“From data science to data culture: the emergence of analytics-powered managers”: now online the new issue of SOMeMagazine

SOMe, our eMagazine which shares stories, points of view and projects around key themes of our mission, has just released its Issue #7.

“From data science to data culture: the emergence of analytics-powered managers” is the topic we discussed with our Faculty.

Carlo Vercellis tells how digital technologies and algorithms analysing data play a crucial role in human evolution and in the transformation of our ways of thinking and living.

Behind the strengthening of data culture in companies lies the need to confront with challenges of the competitive scenario, explains Giuliano Noci. While according to Filomena Canterino, this new approach implies also the revision of organizational and leadership models.

Our “Stories” report the excellent achievement of the Milan’s neighbourhood Hubs against food waste: the project, which the School of Management is partner of since 2017, won the first edition of the prestigious international Earthshot Prize for the best solutions to protect the environment, in the section “a world without waste”.
We share also recent update on the impact of our research with some data from the Research Impact Assessment, a tool recently implemented by our School to assess the impact of our projects on society as a whole. And finally the Erasmus+ project WiTECH (Entrepreneurship for Women in Tech) which promotes the presence of women in the ICT sector.

 

To read SOMe’s #7 click here.

To receive it directly in your inbox, please sign up here.

Previous issues of SOMe:

  • # 1 “Sustainability – Beyond good deeds, a good deal?”
  • Special Issue Covid-19 – “Global transformation, ubiquitous responses”
  • #2 “Being entrepreneurial in a high-tech world”
  • #3 “New connections in the post-covid era”
  • #4 “Multidisciplinarity: a new discipline”
  • #5 “Inclusion: shaping a better society for all”
  • #6 “Innovation with a human touch”

Data-powered management: a multifaceted challenge

Behind a company’s declared need to strengthen its data culture lies a profound need to consolidate, enhance, develop or modify their business model, or the way they manage their business, in an informed manner. This is a compelling and pervasive need, linked to the observation of certain trends that are changing the competitive scenario.

 

Giuliano Noci, Professor of Strategy and Marketing and Vice Rector of the Chinese Campus of Politecnico di Milano

 

People who interact with companies are commonly told, “we need to strengthen our data culture”.

The concept of “data culture” has various undertones: the presence of data analysis skills, the ability to read and interpret analyses, the tendency of individuals and work teams to base their decisions on findings and data rather than on feelings and instinct, and efforts to collect and share the right data to support our own decisions and those of others.

Evidently, “data culture” is a combination of all these dimensions. Behind a company’s declared need to strengthen its data culture lies a profound need to consolidate, enhance, develop or modify their business model, or the way they manage their business, in an informed manner. This is a compelling and pervasive need, linked to the observation of certain trends that are changing the competitive scenario.

First of all, competition pressures, on markets that are increasingly saturated and also more and more interconnected, force us to seek out business models and innovations that enable functionality that is as useful as it is sophisticated. This leads to a quest for enhancement of the range of products and services offered, through data work, but not only. For example, if I want to make an electrical appliance stand out radically on the Western market, I will, within reason, have to connect it to the Internet and use the data it collects to offer value-added services to the customer (for example, in a refrigerator, not only report anomalies to enable technical maintenance in real time, but be able to notice when a milk carton is almost empty, and perhaps, based on the rate at which it is used, estimate when the milk will run out or with what frequency to suggest repurchasing it). What is more, it is clear that this type of innovation may bring about developments in the business model. For example, in the above case, integration with eCommerce systems can offer timely subscription-based refills.

Secondly, diversity in the target markets is calling for increasingly differentiated solutions from market segments that are highly heterogenous in terms of taste, preferences, product/service usage habits, and physical and digital channel usage behaviours in interacting with the company. These aspects require a practically one-to-one response from the company. From marketing automation to service automation, companies are increasingly seeking out models and algorithms that can gauge the health of their relationship with a customer, and how inclined they are to accept a new offer or abandon the company.

Thirdly and in fact as a result of the previous two cases, the focus of managerial activity is more and more characterised by the quest for accuracy, precision and waste reduction – in production, just as it is in marketing, sales, customer service, etc. Also in this case, data and the ability to read it are key levers.

Therefore, apart from the communicative effectiveness of the phrase “data culture”, the issue that arises is the development of an ability to combine advanced analytical skills with business acumen. This is a new skill in companies, and often one that is difficult to attribute to a single professional profile. Instead, it is attributed to a team. In fact, companies often hire data scientists with great analytical and technical expertise, but they do not always have managers able to bridge the gap between business needs and technical and modelling applications. Conversely, their personnel are not always able to translate analytical outputs into action plans that can drive the business.

