Gartner’s 2022 Tech Trends Basics: What’s Next in Tech?

The Tech world progresses at an unprecedented speed. Let alone years, new projects emerge even in weeks today and they have the cutting-edge potential to actually start a completely new era for the future, by shaping global businesses and pointing out new needs for next-level optimization. Notions like blockchain, singularity, Robotic Process Automation, and many others are some of the key trends of contemporary. Yet, many other innovations will join up to this rapid flow of digital transformation. But which others? How to decide which technology trends are going to be relevant in the future?

Gartner, a prominent tech consulting firm, curated some potential IT trends for 2022. In this article, we aim to cite these and try to cover the very basics.

Here’s the list Gartner proposed:

1. Data Fabric
2. Cybersecurity Mesh
3. Privacy-Enhancing Computation
4. Cloud-Native Platforms
5. Composable Applications
6. Decision Intelligence
7. Hyperautomation
8. AI Engineering
9. Distributed Enterprise
10. Total Experience
11. Autonomic Systems
12. Generative AI

Data Fabric

Data Fabric is an information structure that transcends the concept of data storage into a multi-cloud environment, resulting in a consistent design to reach data regardless of business departments, physical places, and the dichotomy of on-premise and cloud solutions. All the data exists in different parts of a cloud system, and it is more rapidly reachable and housed in an integrated form.

The Data Fabric approach may also benefit sustainability as the energy consumption of the hardware will sharply drop. Data management will be easier and data protection & security will also get affected positively. Data Fabric is like a giant spider web that reaches out every bit of data, metaphorically. It forms an overarching structure for centralizing the storage of data that may or may not be stored in different cloud solutions or platforms.

Cybersecurity Mesh

Like Data Fabric, Cybersecurity Mesh is also an integrated structure. This time, it bends towards the safety of data, though. Many businesses and institutions employ a single umbrella perimeter to defend their assets against cyber threats today. Cybersecurity Mesh tackles this problem with a lot more holistic approach.

It’s a strategic set of defense mechanisms that dismembers the data structure in a traceable, broad, and centralized manner. It brings about a distributed practice of cybersecurity for every bit in an IT landscape. A centralized point of authority dominates these interchangeable security measures, enabling traceability and faster response time. Let’s say, a football team that has different defending tactics for every square in the field, all orchestrated by the head coach who is a mastermind.

Privacy-Enhancing Computation

Privacy-Enhancing Computation (PEC) is a definitive practice that enables more secure data sharing across different ecosystems or structures in an institution. It uses different methods to secure these transactions: encrypting, splitting or pre-processing sensitive data, to name a few. Every different part of a structure shares and houses data, but while the data is “on the way” to another system, it gets encrypted. These encryptions are quite original and thus rather secure. A classroom full of students, and every two students have a unique encrypted language for communicating, they understand very well what they are talking about but no one else can do so - a metaphor to picture PEC truly.

Cloud-Native Platforms

Migrating to the cloud is an already-hot topic, as most well known. Its business benefits and created value have been well defined and a certain tendency towards cloud is on the agenda for a lot of institutions. Yet, migrating a legacy system into the cloud has its problems. Cloud-Native Platforms turns a new page regarding this migration process. It proposes “born in the cloud” applications, instead of tailoring applications that have not their roots in the cloud. Gartner proposes that Cloud-Native Platforms will even further speed up the data processes along with supplying a more secure and integrated structure. So, it’s like preferring to construct new furniture perfectly fit for the new house that you will just about to move into, instead of carrying your old furniture there with you.

Composable Applications

Constant technological advancements are not always easy to follow or get adapted to. The concept of Composable Applications offers an API-first or API-only (Application Programming Interface) approach towards this rapid change. That is; instead of relying on legacy and/or robust applications, Composable Applications gives an enterprise chance to code and program in a way faster manner. This is done through low-code or no-code interfaces. You only choose what you need, and the API-first/API-only solution codes it accordingly. Think of it like a coffee machine: you press a button, and the coffee for your needs gets prepared; you don’t personally conduct every step (i.e., coding in IT) to make coffee.

Decision Intelligence

Decision Intelligence, also known as Decision Engineering, is the systematic use of Data Science and Machine Learning in the process of taking a corporate decision. Decision Intelligence creates models and frameworks to speed up taking decisions for similar cases in the future. It analyses already taken decisions and their results, then creates a mindset of focusing on the best practices. Thus, strategic decisions can be done in a more grounded manner thanks to a historic evaluation of similar processes and competitor analyses.

Hyperautomation

Hyperautomation is a broadscale methodology that pushes growth to its maximum. It aims to automate every possible business step and it tries to come up with new solutions so as to automate more and more. The decisions regarding such innovations are taken strategically by understanding and analyzing business agility, potential outcomes, and the time needed for profitability. Its difference from Automation is that Hyperautomation makes plans for tomorrow, rather than automating already-in-use business steps.

AI Engineering

AI holds a great potential that could take some business processes into the next level, this is well-known. Yet, in order to structure an effective AI, an institution should always provide their AI service with necessary updates and developments. Considering the extreme speed of the IT landscape, AI updates have got to be quite agile. Thus, AI Engineering is the new business methodology that allocates time and personnel strictly to updating AI in an institution. It emphasizes the importance of a flawlessly-running environment around AI. Carefully planned AI updates and new ways to teach AI are conducted by this team and the potential benefits of an AI system prosper.

Distributed Enterprise

COVID-19 brought about many trends that shaped (and still shaped) the business. Two of these shifts are the boost of working remotely and the increasing tendency of the customers to prefer digital venues instead of physical, legacy marketplaces. Thus, enterprises had to find ways to exceed their physical boundaries. Naturally, integrating technology to solve this problem is a fruitful effort. Different tech approaches take place such as virtual dressing rooms for trying new clothes without physically going to the shopping centre, or providing fast-paced and secure business network access from every place when working remotely. At the end of the day, one thing is certain: digitalization and passing beyond the physical borders in business is a must.

Total Experience

Total Experience is the unification of 4 notions: customer experience, user experience, employee experience, and multi-experience. Altogether, they form an overall identity and a sphere of influence for a company. Such a holistic approach aims to interconnect and ameliorate the experience of every single person affected by a company in one way or another. This way, experience-related issues of both the customers and the employees might get resolved, benefitting the business quality of the company.

Autonomic Systems

Autonomic Systems are self-managing parts of a business that can adapt to changing dynamics around them by themselves. They can take and execute a decision after monitoring, analyzing, and planning it. Artificial Intelligence and Data Science could be integral parts of an Autonomic System but what defines this trend is the ability that they can make a decision and apply it for the necessary processes. A self-driving taxi’s navigation system when it detects unplanned roadwork, for example. It monitors the situation, analyses if it would be better to wait or find another route, take a decision, and then execute it. Not only once, but each and every time against the unexpected with different dynamics.

Generative AI

AI is “a real dynamo” when it comes to business decisions and processes. It analyses data, learns different methodologies, and then has the potential to produce very yielding results that will boost businesses. Placing it as a “generator” is relatively a new topic, though. The Generative AI notion proposes an AI mechanism to understand a dataset and then create similarly complex ones. It studies an algorithm and then generates equally challenging, but different datasets. Texts, audio files, images can all be subjected to this “reshaping”, and the new output is created according to the underlying pattern. Using quite similar techniques and ingredients for a new dish, let’s say.

Epilogue

All in all, these are the trends Gartner pointed out to be the “case” in the future. In 2022 especially, to be exact. Check out the Loggle Blog for more.