Accelerate Your Application Delivery with QA Automated Testing - Charter Global

2019 will be remembered as an important year for software testing services – with a record 97% of Agile adoption according to Forrester. Upon entering 2020, it’s time to identify several new software testing trends in hopes of achieving new, strategic heights.

As advancements in testing approaches and techniques continue to surge, QA Automation Testing Services teams strive to improve their skill sets. This is crucial in maintaining synchronicity with rapid technological advances that effectively make of break software delivery strategy.

What trends have shown promise in 2019?

Creating a unique, refined, and savory customer experience has taken center stage in 2019 software delivery trends. Growing organizations rely on high-quality software to systematically produce succinct deliverable.

Moving forward, development teams must consider the following trends to maintain a competitive advantage in a fiercely expanding market.

AI Finds its Footing in Business and QA

The rise of automation and Artificial intelligence was predicted in last year’s trends. Nevertheless, it has been included this year as companies will analyze how artificial intelligence applies to their business. According to The World Quality report, 55% of respondents said that this was their main problem with setting up AI projects.

With regards to QA, more companies are expected to adopt machine learning techniques in areas such as predictive analytics (predicting future outcomes of the testing process based on historical data), defect analytics (highlighting at-risk areas of an application) and test suite optimization (identifying redundant test cases).

The use of AI in testing might require newer skills which will eventually lead to organizations creating new roles for AI QA strategists, data scientists, as well as AI test experts in QA and testing teams.

Instant Apps Take the Spotlight

Instant apps are native mobile apps that are smaller in size. Thus, more convenient for the user who is no longer required to download a standalone app.

The demand for better user experiences and shorter load times are constantly increasing.

Google’s introduction of Android App Bundles allows developers to modularize their apps and deliver features on demand; this will make more organizations adopt the Instant Apps approach.

QA Decentralization

The QA and testing department has been fragmented as a result of the transition to agile and DevOps. QA is now embedded into cross-functional teams. So, quality is dependent on the team members’ skills and their responsibility to integrate testing into their product life-cycle.

Continuous Improvement

The trends mentioned above have contributed to the rise in the adoption of Agile & DevOps over the past few years. Testing is an early part of the development process. More companies will adopt more agile/DevOps models to help them release faster and receive quick feedback. Other companies will take it a step further by adopting approaches such as continuous testing and continuous monitoring.

An attitude of “continuous improvement” will enhance the overall performance and quality of products by teams.

Quality Engineering over Quality Assurance  

QA professionals will enhance their technical skills and include some automation-related work organized by quality engineers. The role of Quality Assurance will deviate to implementing the latest technologies to boost the speed of quality checks. This is a trend that will come into play in the coming years.

Big Data Testing

With big data testing, testers have to verify that terabytes of data are successfully processed. Additionally, the aggressive increase in quantity is making it more tasking for companies. More likely, QA will have to deal with validating the quality, accuracy, and consistency of huge datasets.

These trends above demonstrate the crucial transformation both companies and QA Automation Testing Services is undergoing. The expectations of consumers will continue to shoot up. Organizations must adapt and deliver if they must remain in the front line and grow beyond 2019.

If you are interested in learning more about Benefits of QA Automated Testing Services, check out other blogs here.

Get in touch with our team to discuss IT staffing and software development solutions that can supersede your existing solutions on mobile and web applications.

Machine Learning: A Valuable Enterprise

Benefits like cost savings and efficiency make ML an MVP 

 

These benefits are especially prominent when coupled with the IoT and industrial markets

 

Machine Learning is a valuable player in the realm of the Internet of Things. ML and Internet of Things (IoT) have gained tremendous popularity over the past few years, considered by many as revolutionary, game changing tech. Yet, much confusion exists in terms of understanding the purpose of Machine Learning, along with it’s benefits and suitability for use. 

 

Here’s a breakdown of Machine Learning, benefits of ML in AI and IoT, when it should be used, and it’s real-world applications today.
 

Data Analytics vs. Machine Learning

 

With all the aforementioned hype around machine learning, many organizations are asking if applying machine learning could benefit their business model. In the vast majority of cases, the answer is a resounding no. In the case of big data, however, Ml may prove very useful.

