5 Sectors Where AI Is Set To Rule

Last year, IDC predicted AI technology spending would exceed $50 billion by 2021. Cut to the present, more companies have started investing money on AI. From slow adopters like manufacturing to the healthcare industry to small and medium businesses that were unlikely to use AI earlier, there has been an uptick in usage across the board.

AI is helping companies manage back-office work, administrative duties, and in streamlining the supply chain.

It enables companies to maximize the productivity of employees, accelerate the speed of production, and increase their ROI.

As companies enter the post-COVID era, we expect them to embrace AI at an even grander scale.

We expect AI to rule industries in the future.

Let’s look at a few industries that will gain immensely from AI in the future.

5 Sectors Where AI Is Set To Rule

5 Sectors That Will Benefit From AI

  1. Healthcare:

The healthcare industry had always shown interest in using AI for finding innovative solutions to manage care better. It has even helped hospitals to save costs tied to inaccurate diagnosis. It has, for instance, helped hospitals with early detection of cancer and saved patients from unnecessary biopsies. The recent pandemic has brought AI back into the limelight. According to digital health technology funder, Rock Health, $635 million has already been invested in AI by the first quarter of this year.

That’s 4 times more than the amount invested last year during the same period. AI is helping healthcare companies with accurate diagnosis and managing supply chains too. It is also helping pharmaceutical companies with developing drugs by analyzing testing results. Even post-COVID, AI will continue to be an integral part of the healthcare sector as it will strengthen the capabilities of hospitals and streamline repetitive tasks such as analyzing tests and CT scans. The promise is to ramp up the speed and improve the quality of treatment.

  1. Manufacturing:

COVID-19 had led manufacturing units across the world to shut production temporarily. As factories reopen slowly, manufacturing companies are looking at the idea of using AI to automate operations, ramp up production, and minimize human interactions. AI can help manufacturing companies to improve throughput, forecast demand, maintain machinery and production assets, comply with regulatory requirements, and detect and correct inconsistencies in production through real-time monitoring. The Fortune Business Insights expect the market to hit $9.89 billion by 2027 and grow by a CAGR of 24.2%.

  1. Retail:

Even before COVID-19 hit businesses, the retail industry was already facing stiff competition from e-commerce websites. As it happens, e-commerce had already started embracing AI a while ago. The only way for traditional retail to catch up with the e-commerce industry is to enhance customer experience and offer the same level of service and convenience as e-commerce websites.

Several retailers have started to adopt AI to improve customer experience and sales. Sephora, for instance, helps customers walking into their store to find the right makeup for their face based on an AI-led scan. It saves their time and cost in finding the right shades. Similarly, Macy’s AI chatbot lets the customer know if the product they are looking for is in stock and even provides directions to find that product. Besides improving customer experience, AI can help retailers predict customer demands, tweak the inventory to meet those demands, and adjust operations accordingly.

  1. Software:

Customer needs are changing rapidly. Companies have to develop products to keep pace with those demands. Traditional software development processes might not be able to meet customer demands quickly. That’s where AI-powered software development can come to the rescue. AI can reduce the number of keystrokes by half and detect and test bugs early to avoid any errors in the later stage of development.

It can help to create workflows that can improve the productivity of developers. AI could enable companies to release products quickly, accurately, and at a low cost. As companies step out of lockdown, there is immense pressure to ramp up software development. AI could become the perfect solution to help companies accelerate these processes with constrained resources.

  1. Customer Service and Experience:

Way back in 2011, Gartner had predicted that by 2020, 85% of customer relationships would be managed without human interaction. Cut to the present, perhaps we are on the road to meeting that prediction. AI-based chatbots like IBM Watson Assistant are helping companies offer instant responses to customers about queries related to COVID-19, even as call centers are running empty due to lockdowns.

There are plenty of benefits of using AI in customer service. For starters, it offers 24/7 support to customers even without human intervention. This can be a positive brand-building exercise for companies as customers do not like waiting for responses and prefer to be attended to immediately.

AI can also work tirelessly and learn new skills rapidly leading to improved response rates and zero errors. It can also be easily scaled, thus saving money for the company. AI can also help companies to the hyper-personalized customer experience by understanding their behavior. AI has the potential to make companies more customer-centric and help them stay ahead of the competition.

Conclusion:

According to an Accenture study, the inability to scale AI can put 75% of businesses out of business. AI is no longer a ‘nice-to-have’ technology, it has become a ‘must-have’ to strengthen companies and keep them relevant in a dynamic business landscape. If companies want to thrive in the post-COVID era, they will have to consider including AI as a part of their business strategy. It could be safe to say that AI may have become the key to sustain yourself in the new normal world.

The Top 5 Factors To Consider While Developing Products With Remote Teams

COVID-19 has compelled most economies to go into a lockdown since March. Companies had to shut their offices and enforce work from home policies.

Although the decision ensures business continuity and reduces the risk of contracting the virus, one cannot ignore inherent concerns in widespread remote working. Challenges such as cyber-security threats, weak internet connectivity, and disrupted communications between the teams are par for the course when teams work remotely.

Another challenge that product companies face is in being agile. The agile way of functioning requires teams to work in collaboration. Studies have shown that teams that work together in the same place demonstrate better collaboration than fragmented teams. They are more accountable and productive.

Top 5 Factors To Consider While Developing Products With Remote Teams

However, that doesn’t mean that companies cannot be agile when they work remotely. Companies such as Mozello have always worked remotely.

So, what should companies do to develop effective products with remote teams? We have been the remote team that has delivered hundreds of releases for dozens of products over the years. Perhaps our experience will help you get settled into your own remote ways of working.

Top 5 Factors to Consider While Developing Products

1. Skillset and Experience of Team Members:

Zapier is a fully remote company. It believes that there are three ingredients to make remote work successful – team, tools, and process. Tools are easy to buy, and processes can evolve over time and through use. The real challenge lies in managing a workforce that works in several locations, maybe even in different corners of the world. Companies like Zapier have overcome this challenge successfully by focusing on the skills and mindset of the people.

People with self-accountability are ideal for such remote working. Companies must ensure that their members take personal accountability and can work independently without requiring anyone to be perennially on their back to check progress.

Apart from personal accountability, companies should also ensure that they have the right people in their team. Distance could derail a project if the team members are not adequately skilled or have enough experience to be able to do their work alone. Ensure that the team members have the right set of skills and experience to manage their tasks without supervision. If upskilling is necessary, encourage and incentive the members of the team to take up courses so they can learn more. This will help them deliver better quality output. If looking for a new hire, ensure that the person is capable to work without being micromanaged.

2. Clarity of Processes and Road-maps:

Half the battle is won when everyone in the team is clear about the process and the roadmap to be followed while developing a product. It gives clarity about what is expected of each team member during the course of development. After explaining the complete roadmap, have a one-to-one interaction with each team member to gauge if they have understood the process and their role well. Conduct regular roadmap reviews so every team member knows the status of the product and their role in moving it forward. Also, remember to assign clear goals and define feedback sessions with every team member to improve transparency through the entire process.

3.Automation of Functions and Processes:

Agile companies do not need a large team to develop a product. It can be done easily with a small team too. However, the pressure to release can prove to be stressful for the team members. As is acknowledged, one way to solve this problem is by automating certain functions and processes that require less or no human intervention. Identify the functions that can be automated, so that the team can focus on the more important tasks and save time on mundane, repetitive tasks. This is not just about test automation. Automation to extend to other functional areas too, for eg. automating administrative tasks and reporting could ease some of the load on the remote developers.

4. Seamless Collaboration:

To ensure continuous communication and collaboration among team members, companies can use tools such as Slack for communicating, Trello or Asana for project management, and Zoom for video conferencing. There are plenty of collaboration tools available in the market. Some are free, and others are paid. Pick the one that’s convenient for all members and that doesn’t increase their administrative overhead. Keep an open communication policy so there are no misunderstandings or delays in project deliveries due to miscommunication.

5. Motivation Among Team Members:

Working alone from home can be a challenge for team members especially as we live in anxious times. Studies have shown that depression and anxiety are high among people currently due to being cut off from others. Mental pressure and a feeling of isolation can impact the overall morale of the team members and can also, obviously, influence the outcome of the project. The only way to fix this issue is by staying conscious of the morale of employees. Make this the responsibility of the team leads and the managers over them. Regular check-ins, weekly team bonding activities, even if only over Zoom are some ways to stay connected with team members. It’s important to keep an eye out for the members who are especially vulnerable and stay connected with them. Remember that it’s often harder for people to ask for help, technical or personal, when remote. A happy team will lead to a happy outcome from the team members.