Our school recognised this need when interacting with companies. As a result of this we have profoundly enhanced our range of machine learning and applied statistics courses and analytics courses applied to management disciplines (e.g. performance measurement, marketing, and even the public sector), with a Major, or specialisation, of the Master of Science with a strong analytical focus.  A large number of students have decided to enrol in these courses, and this outstanding success demonstrates that our young people know how important it is to acquire the professional expertise to build a strong “data-powered” career.

The pedagogical challenge, in this context, consists in condensing strong analytical training and an equally solid knowledge of the business impacts of the decision-making systems subject to modelling analysis, with an approach focused on studying these models in the context of the areas where their use is beneficial and promoting rich and extensive discussion on the further implications for the operating models of organisations.

 

 

 

Machine Learning & Big Data Analytics

Digital technologies and algorithms to analyse data represent the most recent evolution of intellectual technologies. They have transformed us into what we are today, into what we know, and into our ways of thinking. We live in close symbiosis with intellectual technologies and this will be increasingly the case with artificial intelligence algorithms

 

Carlo Vercellis, Full Professor of Machine Learning at School of Management, Politecnico di Milano

Most of our daily actions, purchases, movements, and personal or professional decisions are guided by a Machine Learning algorithm: it is convenient to receive suggestions about products to buy, hotels and means of transport for travel, and films or music we might like.

Many companies have been collecting large amounts of data in their information systems for decades. Credit card operators, who record almost two billion transactions over the course of a weekend, large retailers, Telco and utility providers.

However, the real revolution that has led to Big Data coincides with the advent of social networks, a phenomenon called the Internet of People. Each of us has gone from being a reader of information into an author of content. The need to store this immense and rapidly growing amount of data has led the large web companies to create a new type of database based on distributed network architectures and, in practice, to bring about the birth of the cloud.

In addition to people, there are now also ‘things’ on the Internet and this Internet of Things consists of countless objects equipped with sensors and often capable of intelligent and autonomous behaviour. We can turn on the lights in our homes from miles away, adjust our thermostats and watch through our video surveillance systems. Cars can drive autonomously without our intervention. This is a universe made up of almost 30 trillion sensors that record numerical values with a very high temporal frequency (one trillion is equal to ‘one’ followed by 18 ‘zeros’!). We also have digital meters for gas and power, capable of accurately recording how much we consume and suggesting behaviours to for more efficient sustainable use of energy. We wear fitness devices and smartwatches on our wrists, which record our physical activity, main vital parameters, eating habits, and the quality of our sleep, and provide us with useful suggestions to improve our physical condition. Smart objects that will help make our lives more and more comfortable.

From what we have said so far, it is clear that predictive value and applicative value help to generate great economic value, for businesses, for public administration, for citizens in general.

However, data in themselves are of no use if they are not automatically analysed by intelligent algorithms. In particular, machine learning algorithms in the field of artificial intelligence are applied to large volumes of data to recognise recurring regularities and to extract useful knowledge that makes it possible to predict future events with considerable accuracy. This is inductive logic, a bit like the learning mechanism of a child, to whom the mother points out a few examples of letters of the alphabet, enabling him in a short time to identify them independently and thus learn to read.

For example, algorithms are able to interpret the mood, the so-called ‘sentiment’, of text posts on social networks with 95-98% accuracy, which is higher than what a human reader could achieve. Similarly, algorithms are now able to perform automatic content and context recognition of analysed images with great precision.

Digital technologies and algorithms for analysing data represent the latest evolution of intellectual technologies and will help us live better. Suffice to think that throughout history, from the first prehistoric tools to the invention of writing, from the invention of printing to the conception of computers, intellectual technologies have been the driver behind human evolution. They have transformed us into what we are today, into what we know, and into our ways of thinking. We live in close symbiosis with intellectual technologies and this will be increasingly the case with artificial intelligence algorithms.

On the economic side, we observe that companies that are more mature in data analysis have a greater ability to compete and continue to strengthen compared companies that are less evolved and not as prompt in their adoption of digital innovation strategies. For years we have been used the term digital divide to refer to the gap between citizens with access to digital resources and those without. As part of the Big Data Analytics Observatory that we started up at Politecnico di Milano in 2008, last year we introduced the term Analytics Divide to indicate the gap that has been created and is unfortunately widening between companies that are virtuous in their use of big data and artificial intelligence and those that are less innovative, which will find it harder to get out of the swamp into which the virus has pushed us.

In order to progress as a data-driven company, it is however necessary to have adequate talent and skills, which can be obtained through the acquisition of new resources or the reskilling of resources already available in the company. With this in mind, at MIP-Politecnico di Milano we have launched several courses on Machine Learning, Artificial Intelligence, Big Data Analytics, and Data Science, such as the international Master in Business Analytics & Big Data and the executive course in Data Science & Business Analytics.