 

Machine learning takes large amounts of collected data and generates useful, real-time insights that help the organization based on it’s inherent learning capabilities. That could mean improving vast amounts of processes, cutting costs, creating a better experience for the customer, or opening up new business models.

 

The thing is, most organizations can get many of these benefits from traditional data analytics, without the need for more complicated machine learning applications.

 

Traditional data analysis are great at explaining data. You can generate reports or models of what happened in the past or of what’s happening today, drawing useful, insightful conclusions about your organization.

 

Data analytics can help quantify and track goals, enable smarter decision making, and then provide the means for measuring success over time.

 

When Is Machine Learning Valuable?

 

In general, machine learning is valuable when you know what you want but you don’t know the important input variables to make that decision. So you give the machine learning algorithm your stated goals, or inputs. Based on learning systems, and then it “learns” from the data which factors are important in achieving that goal.

 

The data models that are typical of traditional data analytics are often static and of limited use in addressing unstructured, fast-changing, sequestered amounts of data. When it comes to IoT, it’s often necessary to identify correlations between dozens of sensor inputs and external factors that are rapidly producing millions of data points.

 

In addition, ML  has the ability to accurately predicting future events. Whereas the data models built using traditional data analytics are static, machine learning algorithms constantly improve over time as more data is captured and assimilated. This means that the machine learning algorithm can make predictions, see what actually happens, compare against its predictions, then adjust to become more accurate.

 

The predictive analytics made possible by machine learning are hugely valuable for many IoT applications. Let’s take a look at a few concrete examples.

 

How are Machine Learning Applications used in IoT?

 

Cost Savings in Industrial Applications:

 

Predictive capabilities are extremely useful in an industrial setting. By drawing data from multiple IoT sensors in or on machines, machine learning algorithms can “learn” what’s typical for the machine and then detect when something abnormal begins to occur.

 

Predicting when a machine needs maintenance via IoT data is incredibly valuable, translating into millions of dollars in saved costs. A great example is Goldcorp, a mining company that uses immense vehicles to haul away materials.

 

When these hauling vehicles break down, it costs Goldcorp $2 million per day in lost productivity. Goldcorp is now using machine learning to predict with over 90% accuracy when machines will need maintenance, meaning huge cost savings.
 

Shaping Experiences to Individuals:

 

We’re actually all familiar with machine learning applications in our everyday lives. Both Amazon and Netflix use machine learning to learn our preferences and provide a better experience for the user. That could mean suggesting products that you might like or providing relevant recommendations for movies and TV shows.

 

Similarly, in IoT machine learning can be extremely valuable in shaping our environment to our personal preferences.

 

The billions of sensors and devices that will continue to power connected devices, smart homes, and IoT devices in the coming years will generate exponentially more data. This huge increase in data will drive great improvements in machine learning, opening countless opportunities for us to reap the benefits.

 

Not only we will be able to predict when machines need maintenance, we’ll be able to predict when we need maintenance too. Machine learning will be applied to the data from our wearables to learn our baseline and determine when our vitals have become abnormal, calling a doctor or ambulance automatically if necessary.

 

Beyond individuals, we’ll be able to use that health data at scale to see trends across entire populations, predicting outbreaks of disease and proactively addressing health problems.

 

Although both machine learning and IoT can be over-hyped, the future of machine learning applications in IoT are worthy of that hype. We’re really just scratching the surface of what’s possible.

 

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4 Tips to make Mobile Recruiting an Essential Part of Your Hiring Strategy - Charter Global

Using mobile technology, an HR unit can engage mobile recruiting to attract, engage as well as convert candidates. Typical examples of mobile recruiting methods include mobile recruiting apps, mobile career sites, social recruiting, and recruiting by text.

IT Staffing Solutions offers an evolving opportunity for employers to connect with candidates in a more efficient manner. A research study showed that more than 89 percent of job candidates confirmed that their mobile devices are going to be an essential tool as well as resource for job search.