Conclusion:

Let’s face it. The situation is unlikely to change soon. Product companies may have to continue working remotely even after the lockdown ends. So, keep the morale high and choose the right tools, right people, and follow the processes correctly to build successful products within the desired time. It’s what we have been doing for years now, so there’s no doubt this works!

How Quantum Computing Can Help Businesses?

Last October, Google created ripples in the technology world by announcing that they have gained an understanding of quantum computing.

Just two days before Google made this announcement, IBM had made its own claim online.

So, what is quantum computing, and why are the two technology giants contesting this battlefield.

To understand what quantum computing is, let’s first understand how it is different from normal computing.

Normal computing chip uses bits that are represented as 0s (off) and 1s (on). Everything that we see is a combination of these two units. Quantum computing works differently. It is represented by qubits. So, instead of just ‘on’ and ‘off,’ qubits can be both at the same time. This is called superposition. This is what makes quantum computing powerful. It prepares people for uncertainty. It helps people do complex calculations that could take normal computer years to complete.

If used the right way, quantum computing can help businesses do things that we read about only in science-fiction novels.

How Quantum Computing Can Help Businesses?

Businesses like Google, IBM, and Alibaba have already started investing and experimenting with quantum computing. Google is using it to improve the software of self-driving cars, while IBM is providing a tool called Circuit Composer to allow developers to write quantum programs.

Alibaba has also signed a memorandum of understanding (MoU) with Quantum Computing Laboratory to do further research in the area of quantum computing and to find ways to use it commercially. It had also launched an 11-Qubit quantum computing cloud service

While we conceptually know how powerful quantum computing can be, let’s understand its business potential.

How Can Quantum Computing Benefit Businesses?

According to a Markets and Markets report, quantum computing will become a $283 million industry by 2024. Businesses like JP Morgan Chase are already experimenting with it to build a tailor-made portfolio for its customers. They are already running quantum computing to improve trading strategy, portfolio, asset pricing, and risk analysis.

Let’s also look at other areas where quantum computing can be used.

1.  Data analytics:

Each day, we collectively generate over 2.5 exabytes of data! Data comes in various forms – videos, photographs, text, and more! The list just goes on. Data is crucial for businesses. As data increases, businesses will have a tough time structuring it to gain business insights from it. That’s where quantum computing can help. Businesses can use quantum computing to sift through the humongous amount of unstructured data, analyze it, and gain some valuable insights on how to use the data for further growth. NASA, for example, is already planning to use quantum computing to analyze the data it collects about the universe for their research purpose.

2.   Forecasting:

In continuation of the point above, classic computing can only handle a limited amount of data. This can limit the potential of forecasting complex situations. Let’s look at weather forecasting. The meteorological departments would require an enormous amount of data to simulate different scenarios and predict climate accurately. Quantum computing can help in accurate forecasting. It can process a large amount of data and use different factors to predict the weather accurately and quickly. It can also potentially deliver economic benefits for countries and businesses as they plan for abnormal weather events or time weather-driven actions (like equipment maintenance) in advance.

3. Medical Research:

Here’s an interesting fact – it takes 12 years for medical researchers to find the best medication for various diseases and viruses with the existing normal computation. Quantum computing can reduce this period and even save R&D costs. If experts are to be believed, quantum computing can help researchers find drugs for treating various types of cancers and even Alzheimer’s.  It will give the researchers an in-depth understanding of the human body at a molecular level and develop drugs with few or no side-effects.

4. Pattern Matching

Volkswagen has been working on a solution that could inform drivers about traffic jams 45 minutes in advance. They used quantum computing to match traffic patterns and predict the behavior of the system. Quantum computing can help businesses find and predict future trends in their data to make more informed business decisions through such sophisticated pattern matching by getting to see beyond the obvious trends.

5. Logistic and Supply Chain Management

According to an IBM study, the entire manufacturing ecosystem can be an early beneficiary of quantum computing. Supply chain and logistics, for example, can be a good use-case for manufacturing businesses to try quantum computing. It can help businesses optimize their logistics by scheduling and planning routing to ensure just-in-time material deliveries. Quantum computing can also run through multiple models simultaneously to find the best route to deliver products in record time! Alibaba, for example, is already banking upon quantum computing to strengthen its hardware and network infrastructure.

What’s the Next Step ahead for Businesses?

Quantum computing is still at a nascent stage. However, given its benefits, businesses are willing to pump in money to research and experiment with it. It’s coming up to the time when more businesses will want to join in the efforts and become quantum-computing ready. To do that, businesses will have to prepare a strategy that would include identifying use cases that would benefit the most from the transition, experimenting with quantum computing, and charting the future roadmap. Of course, or the change to happen successfully, businesses should be willing to embrace change. That should be easy enough to do when you consider the possible benefits that could accrue.

How ThinkSys Is Ensuring It’s As Close As Possible To Business As Usual For Our Customers  

Dear ThinkSys Customer,

My heart goes to all affected by COVID – 19. I hope you are safe at home with your family.

These weeks have been challenging and inspiring for all of us at ThinkSys.

Challenging, as we had to completely change our way of working within a week.

Inspiring, because never in human history have we all collectively been asked to show so much resilience and tenacity in the common interest.

How thinksys helps customers at the time of crisis

The COVID – 19 pandemic may have caused mayhem throughout the globe, but work carries on. We know that companies depend on the work we do so this cannot stop us from delivering on our promise of serving and supporting our customers.

In the 2nd week of March, as the news flow about the potential impact of the virus grew, we started to worry about our employees’ well-being. First, to ensure their safety, we sanitized our entire campus and premises. We provided sanitizers at all locations. We even provided masks to people who came from outside the campus for interviews. It soon became apparent that the situation called for even more drastic measures.

Although none of our employees were detected with COVID – 19, and even before any of the local authorities had mandated it, we started thinking that it would be prudent to maintain social isolation. It seemed apparent that our employees would be safer in their homes.

But that could have led to an interruption in our service.

That’s why we took a few strategic decisions to ensure business continuity throughout the lockdown.

How Are We Ensuring Business Continuity For Our Customers?

We created Workplaces at our Employees’ Homes in Just Two Days

A week before India went into a 21-day lockdown, we, more or less, had decided to let our employees work from home. Rajiv Jha, the COO of ThinkSys and I, had a strategic operational call with our HR, Development, and Q&A team heads. We had a meeting with our team leads and reached a consensus that work from home was the best solution to keep our employees safe and our customer’s business going.

However, we had a challenge. Over 60% of our employees were using desktops in the office for their work. So, we had to get the infrastructure in place for smooth business functioning. We handed over an asset audit form to each of our employees to get a clear picture of the infrastructure like computing hardware, laptop, and internet connection available to them at home. It emerged that over 70% of our employees had the required infrastructure in place. Over the next few hours, we filled the infrastructural gaps. We had some available laptops that we provisioned for usage. We started shifting some desktops to other employees’ homes.

On 18th March, just a few days before the official lockdown began, we informed our employees to start working from home. Over the first couple of days, a very small number of employees came to the office to address backend issues, fix payroll, and some compliance tasks. Today, 100% of Thinksys is working from home.

We did a thorough Analysis of Business impact before taking the Decision

Before shifting the office into our employees’ homes, we wanted to get a complete idea of project complexity, client situations, and other unique challenges. Our COO had a one-to-one discussion with each of our 25 team leads and managers. All potential problems were identified, and we worked to create solutions. Our team leads took the responsibility (as always) of continuing project deliveries.

We even did the Unthinkable – we set Testing Labs at Homes

Some of our projects need multiple OS’ and devices and require our employees to work within a testing lab. We carefully provisioned that infrastructure at our employees’ homes so they can continue working on those projects.

All Data is 100% Secure

We have ensured that all confidential data is 100% secure. Our remote access is securely provisioned using VPN, and our network and security teams are monitoring the situation constantly to ensure that there are no data or security breaches from any of our employees. Rajiv Jha and I have also constituted a task force to monitor potential threats and eliminate them before they escalate.

We Continue to Maintain Operational Excellence

Our support teams and IT teams are working in planned shifts to troubleshoot issues that the remote employees might face while working. Our teams are working tirelessly to provide continuous support to our customers. We are doing everything within our power to maintain our operational excellence consistently.

