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Gartner’s Top Predictions Till 2020

Gartner’s Top Predictions till 2020, focus on disruptions brought about by digital business, the Internet of Things, smart machines and the onset of the Digital Industrial Revolution. IT leaders face a new set of realities in a much larger context.

1. The New Industrial Revolution

a. By 2018, 3D printing will result in the loss of at least $100 billion per year in intellectual property globally.

Intellectual property theft is a multinational issue. The U.S. alone has over $300 billion in intellectual property stolen annually. 3D printers employ one of seven different technologies, giving each printer a unique range of items it can make or copy. The plummeting costs of 3D printers, scanners and 3D modeling technology, combined with improving capabilities, makes the technology for intellectual property theft more accessible to would-be criminals.

b. By 2016, 3D printing of tissues and organs (bioprinting) will cause a global debate about regulating the technology or banning it for both human and nonhuman use.

3D printing represents a different kind of disruption from other IT-related technologies in that it affects things largely centered on the physical world rather than the digital one. In this regard, the use of digital resources to shape our physical reality can also have the effect of shaping our fears of personal impact. The emergence of 3D bioprinting facilities with the ability to print human organs can leave people wondering what will be the effect on society. Many questions will be raised such as “Who will control the ability to receive bioprints?,” “Who will ensure the quality of the organs?” and “Will there be regulation of this emerging industry?”

However, beyond the fears, there is the reality of what 3D bioprinting means in terms of helping people who need organs that are otherwise not readily available. Here, we examine a number of issues surrounding the good and bad of 3D bioprinting.

2. Digital Business

a. By 2017, over half of consumer goods manufacturers will employ crowdsourcing to achieve fully 75% of their consumer innovation and R&D capabilities.

Although the word “crowdsourcing” was coined in 2006, consumer goods manufacturers used elements of crowdsourcing techniques years before that. Consumer goods companies must crowdsource more aggressively than most other industries to stay current with the constant shift in attitudes and habits of millions of consumers. Engineers, scientists, IT professionals and marketers are tapping ever larger (and more anonymous) pools of intellect and opinion through aggressive use of digital channels. Most are piloting or actively using new product innovation (NPI) tools to engage the public, by posing questions (directly and indirectly) via user forums, supply chain collaboration sites and virtual focus groups, or through quantitative research surveys. Today’s technology makes it easier to digitally crowdsource a much broader range of tasks and goals.

b. By 2017, 80% of consumers will collect, track and barter their personal data for cost savings, convenience and customization.

The much-hyped big data movement is set to disrupt the multibillion-dollar consumer data marketplace with transformational results. The escalation in consumer awareness of data collection practices has set the stage for offering consumers more control over the disposition of personal data — collected both online and offline. This affects three sectors in particular: Online portals and retailers, especially Amazon, Google and Facebook, which collect vast troves of data on users Communications service providers, which have visibility into highly revealing data, especially that which is associated with mobile device usage.

c. By 2020, enterprises and governments will fail to protect 75% of sensitive data, and will declassify and grant broad/public access to it.

An ideal way to protect sensitive data is not to have sensitive data. The amount of data stored and used by enterprises and governments is growing exponentially (rapid adoption of big data is just another example). An attempt to protect it all is unrealistic. Organizations’ resources are never sufficient to catch up with the constant growth in the amounts of data to be protected and with the complexity of protection. Moreover, enterprises and governments often do not own and control external entities, such as social and professional communities, open-source organizations, and international hackers’ groups, with their sources of data and their abilities to collect, share and disclose data. By 2015, we will witness several successful, internationally known hacker attacks on sensitive data that governments and prominent enterprises will have failed to protect.

3. Smart Machines

a. By 2024, at least 10% of activities potentially injurious to human life will require mandatory use of a nonoverridable “smart system.”

The increasing deployment of smart systems capable of automatically responding to external events is increasing all the time, but there remains deep-seated resistance to eliminating the option for human intervention. At least in the more litigious Western markets, this is heavily influenced by questions of liability and the need for someone to ultimately be responsible for every event. At the same time, it reflects a highly subjective (and usually incorrect) assessment of real versus perceived risk that appears to be inherent in the human psyche. Simply put, most people believe they can react faster than they actually do, that they can accurately assess risk and predict outcomes (generally untrue), and that automatic smart systems cannot perform as reliably and effectively as they can. In the past, that may have been partially true, but with the ever-advancing power of microprocessors linked to a growing array of real-time sensors, allied with vastly improved analytics, the superior capabilities of smart systems (compared to humans) is increasingly a reality.

b. By 2020, the majority of knowledge worker career paths will be disrupted by smart machines in both positive and negative ways.

A broad and powerful range of new systems — smart machines — is emerging this decade. They do what we thought only people could do and what we didn’t think technology could do. Examples include virtual personal assistants (first envisioned in the IT era by Apple with its 1987 Knowledge Navigator video), and smart advisors (exemplified by the Clinical Oncology Advisor, jointly built by WellPoint and IBM, to stay on top of the huge body of medical and scientific literature and provide advice to clinicians when presented with a patient’s electronic health record).

Smart machines exploit machine learning and deep learning algorithms. They behave autonomously, adapting to their environments. They learn from results, create their own rules, and seek or request additional data to test hypotheses. They are able to detect novel situations, often far more quickly and accurately than people.

c. By 2017, 10% of computers will be learning rather than processing.

Deep learning methods, based on deep neural networks, are currently being applied in speech recognition systems as well as some object recognition applications. They have been shown to be more accurate than the current implementations, which are based on Gaussian mixture models, because they adapt to small speech variations, such as accents.

The Defense Advanced Research Project Agency (DARPA) and Ecole Polytechnique Federal de Lausanne are funding the SyNAPSE and The Human Brain projects, respectively, fostering neuromorphic computing techniques intended for pattern recognition applications, including facial recognition, object recognition, drug discovery and medical diagnostics.

4. Internet of Things

a. By 2020, consumer data collected from wearable devices will drive 5% of sales from the Global 1000.

Wearable computing, or wearables, is quickly moving beyond military applications and the subject of science fiction films into mainstream society, led by the growing, multibillion-dollar health and fitness markets.

Within five years, consumer wearables will become more sophisticated, capturing what the user sees, hears or even feels (through biorhythmic responses). This gives marketers opportunities to connect with buyers with even more intimacy, customization and relevance. (Note: Although this topic has its share of privacy and regulatory issues, they are beyond the scope of this prediction.)

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