Journey 2020

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Navigate through Journey 2020 by clicking on the four sources of digital disruption and explore 19 emerging and evolving research topic areas that we expect to play a major role in business in the Digital Shockwaves context.

Click on the ripple graph to see technologies details

Business Models

Business Model disruptions are arising through new sources of business value and partnership, driven by data and connectivity. Discover how companies are taking advantage of digital technologies to find new markets, business models and revenue streams.

Evolving Challenges

Evolving Challenges are those areas which present familiar challenges, but need to be addressed differently as a result of emerging new influences.

Ways of working

Ways of Working are being disrupted through changes in business processes and the evolution of the very nature of work. We look ahead at how people will collaborate with people, machines and virtual beings in entirely new ways.

Disruptive technologies

Disruptive Technologies explores how certain technologies may create notable societal and economic disruptions.

Evolving Challenges

Addressing ever changing challenges

Include those familiar challenges that need to be addressed differently as a result of emerging new influences, impelling us to deal with them in radically different ways.

Our vision

A number of evolving challenges do not arise from the technologies themselves, but to the way they are used or perhaps abused.

Those familiar challenges that need to be addressed with different perspectives as a result of the emergence of new influences.

Interview with Frédéric Oblé

@fredericoble - Member of the Atos Scientific Community

A growing number of ethical challenges are raised by the application of digital technologies in the IT for Life sciences area, such as advancement in medical technology and food production technology.

In the area of analytics, we are faced with the challenge of Fast Data and the ability to analyse data streams in real time. At the other extreme we have the challenge of Deep Learning as we look to exploit an ever increasing pool of data, using machine learning and algorithmic searching to unlock otherwise hidden insights.

Identity and Privacy and Security debates will continue as the needs, wishes and expectations of stakeholders like citizens, governments and businesses evolve.

With a move to increasingly collaborative working between potentially trustless parties and with the promised advent of high disruptive technologies like Quantum Computing, these topics are more relevant and impactful than ever.

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Emerging & evolving technologies

Privacy, labor and cyber-physical security will remain open challenges by 2020, as businesses and societies continue to learn that no technology can fully solve the world’s problems.


for Life

Demographic trends and climate change are putting increasing pressure on some of the basic human needs like food, drinking water and healthcare.

IT for Life is expected to be a continuing and rapidly evolving topic.

Add to this the challenges of sustainability in areas of energy production and raw material consumption, and it is clear that current thinking and practices need to change.

In a number of respects Information Technology and Digital Technologies can be significant enablers of radically new approaches that could help transform the way we face these challenges. Particularly in the area of Health, our understanding of the human genome together with ever increasing quantities of data relating to patient medical histories is facilitating a shift towards a healthcare model that is predictive, preventative, personalised and participatory.

However the greater insight into our physical conditions opens up a whole new set of ethical challenges in areas of privacy, risk and prioritised use of limited resources.atos_aj2020_it-for-lifeWhere digital technologies will help overcome the challenges of sustainability



Fast Data puts the emphasis on generating actionable intelligence at high speed, enabling immediate response based on insights derived from deep analytics of incoming data streams.

Business in 2020 will be driven by data.

Fast Data is time-critical: its value and derived insights exist within a small window of opportunity as it initiates actions or decisions based on the events identified and on the analytics applied thanks to the historical analysis.

Sources such as Telecom networks, smart sensors, & connected devices provide fast event streams that exceed several thousand events per second and now are approaching millions per second.

This reality has prompted the development of distributed streaming computing platforms: the seed for Industrial Data Platforms.

Algorithms, and their optimized computation architecture, will be the critical differentiator in the successful implementation of Fast Data. HPC technologies will enable multiple use cases such as image analysis in autonomous cars and usage of Blockchain in energy gateways.

The adoption of Fast Data also requires organization agility to address and make real-time decisions, thus driving a need for coordination between networks of people. Since the value generated from fast streaming data depreciates rapidly with time, businesses need to reduce the gap between events and decisions in order to exploit transient business moments



Deep Learning is one branch of technology in the field of Machine Learning and Artificial Intelligence. It involves very large and multi-layered networks of artificial neurons that mimic the behavior of the human brain.

By 2020 Deep Learning will have a wide range of applications in data analysis, Robotics and the Internet of Things.

Deep Learning has already proven its worth, supporting the discovery of the Higgs Boson Particle by classifying decay patterns in data collected from the Large Hadron Collider.

Using large, multi-layered, Neural Networks with advanced algorithms it is possible to model, teach and ultimately analyse sets of data to develop understanding and make decisions.

These techniques will be applied to autonomous vehicle guidance, weather prediction and health monitoring. When used in image processing scenarios, Deep Learning will aid in facial recognition, video surveillance, handwriting analysis and object identification.

With audio, it will provide automatic speech recognition and translation services.

By 2020, Deep Learning will enable machines to evolve from undertaking simple routine tasks to non-routine cognitive pursuits, taking on work, and jobs, that have been hitherto been the reserve of humans.

A simplification

in cybersecurity

Billions of connected devices will be part of new ecosystems, interacting dynamically and without strict supervision, giving rise to new challenges and threats in the context of Cybersecurity.

With the increasing digitalization of the business, Cybersecurity will evolve from an IT Issue to a business issue.

In addition, new and exciting opportunities are provided to offer deeper integrated and open solutions aimed at improving overall situational awareness and increasing organizational and system resilience.

A number of upcoming technologies will by 2020 combine to become important drivers of simplicity and automation in cybersecurity engineering and operations. Those technologies will support concepts for the security of interconnected value chains – like trusted brokers, dynamic access control, application shielding or cyber ecosystems.

New security architectures will break traditional infrastructure silos. Find out what we will foresee as essential abilities for cyber resilient system.

Real-time security analytics will help to reduce the detection time of attacks and their neutralization.

Next generation cryptography with blockchain or homomorphic encryption will secure the communication and the data itself.

Have a deeper look on our prediction to the market adoption of Cybersecurity until 2020 and how it impacts your business.

Cybersecurity will be a key enabler when it comes to a digitalization of business models.

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& privacy

Ubiquitous computing in combination with a high volume of information has all the characteristics to make privacy a concept of the past.

Individuals already demand better privacy protections and better controls in relation to their digital identity.

Consumers are aware that sharing data is key to simplify many of their daily activities, but they expect increased protection of their identity as well as value and rewards in exchange for the exploitation of their usage patterns and data.

Global surveillance, recurring identity thefts and data breaches have created mistrust between individuals, governments and corporations. Data protection legislations will be adopted across the globe to strengthen the obligations of data processing entities.

Data operators will re-invent and re-think the allocation of their respective obligations and their relationships with public authorities. They will progressively adopt and publish their common data protection policies.

Consumers are widely adopting “privacy enhancing” technologies, techniques such as ad blockers or the intentional “dirtying” of databases, and will be ready to swap provider if they do not perceive appropriate data protection.