We are always there for our Customers

Our customer’s business is as valuable to us as it is for them. I am available for our customers 24/7. We are continually monitoring the situation to ensure that there is no impact on the customer’s business. In case of any complaints or issues with our new work model, I can be reached at [email protected]. I will ensure that we solve the problem within the stipulated SLA duration.

We Care for our Employees as much as we do for our Customers

Our employees’ well-being is of paramount importance to me. Hence, we are constantly ensuring that our teams stay connected amidst isolation. It is heartening to see how our employees are managing their work and family with equal efficiency. We are grateful to them for their tireless contribution to keeping our customer’s business up and running.

We would also like to thank all our customers for trusting us and staying with us through these tough times.

I know it is a tough time for you as much as it is for us! But, The Show Must Go On!

We promise to stay with you in these trying times and continue to serve you always!

Stay Safe!

Regards,

Rajiv Jain

CEO of ThinkSys

What Is Edge AI And Why Should Enterprises Care?

The story of the journey of data in the 21st century has been eventful. First, data made the shift from on-premise data centers to the cloud. Now, it is moving towards ‘edge’ points located close to the source of data generation. The dual foundations of Edge AI lie in Edge computing and Artificial Intelligence, two innovative technology trends that have taken the world of business by storm.

Edge computing brings processing, computation, and storage of data closer to where it is generated and collected instead of relying on moving it to a remote location such as a cloud. Edge computing reduces the dependency on bandwidth and curbs long-distance communication between servers and clients. Goodbye latency.

That’s a strong value proposition in itself. Now imagine combining that with the power of AI.

In this article, we will discuss the nitty-gritty of Edge AI and why it can become important for modern enterprises.

edge ai why enterprise should care

What is Edge AI?

Edge AI combines the goodness of Edge computing with Artificial Intelligence. With Edge AI, AI algorithms are locally executed on a, usually remote, hardware device using the data acquired from Edge computing. This device consists of a microprocessor and sensors. Since the data is obtained and processed without using any internet connection, the process happens in real-time and at the exact spot where it can have the most impact.

Edge AI helps in replacing the conventional processing residing on cloud-based data centers that demand heavy computing capacity by making it a part of the AI workflow on a local device.

Edge AI facilitates real-time operations and reduces power consumption as well as data costs since the device doesn’t need to be connected to the internet at all times.

While Edge AI is transforming the way IoT devices deliver value, it is also creating a monumental difference in the way enterprises do business. According to a report by Markets and Markets, the global Edge AI software market size is expected to grow to USD 1152 million in 2023.

As enterprises surge towards embracing ever-more contemporary digital technologies, entwining Edge and AI promises many benefits.

Let’s discuss why enterprises should be caring about Edge AI if they aren’t already.

Why Enterprises Should Care About Adopting Edge AI?

  • Predictive Maintenance:
    To ensure streamlined operations, consistent equipment performance, and reduced cost of repairs, predictive maintenance is extremely critical for enterprises. Today, businesses are investing heavily in digital technologies that help them identify loopholes and failures before they happen, and Edge AI enables this. Edge AI makes use of AI devices and sensors to detect anomalies, without depending on connection-based responses, helping to quickly respond and fix errors.
  • Increased Customer Engagement:
    Today’s customers seek bespoke product experiences that also offer personalization. Every app, website, product or business needs to be engaging enough for the customers to really hit the target. With Edge AI, connecting with customers in real-time is possible. Anything and everything they need is available in an instant, thanks to Edge AI (think smart assistants and offline apps). Edge AI curbs the problem of low latency. To the enterprise, it provides real-time insights that can further help in providing enhanced user experience.
  • Improved Data Analysis and Processing:
    In comparison to the centralized IoT platforms, processes powered by Edge AI enable faster data processing. With Edge AI, the analysis and processing of data is done in milliseconds since it doesn’t have to depend on an off-site cloud location. This improves the speed of response and automates decision-making.
  • Greater Security and Privacy:
    Security and privacy are key concerns for enterprises and Edge AI helps in addressing those challenges. Edge AI-powered systems can trigger alerts in real-time based on behavioral analysis, help to detect phishing attacks, drive password protection and authentication, and examine network traffic patterns to protect corporate networks.  Here’s a great resource to dip into for more on How AI Can Help Combat Security Challenges?

Conclusion:

According to a report by Tactica, the shipment of AI edge devices is expected to grow up to 2.6 billion units by 2025. Several research reports show that AI is becoming more mainstream. As both these technologies mature, Edge AI looks set to enter diverse industries through an array of use-cases.This is the time for visionary enterprises to get serious if they want to be at the vanguard of an exciting new movement!

How AI Can Help Combat Security Challenges?

Today, technology is on the verge of taking over life and work. It is also altering the way we communicate, interact and the way we conduct our day to day transactions. Sophisticated technology is also redefining the way business functions. It may not be wrong to say that we have come to be dependent on technology. That is a bit of a blessing as well as a curse.

The critical question is,how equipped are we to combat the security challenges that come have also been enabled by the rise of technology?
In fact, are we really aware of the potential threats created by the digital revolution and can technology itself combat these security challenges?
Amid the various solutions that are being talked of as a way to combat some of these challenges, AI is showing some remarkable results in protecting data and privacy of the users with its incredible versatility. Be it network security, behavioral analytics, phishing detection or vulnerability issues; AI is stepping in to deal with it all successfully.

Today, the innovation edge of AI provides some of the best solutions that can combat the security challenges.

How AI Can Help Combat Security Challenges?

Let us find out how!

  1. Alerts Based on Behavioral Analysis:
    The behavior-analytics ability of AI can help curb a slew of cybercrimes. Every user has a signature – a specific distinctive pattern of online behavior. This behavior is determined by factors such as the devices that they usually use to login, the geography, IP addresses, typical time slots, their search history,etc. AI learns these behavioral patterns and records them to reproduce it while creating security alerts.
    An AI-enabled solution alerts the user immediately if there is any unusual activity such as login through an unusual device in a different geographical area, mismatched IP address, etc. So, if there’s any malintent lurking around, AI immediately brings it to the notice of the user and even blocks certain elements of the user’s online platforms.
  2. Protecting Against Phishing-
    Almost 1 out of 99 emails is a phishing attack. Phishing has, unfortunately,become quite prevalent today. It is a huge threat to organizations and individuals alike. A combination of AI and Machine Learning can play a crucial role in preventing these phishing attempts to avoid significant damages. AI can scan any phishing activity without being bound by any geographical barriers and can easily detect these wannabe scammers.
    AI can easily differentiate between a fake website and an authentic website. In fact, AI has been found to have the ability to detect close to 10,000 active phishing sites all over the world. AI is not only capable of creating alerts but is also designed to act immediately to avoid damage.
  3. Password Protection and Authentication:
    Passwords are often the last line of defense between cyber criminals and sensitive user-accounts. With users adopting simple passwords and also similar passwords across different accounts, hackers can easily isolate vulnerabilities and slip through a loophole to steal data easily. Biometric identification has become a strong wall between users and hackers;however, it can still be broken down by creative hackers. Even technology such as face recognition has not been fully successful in reducing cyber security attacks.
    Therefore, developers are now using AI in biometrics to make it stronger and more reliable. Apple’s facial recognition technology in the iPhone is a good example of how AI can protect fiercely, where other protection measures may fail.
  4. Protecting Corporate Networks:
    The security policies of a company can play a big role in protecting data.These security policies and a clear understanding of the network topography can determine the robustness of network security. Many a time,loop holes exist in these policies or gaps may creep into their implementation. Also, vulnerabilities in network architecture design can make the data and other assets of an organization vulnerable to potential intrusions and mala fide activity. They can also expose the network to malware and other attacks.
    AI studies network traffic patterns to enable the organization to configure and design the security policies accordingly. AI can examine the network design as well as the implementation to identify possible vulnerabilities that need to be fixed. This helps to direct the time and effort of the organization productively and effectively.

Conclusion-

Sadly, it is impossible to eliminate chinks in the security armor at all levels. Security threats and breaches are now a part and parcel of the digital revolution. Cyber-attacks are commonplace as unsavory elements try to exploit vulnerabilities for financial gain or even just for a cheap thrill. Every person’s and every corporate’s data is vulnerable. In that scenario, it is incredibly important to adopt comprehensive security measures today.In the race to stay ahead of the cyber-malcontents, AI looks like a promising option with its versatility and capability of mitigating risks and alerting users to possible threats.

