What’s new in Gartner’s 2021 emerging technology curve

What’s new in Gartner’s 2021 emerging technology curve

The 2021 maturity emphasizes emerging technologies that will have a significant impact on business and society in the next 2 to 10 years. It includes building trust, accelerating growth, and shaping change to bring order to a changing world.

This year Gartner released a 96-page “Hype Cycle for Emerging Technologies, 2021” report, which also put forward three themes and a total of 25 emerging technologies. I probably took a look at the official PR and felt that this is much more fancy than those few moths last year. This article will give a brief introduction to some of the relatively new technologies in order to give everyone a preliminary understanding of these technologies.

What’s new in Gartner’s 2021 emerging technology curve

Let’s first look at the meaning of these major themes and the division of emerging technologies. (The following are all official Chinese translations)

  Three themes of Gartner’s 2021 emerging technology maturity curve

  establish trust

Trust requires security and reliability as the foundation. This trust also extends to innovation as the core and foundation of flexible IT to deliver business value. This foundation needs to include engineering, reusable, credible, proven, and scalable work practices and innovations. Business risks must be managed or minimized so that IT can deliver. From a business and technical point of view, resilience is the key. Extensible reusability helps build a flexible business core.

Related technologies include:

Sovereign cloud

Machine-readable legislation

Decentralized identity

Decentralized finance

Non-homogeneous tokens (Nonfungible tokens, NFT)

Homomorphic encryption

Active metadata management

Data fabric

Real-time incident center-aaS

Employee communications applications

  Accelerated growth

After establishing a credible core business, recovery and growth will become possible. When managed according to business needs, risk can be managed in the incremental process. In order to ensure the achievability of short-term goals, corporate organizations should balance technical risks and business risk preferences. Once the innovation-led core scale expands, accelerated growth will expand the scope of delivery and increase value. At this point, risk and agility enhance IT delivery for the distant future.

Related technologies include:


Industry cloud

AI-driven innovation

Quantum Machine Learning (Quantum ML)

Generative AI (Quantum ML)

Digital humans

  Shaping change

Change is generally disruptive and is often associated with chaos, but organizations can use innovation to shape change and bring order in chaos. The key is to predict and automatically adjust to changes in demand. Experience helps strengthen business drivers. Risk may help innovation adapt to shape change, but it must be a manageable risk. When evaluating delivery, you can shape change.

Related technologies include:

Composable applications

Composable networks

AI-augmented design

AI-augmented software engineering

Physics-informed AI (Physics-informed AI)

Influence engineering

Digital platform conductor tools

Named data networking

Self-integrating applications

What’s new in Gartner’s 2021 emerging technology curve

 Techniques to remove from the curve

The emerging technology maturity curve is not a special Gartner curve. The content it involves is very extensive and dynamic. Many of its technologies have only one or two years of freshness, after which they will no longer be tracked and will make room for other important technologies. Most of the technologies that are removed will continue to be tracked in other maturity curves. Please refer to Gartner’s maturity curve collection in various fields for details.

For the technologies deleted from the 2020 emerging technology maturity curve, those who are interested can check the official report. Statistics are not made here.

  Newer emerging technologies

Since there are too many emerging technologies in this update report, I probably read it, most of them are very new, and will not discuss them all. Here are a few to briefly introduce.

With assembled, generative, AI-enhanced technologies and multiple experiences, digital humans (called digital individuals last year), you can refer to the introductions written by me last year and related articles on strategic technology trends. There is not much difference in technology, just The application scenarios are different.

This article selects five emerging technologies: sovereign cloud, decentralized identity, assembled network, influence engineering, and machine-readable regulations.

 Sovereign cloud

With the global economy, the protection of intellectual property rights, the expansion of privacy legislation, and the dominance of a few large technology and service providers in China and the United States and the desire to be more self-sufficient, the importance of digital sovereignty has been put on the agenda. The public sector recognizes the value of the digital economy and seeks to develop infrastructure and ecosystems to provide a digital citizenship experience while maintaining autonomy.

Sovereign cloud refers to providing cloud services that meet data residency and legal requirements in a single geographic area. Sovereign clouds help ensure that data is not controlled by external jurisdictions and provide protection for mandatory access by foreign legislation. Countries use sovereign clouds to realize digital and data sovereignty, and provide applicable data protection control, residency requirements, protectionism and intelligence collection regulations and legal requirements.

Legislative authorization can and is being used to restrict the use of multinational supplier services. This affects current investment and future sales growth. National suppliers may see the changing legislative environment as a catalyst for further investment and growth. As a result, end users may find themselves in a regulated/decentralized market, unable to obtain software and services to support their ongoing digital business plans and drive innovation.