No doubts, the use of traditional methods such as phone calls and emails have helped in time past in employment processes. However, the rise in mobile usage among people searching for job has made mobile recruiting a more viable tool for recruitment in 2020 and beyond.

What Research Results are Saying about Mobile Recruiting?

Tip #1: Don’t Ignore Mobile or You will be Ignoring the Best Candidates

If you ignore mobile in your recruitment process, you may end up ignoring the top candidates for those positions you wish to fill. So, why is it quite important to feature your IT requirements analysis on handy devices such as smartphones and tablets? The answer is simple…

“By not integrating mobile strategy in recruitment, companies are giving up skillful and qualified candidates. This is because a good number of highly-talented people are changing the way they search for job, both the entry-level candidates and the senior executives”, says a recruitment expert. And, it’s a pity that a lot of companies are hesitating to swing into mobile recruiting.

Tip #2: Imbibe a Strategic Mobile Hiring Strategy

Wondering where to start when it comes to mobile hiring strategy? For now, don’t focus much on smartphone apps. The first step is to focus on making your company’s career website mobile-friendly. Be the judge of whether or not your career site would enable mobile users to apply for your positions by browsing the site on a mobile device – iPhone, Android phone or tablets. This will help you determine how best to repackage your site to suit the present day job seekers.

Tip #3: Take a Cue from Your Rivals

Another great way to do mobile recruiting is to take a cue on how your competitors are doing it. There’s no need reinventing the wheel — be smart and do less work by taking a cue from your rivals who are already taking advantage of mobile for recruiting. Take a long and insightful look at a well-structured mobile career site.

For More Information, Please visit our website: 

https://charterglobal.com/people-solutions/

Tip #4: Find out and Implement Mobile Users’ Needs 

You should also research on the needs of mobile users. The message is simple; learn about your target audience. Locate a group of persons/candidates critical to your recruiting. Subsequently, you should sit back and find out their needs in terms of what your company has to offer better than the other rival company.

Finally, get to the essentials of mobile recruiting by developing robust mobile devices’ landing pages.

Read More: https://www.charterglobal.com/mobile-recruiting/

Get in touch with our team to discuss IT staffing and software development solutions that can supersede your existing solutions on mobile and web applications.

7 IoT Trends on the Rise

Look Out for These 7 Trends in the Internet of Things (IoT)

 

Trends include the rise of cloud computing, big data convergence, artificial intelligence, smart cities, and more

 

By Leila Kojouri

 

The internet has become a beastly enterprise, with the past ten years seeing more technological advancements than perhaps any other industry. The Internet of Things, or IoT,  for example, is one such advancement gaining immense traction in the marketplace. Here are 7 IoT trends to look out for this year.

 

1. Big Data Convergence:

IoT is changing the way we live and conduct business exponentially, generating a huge amount of data in the process. Big data platforms, for example, are usually made for supporting the demands of large-scale storage and for performing investigative work.

 

Interestingly, IoT and big data have a lot in common. Smart devices, for example, are now being designed specifically for the purpose of digesting massive amounts of system and user generated data. The cloud has proven instrumental in meeting analytic and storage requirements in the realm of big data. Moving forward, the junction between IoT and big data will definitely be a trend on the rise.

 

2. Data Processing with Edge Computing:

Albeit a powerful force, weaker elements in IoT are evident in the addition of devices behind the firewall of the network. While Securing the devices may be easy, securing the IoT itself proves a trickier phenomenon. Thus, security measures must exist between the network connection and the software applications linking to the devices.

 

Perhaps the most notable benefits of IoT are it’s cost-effectiveness and efficiency, especially in data processing. Rapid-fire data processing is prominent in most smart devices, re: self-driving vehicles and intelligent traffic-lights (aptly coined, “smart lights”). Edge computing has been proposed as a potential solution, gaining immense popularity.

 

Edge computing usually outperforms the cloud when it comes to speed and cost. Faster processing translates to lower latency, which is one of the premium benefits of edge computing. Data processing with edge computing will see an uptake in IoT trends in the near future, for certain. 