5 Areas Where eCommerce Sites Can Gain From Artificial Intelligence(AI)

AI (Artificial Intelligence) is the new human-machine that understands customers better than anyone did before! The aim is nothing less than establishing a bridge between technology and its users by being more human-like. AI is being built to make life and businesses easy and convenient, and;eCommerce is one area that’s already feeling the AI magic!

A report by Boston Consulting Group (BCG) shows those retailers who have implemented personalization strategies, gain almost 6 to 10%; a rate that is two to three times faster than other retailers who haven’t adopted the same strategy.

In fact, AI is predicted to boost profitability rates by almost 59% in the wholesale and retail industries by the year 2035!

Personalization is the key to the enormous popularity of AI amid eCommerce.

AI brings with it versatility, convenience, easier transactions and much more! Amazon is an exemplary example of AI revolutionizing eCommerce. With its brilliant AI-driven support; Amazon has created a competitive advantage in the market based on intelligent personalization and uber-relevant customer service.

5 Areas where ecommerce sites can gain from AI

In line with the hype, AI has become the newest sales-magnet. One that redefines marketing goals and strategies and frees up manual time and efforts too. Online businesses are forging ahead with confidence in the power of AI!

Let us see 5 areas where eCommerce can gain from Artificial Intelligence!

  • Increased Personalization:
  • Gone are the days when the sales person had to personally persuade you to make a purchase. In the online world, AI is the new salesperson, much more empathetic and strategic. AI is intuitive enough to read your mind and deliver what you want within the committed time!

    It just takes a robotic human-like chatbot to empathetically enquire about your needs and answer all your questions smartly and elegantly, offering equally interesting and varied solutions! The AI presents the right information at the apt stage of the buyer journey to drive the sale forward.

    AI has enabled eCommerce merchants to know the preferences, likes, and dislikes of customers to serve them exactly with what they need.

  • Smart Purchasing:
  • AI helps customers to find what they need, even if they don’t quite know it yet!Smart assessments of the customer’s needs, the best-bundled offerings, and custom price points power a shopping cart that contains everything the customer could need. This pro-activeness enables the customer to make a quick purchase decision.  AI has created the era of smart purchasing where the technology drives intuitive purchasing options.
    Of course, that’s not the only purchasing where AI can help. Purchase-planning by the business is extremely critical to success in eCommerce. With intelligent systems and machines,they can have enough stocks to dispatch on time, making the necessary goods available to the customers at the right time.AI-enabled demand-forecasting makes purchase-planning for the eCommerce business easier by reliably predicting turnover, seasonal changes, and product trends.

  • Enhanced User Experience:
  • AI enables you to know your customers so well that you can tailor not just the products around their preferences, but also the messaging around their thoughts and sentiments. This sends out a clear message that ‘you care while you sell.’

    Enhanced customer experience is built around effective browsing and searching You should ask the questions, why a customer would prefer my website over others, what it is that can make my customer come back again and again. AI tools can also predict future purchase needs, follow-up and bring them around with the e-mailers and other digital real-time advertising.

  • Customer Relationship Management:
    A neglected customer may never come back to your eCommerce website. Today, it is tougher than everto create loyal followers and customers, or even retain loyal customers for that matter! AI consumes enormous amounts of data, processes it and analyses it to deliver insights around effective engagement alternatives for the customers that need it. This means knowing which customers to target with such strategies as well as creating effective retention strategies.
  • Bigger Sales and Profits:
  • It goes without saying that enhanced customer management, personalized marketing, and better outreach naturally lead to higher sales and profits in the eCommerce business. Convenience and speed matter to most customers. Virtual assistants and chatbots that help customers make the right decisions quickly help deliver exactly that.
    AI delivers profits through quick, action-oriented automated that makes customers want to come back and purchase more. In essence, AI saves time and effort for both, the customer as well as the eCommerce merchant.

Other Benefits of AI Technology:

All the elements that contribute to successful eCommerce business are linked and don’t really work in isolation. Along with a personalized online ambiance that enhances the customer relationship, here are some more advantages of AI in eCommerce.

  1. Inventory Management:
  2. AI’s predictive analytics helps shop-owners to know the present and future demands of the market and the customers. They can now accurately predict the real-time and future inventory needs and plan to make the goods available on time.

  3. Image Classification:
  4. AI is certainly changing the game with its ability to classify, interpret, and understand images. Image classification also gives the advantage of suggesting alternative and similar-looking goods in the form of images to provide customers meaningful options to choose from.

  5. Enhanced Security:
  6. Probably one of the most crucial aspects of online shopping is security. While customers and online users are worried about online fraudsters and fake marketers trying to get information; AI is coming to the rescue. AI solutions that detect patterns and learn from credit card transactions can detect advanced cybercrime techniques and also identify fake and genuine reviews.

Conclusion:

AI has the potential to drive an enormous impact for the eCommerce industry. Right from personalization, to recommending products, easier customer management, and enhanced user experience, the dynamics of online shopping have changed dramatically. Undoubtedly, the eCommerce brands that embrace AI have an advantage over their competitors in getting ahead of the curve to create that ‘wow’ factor for their customers.

What is Distributed Cloud and Should Enterprises Care?

Distributed Cloud Computing has already made it to Gartner’s top ten strategic technology trends of 2020. New-age business processes are being designed to make use of distributed cloud infrastructure, applications,and databases to address modern workflows and their requirements. With the advent of advanced technologies such as distributed cloud computing, companies have accelerated the move away from their traditional ways of thinking and are now open to making the cloud central to their business strategies.

What is Distributed Cloud and Should Enterprises care

To accommodate a huge number of customers and growing volumes of data, traditional companies are now making the strategic shift by embracing the technology necessary for them to become cloud-driven companies. Clearly, cloud-native giants such as Facebook and Google have not only successfully adopted cloud, but they are visibly reaping the benefits of their cloud move. The sense is that to gain a competitive advantage in the market, companies have to scale-up their cloud strategy. And those who won’t make the move will stagnate, or worse.

This is a paradigm shift for many,which also means that there are a lot of questions to be answered before and after they adopt these technologies.

But the most fundamental question is, should companies care about distributed cloud?How can it impact their business!

  1. Understanding Distributed Cloud Computing
    With distributed cloud computing,you get computation, storage, and networking in a micro-cloud located outside the centralized cloud. Edge computing is a very good example of distributed cloud technology. Distributed cloud computing establishes computing, closer to the end-user,thus enhancing security.
    This distributed cloud model brings more agility, thereby giving enterprises better business outcomes.

  2. Personalize Cloud Strategy
    The biggest advantage of the distributed cloud is that it allows companies to diversify and still personalize their cloud strategy. The distributed cloud also allows companies to pick and choose the various capabilities of different cloud providers and solutions to align with their specific business needs.What makes distributed cloud so popular is that it gives immense flexibility to companies to be able to pick different cloud providers, solutions, and infrastructures depending upon the type of applications they want to address.
    So, while companies want to make this shift, they also need to prepare. They will need to make changes to the existing infrastructure to create robust connectivity between different modules of the cloud environment.

  3. Prioritize Business intelligence
    Talking about impactful applications, enterprises now want to weave business intelligence into their IT infrastructure and distributed cloud computing is supporting this move. They are focused on what more they can do with the data available to them. The distributed cloud allows the scaling of data storage and processing capabilities for companies. This is allowing companies to adopt and take advantage of other advanced technologies such as AI, Machine Learning, etc. This integration is helping companies to leverage business intelligence and predictive insights for enhanced profitability.
    Enterprises can now go beyond capturing their operational data and embed intelligence into their core operations, bringing in more agility and productivity. This intelligence also facilitates capturing interactions between the company and the customers to analyze and evaluate products, services, and the effectiveness of overall business functions.
    Going forward, the cloud is also likely to play a bigger role in shaping the overall IT strategy of companies. Offering a more collaborative IT structure; it can help bring together operations data and customer data to create a better product experience and an enhanced workplace experience too.
  4. Data and enhanced Customer experience
    While the cloud facilitates data-processing in real-time, it also enables functions that manage that data.
    Better data management can lead to a meaningful rationalization of the data foundation. Clean, current, accurate, consistent, and relevant data can drive strategies for enhanced customer experience. Data management helps drive agility in responding to the needs of the customer. It can help in understanding the various preferences of the customers, enhance response methods, and power the building of long-term business relations built on a more customized solutions’ approach.
  5. Distributed Cloud and Security
    While enterprises are looking at distributed cloud computing as a boon, some of the companies that still hesitate to adopt cloud are doing so due to some perceived security issues. Distributed cloud has data distributed over a large number of machines and networks, wireless sensors, mobile devices, etc. This scattered data can mean higher risks of security breaches and can make data security in the cloud more complicated than centralized systems.
    However, a lot depends upon the individual company data compliance stance, IT regulations, governance controls, and data security administration policies.Companies that can address security concerns with well-managed cloud computing infrastructure stand to gain, while still staying secure.