Data culture and leadership culture: two sides of the same coin

Data experts are becoming key connectors in relationships within organisations. Data culture therefore brings with it the need to rethink organisational and leadership models

 

Filomena Canterino, Assistant Professor of People Management and Organization at School of Management, Politecnico di Milano 

For several years now, data analytics experts, the so-called data scientists and data analysts, have been among the most sought-after figures by companies across all sectors, from manufacturing and education to publishing. Their job is to gather, structure, analyse, interpret and summarise data, transforming it into information that is useful for the other players and decision-makers in an organisation.

Very often, the people in these roles are key connectors within the organisation, because they interact with individuals at various positions and levels, thus becoming reference points that transcend and, in some cases, even overturn traditional hierarchies. Data experts can in fact deliver great added value to almost all corporate areas, from maintenance and strategy to resources management and marketing. And in doing so, they interact with a host of different corporate players. Let us consider the typical example of the datafication of a production plant, in which a system of sensors is able to continuously gather real-time production performance data (for example, number of items manufactured, number of rejects, duration of downtime, number of breakdowns). By analysing and processing the data, and the information they manage to extrapolate from it, a data scientist or data expert can communicate effectively with operators, team leaders and top managers alike. They are able to give a voice to the machines, but also to the people who, armed with a more complete and detailed idea of the performance and potential areas for improvement, can put forward new solutions and ideas.

Just as often, unfortunately, people occupying these roles are superficially labelled as “nerds” and “geeks”, or other terms that allude to a certain familiarity with and interest in analytical and technical matters, and less interest or self-confidence in relationship, interpersonal and leadership aspects. Besides being narrow-minded – just think how many “nerds” are CEOs and leaders of big successful companies – this view is extremely limiting.

First of all, because it refers to an outdated view of the concept of leadership, i.e. innate, heroic leadership, based on “natural” charisma. Leadership experts and companies at the forefront with regard to these issues know all too well that people are not necessarily born leaders, but can become them – some with more effort than others, of course – simply because leadership is characterised by behaviours, or rather by actions that we can follow, practise and improve, and not by characteristics. So, surely also an individual with outstanding technical and analytical talent can identify and deploy the behaviours needed to interact with others and efficiently lead their team.
What is more, in the field of academic research, in which people have been aware for several decades of the fact that behaviour is more relevant than characteristics, the most recent studies have shown that leadership is actually a complex, dynamic and shared process in most cases, which stems from interaction between the various players of a system. If we look at it in this way, we could almost say that it could be more easily understood by individuals in charge of intercepting and interpreting data flows than by others.

Secondly, this type of view makes managing the development of these figures within organisations ineffective, precisely because it shines the spotlight on the wrong thing, i.e on the personal characteristics of those occupying a specific role, rather than on the organisation’s leadership model.

So what can be done to put these roles in a position to reach their full potential and develop their content- and process-based leadership qualities?

By all means, we can promote a cultural model that views leadership as something that is shared and widespread, based on actions and behaviour and on the concept of accountability – whereby each and every individual or small team is responsible for a small part of the result. All this can be achieved through coherent training and development plans across the entire organisation, as well as through digital technologies, which facilitate data acquisition and sharing to inform decisions and shorten hierarchical chains as a result. Data, accountability and shared leadership: a virtuous circle in which data experts can be true protagonists.

 

“Innovation with a human touch”: now online the new issue of SOMeMagazine

SOMe Issue #6 has been released, the eMagazine of our School which shares stories, points of view and projects around key themes of our mission.

This issue is focused on “Innovation with a human touch”, discussing the role of human and humanities in technological progress and innovation.

We interviewed  Giovanni Valente, who explains how much human and social sciences are essential to face any innovative challenge in the scientific and technological field, making the interdisciplinary approach fundamental in scientific studies.

Man must be at the centre of digital transformation and technologies have to be developed for and not instead of humans, as Raffaella Cagliano, Claudio Dell’Era and Stefano Magistretti tell in their editorials about Industry 4.0 and Design Thinking.

But can technological innovation be truly on a human scale? Giovanni Miragliotta tries to answer to this question considering how much new technologies deeply changed our society and work.

Finally, we feature some of our recent research ”Stories”: the economic impact of climate change, the re-use of electronic waste to create eco-compatible products, the distribution of Venture Capital in Europe.

 

 

To read SOMe’s #6 click here.

To receive it directly in your inbox, please sign up here.

Previous issues of SOMe:

  • # 1 “Sustainability – Beyond good deeds, a good deal?”
  • Special Issue Covid-19 – “Global transformation, ubiquitous responses
  • #2 “Being entrepreneurial in a high-tech world”
  • #3 “New connections in the post-covid era”
  • #4 “Multidisciplinarity: a new discipline”
  • #5 “Inclusion: shaping a better society for all”

Technology and innovation, on a human scale

Scientific progress, the availability of technical facilities, cross-fertilisation between different research communities and combined innovation are giving us an unstoppable progression of human capabilities. But how much, and more importantly, which innovation is really on a human scale?

