In the next 3-8 years, which technologies will subvert and change the entire market?

　　Smart Spaces, Homomorphic Encryption, Generative AI, Graph Technologies, and the MetaverseWill subvert and change the entire market.

Gartner’s Emerging Technologies and Trends Impact Radar Chart this year includes:23 itemsEmerging trends and technologies most likely to bring change and transformation to the market.These trends revolve aroundfourKey Topics:

　smart world: Changing the way people interact with the world around them.

　　Productivity Revolution: Build on core AI technology and expand computing power.

　Ubiquitous, transparent security: Stresses how important it is to protect an increasingly digital world.

　Key Implementation Technology: As a complementary force, incorporate emerging technologies and trends and improve effectiveness by reshaping business practices, processes, methods, models and/or functions in the markets where these technologies are used.

Source | Gartner

A brief explanation on how to understand the above radar chart

The circle represents the range, which provides an estimate of the number of years it will take for a technology or trend to evolve from an early adoption stage to an early majority adoption stage. The size and color of the dots on the emerging technology or trend radar chart represent the quality of the technology, in other words how influential the technology or trend is on existing products and markets.

Most emerging technologies and trends this year will not reach the early majority adoption stage until 3 to 8 years later. This bodes well for major innovations in the coming years. Here are five emerging technologies and trends that Gartner sees as the hottest future:

　1

Smart Spaces

Time to market: 3 to 6 years

Influence: High

Subject: Smart World

A smart space is a physical or digital environment that enables humans and systems enabled by technology to interact in an increasingly open, interconnected, coordinated and intelligent ecosystem. Smart spaces have many other names, including “smart cities,” “digital workspaces,” “smart places,” and “ambient intelligence.”

Common uses of this technology include preventive maintenance of building infrastructure and automated charging and billing. Smart spaces are changing the way people interact with each other and impacting decision support systems within various spaces such as buildings, factories and premises.

Worker safety and social distancing capabilities have become de facto standards, so the COVID-19 pandemic has accelerated the adoption of smart spaces. As organizations begin to combine traditional systems with new technologies such as the Internet of Things through smart spaces, we will increasingly see more connected, coordinated and intelligent solutions emerge across target environments.

In areas where people and mobile traffic need to be observed and managed, smart spaces have a wide-ranging, cross-industry appeal that can make a big impact.

　2

Generative AI

Time to market: 6 to 8 years

Influence: High

Subject: Productivity Revolution

Generative AI refers to learning the features of objects from data and using those features to generate new, completely original artifacts that are similar to the original data.

The field of generative AI will grow rapidly in terms of scientific discovery and technology commercialization. While still a very advanced technology, it has had success in a wide range of applications, from creating new materials to protecting data privacy. But safety concerns and negative uses of generative AI, such as deepfakes, could slow its adoption in some industries.

Generative AI has a high impact because there is an increasing global exploration of generative AI methods and the technology is proving itself in many industries including life sciences, healthcare, manufacturing, materials science, media , entertainment, automotive, aerospace, defense and energy industries.

　3

Homomorphic Encryption

Time to market: 3 to 6 years

Influence: High

Subject: Ubiquitous, transparent security

Homomorphic encryption is an encryption method that returns encrypted results to the data owner. The essence of this technology is to enable third parties to process encrypted data without knowing anything about the data itself or the results. (data availability is not visible)

Several factors hinder near-term adoption of this technology, including performance issues, lack of standardization, and complexity. Homomorphic encryption pioneers understand that the future of the technology is increasingly tied to the growing role of open innovation investment, which goes against the secrecy and silo mentality of traditional corporate research labs.

We believe that homomorphic encryption will be a core technology for many SaaS products in the future, and it will ensure data protection and privacy among third-party data processing and analysis providers. Its primary use will be to eliminate the current data exchange and storage requirements between business partners, third-party analytics companies or other extended data analytics solutions.

　4

Graph Technologies

Time to market: 3 to 6 years

Influence: High

Subject: Key Implementation Technology

Graph technology refers to graph data management and analysis techniques. These technologies are able to explore the relationships between entities such as business institutions, people or transactions. Analyzing relationships between data may involve involving large amounts of heterogeneous data, storage, and analysis, none of which are suitable for relational databases. Graph analytics consists of models that determine “correlations” across data points.

Essentially, graph technology determines the “relatedness”/relationships between different datasets. Taking social media analytics as an example, graph techniques can assess when different paths propagate behavior through unexpected community members, thereby enhancing the identification of influencers and communities in social media networks.

The range of potential applications for graph technology is so broad that it will take 3 to 6 years for the technology to reach an early majority adoption stage. A large portion of graph technologies will be sold as integrated components of existing data platforms, either developed in-house or integrated through resale agreements with specialized vendors.

With their specific graph processing language and capabilities, scalability, and computational power, graph databases are ideal for storing, manipulating, and analyzing large numbers of different views in graph models.

　5

The Metaverse: Outside of the eight-year time frame, but still worth watching

Another trend to watch is the metaverse. While this trend extends beyond the eight-year timeframe that typically determines what we include in this study, it extends computing power to a new order of magnitude beyond current and fundamentally changes the relationship between individuals and corporate institutions and How you interact with the world.

The Metaverse is a persistent, immersive digital environment composed of separate but interconnected networks, and it has not yet been determined what communication protocol the environment will use. The Metaverse enables persistent, decentralized, interoperable collaborative digital content that intersects with the real-time, spatially-oriented, and indexed content of the physical world.

The Metaverse is the next evolutionary stage of the Internet, but it is still in its early stages of development. We expect the transition to the metaverse will be as important as the transition from the analog to the digital world.

While the metaverse cannot provide immediate value and opportunity, innovative metaverse solutions present many potential use cases. Metaverse experiences will not completely replace current digital interactions (through apps, websites, etc.), but will likely replace many digital interactions while opening up new types of interactions and business models that can optimize these new use cases.

With their specific graph processing language and capabilities, scalability, and computational power, graph databases are ideal for storing, manipulating, and analyzing large numbers of different views in graph models.

　　

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