Understanding the Software Development Life Cycle (SDLC)

Breaking Down Each Phase in the SDLC

 

From planning to deploying, and then maintenance, the SDLC involves several different stages

 

With all the complex processes involved in software development, it’s easy to forget the fundamental process for a successful software development life cycle (SDLC). The SDLC process includes planning, designing, developing, testing and deploying with ongoing maintenance to create and manage an applications efficiently. When faced with the task of producing high-quality software that meets a client’s expectations, requirements, time-frame, and cost estimations; understanding the SDLC is crucial. 

 

SDLC models, or methodologies, are used create complex applications of varying sizes and scales, such as Agile, Waterfall and Spiral. Each model follows a particular life cycle in order to ensure success in the process of software development.

 

SDLC Phases:

 

• Planning
• Designing
• Developing
• Testing
• Deploying
• Maintenance

 

Planning and analysis

 

This phase is the most fundamental in the SDLC process. Business requirements are compiled and analyzed by a business analyst, domain expert, and project manager. The business analyst interacts with stakeholders to develop the business requirements document. They also write use cases and share this information with the project team. The aim of the requirements analysis is for quality assurance, technical feasibility, and to identify potential risks to address in order for the software to succeed.

 

Designing the product architecture

 

During the design phase, lead developers and technical architects create the initial high-level design plan for the software and system. This includes delivery of requirements used to create the Design Document Specification (DDS). This document details database tables to be added, new transactions to be defined, security processes, as well as hardware and system requirements.

 

Developing and coding

 

In this phase the database admin creates and imports the necessary data into the database. Programming languages are defined by requirements. Developers create the interface as per the coding guidelines and conduct unit testing. This is an important phase for developers. They need to be open minded and flexible if any changes are introduced by the business analyst.

 

Testing

 

Testers test the software against the requirements to make sure that the software is solving the needs addressed and outlined during the planning phase. All tests are conducted as functional testing, including unit testing, integration testing, system testing, acceptance testing, and non-functional testing.

 

Maintenance

 

In a post-production, live software environment, the system is in maintenance mode. No matter the number of users, the sophistication of the software and rigorous QA testing, issues will occur. That’s the nature of software with managing data, integration, and security, and real world usage. Access to knowledgeable, reliable support resources is essential, as is routine maintenance and staying up to date on upgrades.

 

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7 Reasons why DevOps and MicroServices Work Best Together

When Deployed Together, DevOps and Microservices Form a Perfect Union in Application Development

 

DevOps and MicroServices offer benefits like readability, availability, and scalability when used in conjunction with one another

 

DevOps and Microservices are two very important technological trends gaining traction in the realm of software development. Both practices were designed to provide professional efficiency and better agility for enterprises. When deployed simultaneously, these two technologies form a perfect union in working to create harmony between your IT department and business units. 

 

Monolithic App Conversion

 

The microservices architecture was formed using DevOps ideologies that are in use by major companies such as Facebook, Netflix, SoundCloud, and Amazon. Most of these companies, in fact, started out with monolithic applications and grew rapidly into decomposed services. Their communication via network-based messaging protocols such as RESTful APIs eventually evolved into the microservice architecture.

 

Microservices and DevOps went beyond converting monolithic apps into decomposed services. Basically, DevOps-centric companies changed the way they work: their approaches to software development, development cultures, organizational structures and a strong affinity to cloud-based automation and infrastructure. The same is true with companies with a success record using microservices; they both drive towards development, scalability and speed. These also became the values in agile development.

 

CI/CD

 

With the adoption and growth of agile methods, other innovative microservices-based concepts began to emerge. One good example is Continuous Integration (CI). The CI concept combines agile ideologies with microservices to expedite the production and release of software, leading to the development and practice of Continuous Delivery (CD). CD has a quality-centric ideology that aims to speed up production of shippable goods.

 

Changing the Game with DevOps and Microservices

 

The microservice architecture introduced changes that were well received by most modern application creators. The result of this is evident, as the productivity rate is now higher and more flexible. Scalable solutions are now delivered to clients requesting such applications.

