared first on COR.

Top 5 trending technologies to master

Here are COR’s top advanced technologies making noise in 2021, we’ll explore each one in more detail later in the post.

1. Cybersecurity
2. Blockchain
3. The Internet of Things
4. Quantum Computing
5. Artificial Intelligence

Cybersecurity

What is it?

Cybersecurity is by no means a new trend. For as long as there’s been the internet, there’s been some form of cybersecurity. Effectively it exists to keep data and critical infrastructure safe from hackers and malware. Passwords, two-step authentications and firewalls are some everyday examples of cybersecurity. And on a larger scale, big organizations and governments use more sophisticated methods to keep data safe.

Why is it important?

Society is more connected and online than ever before, with services like wi-fi, once thought of as a luxury, now considered a necessity. But as technology, and our dependence on it, has advanced, so have the threats. 

This is especially true since the beginning of the pandemic. Remote working, online shopping and virtual meetings became the new normal overnight. And technology, threats and cybersecurity rapidly adapted to this new way of living. Hackers are increasingly trying new ways to steal data and breakthrough security.

Aside from protecting personal data, cybersecurity technology is essential in protecting critical infrastructures, like hospitals, airports, and government processes. 

What are the newest trends emerging within cybersecurity?

Virtual Dispersive Network (VDN) is a new cybersecurity trend making waves in the past year. This technology works by sending an intermittent signal, known as a quantum burst, that can’t be read without destroying the content. VDN effectively makes it impossible for hackers to steal information. 

Deep Learning is another new technology being used to develop new cybersecurity measures. It works by using Artificial Intelligence to analyze real-time data to find new threats. We’ll look at AI in more detail later in this post.

Embedded Hardware Authentication is a more advanced way of securing data. It simultaneously combines up to 4 authentication methods when a user tries to log in, which makes it much harder for a hacker to crack passwords or pins. 

Also trending is blockchain security, which we’ll explore in the next section.

What companies are investing in cybersecurity?

Companies investing heavily in this area include the likes of CISCO, IBM and RedHat. According to the International Institute of Business (IIBA), $6 trillion is being spent on cybersecurity in 2021.

Blockchain

What is it?

Blockchain may sound confusing, but the basic principle is quite simple. It’s effectively a type of database that stores data in blocks and chains them together, hence the name. Blockchain technology is mostly used for financial transactions, like money transfers and doesn’t require a third-party intermediary like other transfer technology. 

How does it work?

Data enters a new block in a chain. When each new block is filled with data, it’s chained to the previous block and stores the information in chronological order. This gives it a timestamp that cannot be modified, along with a numerical code that links to the previous chain. If a hacker tries to steal a block, the code will disappear making it obvious to all a block is missing.

What is it used for?

Blockchain is perhaps best known for its use in cryptocurrencies. In the case of Bitcoin, it uses blockchain to record all transactions and the data blocks are stored in mainframe computers. What makes blockchain databases different from other types is that no one person or entity owns or has access to the information. This is known as “decentralized” information. 

For example, let’s say a company with traditional database technology has 1000 mainframe computers storing all its information. This company owns all of that data, which may include sensitive personal and financial information of its customers, employees, and providers. This is obviously a cybersecurity issue if the company is hacked or the data is compromised. But for blockchain databases, this information is decentralized making it more secure.

What are the advantages of blockchain for businesses?

Blockchain’s transparency gives it a major advantage over traditional data storage. All network users can see data in blocks, which can’t be modified or removed without the overall consensus of all users. 

This secure nature means increased trust between organizations and individuals, doing away with the need for third-party intermediaries. As blocks can’t be removed or modified, businesses can share more secure data safe in the knowledge it can’t be stolen or hacked.  As third parties aren’t required to verify or secure transactions, blockchain is, therefore, more efficient than traditional technology. 

Another benefit is automation. Blockchain automates the process of recording transactions in financial ledgers. Traditional, manual methods of bookkeeping can be paper-heavy, time-consuming and prone to error. Through automation, data is more efficiently and accurately recorded. And the way data is recorded makes it easier to understand, meaning financial institutions can process greater volumes of data. Through automation of processes and transparency of information, blockchain technology is also more cost-effective for businesses that use it.

