Trends Identified
Digital Twins
A digital twin is a digital representation of a real-world entity or system (see Figure 5). The implementation of a digital twin is an encapsulated software object or model that mirrors a unique physical object (see Note 1). Data from multiple digital twins can be aggregated for a composite view across a number of real-world entities. The notion of a digital representation of real-world entities or systems is not new. You can argue that this was a central notion in the IT industry with the creation of computer-aided design representations of physical assets or profiles of individual customers. The difference in the latest iteration of digital twins is: The robustness of the models
Digital twins' link to the real world, potentially in real time
The application of advanced big data analytics and AI
The ability to interact with them and evaluate "what if" scenarios
2017
Top 10 Strategic Technology Trends for 2018
Gartner
Cloud to the Edge
Edge computing describes a computing topology in which information processing and content collection and delivery are placed closer to the sources and sinks of this information. Edge computing draws from the concepts of mesh networking and distributed processing. It tries to keep the traffic and processing local, with the goal being to reduce traffic and latency. As such, the notion of edge content delivery has existed for many years. The "where to process the data" pendulum has swung between highly centralized approaches (such as a mainframe or a centralized cloud service) and more decentralized approaches (such as PCs and mobile devices). Connectivity and latency challenges, bandwidth constraints and greater functionality embedded at the edge favor distributed deployment models. The advantages of processing power and low costs of operating at hyperscale, coupled with the complexity of managing and coordinating thousands of geographically separated endpoints, favor the centralized model.
2017
Top 10 Strategic Technology Trends for 2018
Gartner
Conversational Platforms
Conversational platforms will drive the next big paradigm shift in how humans interact with the digital world. They will shift the model from technology-literate people to people-literate technology. The burden of translating intent will move from the user to the computer. The system takes a question or command from the user in natural language. It responds by executing a function, presenting content or asking for additional input.
2017
Top 10 Strategic Technology Trends for 2018
Gartner
Immersive Experience
While conversational platforms are changing the way in which people interact with the digital world, virtual reality (VR), augmented reality (AR) and mixed reality (MR) are changing the way in which people perceive the digital world. This combined shift in perception and interaction models leads to the future immersive user experience.
2017
Top 10 Strategic Technology Trends for 2018
Gartner
Blockchain
Blockchain is evolving from a digital currency infrastructure into a platform for digital transformation. Blockchain and other distributed-ledger technologies provide trust in untrusted environments, eliminating the need for a trusted central authority.
2017
Top 10 Strategic Technology Trends for 2018
Gartner
Event-Driven Model
Business is always sensing, and ready to exploit, new digital business moments (see "Business Events, Business Moments and Event Thinking in Digital Business" (/doc/code/338380?ref=ddisp) ). This is central to digital business. Business events reflect the discovery of notable states or state changes, such as the completion of a purchase order. Some business events, or combinations of events, constitute business moments — detected situations that call for specific business actions. The most significant business moments have implications for multiple parties (for example, separate applications, lines of business or partners).
2017
Top 10 Strategic Technology Trends for 2018
Gartner
Continuous Adaptive Risk and Trust
The intelligent digital mesh and related digital technology platforms and application architectures create an ever-more-complex world for security. 20 The continuing evolution of the "hacker industry" and its use of increasingly sophisticated tools — including the same advanced technologies available to enterprises — significantly raise the threat potential. Relying on perimeter defense and static rule-based security is inadequate and outdated. This is especially so as organizations exploit more mobile devices, cloud-based services, and open APIs for customers and partners to create business ecosystems.
2017
Top 10 Strategic Technology Trends for 2018
Gartner
Autonomous things
Whether it’s cars, robots or agriculture, autonomous things use AI to perform tasks traditionally done by humans. The sophistication of the intelligence varies, but all autonomous things use AI to interact more naturally with their environments. Autonomous things exist across five types: Robotics Vehicles Drones Appliances Agents Those five types occupy four environments: Sea, land, air and digital. They all operate with varying degrees of capability, coordination and intelligence. For example, they can span a drone operated in the air with human-assistance to a farming robot operating completely autonomously in a field. This paints a broad picture of potential applications, and virtually every application, service and IoT object will incorporate some form of AI to automate or augment processes or human actions. Collaborative autonomous things such as drone swarms will increasingly drive the future of AI systems Explore the possibilities of AI-driven autonomous capabilities in any physical object in your organization or customer environment, but keep in mind these devices are best used for narrowly defined purposes. They do not have the same capability as a human brain for decision making, intelligence or general-purpose learning.
2018
Gartner Top 10 Strategic Technology Trends for 2019
Gartner
Augmented analytics
Data scientists now have increasing amounts of data to prepare, analyze and group — and from which to draw conclusions. Given the amount of data, exploring all possibilities becomes impossible. This means businesses can miss key insights from hypotheses the data scientists don’t have the capacity to explore. Augmented analytics represents a third major wave for data and analytics capabilities as data scientists use automated algorithms to explore more hypotheses. Data science and machine learning platforms have transformed how businesses generate analytics insight. “By 2020, more than 40% of data science tasks will be automated” Augmented analytics identify hidden patterns while removing the personal bias. Although businesses run the risk of unintentionally inserting bias into the algorithms, augmented analytics and automated insights will eventually be embedded into enterprise applications. Through 2020, the number of citizen data scientists will grow five times faster than professional data scientists. Citizen data scientists use AI powered augmented analytics tools that automate the data science function automatically identifying data sets, developing hypothesis and identifying patterns in the data. Businesses will look to citizen data scientists as a way to enable and scale data science capabilities. Gartner predicts by 2020, more than 40% of data science tasks will be automated, resulting in increased productivity and broader use by citizen data scientists. Between citizen data scientists and augmented analytics, data insights will be more broadly available across the business, including analysts, decision makers and operational workers.
2018
Gartner Top 10 Strategic Technology Trends for 2019
Gartner
AI-driven development
AI-driven development looks at tools, technologies and best practices for embedding AI into applications and using AI to create AI-powered tools for the development process. This trend is evolving along three dimensions: The tools used to build AI-powered solutions are expanding from tools targeting data scientists (AI infrastructure, AI frameworks and AI platforms) to tools targeting the professional developer community (AI platforms, AI services). With these tools the professional developer can infuse AI powered capabilities and models into an application without involvement of a professional data scientist. The tools used to build AI-powered solutions are being empowered with AI-driven capabilities that assist professional developers and automate tasks related to the development of AI-enhanced solutions. Augmented analytics, automated testing, automated code generation and automated solution development will speed the development process and empower a wider range of users to develop applications. AI-enabled tools are evolving from assisting and automating functions related to application development (AD) to being enhanced with business domain expertise and automating activities higher on the AD process stack (from general development to business solution design). The market will shift from a focus on data scientists partnered with developers to developers operating independently using predefined models delivered as a service. This enables more developers to utilize the services, and increases efficiency. These trends are also leading to more mainstream usage of virtual software developers and nonprofessional “citizen application developers.” Read more: How to Build a Business Case for Artificial Intelligence
2018
Gartner Top 10 Strategic Technology Trends for 2019
Gartner