 

Charter Global has a dedicated team of experts who deploy microservices in our DevOps fields to create automated tasks in sustainable and high-performing environments. We do this in both smaller and larger scales

 

Here are some benefits of the DevOps-microservices synergy:

 

1) Reability: Only minimal errors are experienced with microservices. This is because faults with a microservice function only affect that microservice and its consumers, unlike a monolithic application where a fault can cause the whole monolith to fail.

 

2) Availability: It takes little downtime to release a whole new version of a particular app using microservices.

 

3) Deployability: With microservices, expect increased agility, which makes it extremely easy to update and upgrade into newer versions of a service. Agility makes the build shorter and faster, as well as the testing and deploying cycles. This also enables flexibility, service specificity in the replication, security, persistence, and monitoring of configurations.

 

4) Scalability: You can scale a microservice independently using grids, pools and/or clusters. This characteristic is what makes microservices compatible to cloud’s elasticity.

 

5) Modifiability: Imagine the possibility of easy modification due to its flexibility to new frameworks, data sources, libraries, and other valuable resources.

 

6) Management: DevOps and Microservices leverage agile methodology, which divides the application development effort across smaller and more independent teams.

 

7) Productivity: DevOps works best with Microservices to bring about additional productivity using a common tool set that functions perfectly with both development and operations.

 

 

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Why Big Data Should be Part of Your Recruitment Strategy

Leveraging Big Data to Find and Secure the Right Talent

 

Understanding how big data, automation, and artificial intelligence can help source and keep talent 

 

By Leila Kojouri 

 

Unemployment in the United States is at a record low. Baby boomers are nearing retirement from their professions; while younger generations have a world of options in front of them. Our current economy gives job seekers the upper hand, where they can leverage online resources to comb through a sea of opportunity.

 

As a result, companies are struggling to find and hire top talent from the job market. With that said, the talent-hunting market has become increasingly dynamic. According to research, a number of job candidates use online resources exclusively to look for suitable positions.

 

Through online platforms, potential recruits are offered a window into a company’s values to gain perspective on the culture, salary ranges, career advancement opportunities, and work-life balance. Similarly, the quality of an organization’s management has become a deciding factor in whether or not employees will continue to stay at their current place of work.

 

Using Employer Data to Understand Valuable Workforce Trends

 

Employers are already in possession of a wealth of data. And, when leveraged correctly, this powerful possession can be a great tool in luring, hiring, and most importantly – retaining – the best candidates. 

 

Companies and recruiters can take advantage of the trends and patterns within their own historical data to achieve precision in the course of hiring — they simply need to source through better-fitting candidates to hire and retain.

 

For example, a high turn-over rate can be assessed by examining trends in the data associated with the employees who chose to leave their positions. By understanding why these candidates are coming and going so frequently, it is possible to develop and implement a comprehensive solution. 

 

What’s more, reducing a high turn-over rate is beneficial to an employers reputation, which, these days, is paramount in securing young, fresh talent. A little good reputation goes a long way. During the course of recruitment, the ability to present a stable, yet dynamic workforce is enticing and alluring, and a surefire way to help turn candidates into recruits. 

 

Employers Should Source for the Best Talents from the Onset

 

In today’s hiring niche, transparency is becoming highly prioritized. It is important to determine whether the qualified candidates are interested in the company — and if they are truly eligible for the position in question; along with ensuring they won’t turn elsewhere too soon.

 

On the other hand, job candidates want to determine whether or not the position in question matches their career path as well as skill sets, including the culture practiced in the workplace as well as their management policies.

 

By engaging data, recruiters can shift gears towards the era of transparency, sourcing eligible applicants who are best suited for the positions at hand. In addition to engaging data, automation will also help employers to achieve this feat faster and better than before.

 

Engaging Automation and Data in Recruiting

 

Before now, employers do not have a solid guide, or handbook gauging whether or not a prospective employee has a higher or lower flight risk — they only engage their intuition.

 

Thankfully, employers can now engage tools featuring artificial intelligence as well as in-depth analytic capabilities to parse data on their current workers — to truly determine their skills, previous work experiences and the most recent achievements.

 

Recruiters should engage these tools and even social recruiting methods to hunt for and hire the best talents. After they get in the door, it’s even more important to find ways to keep and retain this talent for as long as possible. 

