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Gartner 2020 Hype Cycle for Emerging Technologies Trends

Gartner 2020 Hype Cycle for Emerging Technologies Trends

Posted | Updated by Insights team:
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
  • Emerging Technologies
  • Sustainable Growth and Tech Trends


Publication | Update: Oct 2020
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According to Brian Burke, of Gartner, in most cities in China, citizens and visitors must download Health Code — an app that indicates COVID-19 status — to access many public and private spaces and services. A green screen means the person is free to travel, yellow indicates required quarantine and red means a confirmed infection.

In India, the Aarogya Setu app indicates which travelers are “safe” to use rail and air travel. The United Arab Emirates recently launched ALHOSN UAE, which also indicates via color if a person is okay, infected or needs to be quarantined, but also has an option for “hasn’t been tested.” ALHOSN UAE is currently being used to grant access to air travel.

The sheer populations in India and China using health passports pushed this technology to a 5% to 20% market penetration, an unprecedented number for a technology just entering the Hype Cycle.

All of these apps, called health passports, are examples of a pandemic/epidemic response technology and one of the new additions to the Gartner Hype Cycle for Emerging Technologies, 2020. The Hype Cycle for Emerging Technologies is a unique Hype Cycle that distills more than 1,700 unique technologies that will significantly affect business, society and people over the next five to 10 years. It includes technologies that enable a composable enterprise, aspire to regain society’s trust in technology and alter the state of your brain.

The Hype Cycle for Emerging Technologies is a unique Hype Cycle that distills more than 1,700 unique technologies into a list of must-know technologies and trends. This year’s list highlights five unique trends:

·       Composite architectures

·       Algorithmic trust

·       Beyond silicon

·       Formative artificial intelligence (AI)

·       Digital me

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Hype Cycle Trend No. 1: Composite architectures

In the face of rapid changes and decentralization, organizations need to shift to more agile, responsive architectures. A composite architecture is made up of packaged business capabilities built on a flexible data fabric. This allows the enterprise to respond to rapidly changing business needs.

For example, a “composable enterprise” supported by a composite architecture offers increased business resilience. This modular design enables organizations to “recompose” when needed, like during a global pandemic or economic recession. The composable enterprise has four core principles: Modularity, efficiency, continuous improvement and adaptive innovation. Although many organizations apply these principles in a piecemeal fashion, a composable enterprise applies all four across all parts of its organization — from business models to how employees work.

This modular business model enables organizations to move from rigid, traditional planning to active agility. Composable enterprise thinking creates more innovation, reduced costs and better partnerships.

Other emerging technologies under this trend include packaged business capabilities, data fabric, private 5G and embedded AI.

Hype Cycle Trend No. 2: Algorithmic trust

Increased amounts of consumer data exposure, fake news and videos, and biased AI, have caused organizations to shift from trusting central authorities (government registrars, clearing houses) to trusting algorithms. Algorithmic trust models ensure the privacy and security of data, provenance of assets, and the identities of people and things.

For example, “authenticated provenance” is a way to authenticate assets on the blockchain and ensure they’re not fake or counterfeit. While blockchain can be used to authenticate goods, it can only track the information that it is given.

To adequately track assets, they must be tracked from their source. For example, if a counterfeit item is added to the blockchain as a genuine version, the blockchain will continue to verify its authenticity based on the bad original data input. Due to the nature of the immutable ledger, it can never be modified or deleted.

Gartner believes increased interest in blockchain will create increased digital authentication and verification options.

Other emerging technologies in the algorithmic trust trend include differential privacy, responsible AI and explainable AI.

Hype Cycle Trend No. 3: Beyond silicon

Moore’s Law predicts that the number of transistors in a dense integrated circuit would double every two years, but technology is quickly reaching the physical limits of silicon. This has led to the evolution of new advanced materials with enhanced capabilities designed to support smaller, faster technologies.

For example, “DNA computing and storage” use DNA and biochemistry in place of silicon or quantum architectures to perform computation or store data. The data is encoded into synthetic DNA strands for storage and enzymes provide the processing capabilities through chemical reactions.

Despite two successful prototypes, the technology is currently rudimentary and expensive with significant technical barriers to mainstream use. However, the impact of a successful DNA computing and storage option would transform data storage, processing parallelism and computing efficiency.

Other emerging technologies in this trend include biodegradable sensors and carbon-based transistors.

Hype Cycle Trend No. 4: Formative AI

Formative AI is a type of AI capable of dynamically changing to respond to a situation. There are a variety of types, ranging from AI that can dynamically adapt over time to technologies that can generate novel models to solve specific problems.

For example, generative AI is a type of AI that can create new novel content (images, video, etc.) or alter existing content. The new artifacts are similar to, but not exactly the same as, the original. This technology is responsible for deep fakes content, which can cause serious disinformation and reputational risk, and is expected to increase in numbers over the next five years. However, less nefarious uses like drug discovery and synthetic data generation — and even AI-generated artwork — are also increasing in popularity.

Other emerging technologies in this trend include composite AI, differential privacy, small data and self-supervising learning.

Hype Cycle Trend No. 5: Digital me

From health passports to digital twins, as technology integrates with people, there are more opportunities to create digital versions of ourselves. These digital models represent humans in both the real and virtual worlds.