What’s new in Gartner’s 2021 emerging technology curve

The introduction of sovereign cloud services can enable the public sector to eliminate the barriers to storing sensitive data and official sensitive data on public clouds. Providing the public sector with the ability to safely and legally use the public cloud means that it can develop simultaneously with the industry, promote better decision-making, and carry out implementation, which may bring huge benefits to the public sector. In general, it should be possible to reduce the cost of data use and make better use of data to create value.

At present, Microsoft has launched related services (Power BI) to support the compliance and supervision systems of China, the United States, and Germany.

(See: https://powerbi.microsoft.com/zh-cn/clouds/)

 Main driving factors

1. The digital plan needs to ensure the security and reliability of access to data sources, as well as the ability to contextualize and aggregate large amounts of internal and external data sources. For platform companies like Ali, Amazon or Tencent, this will bring huge benefits.

2. Currently, the digital and cloud technology service market is dominated by providers in the United States and Asia. Therefore, European companies have to use non-native services and technologies to establish and run digital business models. Therefore, data being stored in non-European cloud and digital service providers can cause political unrest. Sovereign cloud is therefore listed as a future emerging technology trend.

3. As digital services become more important and closely related to systems, companies and regional trade organizations worry about maintaining control of their data to comply with local regulations. This requires someone to provide compliance services and platforms to eliminate this concern.

  Obstacles faced

1. The service range and capabilities of hyperscale cloud providers go far beyond virtualization infrastructure. If users have too high expectations of the sovereign cloud, such as the maturity and scalability of delivery and the comparison of competitors’ functions, this will be a huge technical challenge and an obstacle at the same time.

2. There is an obvious lack of professional and technical personnel. There are too few skilled bosses who can replicate design capabilities in multiple countries at the same time. Due to the low level of available skills, safety and operational maturity will be affected, potentially leading to greater safety and failure hazards. Don’t forget that the latest “Cloud Security Report 2021” shows that the main problems at present are still the shortage of cloud security professionals, difficulty in integrating with traditional solutions and tools, and data leakage.

On the whole, the difficulty of law and cooperation may be greater than that of technology, but even technology is also a big difficulty at present. Therefore, there will be no more complete products or services in the short term, and it will be possible to achieve it in the next 5-10 years. Technology is not an exaggeration.

  Decentralized Identity

Decentralized Identity (DCI) uses technologies such as blockchain or other distributed ledger (DLT) to allow entities to create and control their own digital identities. Therefore, by establishing trust in identity and flexibility in the entire system, an alternative to a centralized IAM architecture is provided, which almost no longer relies on a centralized arbiter for identity storage.

Independent digital identities will not expand with the demands of digital services. As service providers (banks, retailers, social networks, etc.) force consumers to create personal accounts for each service, online and mobile identity authentication has always been in a distributed state. DCI provides a distributed digital identity alternative that has nothing to do with traditional security, privacy, and availability.

With DCI, users can control their identity and data, allowing service providers to interact with users faster and confidently. Currently, providers usually collect user identification information. Using DCI, identity and service providers will be able to improve the security and access convenience of end users, while reducing data leakage and potential privacy violations.

By definition, personal understanding is actually similar to Bitcoin and NFT. Using blockchain technology, users’ sensitive personal information is placed on a third-party platform, and the cost of service provider data management is transferred to the third party, and there is no need to worry about data governance and For leaks, you only need to pay attention to business service customers. Therefore, the service provider mentioned above can interact with users more quickly or confidently. Then it is to separate out the data-related issues, and the transaction is performed on a single platform for authentication, and users and service providers do their own things. This kind of thinking is not bad.

  Main driving factors

1. Suppliers invest in DCI: Since there is no shortage of suppliers who want to invest in this field and they are generally influential, they have great potential to promote the development of the DCI market. IBM and Microsoft have made significant investments.

2. Investment in BYOI (similar to BYOD, I means identity, bring your own identity): Microsoft enables “external identity” through Azure AD.

3. Customers and the overall market are showing interest in DCI. While maintaining customer privacy while creating new digital business opportunities, customer and market interest in this is growing. For example, use DCI to share verified claims, such as age/income, without the need to disclose sensitive personal data.

4. Standards led by the World Wide Web Consortium (W3C) and the Decentralized Identity Foundation (DIF) are emerging to create a consistent DCI approach. These standards will help promote the development of this technology.

  Obstacles faced

1. Most of the large ecosystem participants, CIAM (CloudIAM) vendors, and various IDPs (ID Providers) including the government have not acted, and the progress of implementation has been slow.