 

3. Auto-ML (Machine Learning) for Data Security:

In present days, developers are tasked with finding newer methods in which people can share data securely by the use of block-chain-like technologies. Many industrial companies are learning how to trust and accept the forecast of machine learning models (otherwise known as “predictive analytics”), and will acclimatize their operations for preventing the downtime by model outputs.

 

Machine learning model training will likely become a highly automated process. Industrial companies in particular will increase the large capital assets, particularly in cloud computing, in the near future. 

 

4. IoT – Massive Growth Coming:

When it comes to data analytics, IoT is perhaps the most promising technology to date.  Smart devices ingest more data and information about the devices and users, and by 2020, it is expected that IoT devices will exceed 31 billion. Today, we see IoT devices as the major part for reporting and tracking.

 

 IoT is capable of the extraction, storge, and analysis of massive data stores. When coupled with other ground-breaking technologies, like Artificial Intelligence (AI), for example, the essential data can be appropriately measured, filtered and categorized.  IoT trends will most definitely see an increase in the amount of “work” smart devices are doing. What’s more, they will serve to assist data scientists and technicians in providing powerful, insightful suggestions.

 

5. Better Data Analytics:

Chances are, you’ve heard some of the hype and clamor surrounding artificial intelligence in modern business practices. The merger of IoT and AI has seen many recent developments, as the two can be used interdependently to provide e a centralized decision-making tool for all types and sizes of businesses.

 

AI and machine learning are systems that can easily identify trends, patterns, and unique behaviors otherwise invisible to the naked human eye. Better data analytics and the need to safeguard this said data are two major reasons for the rise of this trend. By collecting insights from this data, we could even suggest it be rendered to help us make better decisions in our personal lives. The sheer intelligence, thoughtfulness and self-learning capabilities make this a hugely popular trend to look out for in the near future.

 

6. Smart Cities to Become Mainstream:

When it comes to data collection, many states have adopted a more technological approach – replacing or improving upon antiquated infrastructure; integrating sensors to reap data that proves invaluable for the purpose of urban planning and development. Prepare yourself for the integration of IoT into just about every sidewalk, crosswalk, highway, and byway, as data collection becomes less of a convenience and more of an evolutionary imperative for American progress.

 

Cities both nationwide and globally will become pioneers for the great data exchanges affording accessibility and empowering evidence for better decision-making. Ultimately, the digestion and dissemination of this unique, all-telling data will provide a fundamental platform for both private and public organizations, and the citizens under their watch.

 

IoT integration for the sake of developing “responsive cities” can accomplish goals such as lessening traffic congestion, unlocking sustainable development and improving safety precautions. 

 

7. Personalization of the Retail Experience:

With IoT, the efficiency of supply chain and information systems management has grown by leaps and bounds with respect to retail. Sensors and other smart beacon technologies are being used to tailor shopping experiences with ease, speed, and accuracy like never before. 

 

In the not-so-distant future,  IoT can be used to monitor, gauge, track, and personalize your investment portfolio – making unique, custom trade recommendations based on your data insights.  Or, imagine getting notified immediately when a highly-sought after product from your favorite shop is discounted via push notification which when expanded, offers an indoor map of your favorite shop – leading you to the exact product you desire. 

 

The value of this trend ensures the better integration of personalized retail experiences which ultimately can bring upon a new era of shopping as we know it. 

 

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Giving the Green Light with QA Automation Testing

Improve customer experience, business processes, and enhance technical skills with QA Automation

 

2019 will be remembered as an important year for software testing – with a record 97% of Agile adoption according to Forrester. Upon entering 2020, it’s time to identify several new software testing trends in hopes of achieving new, strategic heights.

 

As advancements in testing approaches and techniques continue to surge, QA teams strive to improve their skill sets. This is crucial in maintaining synchronicity with rapid technological advances that effectively make of break software delivery strategy.

 

What trends have shown promise in 2019?

Creating a unique, refined, and savory customer experience has taken center stage in 2019 software delivery trends. Growing organizations rely on high-quality software to systematically produce succinct deliverables.