 

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Machine Learning vs. Artificial Intelligence: What’s the Difference?

Understanding the Difference Between Machine Learning vs. Artificial Intelligence

 

AI and ML, although related technologies, differ in their functionality, learning capabilities, and various applications

 

By Leila Kojouri

 

Remember when concepts like Machine Learning (ML)  and Artificial Intelligence (AI) were things found in comic books and movies; strictly concepts, and nothing else?

 

Actors like Arnold Schwarzenegger would light up the big screen, portraying epic battles between man and machine, on a seemingly never-ending quest to find balance between the two entities. 

 

Yet, as 2020 looms near, AI is no longer just the stuffs of pop culture, rather, it is very much becoming a part of our every day reality. In fact, according to a Global Artificial Intelligence Study conducted by PwC, AI could contribute up to $15.7 trillion to the global economy by 2030. 

 

The realm of AI encompasses a variety of technologies, including machine learning, and the two are often used interchangeably. Chances are, you’ve probably overheard conversations at the water cooler, or taken part in a conversation yourself regarding the marvels of AI and ML.

 

That’s because these terms are two of the most popular buzzwords in the analytics market today. Whether you realize it or not, they have become a part of everyday life. And although the two terms are often used interchangeably, they are mutually exclusive concepts – so there is  a difference. 

 

Machine Learning (ML)

 

ML is technically a subset of AI. Essentially, ML provides systems the ability to automatically learn and improve from experience without being explicitly programmed; focusing on the development of computer programs that can access data and use it learn for themselves.

 

In other words, ML relies on processing big datasets, while detecting trends and patterns within that data and essentially “learning” about these trends along the way.

 

Like people, machines have the ability to “learn,” acquiring knowledge and/or skills through their unique experiences. For example, say you have an ML program with  lots of images of skin conditions, along with what those conditions mean.

 

The algorithm examines the images and identifies patterns, allowing it to analyze and predict skin conditions in the future.

 

When the algorithm is given a new, unknown skin image, it will compare the pattern in the current image to the pattern it learned from analyzing past images. In the instance of a new skin condition, however, or if an existing pattern of skin conditions changes, the algorithm will not predict those conditions correctly.

 

This is because one must feed in all the new data so that the algorithm can continue to predict skin conditions accurately.

 

Artificial Intelligence (AI)

 

Unlike machine learning, AI learns by acquiring and then applying knowledge. The goal of AI is to find the most optimal solution possible, by training computers a response mechanism equal to or better than that of a human being. 

 

In the instance of adaptation in new scenarios, Artificial Intelligence is perhaps the most ideal. 

 

Let’s take a simple video game, for example, where the goal is to move through a minefield using a self-driving car. Initially, the car does not know which path to take in order to avoid the landmines.

 

After enough simulated runs, large amounts of data are generated concluding which path works and which paths do not. When we feed this data to the machine learning algorithm, it is able to learn from the past driving experience and navigate the car safely. 

 

But, what if the location of the landmines has changed? Thee ML algorithm does not know these individual landmines exist, rather, it only exclusively knows the  all it knows the pattern resulting from the initial data.

 

Unless we feed the algorithm the new data so it can continue learning, it will continue to guide along that (now incorrect) path.

 

Enter, AI – capable of analyzing new data in an algorithm to determine multiple factors; answering questions like, why did the paths change? Which direction is most ideal, given the new circumstances, and where are the new hot-spots? It will then codify rules for identificiation of those hot-spots where the land mines exist. 

 

Slowly, AI will begin to avoid them altogether by following te new trails – just like people, learning and adapting to new boundaries and environmental challenges. 

 

The Future is Now with AI and ML 

 

So, by now, you’ve learned the basic differentiating factors between ML and AI. Machine learning uses past experiences to look for learned patterns, while Artificial Intelligence uses the experiences to acquire knowledge and skills, then applies that knowledge to new scenarios.

 

It’s clear that both AI and machine learning have valuable business applications, empowering companies to respond quickly and accurately to changes in customer behavior and solve critical business problems.

 

As the adoption of AI and ML become more commonplace, namely predictive analytics and data science will see a massive uptake in virtually all industries across the marketplace. 

