How Future Tech has Evolved with COVID-19

Relatively unfamiliar until a year ago, the word ‘pandemic’ now dictates our daily lives. Unlike other similar events like the Spanish flu or the Plague, the COVID-19 pandemic has happened in an era where the rate of technological development is at a historic high. For instance, Viraj Tyagi, CEO of eGov Foundation, says that the COVID e-pass platform was developed and deployed in eight states within 72 hours of the implementation of the nation-wide lockdown. With trends like remote monitoring of the sick, work-from-home and online shopping becoming the new normal, future tech plays a huge role in how we have adapted to the changing situation.


In this article, we will look at how technology has helped to address the COVID crisis, especially the fields of data science and analytics, Artificial Intelligence (AI) and UI/UX design. We’ll draw inspiration from this for what 2021 will hold and how you can take the driver’s seat for your career.

Future Tech: How COVID is Shaping Tech and Vice Versa

The early fear that career opportunities in tech will fall during the economic downturn is being replaced by the confidence that technology can ably complement human efforts and create jobs. India is at the forefront of this wave: BBC reported that researchers at CSIR-Institute of Genomics and Integrative Biology (IGIB) successfully created a paper-based coronavirus testing kit that, at about Rs 500, would give results in an hour!

With Silicon Valley companies steadily investing big in India and a third IT outsourcing wave expected, the prospects for tech jobs are encouraging. Let’s look back at the most important developments this year and what it means for future tech in the post-COVID world.

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#1 Domain-specific AI/ML Gains Popularity

AI helps to reduce manual effort in an activity by making machines mimic human behaviour. This came in handy, especially in the early days of the pandemic, when it was essential to make sure that the least number of people were exposed to the virus. In 2020, AI technologies changed diagnosis and healthcare delivery.

2020 Use Cases

AI tools helped create simple and effective ways of testing for the virus. A voice-based diagnosis application created by students in Mumbai analyses voice samples according to parameters like frequency and noise distortion and delivers results after comparing them to that of a healthy person.

Robots help reduce patient contact in hospitals and care centres. MeD Robo, fabricated in Visakhapatnam, delivers food, medicines and monitors the temperatures of COVID patients, effectively shielding healthcare workers from frequent interaction.

Healthcare, which has been lacklustre in adopting these technologies, don’t have the choice anymore. Remote consultation, automated lab testing, electronic medical records etc. will soon become the default standard. This will open up unique career opportunities for AI professionals in the domain.

Sreehari Ravindranath, our AI/ML career coach believes that the COVID period is also a good time to upskill to AI/ML if you’re already a healthcare professional — because you know the domain and are likely to help achieve greater accuracy. This also goes for all industries.

2021 possibilities

In 2021, we believe that generic AI will give way to more domain-specific approaches. It will take into account contextual factors, data security, privacy, regional compliance needs etc. to put AI into more effective use.

If you’re looking to grab an opportunity in AI/ML, here are some top skills you’ll need:

  • Python programming
  • Statistical inference
  • TensorFlow
  • Deep learning
  • Machine learning
  • Natural Language Processing (NLP)

#2 Advanced Analytics Becomes Omnipresent

Even if you’ve never seen advanced analytics in action before, you would have since COVID struck. The various kinds of numbers you see everyday — testing rates, number of cases per day, percentage of deaths, vulnerable groups, country-wise reports etc. — are all available only as a result of advanced global analytics systems.

Public health strategies like ‘flattening the curve’ are also devised on the basis of such numbers. One could argue that collecting mass data and analysing it has gained new importance for both the private and public sector, as a legitimate means to solve complex problems.

2020 use cases

Database management systems have helped to handle the large volume of data produced. Home Quarantine & Isolation Management System (HQMIS), is one such, used by the Greater Chennai Corporation to monitor those under home quarantine. Data was collected from 18 sources, cleaned and dispatched to area heads. The HQIMS app also provided a data visualization dashboard with reader-friendly results.

Insights were key to making the ‘new normal’ work. Large companies had to revamp their operations to the work from home style. Cognizant, for example, built an analytics stack to make quicker and more informed decisions.

