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10 strategic trends that will drive data management in 2020
The New Year is only weeks away, but it will mark major changes in how organizations manage and use data. Research firm Gartner has identified the 10 top strategic technology trends that will affect how organizations manage data. Gartner defines a strategic technology trend as one with substantial disruptive potential that is beginning to break out of an emerging state into broader impact and use, or which is rapidly growing with a high degree of volatility, expected to reach a tipping point over the next five years.
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Hyperautomation
Hyperautomation is the combination of multiple machine learning, packaged software and automation tools to deliver work. Hyperautomation refers not only to the breadth of the pallet of tools, but also to all the steps of automation itself (discover, analyze, design, automate, measure, monitor and reassess), Gartner experts contend. Understanding the range of automation mechanisms, how they relate to one another and how they can be combined and coordinated is a major focus for hyperautomation. This trend had its start with robotic process automation, which is not, by itself, fully hyperautomation, which requires a combination of tools to help support replicating pieces of where the human is involved in a task.
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Multiexperience
Through 2028, the user experience will undergo a significant shift in how users perceive the digital world and how they interact with it, Gartner explains. Conversational platforms are changing the way people interact with the digital world. Virtual reality, augmented reality and mixed reality are changing the way in which people perceive the digital world. This combined shift in both perception and interaction models leads multisensory and multimodal experiences expected in the future. “The model will shift from one of technology-literate people to one of people-literate technology. The burden of translating intent will move from the user to the computer,” said Brian Burke, research vice president at Gartner. “This ability to communicate with users across many human senses will provide a richer environment for delivering nuanced information.”
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Democratization of expertise
Democratization aims to give people access to technical expertise—such as machine learning and application development—or business domain expertise (for example, sales process and economic analysis) via a radically simplified experience that doesn’t require extensive and costly training. A “citizen” approach—for example, citizen data scientists and citizen integrators—as well as the evolution of citizen development and no-code models, are examples of democratization.

Gartner expects four key aspects of the democratization trend to accelerate.

• Democratization of data and analytics (tools targeting data scientists expanding to target the professional developer community).

• Democratization of development (AI tools to leverage in custom-developed applications).

• Democratization of design (expanding on the low-code/no-code phenomena with automation of additional application development functions).

• Democratization of knowledge (non-IT professionals gaining access to tools and expert systems that empower them to exploit and apply specialized skills beyond their own expertise and training).
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Human augmentation
Human augmentation explores how technology can be used to deliver cognitive and physical improvements as an integral part of the human experience. Physical augmentation enhances humans by changing their inherent physical capabilities by implanting or hosting a technology element on their bodies, such as a wearable device. Cognitive augmentation can occur through accessing information and exploiting applications on traditional computer systems and the emerging multi-experience interface. In the next decade, increasing levels of physical and cognitive human augmentation will become prevalent as individuals seek personal enhancements. This will create a new “consumerization” effect by which employees seek to exploit their personal enhancements—and even extend them—to improve their office environment.
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Transparency and traceability
Consumers are increasingly aware that their personal information is valuable and are demanding better protection for it, Gartner notes. Organizations recognize the increasing risk of securing and managing personal data, and governments are implementing stricter legislation to ensure they do. Transparency and traceability are critical elements to support these digital ethics and privacy needs. Transparency and traceability refer to a range of attitudes, actions and supporting technologies and practices designed to address regulatory requirements, preserve an ethical approach to use of artificial intelligence and other advanced technologies, and repair the growing lack of trust in companies. As organizations build out transparency and trust practices, they must focus on three areas—artificial intelligence and machine learning; personal data privacy, ownership and control; and ethically aligned design.
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The empowered edge
Edge computing is a topology in which information processing, and content collection and delivery are placed closer to the sources, repositories and consumers of this information. It tries to keep the traffic and processing local to reduce latency, exploit the capabilities of the edge and enable greater autonomy at the edge. “Much of the current focus on edge computing comes from the need for IoT systems to deliver disconnected or distributed capabilities into the embedded IoT world for specific industries,” Burke said. “However, edge computing will become a dominant factor across virtually all industries and use cases, as the edge is empowered with increasingly more sophisticated and specialized compute resources and more data storage.”
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Distributed cloud
A distributed cloud is the distribution of public cloud services to different locations, while the originating public cloud provider assumes responsibility for the operation, governance, updates to and evolution of the services, Gartner says. This represents a significant shift from the centralized model of most public cloud services and will lead to a new era in cloud computing.
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Autonomous things
Autonomous things are physical devices that use artificial intelligence to automate functions previously performed by humans, Gartner explains. The most recognizable forms of autonomous things are robots, drones, autonomous vehicles or ships and appliances. Their automation goes beyond that provided by rigid programming models, and they exploit AI to deliver advanced behaviors that interact more naturally with their surroundings and with people. As the technology capability improves, regulation permits and social acceptance grows, autonomous things will increasingly be deployed in uncontrolled public spaces. “We expect a shift from stand-alone intelligent things to a swarm of collaborative intelligent things where multiple devices will work together, either independently of people or with human input,” Burke said.
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Practical blockchain
Blockchain technology has the potential to reshape industries by enabling trust, providing transparency and enabling value exchange across ecosystems, offering the potential to lower costs, reduce transaction settlement times and improve cash flow, Gartner explains. Assets can be traced to their origin, significantly reducing the opportunities for substitutions with counterfeit goods. Asset tracking also has value in other areas, such as tracing food across a supply chain, to more easily identify the origin of contamination or track individual parts to assist in product recalls. Another area in which blockchain has potential is identity management. Smart contracts can be programmed into the blockchain where events can trigger actions; for example, payment is released when goods are received. While blockchain remains “immature for enterprise deployments due to a range of technical issues,” it has potential for disruption and revenue generation, and organizations should evaluate its potential, Burke said.
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Artificial intelligence-enhanced security
Artificial intelligence and machine learning will continue to be applied to augment human decision-making across a broad set of use cases, Gartner predicts. While this creates great opportunities to enable hyperautomation and leverage autonomous things to deliver business transformation, it creates significant new challenges for the security team and risk leaders with a massive increase in potential points of attack with IoT, cloud computing, microservices and highly connected systems in smart spaces. Security and risk leaders should focus on three key areas—protecting AI-powered systems, leveraging AI to enhance security defense, and anticipating nefarious use of AI by attackers.