Conclusion:

IT is becoming the backbone of business performance. But businesses are looking at solutions that can simplify the complex nature of their IT infrastructure. They want to make IT incredibly manageable,driving better business outcomes. The distributed cloud makes it easy to integrate new technologies within a seemingly simpler IT infrastructure.

While enterprises are already looking beyond just capturing operational data; this method of cloud computing offers a lot more in terms of flexibility, convenience, cost control, and, most importantly,nurturing specific business applications like enterprise intelligence!
There’s no doubt that 2020 will be the year where enterprises will dive into the distributed cloud. The business case is just too compelling.

Thanks to Big Data, Customer Experience is Transforming. Here is How

What is it that makes an Amazon or Netflix so popular among people?

Why is it that we go to a seemingly interesting website and then quickly leave it?

The answer may lie in the experience. You go to the Netflix app because you want to be entertained. You open the Amazon website because you want to compare prices and order a few things. Now, imagine if the design of the website or the app was confusing. What if Netflix did not give you personalized recommendations? What if Amazon did not categorize its items? You might waste a lot of time looking for what you want and might end up exiting the website or the app within a few seconds. For a digital-first company, that spells bad news.

Gone are those days when customer experience was just a way to stand apart from the competitors. Today, for apps and for software products, it is a necessity to survive in the hyper-competitive market. 86% of customers today are willing to pay a premium for excellent customer experience. A study by Walker suggests that in 2020, customer experience will overtake price and product as the key differentiator.

So, how do you create an experience that will make your customers stay on your platform?

To begin with, how do you even determine what good experience means to your customer?

The answer may lie in big data.

Thanks to Big Data, Customer Experience is Transforming. Here is How

How has Big Data Helped Companies to Improve Customer Experience?

Companies have increasingly started relying on data to find ways to make the customer’s experience frictionless.

However, with terabytes and zettabytes of data generated every year, companies find it hard to collect, analyze, and interpret the data correctly.That’s when they use technology like big data.

Big data extracts information from large data sets systematically to enable businesses to make informed decisions.

Here’s how.

  1. Helps in Understanding the Behavior of the Customers:
    When you operate in a physical retail space, it is easier to observe the behavior of people and provide them what they need. But how do you emulate the same thing online? That’s where big data helps. With big data and analytics, you can find out how people interact with your product or website, how they prefer to engage, what prevents them from taking action?
    You can mine insights from customer support queries and online forum responses about what irks the customer? What features are they looking for? Understanding the behavior of the customer will enable you to make improvements to your product in line with the needs of the customer.
  2. Helps in Rectifying Customer Pain Points:
    Taking the previous point further, big data helps you to rectify the issues customers face while using your product. Let’s take Amazon for instance. What made Amazon introduce the 1-click ordering feature on their mobile app?
    It was not a random decision. Amazon realized that most customers backed out on having to enter all their information manually during the checkout process. With the 1-click option, the users can place an order without entering any details. This saves their time and increases conversions for Amazon.
    Once you gather information on what prevents your customers from deriving the necessary value from your product, your UX and UI designers can rectify the design or you can take the call to add a much-needed feature.

  3. Helps in Improving Visualization of the Website or App:
    To design a great product, you have to determine what you want the customers to do and then create the interface such that it helps them do that. Visualizing such a design might look simple, but if you want the design to be effective, you have to turn to big data. Let’s take Instagram’s example.
    Data revealed to the widely popular photo-sharing app that people were unable to find relevant images and videos on their feeds. So, they altered the feed from reverse-sequential order to display posts that they thought people would like to see, like, and share. This helped them to customize the data feed for their users and led to more popularity.
    The data collected through photos and videos let visitors discover new interests relevant to them. So, if you are planning to revamp the design of your product, read the data you collect from your customers and mine those insights to improve the usability.
  4. Removes all guesswork and leads to data-backed experiments:
    Every product change will cost money for your company. It could also earn you bouquets or brickbats depending upon how your customers react to the change. So, if you want to experiment with your UX or UI, conduct data-backed experiments. Conduct usability tests on a small batch of loyal users to know their feedback on the changes and use those insights to improve or modify the design.
    Turn to predictive analytics to get a sense of what could happen. This saves time and money and helps you focus on strategies that have a greater chance of working.
  5. Creates personalized interface for every user:
    Think of the Amazon homepage showing recommendations of products to buy or the personalized content recommendations by Netflix. Conditioned by the experience provided by apps like these, even enterprise and business users are demanding greater levels of customization.
    And the app and product experience can be so much more meaningful to your users too,thanks to big data. Analytics helps you bring that relevance in your products that encourages higher engagement.

Conclusion:

Until a few years ago, software products never thought of combining art and science to improve the customer experience. Today, customer experience has become more data-focused and customer-centric. And all credit for this significant shift must go to big data and analytics. Analytics has enabled companies to adopt science to create personalized experiences that make every customer feel special.

React Native Vs Flutter App Development

The past few years ushered us into the era of mobile applications, where mobile apps have become an integral part of our everyday life. Be it Netflix, Facebook, Instagram, Uber, Skype, or more mobile applications are trending excessively in this day and age. However, with this increasing dependency on mobile applications, the need for niche technologies, frameworks, and platforms is also rapidly increasing, giving way to the advent of new frameworks and platforms that allow developers to create cross-platform apps that are suitable for all platforms.

From simplifying the app development process to making them efficient, these frameworks are helping reduce the complexity of mobile application development and are hence trending among developers. Among these, Flutter and React Native are the two most popular Cross-platform Mobile App Development Frameworks that are enabling developers to create cross-platform mobile applications that work seamlessly across various platforms and devices. Moreover, these two frameworks are competing against each other to prove their worth, making Flutter Vs React Native the most trending topics of the year.

Reactnative vs Flutter

So, let’s try to determine, “What is the difference between Flutter and React Native?” and answer the important question, “Will Flutter replace React Native?”. But before we delve deep into this discussion on React Native vs Flutter, it is important that we understand the need for cross-platform development frameworks.  

The Need for Cross-Platform Mobile Development Framework:

Nowadays, Android and iOS are two of the most widely used mobile platforms, with a completely different application development process. Android requires developers with extensive knowledge of Java or Kotlin, whereas iOS needs developers well versed in Swift programming language, making the development process expensive and time-consuming.

Cross-platform app development came into the inception to overcome this drawback and has become the need of the hour. Industries are using frameworks like React Native, Flutter, Xamarin, PhoneGap, and more, to create cross-platform applications, as they enable a single team to create apps with a single code base that works on multiple operating systems (OS), like iOS and Android. Due to this, most of the applications developed today are either cross-platform or hybrid and can run seamlessly on iOS and Android. Other advantages offered by cross-platform mobile application development frameworks are:

  • It offers UX uniformity.
  • Ideal for prototyping.
  • Requires one team to create one product for two or more platforms.
  • The code can be reused across platforms.
  • Quicker Development.
  • Easier Implementation.

Now that we know the reason for the shift from native app development to cross-platform and hybrid app development, let’s compare the two important cross-platform mobile app development technology.

React Native: Understanding the Basics:

Launched by Facebook in 2015, React Native is an open-source JavaScript framework built upon the React library and used to build natively rendering, mobile applications for iOS and Android. One of the most reliable and popular JavaScript frameworks used for developing mobile apps, React Native combines native components with React, the best-in-class JavaScript library for building the User Interface (UI). React Native enables developers to create react native apps for iOS, Android, Windows, and Linux, though the latter two require dependency managers like HomeBrew package manager.

Used by Facebook, Instagram, Airbnb, Skype, Tesla, Walmart, etc. and backed up by a huge developer community, React Native popularity is tremendous due to its ability to build applications efficiently, in less time as well as its use of Node Package Manager (NPM) for installation, excellent UI rendering, GPU oriented application development, seamless integration and quick load time, etc. Additionally, its features like platform-specific code and hot reload make it a common choice for developers for mobile application development.

What is Flutter?