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2021 possibilities

The prospects look good too. Salesforce alone plans to add 5,48,00 direct jobs and 1.3 million indirect jobs in India, according to chief data evangelist Vala Afshar. This is good news. If you’re hoping to prepare for a career in data analytics, here are some skills and tools you’ll need:

  • MS Excel
  • Programming with R or Python
  • Building queries with SQL
  • Data visualisation
  • Software libraries like Apache Hadoop

#3 Intuitive Design as the Foundation of Better Virtual Experiences

A natural consequence of being homebound for months together is that people now rely on their phones and computers even for activities that were hitherto done offline. This has opened a window for companies to strengthen their online audience by giving them the best virtual experience.

2020 use cases

Companies have rethought their virtual offering to improve online brand engagement. Luxury shoe brand Christian Louboutin recently launched a virtual universe on a South Korean gaming platform that lets users’ avatars try on collections showcased in the Paris Fashion Week.

COVID related apps have increased the demand for good UI/UX design. These include apps for receiving information, tracing (eg: Aarogya Setu app), monitoring, to online shopping, delivery, fitness etc.

2021 possibilities

Unlike data jobs, UI/UX design is accessible to a larger population as it does not require a tech background. The most important tool for a designer is an intuition for good design, and comfort with a few design tools. These might be:

  • Research tools like Google Analytics, UserZoom
  • Balsamiq for wireframing
  • Invision for product design
  • Figma for interface design
  • Presentation tools such as Powerpoint

Launch a Future Tech Career in 2021

The biggest lesson from 2020 is that: Technology is only as good as the impact it can make. We noticed that experimental technology projects were growing slower when compared to existing technology adoption. This meant that:

  1. Domain experts are on par with technology experts.
  2. Analytics that enabled decisions were valued higher than reports that just presented numbers.
  3. Design is used to shape a user’s feelings, emotions and decisions, far beyond simply being pretty.

At Springboard, our experts keep an eye out for these trends and regularly upgrade the programs to be job-ready. All of Springboard’s programs prioritise practical expertise and industry application.

What is Tokenization?

While by no means a new concept, tokenization has become a salient topic as of late due to the potential role it may play. As technologies like host card emulation become more prominent, tokenization is viewed by many as the solution to helping cloud-based mobile payments become more secure, and thus more viable for mass adoption.


What is Tokenization?

But what is tokenization? In simple terms, it is a form of data security.

As the name suggests, tokenization is the process of creating a “token” – in this context, it is sensitive data that is tokenized. The token produced is a non-sensitive, unique piece of data that retains essential information about the sensitive data it represents, without compromising its security. The concept is similar to how a voucher, coupon, or casino chip represent money.

In the context of electronic payments, the ultimate purpose of tokenization is to act as a security measure that works to lessen the threat to sensitive information.

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What Role Does Tokenization Play in Mobile Payments?

In order for mobile transactions to be completed, a Personal Account Number (PAN) is required. However, there is an inherent security risk in storing PAN data on the mobile device itself. If somebody were to hack the device and gain access to the PAN, they could theoretically carry out multiple transactions as they have the fixed account number needed to do so.

Tokenization helps to mitigate this risk. It obscures the 16-digit PAN data by masking it as a token so that the information is not sent as plain text. Furthermore, because the token is not the PAN itself, it cannot be used outside of a specific, unique transaction. Even if there were a data breach, the account information would still be inaccessible, unless the secure servers which held the information were breached.

Rather than transferring track 2 data, tokenization sends a token to the NFC terminal, which is then relayed to the cloud. The cloud decrypts the token, associates it with the right PAN, and sends the PAN data back to the NFC terminal.

Tokenization and Host Card Emulation

Due to the nature of tokenization, it is being considered as one of the best solutions for securing HCE mobile transactions.

HCE moves the account information into the cloud, which eliminates the need to store it in the mobile device’s secure element. But, to enable mobile transactions to take place securely, there needs to be a way to ensure the security of the sensitive information. Along with measures like secure account reload and limited use keys, tokenization is at the top of the list as a potential solution.

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Barriers to Market Adoption of Tokenization

Though we have yet to see the storage, creation, and validation of tokens enter the market on any significant scale, there is massive potential for tokenization to play an integral role in the way that mobile payments develop and evolve. It may very well be the key to transforming the mobile wallet from closed to open loop. As it stands, there are a number of significant barriers preventing the adoption of tokenization, including:

  • developing the technological infrastructure
  • developing the institutional infrastructure
  • having a token generation/validation body with common standards
  • consumer adoption

But, as Jay Weber of FIS points out, the benefits of tokenization far outweigh the barriers to adoption. For one, it lowers the likelihood of sensitive account information being compromised in the event of a data breach. Given the succession of data breaches that have plagued some larger corporations, this is a major advantage. It can also potentially reduce the compliance burden if correctly implemented, among myriad other benefits.