 

Giovanni Miragliotta, Professor of Advanced Planning, Co-Director of the Industry 4.0 Observatory, Politecnico di Milano

 

Everywhere we look, as citizens and as researchers, we read about the “magnifiche sorti e progressive[1]” that, by means of new technologies, are changing our society and our lives. From the more familiar ones, such as broadband communication networks, to the more advanced, such as bioengineering, to those operating behind the scenes, such as cryptography, it all comes together to the point where it is almost difficult to realise the potential for change in the research and innovation system we have built up in developed countries. This potential is realised from time to time by some unexpected  discontinuities, such as the pandemic we are currently experiencing, which, by combining the various existing innovations, show us how the way we work, teach, plan and treat can be overturned in just a few months. A very powerful reflection in this sense, also and above all because it comes from a man of letters and not from a scientist, is the one recently published by Alessandro Baricco[2].

This occasion, which has shown us the extent and speed of possible change, can be used to elaborate on what innovation is at a human scale; it more important than ever to do so right now, in view of what is being developed in universities and laboratories all over the world, since the forthcoming technological breakthroughs could materialise a change, which many believe (and I am one of them) could be disruptive to the very core of our society.

If we consider western democratic states as the main scope, our society rests on a set of pillars, a mix of worldview ideology, morals and common sense, which form the glue. Some technological innovations (first and foremost bioengineering and artificial intelligence) are, so to speak, on a collision course with these pillars, and could lead to new societies, the extent to which they will be on a human scale is difficult to predict, at least as we currently interpret that scale.

Let’s us consider the central role that the work plays in the structure of society, even just focusing on its economic value and disregard the psychological aspects or that of personal fulfilment; for the first time in history we are beginning to glimpse a possible future in which not only we can no longer predict what our children’s jobs will be in 30 years’ time, but we are beginning to doubt that there may even be any jobs left. In an increasing number of specific fields of work (=Narrow AI), in fact, machines have already achieved superhuman abilities and, as you probably know, there is a huge debate about the balance between jobs created and lost. The analyses carried out in the Artificial Intelligence Observatory, at least for the next decade, seem to indicate a positive scenario[3], but if we extend the horizon of analysis, we cannot exclude a situation in which the demand for human labour will be much lower, made unprofitable or useless by the new skills of machines[4].  In the context of fragile monetary and fiscal equilibrium of nations, a significant alteration in the labour market could represent a strong element of instability.

Changing the technology of choice, the advent of biotechnology could in the near future bring about such major changes that the very foundations of society will be shaken: how will the concept of the family evolve if it were normal for human beings to live to be 120 years old, with youth lasting over 40 years?  What will happen when the wealthier classes, in addition to being able to afford better traditional health care, can also afford to take steps to improve their genetic set-up in a way that cannot be matched by most people? Will we, for the first time in history, observe a divergence in our species, with a (small) fraction of the population having more capable, durable and long-lasting “hardware” (body + brain) than the majority of the population?

These examples make us think about the extent of possible economic and social change, but they do not yet seem to affect the ideological foundations of the society we have built in the West since the American and French revolutions, namely the profound belief in the value of freedom and the uniqueness and individuality of the person. But what if, in principle, by observing all the interactions of a person with their environment and their fellow human beings, it were possible to predict exactly what their feelings and needs would be? What would happen if Google or Facebook or others, on the strength of the immense amount of data they collect about us, knew how to advise us on the right book, the right job, the right investment, the right wife, the right preventive surgery, much better than we would know how to do on our own, confused and lost in an endless number of important decisions to be taken dozens of times in our (very long) lives? Would we then still be “free”? And if there is any freedom left, should we make use of it, or would it not be more convenient to delegate our decisions to a “life advisor” technology that would achieve to us a much higher probability of success and happiness than we could do with our own hands?

This last scenario, envisaged by many thinkers, opens up a radical rethinking of the founding principles of our society, first and foremost the liberal principle, leading to outcomes that could range from a further loosening of existing points of reference (in the wake of Bauman’s liquidity) to its total opposite, a very rigid technocracy.

The point is always the same: it is not possible to make predictions of any kind and, after all, the little that needs to be known, of pure speculation on the future, has already been written. These reflections, on the contrary, bring us to a very great responsibility, that of remaining very vigilant over the changes, even the slight ones, that technological innovation is imprinting on our society.

A future awaits us which can only be on a human scale if we will care about building it.

 

 

Reading notes

This reflection arises, and can be further developed, by drawing on the insights of the following authors:

  • Yuval Harari: I recommend the whole trilogy on man’s past, future and present;
  • Mark Tegmar, “Life 3.0”, and the debate at the Future of life Institute;
  • Zygmunt Bauman, in particular his key text “Liquid Modernity”.