What businesses are investing in blockchain technology?

IBM is the one of biggest tech players investing in blockchain, with $200 million invested in research as of 2021. And of course, cryptocurrencies like Bitcoin continue to invest in this new trend, as well as trading platforms like Coinbase.

The Internet of Things

What is it?

The Internet of Things (IoT) is an emerging tech trend that has been gaining ground over the past decade. In this increasingly connected and digital age, “things” are being constructed and designed to connect to each other, hence the name the “Internet of Things”.

Take Apple as a perfect example. The iPhone connects to the iWatch, which connects to other mobile devices, and not just Apple’s own. This intra-connectivity of appliances has become standard in the last decade, and the technology is expected to evolve even quicker in the coming decade. Smart cities are another example of IoT, with entire urban centers intra-connected.

How is it used in business?

But it’s not just consumers benefiting from this new technology, IoT is revolutionizing how businesses are operating. This trend has been dubbed the “industrial internet of things” (IIoT). For example, IoT facilitates more accurate management reporting by connecting operational machinery or technology to computers. Let’s take a car manufacturer that tracks data from its assembly line, like employee utilization and use of materials. The IoT allows the company to have a real-time, panoramic view of the operation which facilitates more data-driven decision making. 

How does IoT benefit businesses?

Improved efficiency 

Data helps businesses to improve efficiency in their operations. By tracking real-time data, analysts can determine where inefficiencies may lie and how to improve. This in turn allows the business to continually optimize its operations. For example, our car manufacturer could identify time-consuming tasks on the distribution line or in the supply chain. It could use the data to determine whether these tasks could be automated to free up employees’ time or obtain materials quicker. They could then be re-deployed to other parts of the production line that would allow for quicker or more cost-effective output. By using IoT data, this company can improve its operations and become more profitable.

Automation

The IoT also allows businesses to automate it’s maintenance, protocols and failure responses. For example, let’s take the construction industry which was one of the worst affected by the Covid-19 pandemic. Operations came to a standstill as restrictions were imposed across the world and workers were forced to stay home. To resume operations, construction sites had to follow strict protocols, like social distancing. The IoT has helped these sites to ensure compliance with these new safety measures. Some construction sites issued tagged helmets which alert the wearer if they come within a certain distance of another employee. Another example is TrenItalia, Italy’s train operator, whose trains are constantly monitored for maintenance and safety issues. 

(You can read more about how the Covid-19 pandemic affected the construction industry here)

Improved service

Businesses can also use IoT technology to provide better customer service. Mobile delivery app Truck Advisor is a perfect example of improved services through IoT. Used by big names like Unilever, its entire fleet of delivery trucks are connected through a cloud platform. This allows it to track in real-time the progress of its deliveries without attaching a GPS, meaning greater accuracy and security. IoT technology also allows it to forecast potential delivery delays, and communicate estimated arrival times to its customers. IoT technology is used by all types of businesses, from startups to big brands.

What businesses are investing in IoT?

The biggest players investing in IoT technology include Apple, Cisco and IBM. In 2020, global market research company Garter surveyed 402 businesses from across the USA, UK, Germany, Singapore, India and Australia. It found that 47% planned to invest in IoT technology despite the economic slowdown caused by the pandemic. 

Quantum Computing

What is it?

Quantum computing is an advanced technology that has created a big buzz within the tech sector in recent years, although many experts believe we’re still a few years away from realizing its full potential. It’s effectively a far quicker way of processing data.

How does it work?

First, it’s important to define a little about quantum physics: atoms do not follow the usual rules, as they’re able to travel in time and be in two places at once. To understand how this links to quantum computing, let’s look at how traditional computers work.

Classic computers use two states to do calculations. These states are called bits and are usually denoted as 1 or 0. A really simple example of this is a light switch with its ‘on’ and ‘off’ setting. Quantum Computing, on the other hand, leverages the versatility of atoms, to do calculations. As they can be in the same place at once, quantum computing has multiple states, as well as the standard 1 or 0. These states are known as qubits.

What are the benefits of quantum computing?

