Identifying future trends in tech is always a massive challenge because variables like innovation, political instability, and epidemics — to say nothing of a once-in-a-century global pandemic — can change everything instantaneously.
But even without a crystal ball, we can now spot upcoming trends by factoring in previous trends, market behavior, data analysis, and the pace of innovation.
This article covers a research-backed analysis of upcoming tech trends. If you are a techpreneur, business executive, or tech enthusiast, here are the trends to brace for in 2022.
Data has always been the lifeblood of every successful business. And with data management systems becoming readily available, companies will continue extending their access to several data nodes and robust analytics.
To this end, companies will adopt AI-enabled integrations into data fabric architecture to reduce human fallibility and increase accuracy in decision-making processes.
According to IBM, a data fabric provides fast-paced data processing capabilities through a self-governing architecture, which is a significant leap from traditional data management systems.
But how will data fabrics improve the way we handle information in 2022?
Data fabrics also enable businesses to retrieve process-relevant data from datasets and multi-cloud platforms in real-time, regardless of storage location (cloud or on-prem).
Not only that, DataOps teams — and other technical teams — can use data fabrics to simplify complex data management and governance problems without exposing the infrastructure to security risks.
Cybersecurity is one of the evergreen trends in information technology because companies and private individuals are under constant threat of attacks on their infrastructure, mostly financially motivated.
But despite preemptive and preventive measures that companies employ against cybersecurity threats, cybercrime went up 6x over the past 18 months. And since most businesses still use centralized networks, the rate of cybercrime will continue to rise.
Consequently, companies will experience significant financial losses stemming from maintenance costs and unprecedented downtimes.
However, with a cybersecurity mesh, technical teams can establish perimeters around data access points and provide privileged access management (PAM) to users based on their security clearance and position within the organization’s framework.
In addition to perimeter-based protection, cybersecurity meshes employ a Zero Trust strategy to deter internal attacks on the network.
With this protective barrier in place, companies can revoke unauthorized access, detect service impersonation attempts, and repel unauthorized data exfiltration.
To this effect, managers and data scientists will continue to “weave” the DataSecsOps philosophy into the infrastructure to boost security.
Controversies involving Facebook and Google have propelled the issue of privacy beyond the realm of technology trends; it is now a core aspect of the user experience in every industry.
According to data from Pew Research Center, 79% of internet users are worried about online privacy. Therefore, businesses will have to employ privacy-enhancing technology (PET) in the coming year to improve users’ privacy and earn trust.
Here are some notable PETs that will peak in 2022:
- Homomorphic encryption — a protocol that uses advanced algorithms to encrypt core business functions like search and analytics.
- Trusted execution environments (TEE) — a perimeter-based approach for protecting the execution environment from the rest of the operating system.
- Multi-party computation — a cryptographic protocol that facilitates data management and processing without providing access to the main database.
By implementing these PETs, businesses can protect user information from irretrievable loss and unauthorized third-party access. Besides, companies can also use PETs when collecting, sharing, analyzing, and managing data.
And most importantly, PETs will improve the overall customer experience by protecting customers from potential fraud and cyber attacks.
As the size of data continues to increase, companies remain on the lookout for better ways to store their data to prevent frequent downtime and losses.
Previously, companies like Netflix relied on cloud-enabled technologies to deliver their services. However, cloud-based services like Azure have redefined the way companies build, integrate, and deploy applications — the new answer is cloud-native computing.
Unlike cloud-enabled solutions, cloud-native is “born” in the cloud and remains there throughout its life cycle. Essentially, cloud-native infrastructure allows developers to deploy services faster and more accurately.
Furthermore, cloud-native technology integrates with container engines like Kubernetes and Docker, which also rely on a microservices-based architecture rather than the monolithic, centralized architecture of cloud-based systems.
As a result, companies can continue building and offering solutions even when their microservice framework experiences downtime. At the same time, developers can spot issues in the infrastructure easily and address them faster without dismantling the entire network.
Despite the current promise of automation and artificial intelligence, Deloitte estimates that only 36% of businesses have implemented workflow automation tools in their business processes.
But this hesitancy in adopting automation is reaching a breaking point in IT, judging from the fact that 97% of IT leaders referenced in the same Deloitte report are both willing and eager to implement hyperautomation.
How does hyperautomation differ from automation?