 

 


[1] Citation of the Italian romantic poet Giacomo Leopardi, “magnificent destiny and progressions”

[2] Alessandro Baricco, “Five years in one”, https://www.ilpost.it/2021/05/28/baricco-2025/

[3] See report Artificial Intelligence Observatory, “On your marks”, ed. 2019.

[4] Consider, for example, “A 3D printed car which is designed by AI”, www.thereviewstories.com/czinger-21c-ai-3d-printed-car/

 

Human Centered Industry 4.0

Industry 4.0 is often referred to as a new industrial revolution and the recent COVID pandemic has further accelerated the already impressive level of investment in new technologies. However, no real transformation can happen if people are not put at the centre of the transformation. Successful implementation of the Industry 4.0 paradigm requires a joint design of technological and organizational variables, with the aim of designing technologies for humans and not instead of humans. Augmentation strategies through participatory design is the promising avenue to a more resilient and smarter manufacturing

 

Raffaella Cagliano, Professor of People Management and Organization, Co-Director Obstervatory Industry 4.0 Transition, Politecnico di Milano

Digital technologies are nowadays one of the central factors in the transformation of any organization. In the manufacturing context, digitalization is often associated to the concept of Smart Manufacturing or Industry 4.0. Someone even talks about a fourth industrial revolution, referring to the transition towards a new paradigm of interconnected, digitalized and intelligent production systems.

The recent COVID-19 pandemic has been a kind of turning point in this process. As also clearly stated in the recent sixth annual State of Manufacturing Report (Fictiv, 2021), digital transformation has become a business imperative, and no longer a “nice to have” or an optional strategic lever. In fact, those companies that have been able to thrive during the COVID-19 year and have shown higher resilience are the ones that invested more in digital technologies in the years before the pandemic. Even during the crisis, investment in digital transformation – also in manufacturing – increased hugely (see e.g. Deloitte, 2021).

Despite this, the results of the introduction of new technologies do not always fulfil promises and in many cases the investments tend to be higher than the advantages. Many change management problems are mentioned as possible cause, and many lament a lack of competencies within the organization, or a lack of right culture, mindset or other.

During our recent years of research on Smart Manufacturing at the School of Management of the Politecnico di Milano, we had the opportunity to study many successful cases of companies that were able to transform their manufacturing systems into completely new models and to improve their operations significantly; often they were even able to rethink their business model and to offer completely new lines of products or services as a consequence of the new capabilities developed and the opportunities brought by the introduction of the new technologies. At the same time, many of these companies were also able to readily react to the COVID crisis, showing a resilience that was higher than the average. They were able to move many activities to a remote or virtual space, to schedule work in a flexible way to accommodate the needs and constraints of people during the emergency, and to introduce health and safety measures more rapidly and effectively.

These companies have a common approach to digital transformation: to put people at the centre of the transformation. We can recognize this approach from two main elements. First of all, they introduced digital technologies within the context of a clear strategy for operations improvement, where technology is seen mainly as a way to facilitate or augment human physical or cognitive capabilities, rather than substitute them. Technologies, on the one hand, are used to facilitate the work of operators by providing all the relevant information, guidance and support that is needed to operate in the most effective way, and to take away those tasks that are heavy, dangerous or where humans don’t add specific value compared to machines, leaving in this way more space to people to contribute according to their most valuable characteristics. Even more, some applications of Industry 4.0 technologies are designed to augment the operators’ potential by providing them with all the data and information needed to make them able to manage complex production systems autonomously and contribute to continuously improve the processes and the systems themselves. Thus, technologies are not used instead of humans, but for humans to enhance their work and contribution.

Second, these companies adopted a systemic approach to technology design and implementation that allowed them to design a system where technology works for humans. This systemic approach requires that technological and organizational factors are designed together, according to the well-known – but not so often used – socio-technical approach. If technology has to support human work, the technical and social systems should be designed together to exploit the joint advantage of the two systems and to design work and processes where the potential of technology and humans are fully exploited. A more common approach is instead the one where technology is designed first, and the consequences of technology on people are managed afterward, trying to adapt a posteriori the knowledge, culture but even the predisposition of people to the technology, with poor results in most cases. This mistake has been perpetuated in every major technological wave or revolution.

Instead, in many successful cases we observed that the joint design of the technology and the work system is realized though participatory approaches, where people are engaged not just in the last phases of change, to inform them or to test the new systems, but instead since the early phases of the project. Operators are asked to express their needs, to provide early feedback on the new systems and sometimes even to provide ideas to further improve or innovate the production systems. When this level of involvement is achieved, the manufacturing system will benefit from the transformation even after the implementation of the technologies, since people are able to continuously improve the way they work and they use the technology, crafting their jobs according to the potentialities discovered in the technologies and in the data that have been made available. This idea of participation, involvement and diffused creativity is coherent with the principles of design thinking that we have seen used in some of the most advanced cases in our study, and that can constitute a new frontier for the application of the methodology outside the context in which it originated.