2. The standard is under development.

3. DLT lacks clear security standards, such as encryption flexibility, wallet standards and security.

4. The lack of a production-level solution makes some organizations unable to deploy, and they worry about the change after the solution is stable in the near future.

This technology covers a wide range and requires the cooperation of government departments and the joint efforts of all major platforms. In fact, it is somewhat similar to the sovereign cloud. It is difficult to drive cooperation with technology, and the time required will not be short. But given the urgency, it may deliver a partially feasible solution to deal with some needs, just like the current zero trust, and then slowly polish it in the future. It is reasonable to set its time to be between 2-5 years in the future.

  Composable Networks

The assemblable network is composed of de-aggregated and reusable network functions and elements, which can be easily integrated and used as a shared resource pool. Assemblable networks are constructed by modular and automated components (similar to service grids and network security grids) to support the dynamic needs of assemblable digital services. Telecommunications network technology will develop with the modularization of container-based microservices and use open APIs to integrate with other interoperable components.

The assemblable network of CSP (cloud security provider) can flexibly provide customers with DevOps and low/no code value propositions (refer to the cloud value proposition of distributed clouds), while reusing integrated and automated network components to improve efficiency . The use of an assemblable network to support a wide range of composable business thinking and design can improve the way CSP interact with customers and ecosystem partners. By using more fine-grained and modular components to build a network structure, CSP can quickly combine workflow and service chains.

It may not be easy to understand if you look at it alone. Later, I will post an interpretation of Gartner’s network security grid technology.

For the time being, you can take a look at last year’s interpretation of the technology of composite architectures to help understand.

  Main driving factors

1. Adopt a cloud native architecture, use microservices and de-aggregated network functions.

2. Support API architecture and wider OpenAPI; API-first structure will promote discovery, orchestration and automation, which is the way to achieve modularity.

3. As products mature and supplier ecosystems further adopt open frameworks, microservices/containerized solutions, and Open APIs, the level of composability will continue to increase.

4. CSPs are increasingly participating in the digital ecosystem to discover new and differentiated values.

Obstacles faced

1. Re-aggregating functions to achieve high reliability and interoperability is a challenge.

2. Cross-component orchestration can be very difficult in a multi-vendor network environment.

3. Establish a variety of assembleable/modular network component management CI/CD pipelines across multiple suppliers.

4. CSP’s island organization structure, independent teams deal with IT and OT and network domains.

It looks like a cloud service grid (or network security grid). At present, it looks more like SASE in shape, but it seems to be different, but the ideas are similar. I hope that Gartner will introduce some cases in detail in other reports in the future.

 Influence Engineering (InfluenceEngineering)

Impact Engineering (IE) refers to the production of algorithms for automating digital experience elements through the learning and application of behavioral science and technology, and guiding users to scale their choices (similar to Agile at scale).

Abundant data sources and machine learning capabilities can build a new influence system. Although it is theoretical, breakthroughs in areas such as emotion detection and language generation have shown its potential to automatically affect all aspects of communication. Existing examples have shown that artificial intelligence can amplify prejudice and other harmful effects, and beneficial goals may accelerate positive social changes. This shows the need for new forms of governance to oversee the research and deployment of IE.

While achieving profitable growth, companies are also facing increasing demands and need to be responsible and transparent to achieve environmental and social goals (referring to ESG and sustainable development). The success of the changes required to meet these needs depends on market adoption. As IE technology matures, their ability to form opinions and choices will increase, which has both advantages and disadvantages for transformation. Therefore, the ability to use these tools effectively in a beneficial way will affect the long-term health of the company.

  the main factor of influence

1. Global platform vendors (such as Google, Apple, Facebook, and Amazon) and market technology vendors (such as Adobe, Salesforce, and Oracle) have invested and made breakthroughs in AI, eliminating the barriers to adopting AI in marketing.

2. The emergence of technologies such as deep forgery and chatbots shows that AI has the ability to create realistic experiences.

3. The shift of consumer behavior to digital channels creates more opportunities for automated experience elements.

4. Enterprises are also facing increasing pressure and need to properly respond to social impacts. This is reflected in the environmental, social and corporate governance (ESG) ratings of investors, prompting consumers to choose a more sustainable and fair lifestyle . (E.g. carbon neutral)

  Obstacles faced

1. The abandonment of current mainstream personal data collection mechanisms (such as browser cookies and mobile device ids) (these mechanisms provide behavioral data sets for training personalization algorithms) has created a need for new sources of training data.

2. The government is taking more and more actions, including restricting the use of personal data and unexplained analysis; monitoring AI spreading prejudice and discrimination (such as AI ethics).