 

 

 

 

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Gain These 3 Benefits by Switching to Open Source Software

Switching to Open Source Brings These 3 Benefits in Software Development

 
When it comes to enterprise software solutions, businesses may end up shelling out hundreds of thousands of dollars in licensing fees alone. Popular platforms, like big-brand customer relationship management (CRM) tools, are not only pricey, but lack the freedom otherwise found in open source software and hardware options available in creative commons.

 

Open source products can transform your infrastructure, reducing costs and streamlining upgrades, making it a reliable, agile setup for your organization.

 

While the cost effective advantages of open source solutions are huge, the benefits extend far beyond the savings. Other open source perks, for example, include Agile improvements via upgrades and development.

 

Readily available hardware plans, and community software vetting, are also a few major key takeaways when considering the proactive impact open source software could deliver. In enterprise use cases, open source infrastructure has a wealth of features that closed-source options can just not compete with. 

 

Flexibility

 

Perhaps one of the most notable characteristics of open source software is it’s flexible nature. Like clay, open source infrastructure can be molded – shaped and reshaped, over time, to adjust to varying business needs. This is especially useful when considering changing priorities and business objectives, and the technology corresponding to these priorities.

 

Developers can make huge changes to their operating systems, without the need for third party interference, because the source code is readily available for both software and hardware. 

 

When solving a problem, open source options allow for numerous paths to reach a solution. What’s more, you can collaborate with the open source community every step of the way. By reviewing code and adding various features without having to rely on a third-party, developers are able to rework the software to adapt to your department’s needs.

 

The adaptable, agile nature of open source software simply doesn’t exist elsewhere – and if it does, you’re likely paying the price with costly proprietary software. 

 

Speed-to-Market

 

Ultimately, we have an overwhelming dependence on technological deployment cycles for patches, upgrades, and new features. With closed source, proprietary software, you are at the mercy of another company’s development, vetting and release cycle.

 

By switching in your cap for a red hat, you’ll find a lot less time is spent on the preamble associated with each and every roll-out of a new patch or upgrade.

 

With open source software, however, you own your timeline, meaning you can have and implement those patches and upgrades much faster.

 

Think about it this way: There is no board of directors or corporate lawyers to hold you back from immediately releasing an upgrade, patch or feature once its development is complete. 

 

Cost Effectiveness

 

Software expenses can eat through your budget very quickly, rapidly depleting your resources in accordance with the terms of the contractual agreement issued by the proprietor, or a “vendor lock.”

 

While switching to a more open infrastructure may not be absolutely free, you can expect to significantly reduce operating costs when following through with your open source project. 

 

A wealth of information exists to support your open source code endeavors. The Open Compute Project, or the Open19 specification, for example, can help in supporting sustainable infrastructure with respect to the redesign of hardware technology.

 

These initiatives enable streamlined, modular hardware configurations allowing you to inexpensively scale data center racks to meet growing data processing demands for both on premises and edge deployments. 

 

Open source infrastructure initiatives make all of their documentation available online. As such, your department can download specs, setup instructions, and tutorials directly, without the need for a service contract to keep systems running in an on-site data center post-installation.

 

Peer-to-peer support is overwhelmingly prominent in various forums and online communities, again easily accessible by users looking for specific information and answers to their queries.
 

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Don’t Ignore These 3 Trends in Software Development

Machine Learning/AI, DevOps, and MicroServices

 

Technological trends have always influenced how industries, business enterprises, and investment parties make decisions.

 

Constantly changing digital landscapes require software development teams to evolve and adopt certain trends to maintain relevance and participate in the modern technological climate. Don’t let your business get left behind.

 

The following software development and testing trends will help you drive digital transformation and increase productivity in your business.
 

1. Machine learning and artificial intelligence (AI)

 

The major scientific breakthroughs from machine learning and AI will continue to receive a lot of buzz this year.

 

Many advancements in these areas are on the horizon. 

 

International Data Corporation (IDC) has predicted that there will be more than 50% of growth in AI by 2021. Without a doubt, AI and Robotics are the biggest and most exciting trends to change industries in 2019.