 

Innovation with a human touch

Conversation with Giovanni Valente, Professor of Logic and Philosophy of Science in the Mathematics Department at Politecnico di Milano and Member of the inter-departmental Unit of Study META

 

The Politecnico di Milano, a technical University, has promoted the creation of an interdisciplinary network of scholars from its various departments of engineering, architecture and design with skills in human and social sciences to provide expertise in philosophical, epistemological, ethical and social issues related to processes of science, technology and innovation. Why was this decision made?

At international level, there is a relatively widespread tradition of promoting the presence of scholars working in the social sciences and humanities within the major polytechnical universities. In fact, some of the world’s leading academic institutions, such as the MIT in Boston, even feature entire departments dedicated to specific fields of humanities. The reason for this choice is that the humanities, if they are scientifically-informed, can complement technical knowledge by adding a critical and reflective perspective.

Research and teaching in the areas of philosophy and sociology of science and technology have been present at the Politecnico di Milano for quite some time. However, they began to acquire a systematic form of coordination only recently with the development of the Unit of Study META, which was officially created in the academic year 2017-2018 in the form of a collaboration between various departments. Throughout the years that followed, the group progressively expanded with the addition of more tenure-track faculty, post-doctoral researchers and PhD students, who have been recruited thanks to external funding as well as the direct endowment of the Rector. The current administration of the Politecnico di Milano has indeed realised the importance of developing the humanities in order to enhance interdisciplinary research and enrich the educational offer for engineers, architects and designers along the tradition of the most prestigious polytechnical universities around the world.

Specifically, META aims to produce and disseminate knowledge and offer expertise in the philosophical, ethical and social dimensions of science and technology by organising research and teaching activities as well as academic and public events, which have received a great deal of attention even outside the university itself. A distinctive feature of this network is that its members are based in different departments, so that, besides collaborating with each other, they can also interact directly with colleagues working in relevant fields of science and technology. Such an interaction thus fosters an interdisciplinary approach whereby expertise in the humanities and social sciences is well integrated in the research processes.

So, especially for an engineer, why is it important to have humanities skills and how do they fit into the training path?

As the British novelist and physical chemist Charles Percy Snow famously explained in his influential 1959 book on “The Two Cultures and the Scientific Revolution”, the alleged contrast between scientific and humanistic knowledge (namely, the two “cultures” into which the Western world seems to be split) can have dramatic consequences for society, especially in as much as the educational system tends to favour one at the expense of the other. The call for a properly balanced and multi-disciplinary preparation for our students is even more earnest now that we live in an era of high fragmentation of knowledge and hyper-specialisation, in that there often lacks a dialogue between different scientific fields, let alone between science and humanities.  Disciplines such as philosophy and sociology of science and technology are interdisciplinary by their own nature, and therefore they are suitably apt to bridge the gap across “the two cultures”, even more so when they are taught at a polytechnical university.

Indeed, philosophy and sociology prompt students to reflect upon the foundations of their own scientific and technological disciplines, thereby refining their critical thinking. To give an example, scientific models often resort to assumptions that are, strictly speaking, false, and yet they can be applied to concrete systems: that gives rise to outstanding conceptual questions about the justification of such unrealistic idealisations. Furthermore, philosophical and sociological studies put polytechnical students in a position to develop awareness of the ethical and social consequences of the use of the technologies they will employ in their future jobs. For instance, an extremely popular course META has introduced for engineering degrees at the Politecnico di Milano is called “Ethics for Technology”, which is the first course of its kind established in the Italian educational system.  Last but not least, since the courses designed by META typically require the enrolled students to submit written essays, they offer them the opportunity to practise and improve their own writing and communication skills, an opportunity they would otherwise seldom encounter in other more technical courses. This actually contributes to filling an important gap in the engineering curricula.

With reference to innovation processes that are increasingly data-dependent or data driven, what is the role of social sciences and humanities, in particular with respect to the implications of the use of artificial intelligence in the context of social phenomena?

In the current digital era, the massive and growing use of technological innovations that can process huge amounts of data with unprecedented power poses ever more pressing epistemological and ethical issues. In this respect, the long-standing discussion in philosophy and sociology about the nature of scientific data can be highly beneficial to the research on artificial intelligence, especially when it is applied to the analysis and prediction of social phenomena. Indeed, from an epistemological point of view, it is a recognized fact that there does not exist such a thing as “brute data”. For the process of collecting and elaborating data is not at all neutral but rather it is theory-laden, in the sense that the selection of the dataset relevant for the study of a certain phenomenon as well as the subsequent interpretation of computational outcomes are always driven by contextual background knowledge.