 

Moving forward, development teams must consider the following trends to maintain a competitive advantage in a fiercely expanding market.

 

Crafting a Quality Customer Experience

Remember testing was merely about bug finding, reporting, and fixing? We’ve come a long way. Now, testing trends are aligned with a central theme: customer satisfaction.  

 

As it stands, customer experience is the key differentiator for consumers when it comes to product choices. Customers expect more from their suppliers than ever. The advancement in technical areas like performance and DevOps are fundamental in creating a great end-user experience.

 

Performance engineering seeks to create a phenomenal,  one-of-a-kind customer experience. This is done by observing how the entire system, including hardware, software, configuration, etc. communicate and collaborate together to generate business value.

 

AI Finds its Footing in Business and QA

The rise of automation and Artificial intelligence was predicted in last year’s trends. Nevertheless, it has been included this year as companies will analyze how artificial intelligence applies to their business. According to The World Quality report, 55% of respondents said that this was their main problem with setting up AI projects.

 

With regards to QA, more companies are expected to adopt machine learning techniques in areas such as predictive analytics (predicting future outcomes of the testing process based on historical data), defect analytics (highlighting at-risk areas of an application) and test suite optimization (identifying redundant test cases).

 

The use of AI in testing might require newer skills which will eventually lead to organizations creating new roles for AI QA strategists, data scientists, as well as AI test experts in QA and testing teams.

 

Instant Apps Take the Spotlight

Instant apps are native mobile apps that are smaller in size. Thus, more convenient for the user who is no longer required to download a standalone app.

 

The demand for better user experiences and shorter load times are constantly increasing.

 

Google’s introduction of Android App Bundles allows developers to modularize their apps and deliver features on demand; this will make more organizations adopt the Instant Apps approach.

 

QA Decentralization

The QA and testing department has been fragmented as a result of the transition to agile and DevOps. QA is now embedded into cross-functional teams. So, quality is dependent on the team members’ skills and their responsibility to integrate testing into their product lifecycle.

 

Continuous Improvement

The trends mentioned above have contributed to the rise in the adoption of Agile & DevOps over the past few years. Testing is an early part of the development process. More companies will adopt more agile/DevOps models to help them release faster and receive quick feedback. Other companies will take it a step further by adopting approaches such as continuous testing and continuous monitoring.

 

An attitude of “continuous improvement” will enhance the overall performance and quality of products by teams.

 

Quality Engineering over Quality Assurance

QA professionals will enhance their technical skills and include some automation-related work organized by quality engineers. The role of Quality Assurance will deviate to implementing the latest technologies to boost the speed of quality checks. This is a trend that will come into play in the coming years.

 

Big Data Testing

With big data testing, testers have to verify that terabytes of data are successfully processed. Additionally, the aggressive increase in quantity is making it more tasking for companies. More likely, QA will have to deal with validating the quality, accuracy, and consistency of huge datasets.

 

These trends above demonstrate the crucial transformation both companies and QA is undergoing. The expectations of consumers will continue to shoot up. Organizations must adapt and deliver if they must remain in the front line and grow beyond 2019.

 

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3 Ways IT Leaders Can Save Time and Boost Productivity

Optimize time and productivity by eliminating redundant practices and adopting efficiencies in your workflow

 

Time moves unforgivingly fast in the world of information technology.

 

So fast, in fact, that keeping a well-oiled IT machine requires finding efficiencies, eliminating time-wasting approaches and taming bad habits at every juncture.

 

The following tips can help you maximize time within your organization.

 

1. Maximize Cloud Usage

The cloud is an amazingly effective tool for collaboration, allowing colleagues to view progress and make rapid-fire changes quickly. However, there are risks involved, as cloud solutions can attribute to new problems, and waste time if not properly implemented.

 

Because so many point solutions exist, organizations at times end up with application sprawl and fail to share valuable information among cloud solutions.

 

Further, caution should be exercised with shadow IT – making absolutely certain that services are secure, functional, and deliver the expected results.

 

2. Make E-Mail a Cleaner Medium

When technology wastes more time than it saves, it backfires. E-mail represents this dilemma well. The primary form of communication for most all organizations, e-mail messaging is quite hard to abandon for other forms of messaging.