As qubits are more dynamic than traditional bits, quantum computers can handle more calculations, allowing for far faster processing. It’s also expected quantum computers will be able to crack calculations and codes no traditional computer ever could. 

That said, there are some tasks which classic computers are better at, such as spreadsheets and emails. Quantum computing is a different method to address different problems, and is not intended to replace traditional computing. Uses of quantum computing include scheduling flights and calculating the best journey routes for delivery trucks. 

What does quantum computing mean for the future?

Well, in an increasingly digital age of data analytics, quantum computing allows for more agile processing of all the data we produce daily. Quantum computing is instrumental in the development of emerging technologies like 5G networks, augmented reality and virtual reality. 5G’s increased bandwidth means it can process more data in less time than 3G and 4G.

What businesses are investing in Quantum Computing?

Big tech firms, like Google and Microsoft, are investing heavily, with the former having engineered a computer 110 million times faster than a traditional computer back in 2019.

Artificial Intelligence

What is Artificial Intelligence? 

Artificial Intelligence is data science where computers and machines carry out tasks that traditionally require human intelligence, such as decision making and problem-solving. Classic computers and machines use data to define all possible outcomes and are programmed to function within these parameters. Whereas AI works by creating algorithms that analyze and organize existing data to help it predict outcomes. Over time, it collects new data, which helps it improve predictions and outcomes. This is called machine learning, which in many ways, it’s just like humans: we learn from experiences which in turn affects the way we act in the future. 

Why is it important?

Artificial Intelligence can interpret and learn from data much quicker than humans. This means businesses can use AI to automate processes and improve operational efficiency.  

What are some examples of AI?

Although in its infancy, AI is also already used in much of today’s society. Here are some everyday examples:

1. Amazon’s Alexa and other speech recognition software use AI to respond to questions. It uses a method called natural language processing that converts speech into text. This text is then converted into a programming language so the algorithm can read it. As Alexa gathers more data over time, its answers become more accurate and lifelike.
2. Netflix uses AI to make personal recommendations of series to watch. It analyzes your viewing data and uses an algorithm to recommend series based on your trends. And social media platforms, like Facebook, make use of AI technology to recognize faces in photos. And self-driving cars are equipped with computer vision that uses AI.

How has AI helped in the healthcare industry?

But aside from these uses by big brands, artificial intelligence also allows for groundbreaking progress in the healthcare sector. From robot-assisted surgeries to protecting patient records, AI has revolutionized this overworked and traditionally inefficient industry. For example, AI-based virtual assistants allow patients to undergo certain processes remotely, reducing hospital visits and freeing up nurses’ time. Doctors have benefitted from AI-improved processes and automation, which also frees up their time to treat more patients.  

AI has helped with the fight against Covid-19. Indeed, the virus was detected by AI-based software designed by Canadian firm Bluedot. It uses machine learning to read epidemiological data and make predictions and alerts about the spread of infectious diseases. Contact-tracing apps in use in many countries rely on AI technology and have helped control the virus. 

What businesses are investing in AI?

Apple, Amazon and IBM are just some of the big names investing in this top technology.

Conclusion 

In the age of big data and fast technological advances,  new trends are constantly emerging that improve our personal lives and help businesses be more profitable. The most popular trending technologies all make use of data to automate processes and save human time. Cybersecurity, Blockchain, the Internet of Things, Quantum Computing and Artificial Intelligence are our top 5 new tech trends to follow in the next year.

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The interesting aspect is that even though a big part of our day to day activities are supported by AI, most people don’t have a clear grasp of what that really means. As the development of this type of technology increases, the informational gap does so as well. There’s one thing that we can say with certainty, even if we ignore how AI affects our lives, it will still continue to evolve. 

Since it’s a new technology, the implementations and ethics involved are still up for debate, the policymakers have a tough time agreeing upon regulating laws. But it’s possible, recently the European Union made great progress in the field, with their European Commision.

Today we wanted to explore the many positive uses AI has, and why the development of this technology is so important. 

 Qualities of Artificial Intelligence

Let’s begin with a possible definition. According to research from Shubhendu and Vijay, what we refer to as AI are machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment and intention. These systems and procedures are able to make decisions that would require a human level of expertise. It requires key technologies to function, such as machine learning, natural language processing, rule-based expert systems, neural networks, deep learning, physical robots, and robotic process automation. They can help us anticipate problems or face issues as they come up. They carry 3 central qualities. 