Accordingly, if we wish to draw meaningful and reliable conclusions from the available data, we ought to understand the extent to which they depend on the theoretical assumptions underlying the construction and implementation of the algorithms we employ. Moreover, from an ethical point of view, when we deal with sensitive data that reveal personal information, as often happens in the context of social phenomena, there arise delicate and controversial ethical issues, for instance, concerning the protection of individual privacy. Data security is actually one of the major problems stemming from the use of powerful computational algorithms, together with bias problem  namely the fact that AI systems are trained on data that are only representative of a limited sample of the population, and the trust deficit problem, namely the fact that the procedures by which deep learning models predict the outcomes remain largely unknown.

In order to face these outstanding challenges of artificial intelligence, the vast philosophical and sociological literature on epistemological and ethical issues concerning scientific data can thus be fruitfully combined with scientific and technological practice so as to develop an effective integrated approach.

Designing for the digital society: unveiling the opportunities embedded in digital technologies through Design Thinking

Nowadays, digital technologies are providing incredible options; we live in a world where technological opportunities are cascading over society at an unprecedented speed. Humans are central to understanding how the technology can be better aligned with end-user needs and their willingness to adopt it. Design Thinking is an approach that looks at value and change from the perspective of people

 

Claudio Dell’Era, Associate Professor of Design Strategy at School of Management, Politecnico di Milano
Stefano Magistretti, Assistant Professor of Innovation and Design Management at School of Management, Politecnico di Milano

We live in a digital society where digital technologies are all being used for work, monitoring health and habits, staying connected, seeking information and getting the news, shopping for groceries, travelling, managing finances and more. Digital technologies are widespread throughout the world, and their presence in our daily life is booming. In the last few decades, several different digital technologies have reshaped the way people live and the way companies develop new products and services. Nowadays, digital technologies are providing incredible options; we live in a world where technological opportunities are cascading over society at an unprecedented speed.

A world awash with technologies and information. But humans do not use digital technologies or data; they need products and services. Artificial Intelligence (AI), in particular, has the potential to transform our world for the better: it can improve healthcare, reduce energy consumption, make cars safer and enable farmers to use water and natural resources more efficiently. AI can be used to predict environmental and climate change, improve financial risk management and provides the tools to manufacture  products tailored to our needs with less waste. AI can also help to detect fraud and cybersecurity threats, and enables law enforcement agencies to fight crime more efficiently. AI can benefit the whole of society and the economy. It is a strategic technology that is now being developed and used at a rapid pace across the world.

Nevertheless, AI also brings new challenges for the future of work, and raises legal and ethical questions. To address these challenges and make the most of the opportunities which AI offers, the Commission published a European strategy in April 2018. The strategy places people at the centre of the development of AI — human-centric AI. According to the report “Tech for Good – Smoothing disruption, improving well-being” developed by McKinsey, the development and adoption of AI-driven solutions has the potential not only to raise productivity and GDP growth, but also to improve wellbeing more broadly, including through healthier living and longevity and more leisure.

Technology has for centuries both excited the human imagination and prompted fears about its effects. In this changing context, the challenge is to build AI solutions to improve and not damage wellbeing. Researchers and practitioners are acknowledging that this is a problem of design, which acts as a driver of innovation and change and which is able to keep humans at the centre when building solutions. Humans are central to understanding how the technology can be better aligned with end-user needs and their willingness to adopt it.

Design Thinking is an approach that looks at value and change from the perspective of people. Or, even better, from the perspective of what is meaningful to people. Similar to many other approaches, Design Thinking also combines three factors: (i) technologies, how things are made and their improved performance; (ii) people, how these things are valuable for customers; (iii) business, how organisations can profit from offering them.
The perspective embedded in Design Thinking makes it unique: Design Thinking starts with people. This approach allows leaders to look at value created for individuals and assume their perspective, conceiving innovation not primarily as a source of competitive advantage and profit, but as a means to generate value for end-users.

Design Thinking is usually characterised by three traits: a human-centred perspective, where innovators build empathy with users; the leverage of creativity as a driver of innovation (sometimes even in contrast to assets as knowledge, technology and competitive positioning); and an intense use of prototyping as a rapid and effective source of communication and learning among stakeholders.

Human centeredness in Design Thinking means that what drives the entire innovation process is the identification and satisfaction of user needs. The success of any innovation depends on simultaneously achieving user desirability, technology feasibility and financial viability, yet Design Thinking almost prescriptively instructs innovators to address desirability first.
By continuously involving end users in the iterative co-creation and testing of ideas and prototypes, design thinkers ensure that the outcomes of their innovation effort add value to the human experience and are meaningful and affordable. In so doing, Design Thinking overturns the traditional business perspective that is technology driven: companies first determine what is feasible for them to develop and then push their new products and services through marketing campaigns hoping that they address people’s search for value and meaning.