For example, bidirectional brain-machine interfaces (BMIs), are brain-altering wearables that enable two-way communication between a human brain and a computer or machine interface. BMIs can be either wearables or implants that monitor EEGs (electrical activity in the brain) and individuals’ mental states. The difference between regular monitoring BMIs and bidirectional BMI is that the latter can use electrostimulation to modify the mental state of the person.

In the business world, potential applications include authentication, access and payment, immersive analytics and exoskeletons. But other applications, which have their own social and ethical concerns, might include using stimulation to boost alertness in a fatigued employee or changing the mood of an irritable teacher by applying currents to the brain. While there are many potential use cases, BMIs also introduce an additional avenue of vulnerability for would-be attackers to exploit.

The original article by Brian Burke, research vice president at Gartner, is here

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Objectives and Study Scope

This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.

The report in its entirety provides a comprehensive overview of the current global condition, as well as notable opportunities and challenges. The analysis reflects market size, latest trends, growth drivers, threats, opportunities, as well as key market segments. The study addresses market dynamics in several geographic segments along with market analysis for the current market environment and future scenario over the forecast period. The report also segments the market into various categories based on the product, end user, application, type, and region.
The report also studies various growth drivers and restraints impacting the  market, plus a comprehensive market and vendor landscape in addition to a SWOT analysis of the key players.  This analysis also examines the competitive landscape within each market. Market factors are assessed by examining barriers to entry and market opportunities. Strategies adopted by key players including recent developments, new product launches, merger and acquisitions, and other insightful updates are provided.

Research Process & Methodology

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We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.

We draw on available data sources and methods to profile developments. We use computerised data mining methods and analytical techniques, including cluster and regression modelling, to identify patterns from publicly available online information on enterprise web sites.
Historical, qualitative and quantitative information is obtained principally from confidential and proprietary sources, professional network, annual reports, investor relationship presentations, and expert interviews, about key factors, such as recent trends in industry performance and identify factors underlying those trends - drivers, restraints, opportunities, and challenges influencing the growth of the market, for both, the supply and demand sides.
In addition to our own desk research, various secondary sources, such as Hoovers, Dun & Bradstreet, Bloomberg BusinessWeek, Statista, are referred to identify key players in the industry, supply chain and market size, percentage shares, splits, and breakdowns into segments and subsegments with respect to individual growth trends, prospects, and contribution to the total market.

Research Portfolio Sources:

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  • Dun & Bradstreet: Country Reports, Country Riskline Reports, Economic Indicators 5yr Forecast, and Industry Reports

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  • OECD: Economic Outlook, Economic Surveys, Energy Prices and Taxes, Main Economic Indicators, Main Science and Technology Indicators, National Accounts, Quarterly International Trade Statistics

  • Oxford Economics: Global Industry Forecasts, Country Economic Forecasts, Industry Forecast Data, and Monthly Industry Briefings

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  • Technavio: Global Market Assessment Reports, Regional Market Assessment Reports, and Market Assessment Country Reports

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Global Business Reviews, Research Papers, Commentary & Strategy Reports

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M&A and Risk Management | Regulation

  • Thomson Mergers & Acquisitions

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  • CoreCompensation

  • CCH Research Network

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Forecast methodology

The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.

Forecasts, Data modelling and indicator normalisation

Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:

  • Cambridge Econometrics (CE)

  • The Centre for Economic and Business Research (CEBR)

  • Experian Economics (EE)

  • Oxford Economics (OE)

As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.

Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.

Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pre­tax revenue and its total bought­in costs (costs excluding wages and salaries).

Forecasts of GDP growth: GDP = CN+IN+GS+NEX

GDP growth estimates take into account:

  • Consumption, expressed as a function of income, wealth, prices and interest rates;

  • Investment as a function of the return on capital and changes in capacity utilization; Government spending as a function of intervention initiatives and state of the economy;

  • Net exports as a function of global economic conditions.

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Market Quantification
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.

Revenues

Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.

Industry Life Cycle Market Phase

Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:

  • Nascent: New market need not yet determined; growth begins increasing toward end of cycle

  • Growth: Growth trajectory picks up; high growth rates

  • Mature: Typically fewer firms than growth phase, as dominant solutions continue to capture the majority of market share and market consolidation occurs, displaying lower growth rates that are typically on par with the general economy

  • Decline: Further market consolidation, rapidly declining growth rates

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The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.

The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.

  • Vector Auto Regression (VAR) statistical models capturing the linear interdependencies among multiple time series, are best used for short-term forecasting, whereby shocks to demand will generate economic cycles that can be influenced by fiscal and monetary policy.

  • Dynamic-Stochastic Equilibrium (DSE) models replicate the behaviour of the economy by analyzing the interaction of economic variables, whereby output is determined by supply side factors, such as investment, demographics, labour participation and productivity.

  • Dynamic Econometric Error Correction (DEEC) modelling combines VAR and DSE models by estimating the speed at which a dependent variable returns to its equilibrium after a shock, as well as assessing the impact of a company, industry, new technology, regulation, or market change. DEEC modelling is best suited for forecasting.

Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from official sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reflect different assumptions about their relative importance.

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The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.

Elasticities
Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.

Prices
Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.

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