Flutter, one of the biggest React Native competitors, is a free and open-source mobile UI framework created by Google and released in 2017. Though new to the spectrum of mobile application development, it is gaining popularity and momentum among web and mobile developers for creating native applications, with a single codebase. In short, with Flutter, developers can use one programming language and codebase to create two different apps for different platforms.

Unlike React Native, Flutter does not use JavaScript, but rather a less known programming language Dart, which was created by Google in 2011. Dart programming language is focused on front-end development and can be used to create applications for both web and mobile. Though most of the systems are implemented in Dart, the factor that differentiates Flutter from other mobile application SDK is that it has a thin layer of C++ or C. Moreover, it is supported on Android Studio, IntelliJ Idea & Visual studio code.

Flutter Vs. React Native: Key Points

From being open-sourced, fast and free to offering excellent UI support and native-like experience, React Native and Flutter, two major competitors offering cross-platform solutions, share various similarities. However, there are certain aspects of these two frameworks that make one framework superior from the other, which will be highlighted in the following comparison:

  1. Programming Languages: Flutter Dart vs React Native JavaScript
  2. A major advantage of using cross-platform mobile app development frameworks is it allows developers to create applications for both iOS and Android using a single programming language.

    • Flutter uses Dart programming language to create a Flutter app. Though new for mobile application developers, Dart is easy-to-use for developers experienced in different OOP languages such as Java and C++.
    • React Native: Uses JavaScript to build cross-platform apps. Extremely popular among developers, this programming language helps web developers build apps with little training and hence is a winner compared to Flutter. 
  3. User Interface:
  4. As React Native is purely focused on UI design, it has a large number of React UI components that are more extensive than that of Flutter. This is because application development with React Native is highly based on native components, whereas Flutter works flawlessly with the owner widget sets. One advantage of these widgets is that they prevent the developer from being dependent on third-party UI libraries. 

  5. Development Time: 
  6. Development time and process are the two most critical aspects that need consideration during mobile application development. Flutter, prominently known for fast and simple development, is lauded for its hot reload feature, which enables developers to instantly view changes and implement modifications. Moreover, it provides a full suite of extensible components that are built from scratch. Whereas, React Native, though popular, relies heavily on third-party libraries, which becomes makes the process comparatively slower. 

  7. React Native Vs. Flutter Performance: 
  8. React Native, though popular for providing high-quality user experience, is considered less suitable for application development because of its architecture, which impacts its performance and makes it slower. Whereas, Flutter’s ability to reuse the same code for creating applications for different platforms as well as its use of widgets and GPU to render it on apps on the screen helps create apps with best-in-class performance and speed.

  9. Documentation & Toolkit: 
  10. Google, like always, provides clear, structured, and in-depth documentation for their products, with Flutter being no exception. From installation to widgets, testing, more Flutter offers proper documentation for all and is backed by the Flutter team. React Native lags behind in this aspect, as it has a poorly maintained and unclear documentation, with not much explained like installation and configuration setup. Moreover, it lacks official documentation for Continuous Integration & Continuous Delivery (CI/CD).

  11. Technical Architecture: 
  12. Another important aspect that needs consideration when comparing React Native and Flutter is technical architecture. 

    • Flux Architecture: 
    • As React Native relies heavily on JS runtime environment architecture known as JavaScript bridge, it uses Facebook’s Flux architecture, which helps it to communicate with native modules. This though beneficial results in poor performance. 

    • Skia:
    • On the other hand, Flutter uses Dart Framework, which has most of the components inbuilt and does not require JavaScript bridge to communicate with the native UI component. Moreover, it further uses the Skia C++ engine which consists of all the protocols, compositions, and channels needed to develop a mobile app. This independency of Flutter makes its architecture more beneficial than React Native.

  13. DevOps and CI/CD Support: 
  14. To ensure an application receives continuous feedback and is not released with bugs, it crucial to adopt Continuous Integration and Continuous Delivery practices. This is ensured by Flutter, in its section on Continuous Integration and Testing, where its rich Command Line Interface (CLI) allows easy set up on CI/CD services strong CLI tools, whereas, React Native does not provide any instructions on CI/CD practices.

  15. Installation: 
  16. The installation process with Flutter is more straightforward, with the added advantage of automated checkup of system problems. On the other hand, react-native lacks a streamlining setup and configuration. 

  17. Development Tools: 
  18. Some of the tools used in both React Native and Flutter mobile application development are:

    1. Flutter: 
      • Flutter SDK.
      • DevTools.
      • Hot Reload.
    2. React:
      • Expo.
      • Redux.
      • Flow.
      • Ignite.
      • React Navigation.

    Conclusion:

    React Native is currently ruling the spectrum of cross-platform mobile application development, however, there is no doubt that Flutter is working hard to prove its worth and is slowly taking over the future mobile application development, by making the development more streamlined, introduction of new features and functionalities, as well as by saving the developer time and effort. However, it is still too soon to answer to questions like “Is Flutter better than React Native?”, as Flutter is still climbing the ladder to achieve the popularity and reliability that React Native is currently enjoying. 

Angular vs Reactjs: Our Updated View

Our previous article touched upon the key features of AngularJS and ReactJS. But it’s been a while since then. Technology never stands still. There is now a considerable debate over which of those is better suited for front-end development. And here we are again with an updated account of what differentiates the two technologies, and which could be the better option for specific situations.

Web development is progressing at lightning speed. Something that was hot and relevant in 2017 may be seen as archaic today. Today, users have even more control and power over what they want. Product and app companies are being forced to frantically try and stay on-trend while meeting those growing demands.

In the software and mobile app universe, two technologies are being spoken of far and wide- Angular and ReactJS

While Angular is an MVC framework, React is a JS library that handles only the Views in an MVC framework. Despite, or perhaps because of, some foundational differences between the two technologies, there are several debates about the capabilities and preference of one over another. 

angular vs reactjs

Angular handles many functionalities out of the box and is centered around the concept of an application. React is more lightweight and does not handle quite as many functionalities out of the box. It comes with the concept of components, each with their own properties.

With that snippet of an introduction of both technologies, let’s try to (again) nail down the differences between them to help you pick one best suited to your unique needs.

Angular vs. ReactJS

With several out-of-the-box functions, Angular can help get you started quickly without feeling overwhelmed or intimidated by choices. Usually, developers feel at home quicker and switch between roles more comfortably with Angular.

For the web app-focused folks, while all Javascript frameworks have SEO capabilities, ReactJS does a particularly great job here. Run ReactJS on the server and the virtual DOM will be returned to your browser as a consistent web page.

Earlier, Angular was built to offer ease of software development, particularly through the use of modules. In that light, Angular also provides ease of testing, another advantage of the technology.

ReactJS Native is more focused around UI, unlike Angular. It allows for a more responsive interface with Javascript communications between the Native environment of a device and the framework. Therefore, it impacts the app’s load time and keeps it running seamlessly without interference.

Use cases of Angular vs. ReactJS

Use ReactJS when there is a lot of dynamic content in your software or applications. Top brands such as Facebook, Netflix, Uber, Dropbox, PayPal, Flipkart, and Instagram prefer ReactJS to drive their apps for this dynamic nature.

ReactJS could also be the choice for you when you plan on expanding the functionality of your mobile app in the near future.

On the other hand, choose Angular if you are just starting with app development, and want to be done quickly. Angular may also be the right choice out of the two when you are looking for a robust and well-maintained framework. Angular may work better when you have a team of experienced developers with a good hold on TypeScript.

Performance of Angular vs. ReactJS

React is racing ahead in the popularity charts for its fantastic rendering speed. The technology derives its name from its ability to react to change with minimal delay. In response, Angular 2 has been tried to improve its performance by modifying its ‘change detection’ algorithm.

When it comes to smaller applications, both technologies stand neck to neck. However, when apps get more complex and larger in size, React outperforms Angular. React can also be easily combined with Redux or Flux to build bigger applications.

React developers also have the benefit of a built-in virtual DOM feature that allows a server to save a light DOM tree, leading to less loading time on browsers and high performance.

The Learning Curve of  Angular vs. ReactJS

One of the distinguishing factors of ReactJS is that it is easy for anyone to learn. Since it is purely based on JavaScript, even newbies can familiarize themselves with the technology in no time. The learning curve associated with React is shallower and better suits beginning developers while Angular comes with a steep learning curve. This is essentially because developers are expected to be well-versed with the additional dependency of TypeScript, a statically typed programming language from Microsoft.

Why Choose Angular vs. ReactJS?