 

We can begin to write and predict new test cases based on the following sub-topics under the ML/AI umbrella:

 

1. Log files
2. Defect analysis
3. Test case optimization
4. Historical data
5. Real-time interaction with the application

 

2. DevOps

 

DevOps actually combines software development (Dev) with information technology operations (Ops). The goal is to shorten the development life cycle while delivering features, fixes, and updates frequently.

 

This development approach facilitates Continuous Integration and Continues Delivery and allow testers to perform Continuous Testing and Continuous Monitoring to validate if the application was successfully built.

 
DevOps is rapidly evolving and making progressive advancements in the world of software. Since 2015, it has been in the spotlight, automating the processes between software development and IT teams. Software developers and testing teams are now able to build, test, and release software quickly, efficiently and more reliably through DevOps.

 

Apart from high speed, functionality and innovation benefits, DevOps also helps organizations improve their performance levels by fixing complex problems and by delivering faster resolutions.

 

3. MicroServices

 

MicroServices has gained immense popularity in recent years, quickly becoming the preferred method for many software system developers. Also referred to as “MicroService Architecture,” this distinctive method is designed as a functional element in the DevOps process.

 

Microservice architecture is an architectural style to develop a single application that can work together in a suite of small services. It is expected that developing microservice applications can reduce the complexity of testing large applications as each microservice can be tested as a separate process. 

 

It improves the quality of code, reduces maintenance, decreases complexity, and increases scalability.
 

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Cloud Computing, Debunked

It’s Time to Think About Your Cloud Migration Strategy

 

What is cloud migration?

 

Cloud migration is the process of transferring data, applications and/or other business elements to a cloud computing environment.

 

Cloud migration also means moving data and applications from one cloud platform or provider to another (known as cloud-to-cloud migration). There are several tools that can be used for cloud migration like 5nine, ZConverter, VM-Converter, etc.

 

Several reasons have informed the decision of many organizations to migrate to cloud. From the evolvement of the cloud technology to something more recognized and relied upon by many today, to data protection, scalability, flexibility, cost efficiency and so on.

 

Successful digital businesses are holistically applying the concepts of mobile, social, and big data to re-imagine their enterprise – and perhaps it’s time for you to do the same. 

 

What are the risks?

 

Cloud computing is a very broad topic and by nature, draws many myths associated with it’s benefits and risks. For example, cloud computing is perceived as less secure, even though most of the breaches to date have been within on-premise data center environments.

 

Many organizations are adopting hybrid cloud strategies and deploying the business applications that meet their workload demands, many of which are new applications, but in a phased approach that’s aligned with their data center strategies. 

 

Like any technology, cloud computing may not be advisable for your business based on the size and scale of data you’re working with. It is, however, worth considering as advancements in the realm of migration continue to evolve and dominate the landscape.

 

Who are the major players? 

 

Cloud providers are constantly evolving and increasing their overall security capabilities and so far, the 3 dominant public cloud offerings are Amazon Web Services, Microsoft Azure, and Google Cloud.

 

As an example, Microsoft and its SharePoint collaboration platform is being leveraged more than ever by many organizations who are replacing in-house legacy applications and centralizing email and messaging for pennies on the dollar, as seen by the rapid rise of Office 365 applications.

 

Examples include replacement of in-house business intelligence (KPIs), reporting engines and state-of-the art customer relationship management (CRM) solutions.

 

The availability of SharePoint in the cloud represents a point of inflection in technology and strategic differentiation for enterprises.

 

Is it right for us? 

 

All businesses – no matter what their size – can reap huge rewards from cloud computing. The most significant benefit of cloud computing is the reduction in IT maintenance costs, thus helping to improve your cash flow and keeping operational expenses to a minimum.

 

Cloud platforms also enable a new wave of innovation and automation of manual and e-mail centric processes, for example, which is why it’s so cost effective.

 

No matter what strategy your organization is taking to reap the benefits of the cloud, CGI can provide current expertise to help you architect and migrate your SharePoint content to the cloud, Office 365, or streamline your current business processes and workflow for optimum results.

 

 

 

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Priorities to Consider When Making an App

Prioritize These Features in Your Application Development Process

 

Mobile device usage has seen incredible growth in the past decade. As such, software development has seen a huge impact in the amount of technology geared towards the mobile market.

