Gartner has highlighted the top 10 technology trends that will be strategy for most organisations in 2016.
The first three trends address merging the physical and virtual worlds and the emergence of the digital mesh. While organisations focus on digital business today, algorithmic business is emerging. Algorithms — relationships and interconnections — define the future of business. In algorithmic business, much happens in the background in which people are not directly involved. This is enabled by smart machines, which our next three trends address. Our final four trends address the new IT reality, the new architecture and platform trends needed to support digital and algorithmic business.
The top 10 strategic technology trends for 2016 are:
The device mesh refers to an expanding set of endpoints people use to access applications and information or to interact with people, social communities, governments and businesses. The device mesh includes mobile devices, wearable tech, consumer and home electronic devices, automotive devices and environmental devices — such as sensors in the Internet of Things (IoT).
A variety of other trends have led to an increased number of sensors embedded in many technologies and devices that we use personally and professionally. Devices become smarter as they gather more data on our daily patterns. Gartner predicts that these sensors, which tend to work in silos today, will increasingly work in concert, leading to even greater insights about our daily patterns.
The device mesh creates the foundation for a new continuous and ambient user experience. Immersive environments delivering augmented and virtual reality hold significant potential but are only one aspect of the experience. The ambient user experience preserves continuity across boundaries of device mesh, time and space. The experience seamlessly flows across a shifting set of devices and interaction channels blending physical, virtual and electronic environment as the user moves from one place to another.
Gartner predicts that the devices and sensors will become so smart that they will be able to organise our lives without us even noticing that they are doing so.
Advances in 3D printing have already enabled machines to use a wide range of materials, including advanced nickel alloys, carbon fiber, glass, conductive ink, electronics, pharmaceuticals and biological materials. These innovations are driving user demand, as the practical applications for 3D printers expand to more sectors, including aerospace, medical, automotive, energy and the military. The growing range of 3D-printable materials will drive a compound annual growth rate of 64.1% for enterprise 3D-printer shipments through 2019. These advances will necessitate an altering of assembly line and supply chain processes to utilise 3D printing.
Everything in the digital mesh produces, uses and transmits information. This information goes beyond textual, audio and video information to include sensory and contextual information. Information of everything addresses this influx with strategies and technologies to link data from all these different data sources.
Information has always existed everywhere but has often been isolated, incomplete, unavailable or unintelligible. Advances in semantic tools such as graph databases as well as other emerging data classification and information analysis techniques will bring meaning to the often chaotic flood of information.
DNNs (an advanced form of machine learning particularly applicable to large, complex datasets) are what make smart machines appear “intelligent.” DNNs enable hardware- or software-based machines to learn all the features in their environment, from the finest details to abstract pieces of content. This area is evolving quickly, and organizations must assess how they can apply these technologies to gain competitive advantage.
The explosion of data sources and complexity of information makes manual classification and analysis difficult and uneconomic. DNNs automate these tasks and make it possible to address key challenges related to the information of everything trend.
Machine knowledge gives rise to a wide array of smart machine implementations including robots, autonomous vehicles, virtual personal assistants (VPAs) and smart advisors that act in an autonomous or semi-autonomous manner. Perhaps the most prominent example is the autonomous driving car, which leverages information from autonomous vehicles that have been used within controlled environments for years. Masdar City in the United Arab Emirates is another example of prominent controlled environments. On the software side, VPAs such as Google Now, Microsoft’s Cortana and Apple’s Siri are also becoming smarter. Instead of interacting with menus, forms and buttons on a smartphone, the user speaks to an app, which is really an intelligent agent.
IT leaders should explore how they can use autonomous objects and agents to augment human activity and free people for work that only people can do. However, they must recognize that smart agents and things are a long-term investment that will continually evolve for the next 20 years.
The complexities of digital business and the algorithmic economy, combined with an emerging “hacker industry” significantly increase the threat surface for an organization. Relying on perimeter defence and rule-based security is inadequate, especially as organisations exploit more cloud-based services and open APIs for customers and partners to integrate with their systems. IT leaders must focus on detecting and responding to threats, as well as more traditional blocking and other measures to prevent attacks. Application self-protection, as well as user and entity behaviour analytics, will help fulfil the adaptive security architecture.
The digital mesh and smart machines require intense computing architecture demands to make them viable for organisations and their customers. Providing this required boost are high-powered and ultra-efficient neuromorphic architectures. Fuelled by field-programmable gate arrays (FPGAs) as an underlining technology for neuromorphic architectures, there are significant gains to this architecture, such as being able to run at speeds of greater than a teraflop with high-energy efficiency.
Systems built on graphics processing units (GPUs) and FPGAs will function similarly to human brains that are particularly suited to deep learning and other pattern-matching algorithms that smart machines use.
More apps are being built that can work together, and the value of the combination is much greater than the sum of the parts. Enabled by software-defined application services, this new approach enables Web-scale performance, flexibility and agility.
Microservice architecture is an emerging pattern for building distributed applications that support agile delivery and scalable deployment, both on-premises and in the cloud. Bringing mobile and Internet of Things (IoT) elements into the app and service architecture creates a comprehensive model to address back-end cloud growth and front-end device mesh experiences. Application teams must create new modern architectures to deliver agile, flexible and cloud-based applications with dynamic user experiences that span the digital mesh.
Internet of Things ( IoT) platforms compliment the mesh app and service architecture. The management, security, integration and other technologies and standards of the IoT platform are the base set of capabilities for building, managing and securing elements in the IoT. IoT platforms constitute the work IT does behind the scenes from an architectural and a technology standpoint to make the IoT a reality. Gartner indicates that the providers of Internet of Things platforms are fragmented today, and would benefit greatly from patching together a better ecosystem where data is shared more broadly.by