Choose Angular for its component structure, where you can utilize its components with several frameworks without having to stretch. Angular binding permits you to build a simple-to-maintain, logical connection between the model and the data view. It also allows for smooth testing and the flexibility of choosing your preferred environment for app development.

Choose ReactJS for its Virtual DOM, which increases the speed of the framework. Also, for its Simple State Machine components that allow you to modify the state of an object and apply all updates with ease. Developers also love the fact that the view is not disparate from the logic in ReactJS. That, and it’s fast to learn.

While there is no black and white here, the situation your product or app is in will help you make an educated decision on whether Angular or ReactJS would better suit your needs. For further assistance, reach out to us, and we will help you define the best roadmap for your application development needs.

The Special Role of Regression Testing in Agile Development

Presumably, everyone here who has developed products knows that regression testing is done to validate the existing code after a change in software. Unlike most other testing, it validates that nothing got broken in the already existing functionality of the software product even as changes were made to other parts. In a nutshell, the aim is to confirm that the product isn’t affected by the addition of any new features or bug fixes. Often, older test cases are, re-executed for reassurance that there were no ill-effects of changes.

role of regression in agile development

Regression testing is necessary for all product development where the product is evolving, that is, in effect for all products!

Which Brings is to Agile Software Development?

The Agile method calls for rapid product iterations and frequent releases. Obviously, this includes shorter and more frequent testing cycles. This is to ensure that the quality of the output of the sprints is intact whenever the software is released. These constant churns call for a massive focus on regression testing.

A sound regression testing strategy mainly helps the teams focus on new functionalities and maintain stability as the product increments take place. It makes sure that the earlier release and the new code are both in-sync. This is how the software’s functionality, quality, and performance remain intact even after going through several modifications.

To put things into perspective – the Agile method is all about iterative development and regression testing is all about focusing on the effects that occur due to that iterative new development.

What Makes Regression Testing Special in Agile Development?

  • Helps Identify Issues Early– One of the ways in which Agile teams build their regression testing strategy is to identify the improvements or the error-prone areas and gather all the test cases to execute for those cases. This preparation helps them gear up for the accelerated tests and also, prioritize the test cases. This way they can target the product areas that need more focus on quality. Additionally, by detecting defects early in the development cycle, regression testing can help reduce excessive rework. This helps release the product on time.
  • Facilitates Localized Changes – Regression testing makes it possible for development teams to confidently carry out localized changes to the software or sometimes, even for bigger changes. The teams mainly focus on the functionality that they planned for the sprint secure in the knowledge that the regression tests will highlight the areas that are affected by the most recent changes across the codebase.
  • Business Functionality Continuity – Since regression testing usually takes into consideration various aspects of the business functions, it can cover the entire system. The aim is to run a series of similar tests repeatedly over a period of time in which the results should remain stable. For each sprint, this helps test new functionality and it makes sure that the entire system continues to work in an integrated manner and the business functionality continues in the long run.
  • Errors Are Reduced to a Large Extent – The thing with an Agile development environment is that there is a reduced scope for errors during the accelerated release cycles. The series of regression tests at each level of the release ensures that the product is robust and resistant to bugs. This helps in enhancing the software’s stability and improves its overall quality.
  • Offers Scope to Add Better Functionalities – Introducing new functionalities in any application can be time-consuming because there are several aspects that need to be taken into consideration. This process becomes less cumbersome with Agile development, which can boost gradual changes. Regression tests amp up the power of the methodology by giving the scope of introducing several functionalities in seamlessly.
  • Quicker Turnaround – There are multiple tools for regression testing. It’s also possible to automate significant portions of the regression testing given the repetitive nature of the tests. This offers the Agile development team faster feedback. They can achieve faster turnarounds and can accelerate releases confidently.

To Sum Up:

Regression testing is a staple while developing a well-integrated, robust software as it evolves. In the accelerated Agile environment, it helps ensure that any newly developed sprint has no adverse effect on the existing code or functionality of the business. Furthermore, a carefully considered regression testing strategy helps the Agile teams be confident that every feature in the software is in perfect condition with all the updates and fixes required. It’s the insurance policy that Agile product development teams need.

Where AI Could Fall Short In Software Testing?

We have written earlier how Artificial Intelligence can increase the efficiency and speed of software product development. Now that AI in software development is gaining acceptance, let’s look at how AI can play out in software testing- its potential as well as shortcomings.

Where AI could fall short in Software Testing

After test automation, AI-based testing looks like the obvious next step. Here’s how things have rolled out in the software testing space:

  • Traditionally, manual testing has always had a role to play, because no software is produced sans bugs. Even with all the tools available, a key part of the process is handled manually by specialized testers.
  • Over time, test automation took root. In several cases, test automation is the only feasible approach when you need to run a large number of test cases, fast and with high efficiency.
  • AI-enabled testing is making test automation smarter by using quantities of data. QA engineers can feed historical data into algorithms to increase detection rates, implement automated code reviews, and automatically generate test cases.

Let’s take an overview of what AI can do in Software Testing.

The Potential of AI in Software Testing:

​As organizations aim for continuous delivery and faster software development cycles, AI-led testing will become a more established part of quality assurance. When considering only software testing tasks, there are several tasks that quality Assurance engineers perform multiple times. Automating them can drive huge increases in productivity and efficiency.

In addition to the repetitive tasks, there are also several tasks that are similar in nature, which, if automated, will make the life of a software tester easier. And AI can help identify such fit cases for automation. For instance, the automated UI test cases that fail every time we make a change in a UI element’s name can be fixed by changing the name of an element in the test automation tool.

Artificial Intelligence has several use cases in software testing, including test case execution, test planning, automation of workflows, and maintenance of test cases when there are changes in the code.

But what are the limitations?

Why AI will not take over entire QA phases?

Even though Artificial Intelligence holds strong promise for testing, it will be hard for mere technology to completely take over.

  1. Humans need to oversee AI:
  2. Artificial Intelligence can’t (yet) function on its own without human interference. Until then, organizations need human specialists to create the AI and to oversee operational aspects that are automated with AI. In short manual testers will always be a part of the testing strategy to ensure bug-free software.

  3. AI is not as sophisticated as human logic:

    While there have been significant advancements in Artificial Intelligence, it does not beat the logic, intuitiveness, and empathy inherent in humans. AI will bring about more impactful change in the way it assists software testers to help them perform their tasks with more accuracy, precision, and efficiency. But for all tasks that need more creativity, intuitive decision making, and user-focused assessments, it may have to be human software testers who hold the fort. For a while at least!

  4. AI can’t, and never will, eliminate the need for humans in Testing:
  5. Organizations can use AI-based testing tools to cover the basics of software testing, and easily uncover defects by auto-generating test cases and executing them for desktop or mobile. However, such an approach isn’t feasible when you need to assess a complex software product with various functions and features to test. Experienced software QA engineers bring a wealth of insights to the table that goes beyond the data. They can make the decisions that must be made even when data doesn’t exist. When a new feature is being implemented, AI may struggle to find enough solid data to define the way forward. Experienced software testers may be better suited to such situations where they can make intuitive leaps based on nothing more than their judgment.

  6. Functions in Software Testing that can’t be entirely trusted to AI:
  7. AI can seamlessly help with tasks that are repetitive in nature and have been done before. But, even if we leverage AI to its full potential, there are jobs within QA that demand human assistance.

    • Documentation Review – Comprehensively learning about the ins and outs of a software system and determining the length and breadth of testing required in it is something better trusted to a human.
    • Creating Tests for Complex Scenarios – Complex test cases that span several features within a software solution may be better done by a QA tester.
    • UX Testing – User experience can be tested and assured only when a user navigates the software or application. How something looks to the users and, more importantly, how it feels to them, is a task beyond the likely capabilities of AI.

Just like automation aims at reducing manual labor by addressing monotonous tasks, AI-led QA minimizes repetitive work with added intelligence by taking it up a notch up.

This means QA engineers should keep doing what they do best. However, it will help QA testers to familiarize themselves with technologies AI to advance their career when these tools become commonplace. The truth is that AI is making a stand, but we still need diligent, creative, and expert QA engineers on our product development teams.

How Microservices Comes Together Brilliantly with DevOps?

Do you know what’s common to Amazon, Netflix, and NASA?

All three of them use DevOps.

Amazon uses it to deploy new software to production at an average of every 11.6 seconds!