 

More shoppers have been using their mobile phones and tablets because of its convenience. As a result, businesses are developing apps or optimizing web design specifically for mobile users.

 

Having apps and mobile-friendly web design helps maximize potential sales and growth. Here are the essential features a mobile app should include.
 

1. Security

 

Identity theft and financial hacks are becoming common.  For preventative purposes, it is important to make sure that an app is secure and protected against these threats. Many apps require credit card information, especially when an e-commerce function is integrated.

 

Businesses should always ensure that their clients are protected.  They should be transparent on how the client data are being used in the app. The policies and practices should be clearly outlined before users are being asked to provide personal information. In this way, the users will trust the company, and will feel that the business has credibility. Users providing their financial information online shows their trust in the app and will likely use it more.
 

2. Ease of Navigation

 
Poor design is one of the main reasons why an app is deleted. Most users consider the overall usability and user-friendly design as key functions of a great app.
 
A mobile device does not have a keyboard nor a mouse, so typing should be minimized when designing an app. Users also favor having to scroll instead of clicking. Scrolling feels natural and shows more content quickly because there is no need to wait for pages to load. Imagine having to click through 5 pages of a top 10 list instead of just scrolling through it on a single page.
 

3. Social Media Integration

 

In 2017, it was reported that there are over 3 billion active social media users globally.  That being said, social media has an extensive influence in our lives, including the way we do business.  More than being a platform to share photos and status updates, social media has transformed into an essential tool for businesses to reach their customers.
 
Integrating social media in mobile apps offers expanded brand awareness. Users can share posts about the business, thus reaching more people. Social media integration brings increased visibility and recognition to potential customers or users of the app.
 

4. User Feedback

 
Having the ability to provide feedback gives users the satisfaction that they are being heard. This also lessens the need for the them to call or email the support team. It will help the business by having an understanding to what the users want, the things that need to be changed, or the things the users want to see in the app. It is imperative that businesses constantly improve their apps to provide value to their clients.
 

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3 Ways to Own Your Org Chart in Dynamics 365

 

By Leila Kojouri

 

One of the most important facets of client relationship management involves understanding the chain of a command within an organization.

 

Creating an org chart with Microsoft Dynamics 365 enables a powerful visual representation of this hierarchical infrastructure: including employees, their roles, and direct reports.

 

Here are 3 benefits an org chart provides:

 

1) Get the Big Picture

Understand the big picture by creating a customized organizational chart in Dynamics 365.

 

Rather than a standard outline or diagram, Dynamics 365 provides a visual representation, offering a birds-eye view of the reporting structure within a company (or potential sales target) of interest.

 

This hierarchy is established in the case of creating a new account and when adding a contact hierarchy to an existing account.

 

Empowering yourself with the big picture will help you save a sizable amount of time and frustration to get what you’re looking for.

 

 

2) Connecting the Dots

Sometimes, our business objectives require us to do a lot of digging in order to deliver results.

 

Identifying the right contacts needed to get the information you’re looking for is often time-consuming and tiresome.

 

This is especially true in the case of large corporations, where finding the right contact relies on combing through an extensive list of candidates with similar, yet wholly different job titles and responsibilities.

 

Dynamic’s organizational chart functionality mitigate these complexities and helps you find exactly what you’re looking for, the first time around.

 

 

3) Leverage Your LinkedIn

Do you have a LinkedIn Sales Navigator (LISN) license? Leverage the power of LISN in your Dynamics 365 with an easily accessible, embedded widget option ensuring your contacts are kept up to date. 

 

Enhance your perspective by viewing all of the common connections between you and a new lead immediately upon creation – allowing for more warm introductions and less cold-calling.

 

Interested in making an org chart of your own? Check out this step-by-step guide to help you maximize these features and more in your CRM.

 

 

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Don’t Leave these 5 Things Out of Your IoT Game Plan

 

Traditionally, software is manually developed and operated by humans using devices like desktop computers, and more recently, on smartphones and tablets. 

 

The Internet of Things (IoT)  introduces a complex combination of hardware and software that work together flawlessly in a wide variety of environments and, in many cases, with no human interaction whatsoever. 