Intelligence

AI is developed alongside machine learning and data analytics. Machine learning analysis data and searches for trends among it. Once it finds something relevant, software engineers can use that information in order to face a certain problem. The only thing it requires is a strong and voluminous set of data, in order to correctly reach useful and significant patterns. These data inputs don’t have to follow a specific type of media or digital form of information. It can be text, photographic information, or even unstructured data. 

Intentionality

There’s a somewhat clear objective when designing an AI algorithm, it is programmed for decision making, for extremely fast and updated decision making. There are not passive machines, since they reach conclusions that are not predetermined or known by their creators. With information gathered from sensors, remotely inputs, and/or digital data, they can combine multiple layers of information from different sources, analyze it in mere seconds, and reach valuable conclusions from the data. Depending on the type of AI set in place it can even make decisions or act upon the gathered insight. Thanks to the enormous technological advances in the field of IT, mechanical and electronic engineering, we now have massive storage systems, rapid processing capabilities and state of the art analytic techniques. All of these make AI capable of near human sophistication in decision making. 

Adaptability

One of the most interesting aspects of AI is how it can help us in real time, almost instantly. AI systems have the capability of learning and adapting as they integrate new data, thus changing the outcome of their decisions. Imagine you are in your car, driving with the help of a GPS app. Most of these types of map and commuting apps are able to adapt to real time diving conditions thanks to AI, and the data their systems gather from other drivers up ahead. They can inform on real time congestion due to a car crash, show certain spots with a lot of traffic or potholes, among others. Human interference is not even needed, since the simple action of keeping the app open while driving is enough. Enough and immediate, since the data travels instantly, letting the system know what is going on, and letting drivers know what is up ahead.

Types of AI technologies

There are several possibilities for implementing AI technologies, and many fields are already deploying systems such as the one mentioned. Today we will focus on the business applications AI has.

Process automation

This is the most common type of use general business and companies get out of AI. Automating digital or physical tasks that are mundane and time consuming for employees, like administrative and finance activities.

For example, some state of the art project management softwares have features that automatically help in day to day tasks. With routine data input, such as the amount of billable hours, and type of project carried out, these tools can create profitability estimations, and do so in an automated way, just feeding on the regular information the systems uses to function. Imagine being able to modify whatever a project needs to be profitable, without having to waste any resources.

These sorts of softwares also include finance reporting capabilities that allow employees the possibility of cross referencing data sets in order to analyze certain topics. You could check the historical data of a single client, and compare it with revenue rates. This or any report only a click away, and without having to input new data. 

This is usually carried out by using robotic process automation tools that perform as a human introducing and consuming information from multiple and varying sources or information ecosystems.

Process automation can take the form of data entry tasks, introducing information from call centers and e-mail into the business’s record, or constantly updating the client’s information. It can also process legal and contractual documentation, by using natural language processing. These are tasks that are very easy for human intelligence, but would take a lot of time in order to complete. And that’s the driving force behind AI tools, especially process automation, it saves time, and frees the human brain for more challenging or creative work.

This does create some worry for the loss of jobs. But most of the time tasks that can be automated are already being outsourced. Replacing workers is not the objective here, and it rarely happens.

Cognitive insight

Another use of AI is cognitive insight. It’s the ability of reading big data, enormous amounts of information, and deploying a pattern recognition for detecting trends and interpreting their meaning. These machine learning algorithms can help with large scale predicting, like forecasting the next item a customer will buy. Or analyzing data in real time, finding credit fraud and insurance claims fraud almost instantly. They can also check warranty information in order to identify safety or quality issues in manufactured products. 

These are not the regular data analytics employed by traditional data analysis systems. The development of AI such as this is trained, the model in itself learns and gets better overtime. This ability allows the system to improve its predicting and processing abilities as it continues to carry more intensive data and detailed inputs.