3. Lack of mature methods or tools. As investors and companies seek a hype cycle that they can take advantage of, this market will experience a difficult period of conflicting opinions.

4. It is right to have doubts, because the actual potential of these technologies is still biased towards speculation, and many experts will question the feasibility assumptions.

On the whole, it is a trend that is relatively macro and social development. In order to realize this trend, AI and automation technology are used. It mentioned scale, chatbots, and ESG. In fact, I didn’t understand what I mainly wanted to express, and there are currently no reference materials, cases, and vendors. Personally, I feel that AI (machine learning, deep learning, general AI) should be used to help people realize some daily automated decision-making, but this process requires human guidance (AI ethics), and the use of AI to bring sustainable development to the enterprise (Investment decision-making, environmental protection decision-making, corporate governance decision-making), so as to promote the sustainable development of society.

  Machine-readable legislation

Machine Readable Regulations (MRL) refers to the generation of computer code that will be used to implement these legislations or policies while making legislation or policies. Through parallel development, the technical challenges faced by policy implementation will be reduced; but the system must be constructed in a modular way to allow the realization of this business logic. MRL enables the government to implement more consistent and fair applicability of the law.

MRL ensures that policies are designed and implemented as expected, reflected in subsequent management rules, and executed automatically by various systems. Enforcing laws in computer programs is usually more difficult because laws are not necessarily written in binary logic. However, if policy is technology and technology is policy, and the two are inseparable in a digital society, it is different. When cross-industry systems are implemented in a consistent manner, society as a whole will benefit.

Implementing MRL will be an interdisciplinary activity that requires the necessary skills in policy and IT departments, technology implementation, and ecosystem participation to ensure that the developed “business logic” is created and used to improve regulatory processes and promote economic benefits. MRL will be a basic technical element to create an assemblable government, supporting the digital society by making the writing of laws more data-driven and making their implementation more consistent.

It can be regarded as a domestic “three-synchronization” strategy, but here is “synchronized legislation, synchronized code, synchronized landing”. While considering the legislation/policy, we started to implement the implementation plan. The purpose is to guide the company on how to integrate with the existing environment along with the official solution when the policy is introduced. Eliminate the dilemma that companies have to make a large number of changes due to regulatory requirements. At the same time, the issue of judicial evidence collection of cybercrime should also be considered. (Personal understanding, for reference only)

 Main driving factors

As a new innovation, the driving factor of MRL is not so much concrete as it is fantasy. However, the government faces some existing challenges in the transition to a digital government, which can be solved through MRL. (This sentence is officially said, the fantasy stage)

1. The gap between legislative intent and implementation. By implementing the MRL, the (excessive) interpretation of legislative or administrative intent is excluded from the procedure, and the legislation is instead aligned with law enforcement.

2. Existing legislative procedures have limitations in quickly responding to necessary changes. In the existing legislative procedures, giving the government the ability to repeatedly amend the law can easily achieve the result of maximizing social benefits. When MRL is combined with other emerging technologies (such as machine learning and government digital twins), a large number of scenario iterations that affect multiple public projects can be achieved. This approach can make wider use of data-driven strategies and decisions.

3. Reduce the need to formulate new laws or update the cost of existing legislation. By implementing MRL and disclosing the approved “business logic” as an API to the partner community, the government will be able to reduce the cost of implementing policy changes and auditing systems. Eliminating the economic burden associated with implementing policy and legal changes can make the changed laws more suitable for the wider ecosystem.

  Obstacles faced

1. MRL needs to change the way policies and legislation are formulated and implemented, which is beyond the control of most CIOs. For those who can influence the passage of the MRL, obstacles need to be resolved at the executive and legislative levels.

2. The MRL will change the existing dynamics and power structures related to legal and policy developments, which may cause the current leadership to resist its adoption.

3. The government needs to invest in testing capabilities to test frequent system updates.

In fact, for obstacles, it is enough to say that it is a fantasy at present. Whether it is “triple synchronization” or corporate support for legislation, it is currently impossible. Although this idea is bold, it needs to be truly on track. The effort is still considerable. If this trend still exists in the next 2 years and is not abandoned, I will continue to follow the progress.


The five emerging trends selected above were selected based on personal feelings, relatively new and seemingly incomprehensible. They may not be trends that everyone cares about, but they should be trends that most people cannot explain clearly. Gartner still dare to think and dare to say that many trends are very far-sighted, not limited to the current and a certain field, and continue to interweave their own things as always. You have me in you, and I have you in me. Anyway, one trend will be formed. A system. I remember this from 2019, and the first systemic thing in my impression is SASE.

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