 

Stress-inducing components involve scanning through endless waves of unnecessary or uninteresting e-mails, which can often distract one from seeing an important email needing urgent attention. What’s more, ineffective email practices also serve to waste your time, and your coworkers.

 

For example, cc’ing several colleagues on every email causes those colleagues to spend time opening your email, consuming the information and then making a judgment if they should take action or not – when most of these emails weren’t intended for these people anyway.

 

As a result, this bombardment of messaging creates huge inefficiencies in your organization. Add new people to a message thread with purpose – summarizing information or action points on an as-needed basis, to save the recipient precious time and provide a concise action-item.  

 

3. Expand Your Network

An often highly-underestimated tactic involves seeking out the experience of our peers. Doing so expands your professional network — and incorporates your network into decision making – that can save you loads of time and stress.

 

Avoid getting burn-out by bouncing ideas and questions around, an incredibly important philosophy in sustainable IT practices.

 

By speaking with others, we’re quickly reminded that certain problems are not unique to our own organizations – and we can collaborate, with ease, to work through similar issues and help one another.

 

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Big Data: More than a Numbers Game

Big Data capabilities are a vital aspect of identifying, assessing, and leveraging insight for competitive advantage.

 

Typically, you can rely on your IT team when it comes to everything big-data related. On the other hand, your business team is faced with the challenge to identify opportunities. Understanding how to link data to customer demands and marketplace metrics is essential for producing significant outcomes. Minding the gap between teams is one of the biggest obstacles while undergoing this process.

 

How do we overcome this obstacle, you ask? Start with acquiring a C-Level Sponsor who leads, commits and holds organizations accountable to a Big Data strategy. The three goals of the C-Level Sponsor are:

 

• First, you have to have a plan. It takes a strategy to get you there.
• Second, the plan must put together a proven set of formulas, approaches and methods to win. There is no way to luck into success. As my high school football coach used to say, “Luck is when preparation meets opportunity”.
• Third, is to establish the strategy outcomes by answering the question, “Why are we doing this?” This answer should be quantified in business value creation dollars and remain the focus and measurement of the success of the strategy.

 

CEOs are being asked by the investor community and/or industry analysts to share their organization’s Big Data strategy. The investment community is wise to the fact that organizations that deploy and adopt Big Data and Predictive Analytics provide for long term stability and value gain to the shareholders. With the failure rate at approximately 70% for Big Data projects, it is important for the C-level Sponsor to mitigate risk and dollars in establishing a successful winning strategy. A winning strategy aligns executive, operational and IT goals into a single focus for significant value gain to the organization, shared responsibility and transparent accountability.

Seek outside help with an IT Consulting Partner who earns the trust of IT, Business & C-Level to help guide your organization through the strategy, process, methods and multiple POCs that are needed. There are many advantages for partnering with an outside trusted advisor:

 

• They accelerate your organization’s adoption of proven methods and approaches that guarantee success.
• There are politics in everything these days and Big Data has Big Politics attached internally with careers made and loss on such decisions; leverage an outsider to help manage through the status quo successfully because they have done it many times before.
• Having a trusted voice that is vendor neutral and is concentrating specifically on your success. Assisting you in managing vendors and proving out their claims before investing millions of dollars.
• Your team gets immediate access to critical key talent with a proven track record that you currently do not possess internally or cannot find.
• A trusted advisor guides the building of a cross-functional team and helps create a common set of semantics for communication. Big Data problems are modeling problems, and the models you are trying to create are those of the entities on which you gather data. The dynamics of the data and the products that derive from them are so dynamic that the business and IT folks need to be part of the same discussion and accountable as a team.
• Making the project successful, will require that the cross-functional team avoids any folks that are part of the old way of thinking; otherwise, the gap will persist and widen. A trusted advisor keeps a keen eye on the team and engages the C-level sponsor as needed to guide focus and outcome.
• Most importantly, they keep the focus of Big Data directed on outcomes that support the business and valuable outcome.

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