Cognitive engagement

Another application of AI, although not as common in business as the last two, is the use of machine learning, chatbots and intelligent agents. These systems can be incredibly helpful, for example they can offer customer service all year round. This means help with an array of things, from password change requests to technical support. Some even carry speech recognition capabilities, and are able to correctly employ problem solving tools for audio inquiries.

Chatbots are very common, you will come across one very often, even in social media and regular business’s websites. Some companies have begun to use these for internal purposes, as well as client related tasks. They can answer on employee related topics, like benefits or HR policies. 

Another form cognitive engagement can take is an array of recommendation systems for retailers. These have increased greatly in their ability to create accurate and personalized engagement with clients. It is also popular in the healthcare field, where it can help create care plans taking into account previous information from a single patient. 

Business benefits of AI systems and Machine Learning

One of the main reasons there’s a growing body of AI research in academia is that there’s a lot of interest in developing the economical and financial opportunities machine intelligence presents. In a 2017 article, PriceWaterhouseCoopers estimated that by 2030, AI technologies could increase global GDP by $15.7 trillion, about 14%.

The financial benefits are very alluring, and the practical applications are uncountable. The current practical uses for AI in business are as follows:

• Controller of numerical data: This the use of statistical analyses of numeric data via machine learning. This is generally used to optimize prices and to set pricing strategies that are low enough for attracting customers, and high enough to make a profit.

• Controller of other types of data: Non-numeric data is a more complex set of information than numbers. These systems use speech recognition and image recognition developed thanks to deep learning neural networks. Some practical examples include email marketing for lead generation, some programs can respond to inquiries and differentiate the promising ones, in order to derive it to a sales operative. 

• Numerical data robot: almost the same as a Controller of numerical data, but this iteration has a physical body. You can find these types of smart robots in retail spaces with an extremely well-structured operation. These robots are capable of doing mechanical tasks, like serving coffee, folding clothes, and taking products from one point in a warehouse to a delivery gate, like they use in Amazon. 

• Data robot: Similar to the last one, but these types of robots can actually process all types of data. Imagine a robotic assistant in a large store that can respond to verbal queries, scan products, and roll towards a certain area of the store leading the way for a customer. There are certain robots that can also help in security, incorporating thermal vision in order to aid security guards in their patrols. The objective is to liberate humans to focus on servicing customers and taking care of the more complex tasks. 

Of course the main financial drive does not come in the form of service robots pouring a coffee with latte foam art, or a science fiction robot butler. Maybe there’s a market for autonomous vehicles in the future. But for now it’s in the analysis performed in order to predict market shifts, and the generation of leads and sales, as well as a driving force of competitiveness . There are multiple business capabilities in AI, such as digital marketing, healthcare, financial, agricultural, to name a few. 

The future of AI

The next frontier to access in the field of AI research would be the incorporation of contextual data in order to make better predictions and to manage more complex tasks. Self-driving cars, for example, are still in development. They seem to have trouble performing under non-ideal weather scenarios.

Another possible use in the future is in the medical research field. There’s a very important aspect of researching new cures for diseases that involves understanding how certain proteins behave. Basically, if you understand the complete shape of a protein you know how it will affect the human body, and how to target it, this is especially important for autoimmune diseases. Proteins can also become cures in themselves, and here lies the true value of this practice.  But the problem is there are millions of different ways a protein can behave and take shape. 

The process of understanding this can be extremely costly and time consuming, even for AI. Although it’s hard, human ingenuity is a force to be reckoned with. Just look at the crowdsourcing game FoldIt. They came up with a way of using the capabilities of humans, and their natural gift with puzzles in order to start shaping the way certain amino acids, the basic components of proteins, are shaped. This protein structure prediction is something that can’t be processed by our current technology in an efficient and cheap way.

With the advances in the field of AI, maybe there’s a near future where they can analyze the human factor and program AI algorithms in order to unlock these puzzles faster. This could mean giant steps forward for the discovery of cures for HIV, Cancer and Alzheimer’s.

Another possible breakthrough we could be able to see in our lifetime is AI gaining the ability of understanding the content of human language. This could help translate and share multiple resources around the world, and could empower each individual person to understand other languages with every bit of important context in the form of portable translation devices.