Netflix uses it to deploy web images into its web-based platform. They have even automated monitoring wherein they ensure that in the event of a failure in implementing the images, the new images are rolled back, and the traffic is rerouted to the old version.

NASA, on the other hand, used it to analyze data collected from the Mars Rover Curiosity.

It’s become such that every organization that focuses on quick deployments of software and faster go-to-market uses DevOps.

How microservices comes together brilliantly with DevOps?

Statista reveals that 17% of enterprises had fully embraced DevOps in 2018 as compared to 10% in 2017.

Given the advantages, these numbers will only grow every year as companies transition from the waterfall approaches to develop fast, fail quickly, and move ahead on the principles of the agile approach.

But for DevOps to deliver to its fullest potential, companies need to move from the monolithic architecture of application development to microservices architecture.

What is Microservices Architecture?

Unlike monolithic architecture, where the entire application is developed as a single unit, Microservices structures applications as a collection of services.It enables the team to build and deliver large, complex applications within a short duration.

How can Microservices Work with DevOps?

Microservices architecture enables organizations to adopt a decentralized approach to building software. This allows the developers to break the software development process into small, independent pieces that can be managed easily. These developed pieces can communicate with each other and work seamlessly. The best part about microservices architecture is it allows you to trace bugs easily and debug them without leading to redeveloping the entire software. This is also great from the customer experience perspective as they can still use the software without any significant downtime or disruption. It’s a perfect fit for organizations that use DevOps to deploy software products.

No wonder organizations like Netflix, Amazon, and Twitter that were using a monolithic architecture have transitioned towards a microservices architecture.

Let’s look at the benefits of Combining DevOps with Microservice Architecture:-

  • Continuous Deployment: Remember the Netflix example we gave at the beginning about how Netflix reroutes the traffic to the old version of web images if they are not deployed on time? Imagine if Netflix still used monolithic architecture or the waterfall method of software deployment, do you think they would have been able to give the same kind of customer experience you witness today? Most likely, not! Microservices architecture coupled with DevOps enables continuous delivery and deployment of software, which means more software releases and better quality codes.
  • More innovations and More Motivation: Imagine working on a product for 2-3 years and then knowing it is not acceptable to the market!It becomes hard to pivot too. Often you realize that there are several bugs, the process has become unnecessarily lengthy, and you have no clue which team is working on what. Wouldn’t it lower your morale? However, those days have gone. Today, organizations have transitioned from a project to a product approach. There are smaller decentralized teams of 5-7 people that have their own set of KPIs and success metrics to achieve. This allows them to take ownership of “their” product and it gives them better clarity on the progress. It also gives them the freedom to innovate, which boosts their morale.
  • High-quality Products: With the power of continuous deployment and the freedom to experiment and innovate, organizations can continuously make incremental changes to the code leading to better quality products. It allows teams to mitigate risks by plugging the security loopholes, make changes to the product based on customer feedback, and reduce downtimes.

As you can see, using DevOps and microservices architecture together will not only boost the productivity of the team, but it will also enable them to develop a more innovative and better quality product at a faster pace. It helps product teams develop products in a granular manner rather than taking a “do it all at once” approach.

However, to embrace DevOps and microservices, you have to ensure that your teams understand the core benefits and make the most of the change.

Teams usually work in silos – the development team works independently, the testing team does its job, and so on. There is an obvious gap in communication, which leads to a delay in completing development and testing. DevOps and microservices require teams to work in tight collaboration. You will have to foster an environment where there are cross-functional teams of testers and developers communicating and working together to complete a task. This will help the teams to accelerate the process of developing, testing, and deploying their piece of work at a faster pace.

Of course, it is not easy to introduce a culture of collaboration, given that people are accustomed to working in silos. Hence, it is essential to reduce friction before starting the initiative. Once everyone shares in the vision and understands their own role in getting there, developing products with DevOps while leveraging a microservices architecture will become much easier.

Key Considerations While Shifting to Microservices Architecture

 

What Exactly is a Microservice?

Microservices is a method of structuring a software or an application as a loosely coupled service. Each service executes one function and is a component of the system that can be developed, maintained, and scaled individually. Each module supports one goal and uses a simple interface to communicate with other services.

Traditionally, software programs were built using a monolithic approach, where the entire software was built as a single unit. For eg., the entire unit would be responsible for database operations, executing business logic, handling HTTPS requests, background processing, client communication, user authentication, and so on.

Key considerations while shifting to a microservices architecture

In this structure, every tiny change in the system would require rebuilding, checking, and deploying the entire application. Change, even in a line of code, would prompt developers to make a new build of the complete system, which was time-consuming.

It became clear that the monolithic architecture was holding back innovation, scalability, agility, and independence. The microservices approach brings all this and more. Where there is a large system with intricate component management, scaling, and individual development, microservices has become the obvious choice.

However, making that move from monolithic to microservices isn’t easy. There are several considerations to factors in.

Key Considerations for Businesses Shifiting To Microservices Architecture :

  • Degree of Independence – The first thing to consider is the level of independence you want for the services in your microservices architecture. In the first approach, each service is completely independent with its own UI and database. This would be an instance of an extreme microservices architecture where services are entirely decoupled and share nothing. The difficulty arises in ensuring all datastores stay in sync and updated at all times. To rectify this, in the second approach, you can choose to share some components between the services, such as the database. This would make it easier for you to ensure data consistency and enforce data standards, regulations, and compliances within the software.
  • Technology Stack – It’s hard enough to decide a tech stack for a monolithic application. Now, imagine doing that for each service within a microservices architecture. If your services are heterogeneous, this could create an issue with standardization. Moreover, it becomes harder for your people to move between teams if every team uses a different tech stack. A recommended approach is to take a balanced approach in deciding the desirable tech stack across the application. If a team wants to override the default choice, they will have to support their decision with the pros and cons of the change that compelled their decision. The ideal tech stack may include the cloud provider, infrastructure, storage, monitoring, programming language, and a testing and logging framework.
  • Complexity – Microservices impacts the operational complexity of the underlying application. You need to consider aspects such as the infrastructure which needs to be scaled up and down for a sophisticated level of automation. Load balancing and scaling, which will happen either for all services or only a few, will be a concern. Service discovery as services in a microservices world changes dynamically due to upgrades and scaling and that should be part of the consideration set. As should be monitoring, which needs to be configured for each service individually. And, think of the capability to handle scenarios when a subset of services are scaled up or down.
  • Decouple Capabilities – There is a definite cost associated with moving to microservices from monolithic systems. Therefore, you’d want to consider what capability your systems have that can be decoupled and how you can migrate incrementally. Assess this in your system by taking a look at the operational readiness for creating services or migrating them. The primary idea is to start with capabilities that are fairly decoupled right in the monolith. These could be services that don’t require changes to several client-facing applications that use monolith and don’t use a data store. After decoupling simple edge services, consider those deeply embedded in the monolithic architecture.
  • Continuous Delivery – As Martin Fowler points out in his article on microservices tradeoffs, easily deploying small independent units is a blessing for development, but operations get more complicated as a few applications become hundreds of microservices. This reinforces the vital role of continuous delivery. While CD is a valuable skill for monoliths, it’s absolutely essential for microservices. Organizations such as Netflix and Amazon have spent their energy in building homegrown custom continuous delivery pipelines for microservices. As an alternative, organizations can choose a CD automation platform for a less intensive choice.
  • Data Services – Refactoring the underlying data structures is one of the most complex issues of migrating to microservices. There are several models that you can follow. Use reference data to populate drop-downs in GUIs, Master Data Management to eliminate several views of an entity such as the customer within a database, flat object structure to store documents such as feedback surveys, independent tables to support data retrieval with SQL, and blob storage for storing a structured Java object, for instance.
  • Team Organization – Lastly, you must reorganize your teams to ensure that all services are developed, deployed, and maintained independently. You may need an independent team to work on each microservice because when engineers work on multiple microservices, they might make optimization decisions that are not in the best interest of all associated services. Each team may need to have capabilities such as development, testing, Ops, database administration, UX, and product management. The central idea is to organize teams for maximum optimization of each microservice, without dependence on other teams.

Challenges of Shifting to Microservices and Why you need a Reliable Partner:

As you would have inferred, migrating from a monolithic architecture to microservices isn’t a job for everyone. When you need to make so many key considerations and weigh several pros and cons, you need specialized assistance from a reliable partner.

At ThinkSys, we make microservices possible even when you have a hyper-complex monolithic application architecture. We can ease the many hiccups you might encounter in that shift. 

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