Ultimately, a number of factors will influence the role of tokenization in regard to mobile payments. The payments industry will need to find some common ground on how tokenization manifests, for both security and practicality reasons. As it stands, tokenization is becoming a battleground for competing proposals. The lack of consensus on this level consequently affects merchant adoption. To see true market adoption, there needs to be a consistent framework to introduce a solution that works for issuers, processors and merchants alike.

C vs. C++: Which One is Better?

Which Both C and C++ are general-purpose programming languages. In fact, C++ is a descendant of C, which means they share some features. However, over the last few years, C vs. C++ has become a hot topic because these programming languages started to differentiate more and more.


What is the difference between C and C++? Well, C++ is for handling complex tasks that C was not able to perform. For instance, C++ offers a stronger type checking and allows more programming styles than C.

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This programming language is called C because it was based on a little-known language called B and the name C was an alphabetical joke. However, this improvement of B led to the creation of an entirely new programming language.

One difference between C and C++ is that C is a procedural language since it follows a step-by-step procedure consisting of functions. Additionally, C is a low-level language that is more complicated for beginners to learn than high-level languages like Python or C#.

In other words, C provides instructions for the computer in the top-down approach. In contrast, C++ is object-oriented instead of procedure-oriented. It means that C++ focuses on inheritance (when a class gets properties and characteristics from another class), code reusability, encapsulation (hiding information about objects), and creating objects.

C programs are usually high-speed. Why? Well, programming languages like Python offer additional procedures that make their programs slower. However, C is a language that lets developers handle computer hardware manually. While this is an advantage in terms of performance, it means that C developers have to prevent memory leaks and allocate memory themselves.

What is C used for? C is mostly involved in the creation of operating systems, language compilers or interpreters, embedded systems, microcontrollers, etc. For instance, C is very useful for machine learning as well. However, the leading language for ML is Python due to its simplicity and user-friendliness. Game development is also a rich area for C developers.


In terms of C vs. C++, the latter can do everything that C can. Both of them are general-purpose, low-level programming languages, and they have multiple similarities:

  • They require the compilation in every operating system to work.
  • Both support manual memory management. They do not offer garbage collectors that would free memory from unused objects.
  • Due to the lack of additional processing (such as automatic garbage collection), C and C++ are more lightweight and offer high-performance.
  • Since they both are general-purpose programming languages, their use cases are very broad (starting from the creation of operating systems and leading to machine learning).
  • Both are low-level languages, meaning that they are closer to the hardware and provide more control over projects.

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Main differences between C and C++

Comparing C++ vs. C reveals a few differences between these two programming languages:

  • C is a procedural language, while C++ is object-oriented. This feature refers to the programming style that developers follow. For instance, procedural programming follows step-by-step guidelines of functions, while object-oriented programming focuses on objects, inheritance, etc.
  • C++ has a well-designed exception handling (Try and Catch blocks), which makes the debugging process easier than in C. This feature is especially useful for finding difficult errors. In C, error handling occurs through functions.
  • C++ also supports information hiding (closely related to encapsulation).
  • Data is more secure in C++ than in C because C++ offers modifiers to limit user access.
  • C++ supports function overloading, which means that a function with the same name can be declared for different purposes.
  • C++ also uses namespaces, which let you organize code according to the desired scope. For instance, grouped entities can be put into a narrower scope referred to as namespace scope. C does not support this feature.
  • Specialists relate C++ to the concept of multi-paradigm. Even though we classified C++ as an object-oriented language, it has features of procedural one as well. Therefore, C++ is more flexible than C since C only follows the procedural logic.

Therefore, the comparison of C vs. C++ syntax rules leads to a few important conclusions:

  • The use of C and C++ differs in a way that you will follow different programming approaches.
  • With C++, developers can follow both procedural and object-oriented programming.
  • C allows only procedural programming.
  • C++ offers more features such as error handling, data security, scope management, information hiding, etc.
  • However, for beginners, C language might be more straightforward and helpful in terms of understanding the main concepts of low-level programming.