 

This variation and complexity puts a significant strain on the skills of  an inernet of Things Software Development team, so it’s important to avoid leaving out these three components in your IoT strategy. 

 

1) Cross-Platform Considerations:

The internet today is largely decentralized. Many devices work together in concert for a variety of purposes. For example, edge computing uses the Internet of Things (IoT) devices as a means of more evenly distributing the computational load. To understand, consider cloud computing briefly.

 

Cloud computing networks multiple servers together. If one computer could process a terabyte of data in an hour, two could do it in thirty minutes, three could do it in fifteen, four could do it in seven and a half, five could do it in half that time, and as you increase available networked servers, the time continues to reduce until a terabyte can essentially be processed in real time.

 

Amazon has a cloud computing server array which puts more than a million servers to the task of creating a cloud array. But you don’t need that many to spread out the load of data processing. Many businesses use IoT devices as a means of facilitating what’s known as an “edge” network.

 

An edge network substitutes IoT tech for servers to the same effect or that of whatever a business using such solutions requires. This technique has expanded such that the web itself is, essentially decentralized. Accordingly, IoT software development today seeks to keep pace with this trend.

 

2) Comprehensive Understanding of IoT Companies:

Internet of Thing software development companies must answer an increasing demand for decentralized solutions which have compatibility across the board. Smartphones, tablets, laptops, smart cars, smart homes—there are many “smart” IoT devices. Not all of them have been designed with software or hardware that is in agreement.

 

Accordingly, internet of things software developers are increasingly seeking to develop programs which facilitate cross-platform utility. But there are always bugs. Even the most well-designed program will have hidden errors in code, or coming from how that code interfaces with other programming languages. Beta testing is key, but that’s not enough.

 

3) Performance Monitoring Solutions:

The performance of your program must be gauged continuously, and issues addressed as soon as they manifest. Cloud computing facilitates various design tools that provide solutions, and there are also APM options out there worth considering.

 

Programs which gauge the effectiveness of other software necessarily have their own coding, and as a result, associated strengths and weaknesses. What this means is that varying startups are carving a notable niche in the market through the facilitation of IoT solutions. Some IoT software solutions pertain to interface between different devices and platforms, some IoT applications have to do with monitoring.

 

There are additionally organizational protocols and security solutions which have a root in the cloud, and help facilitate IoT options across the world. Other considerations for businesses either looking to branch into IoT, or more securely establish themselves, include a number of surprising trends.

 

4) Research on Trends and Opportunity:

Blockchain technology may become more relevant as cryptocurrency comes to dominate the market. Internet of Things software development teams are expected to consider security and connectivity simultaneously. IoT data has reached a level of integrity that has become vastly commercialized. One of the most important applications of IoT technology now involves manufacturing, which has some surprising modern innovation.

 

“Smart” manufacturing uses cloud computing and edge computing techniques as outlined earlier in this writing to help monitor and optimize production. For example, a given machine may be fitted with an IoT device at an operational “bottleneck”. Vehicles have “check engine” lights connected to an internal CPU. When something goes awry, a service indicator appears which helps a mechanic know what to fix.

 

IoT devices use similar technology in a way which allows those running manufacturing operations to maximize equipment utility, fixing problems before they knock a device out of commission, and helping regularly scheduled maintenance to become more efficient.

 

There’s a lot of software needs here, and many startups focused on facilitating them. Internet of Things software development will only get more complex as new ideas evolve. The industry is wide open, and likely to continue becoming more integral as technology advances.

 

5) Regular Assessment and Updates:

There’s a lot of software needs here, and many startups focused on facilitating them. Internet of Things software development will only get more complex as new ideas evolve. The industry is wide open, and likely to continue becoming more integral as technology advances.

 

With all the aspects considered, it is very obvious that IoT is as of now, the biggest revolution in the technology industry! The arrival of IoT happened at the most accurate time. When the users were looking for some technology to increase their convenience.

 

The acceptance and engagement of Internet of Things in software industry have seen a rise, and overall, the technology of IoT is not only benefiting the users but is simultaneously benefiting the businesses and software developers as well.

The post Don’t Leave these 5 Things Out of Your IoT Game Plan appeared first on Software Development & IT Staffing Company.

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