Gartner presented the main strategic technological trends that will mark 2019, with their potential industrial impacts as well as their potential for disruption.
“The Intelligent Digital Mesh has been a recurring theme for two years and remains a major driver until 2019. The trends under each of these three themes are key to driving an innovation process. continued as part of a Continuous NEXT strategy, “said David Charley of Gartner. “For example, artificial intelligence (AI) in the form of automated objects and augmented intelligence is used in conjunction with IoT, the cutting-edge computing to create highly integrated smart spaces. This combinatorial effect of several merging trends to create new opportunities and create new impacts is one of the hallmarks of Gartner’s top 10 strategic technology trends for 2019 “.
Here are the top 05 strategic technology trends for 2019:
Objects, such as robots, drones, and autonomous vehicles, use artificial intelligence to automate functions previously performed by humans. Their automation goes beyond the automation provided by rigid programming models and leverages artificial intelligence to offer advanced behaviors that interact more naturally with their environment and with people
“As autonomous objects proliferate, we expect a shift from autonomous smart objects to a multitude of collaborative smart objects, with multiple devices working together, regardless of people or with human participation,” said Cearley. “For example, if a drone examined a large field and found that it was ready for harvest, it could send an” independent harvester “. Or, in the delivery market, the most effective solution could be to use a stand-alone vehicle to transport parcels to the target area. Robots and drones in the vehicle could then ensure the final delivery of the package.
The augmented analysis focuses on a specific area of augmented intelligence, using machine learning to transform how analytic content is developed, used, and shared. The capabilities of augmented analytics will evolve rapidly to mainstream adoption as an essential feature of data preparation, data management, modern analytics, business process management, process discovery, and data science platforms. Automated information from augmented analytics will also be integrated into enterprise applications, such as Human Resources, Finance, Sales, Marketing, Customer Service, Purchasing, and Asset Management. to optimize the decisions and actions of all employees in their context (and not just in the context of analysts and data scientists). Enhanced analysis automates the process of data preparation, information generation, and information visualization, eliminating the need for professional data specialists in many situations.
“This will lead to citizen data science, an emerging set of features and practices that allow users whose main job is not in the field of statistics and analytics to analyze predictive and normative information from data,” said Cearley. “Until 2020, the number of citizen data science will increase five times faster than the number of data science experts. Organizations can use citizen data science to fill the gap in talent in data science and machine learning caused by the shortage and high cost of data scientists. “
Development boosted by the AI
The market is evolving rapidly, from an approach in which professional data scientists must partner with application developers to create most of the improved AI solutions, to a model in which the professional developer can operate on their own. Predefined templates provided as a service. This provides the developer with an ecosystem of algorithms and artificial intelligence models, as well as development tools designed to integrate artificial intelligence features and models into a solution. Another level of opportunity for professional application development comes when artificial intelligence is applied to the development process itself to automate various data science functions, application development, and testing. According to Gartner, by 2022, at least 40% of new application development projects will have AI co-developers in their team.
“Ultimately, advanced AI-based development environments, automating both the functional and non-functional aspects of applications, will open a new era for the” citizen application developer “, where non-professionals will be able to use AI-based tools to automatically generate new solutions. The tools that allow non-professionals to generate applications without coding are not new, but we expect systems running with artificial intelligence to bring a new level of flexibility, “said Cearley.
A digital twin is a digital replica of an object, process, or system that can be used for a variety of purposes. Digital representation provides both the elements and the operating dynamics of an IoT device throughout its life cycle. According to Gartner, by 2020, there will be more than 20 billion connected sensors and terminals. Digital twins will potentially exist for billions of things. Initially, organizations will implement digital twins that they will simply evolve over time, improving their ability to collect and visualize the right data, apply appropriate analytics and rules, and effectively meet the goals. of the company.
“One of the aspects of the evolution of the digital twin, which will go beyond the IoT, will be for companies implementing Digital Twinning of their organizations (DTO). A DTO is a dynamic software model that relies on operational or other data to understand how an organization is operationalizing its business model, connecting to its current state, deploying resources, and responding to changes to deliver the expected customer value,” said Cearley. “DTOs help improve the efficiency of business processes and create more flexible, dynamic and responsive processes that can potentially respond automatically to changing conditions.”
Edge (tip/end/ tip) refers to terminals used by people or integrated into the world around us. Edge Computing is a form of IT architecture that serves as an alternative to cloud computing. Rather than transfer data generated by connected IoT devices to the Cloud or a Data Center, it is about processing the data at the edge of the network directly where it is generated. Edge computing, therefore, describes a computer topology in which the processing of information, the collection, and dissemination of content are placed closer to these endpoints. It tries to keep the traffic and processing locally, the goal being to reduce traffic and latency.
In the short term, Gartner believes that the terminals will be boosted by IoT and the need to keep the processing close to the terminal rather than a centralized cloud server. However, rather than creating a new architecture, cloud computing and Edge computing will evolve into complementary models, with cloud services being managed as a centralized service running not only on centralized servers but also on cloud computing. Servers distributed locally and on the devices themselves.
Over the next five years, specialized AI chips, combined with processing power, storage, and other advanced features will be added to more devices. The extreme heterogeneity of this integrated IoT world and the long life of assets such as industrial systems will create significant management challenges. In the longer term, as 5G matures, the expanding Edge computing environment will allow for more robust communication with centralized services. The 5G offers lower latency, higher bandwidth and a dramatic increase in the number of knots per square kilometer.