When people translate between languages they understand the content and then reproduce it in a different tonge, with the necessary context and character originally expressed. Machines can’t do this yet. They can’t contextualize or comprehend the meaning behind language, what they achieve now is a certain set of responses, but those do not amount to what they could do in the future.

AI basic components

 In order to gain a better understanding of how AI systems work let’s look at some of its basic components. 

Computer Science and Algorithms

Computer Science is the study of computers and computational systems. This discipline studies software and software systems, which involves the theory behind them, its design, the development, and applications. 

One of the main purposes of this field is the creation of computational systems, which are calculations in arithmetical and non-arithmetical procedures. These systems follow a structured and well-defined model that commands its working procedure; we know these models by the name of “algorithm”. They basically are the set of rules and instructions given to the system that orders how it functions.

Data scientist and the importance of data sets

In machine learning, and other AI sub-fields like neural networks or learning systems, this algorithm is what provides the system the capability of learning on its own and reaching new conclusions.

These systems can be programmed by Data scientists. It’s the study of how to extract meaningful and valuable insights from data. They manage to do this with a combination of domain expertise, programming skills, knowledge of math and statistics. The insights gained by the analysis of data sets can be translated into tangible and operational business value. 

AI Bias

In order to reach AI conclusions and insights we need: 

1. An AI algorithm, with a set of rules, 
2. programmed by a Data Scientist,
3. feeding on a data set.

It looks simple, but there are many ways in which bias can occur without the programmer even knowing. In a truly eye opening article published in Nature, which evaluates the role of AI in reaching Sustainable Development Goals, shows the many ways in which AI can be fallible to racisms, sexisms and low income discrimination. As well as not being able to perform equally for developing countries and wealthy nations. 

There are many reasons for this: the programmer’s own internal biases, since most of the AI development is done by male programmer’s living in wealthy nations there’s a lot of mistakes in the data selected, and the way in which the models behave doesn’t always take into account minorities.  

Another problem surrounding AI is the climate impact it has with the current state of technological hardware we have. The centers for data storage and servers leave behind a terribly high carbon footprint and consume large amounts of electricity, and the frontrunners of these types of computational systems are usually carried out by wealthy nations, but affect everyone. There’s hope yet, with the development of more efficient cooling systems and renewable energy usage in the field. 

The datasets used for calculation are extremely important, a recent example of how bias affects us directly is related to the Covid-Vaccines. There have been numerous testimonies around the world that they may have some effects on women’s menstruation cycles. The reason we don’t know for certain is that no one included that information, that subset in the testing research, and that’s caused by a bias. 

Other ways AI can leave a great amount of population behind, is by not realizing there are no datasets that include people living in extreme poverty. By reaching conclusions that don’t involve the whole population, those conclusions are not universal and lack the power to be used effectively during the implementation of government policies without a high-risk effect. Other examples involve law enforcement and racial profiling, feeding predictive criminal AI systems that use facial recognition. These mistakes lower the validation the field has.

These are not reasons to stop the development of new AI technologies. Whenever a new technology appears, there is an expected adjustment period in which the actual reach is still being tested. But since we, as a global society, keep using the amazing possibilities AI gives us, we do need to be conscious of the unintended biases we could fall into.

This means we need to keep researching and understanding the many ways in which we may make a mistake when designing new algorithms and AI systems. Especially during research projects and for AI researchers. The abilities AI has in order to help businesses and reach insightful conclusions is not diminished.

Why is artificial intelligence important?

The simple amount of information and data generated each moment has reached never before seen numbers. Humans, machines, and current AI, are hard at work creating more data by the minute. It’s only logical that we started to require assistance in the new endeavour of analyzing this information, since a human brain simply cannot process the amount of information out there.

We need help. We are at the height of an ongoing informational revolution, and we should be using all the tools we have at our disposal. The benefits of AI powered software can be inmense for businesses, they can help make better, more informed business decisions. They can bring unexpected problems to the light, and help with solutions for them. They can even help make a sinking operation, a profitable endeavour once again. 

We, at COR, are in the business of helping professional service operations reach their maximum capacity, while integrating better care of the talent, profitability estimates and better communication. Working smarter is the way to a better and more proficient business. Let us know if you want to take a step in the smart direction, and request a demo today.

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