For starters, hyperautomation involves implementing a blend of AI and machine learning algorithms across all processes that can be automated within a company.
Traditional automation focuses on a specific task, but hyperautomation uses AI-powered tools like optical character recognition (OCR) and natural language processing (NLP) to understand and predict the best course of action within a workflow.
According to Gartner, here are other technologies that constitute the hyperautomation model:
- Machine Learning (ML)
- Event-driven software architecture
- Robotic Process Automation (RPA)Business Process
- Management (BPM) and intelligent Business Process
- Management Suites (iBPMS)
- Integration-platform-as-a-Service (iPaaS)
- Low-code or no-code tools.
Since hyperautomation has variable applicability, it reduces the cost of automating different aspects of the business as separate entities. Also, companies can optimize their business processes in real-time using hyperautomation tools like UIPath and Automation Anywhere.
Total experience (TX) is a supercharged strategy that combines and interlinks customer experience, user experience, multi-experience, and employee experience to boost outcomes across intersecting constituents.
Here are ways to revamp your business by improving the total experience:
- TX strategies disintegrate data silos and provide actionable intel from which your business can improve the customers’ unified experience.
- TX strategies address how you treat your employees, which determines your brand perception. Over 49% of consumers consider the welfare of employees before making a purchase.
- The right TX strategy improves engagement by encouraging human interactions across multiple communication channels.
According to Zain Ventures CEO Zain Jaffer, “Total experience takes into consideration the experience of an organization’s employees and users, in addition to their clients.”
Essentially, improving the total experience benefits your business, employees, and consumers — and boosts your bottom line.
Ethical AI engineering
According to Statista, the AI market will reach 51 billion USD in 2022. But AI is not really one of the emerging trends in technology today; it has been around for some time.
So why should you care about AI engineering in 2022?
The answer is Ethical AI.
Ethical AI is a philosophy that focuses on engineering AI tools to exhibit social values like equal representation, anti-discrimination, and privacy. This philosophy resulted from a period of anti-AI protests by human-centric groups.
Going into 2022, developers and DevOps team leaders must maintain the core tenets of ethical AI engineering: human-centered, scalable, and secure.
According to Brad Smith, President of Microsoft, companies should maintain practices that prioritize human rights, even if these laws defy government and law enforcement establishments.
Also, Microsoft recently came under scrutiny for its facial recognition AI, which discriminated against people of color. With that in mind, the company’s refusal to work with police and ICE agents highlights their willingness to do better.
Internet of Behaviors (IoB)
With the Internet of Things (IoT) already in full flow, companies like Google and Amazon are extending their tentacles to human psychology and behavior. To this end, these companies are moving to an Internet of Behavior (IoB).
Unlike IoT services that focus on things, IoB focuses on users’ online activity (and inactivity) as well as interactions with neural networks. Businesses now use IoB systems to monitor how users interact with their services, which offers valuable insights into profitable ways of adjusting marketing strategies.
For example, IoB data from drivers can shed light on driving behaviors within a specific demographic. From that data, Tesla can fine-tune machine learning algorithms native to self-driving cars.
Google, YouTube, and Facebook already use IoB data to recommend content to users. And companies like Notify Nearby send you coupon notifications when you are in proximity of your favorite store.
Effective but spooky. This perceived “invasion of privacy” presents an ethical quandary regarding the application of IoB:
- Should companies have unbridled access to users’ personal data?
- Can data-based recommendations create echo chambers and polarize populations?
- Can companies like Facebook sway significant political outcomes?
- Can location-based IoB services stop crime by locating and reporting potential perpetrators — like in pre-crime?
Regardless of these ethical dilemmas, companies will continue developing IoB services to boost their bottom line and improve the overall (total) consumer experience.
Any company that wants to improve sales and outperform the competition must adopt a data fabric. But at the same time, businesses must take ample precautions to anticipate and prevent cyberattacks and other potential privacy breaches. To this end, a cybersecurity mesh can protect the user’s and company’s sensitive information from unsanctioned third-party access.
Also, businesses can use hyperautomation tools to improve their brands’ total experience. But they must maintain ethical AI engineering standards throughout the entire process. And most importantly, institutions should extend their understanding of users by gathering and analyzing IoB data.
The emerging trends discussed in this article are still in their nascent stages, which gives you a chance to hop on the train before the market gets saturated.