The need for a human-centred approach also stems from the wicked nature of the problems addressed in Design Thinking projects. Wicked problems are defined as a class of social system problems that are ill-formulated, where the information is confusing, and where many customers and decision-makers have conflicting values. These types of problems should be addressed with a human perspective to grasp their complexity, make sense of them and make them tractable.

Human centredness in Design Thinking is achieved through the innovator’s empathy with users. Empathy consists of perspective taking, namely the ability to adopt the perspective of another person or recognise their perspective as their truth, be open to various inputs, suspend judgement, recognise other people’s emotions and communicate by mirroring back.

 

Would you prefer to live longer or healthier? Easy! I want to live a longer and healthier life! The societal challenge of Healthy Ageing

The quest for living longer at any cost – the dream to live for 150+ years – has been replaced by the search of how to improve life quality for better ageing, in order not to lose self-suffiency as well as physical and cognitive capabilities. 

 

Emanuele Lettieri
Full Professor of Health Care Management at the School of Management of Politecnico di Milano
Scientific Director of the Permanent Observatory of Digital Innovation in Healthcare at Politecnico di Milano

Population ageing is a double-edged sword. On the one hand, this is definitively good news. We live longer and life expectancy at birth is increasing generation after generation. This is the result of paramount discoveries in medicine and ground-breaking innovations in medical technology. On the other hand, this is very bad news. Disabilities and chronic diseases will probably characterize the last years of our lives, limiting our possibility to live a full life. Moreover, a significant portion of healthcare expenditure – that is expected to increase year after year – is currently allocated to the management of elderly fragility and chronic diseases. This obliges to reduce the financial resources that could have been allocated to the younger citizens.

But how to jump out of this vicious circle?
A straightforward solution comes from what has already happened in other industries – e.g., in the automotive industry. The healthcare system must treat citizens when they are still healthy, helping them postpone as long as possible the moment when they will need specialized treatment for either fragility or chronic diseases. This vision requires healthcare professionals to turn upside down their current approach to health care delivery. Prevention, lifestyle improvement, empowerment and co-responsibility are the “silver bullets” to help citizens live longer and be healthier.

This is the challenge of Healthy Ageing. This challenge is of paramount relevance for the sustainability over time of our society and it is fully coherent with the Societal Development Goal number 3 – ensuring healthy lives and promoting well-being for everyone at all ages.
In 2015, the World Health Organization (WHO) introduced the concept of Healthy Ageing, intended as the process through which an individual can maintain or enhance her/his well-being within the ageing process. The WHO launched the Decade of Healthy Ageing (2021-2030) that is intended as a decade of concerted global actions on Healthy Ageing. The motivation is that populations around the world are ageing at a faster pace than in the past and this demographic transition will have an impact on almost all aspects of society.
In this view, finding previously unexplored pathways for enhancing the ability of citizens aged 50+ to live a longer and healthier life is top of the agenda for policymakers, professionals, entrepreneurs and management scholars. The initiatives on the table are numerous and they are contributing to the growth of the so-called Silver Economy. This term was coined to describe the economy linked to products and services targeted at citizens aged 50+. Its extent has been estimated in Europe at €5.7 trillion in 2025.

The ageing population can be divided into active, fragile and dependant. For a sustainable society, it is important to support active and healthy ageing among the 50+ citizens so that they can be part of the active workforce for as long as possible. In this view, healthcare must keep pace with their needs. Care delivery must become personalized, participative, preventive and predictive. This is at hand nowadays! Digital solutions might offer the extraordinary opportunity to respond successfully to the challenges of the Silver Economy. Digital technologies might contribute to the development of next-generation techniques for fragility and disease prevention, as well as new treatments to ensure a healthy, active and productive life to the population aged 50+.

The School of Management of Politecnico di Milano stands up for Healthy Ageing and is contributing through different research and educational initiatives.

First, the permanent Observatory of Digital Innovation of Healthcare is collecting data from the field about the transition toward a new paradigm of health care delivery that has been defined as Connected Care. Ageing citizens are searching for an ecosystem of healthcare services and tools that are consistent and interoperable. Considering four main phases – namely, (1) information seeking & primary prevention, (2) access to care; (3) diagnosis & therapy; and (4) follow-up & engagement into new lifestyles – the present Covid-19 pandemics has accelerated the adoption of digitally-enabled behaviours for all phases.
Four citizens out of five searched for information on the internet about healthier behaviours and disease prevention. Two citizens out of five tried smartwatches or Apps to monitor their physical activity, improve their nutritional behaviour or test their cognitive capabilities. One citizen out of three is interested in interacting with their doctor through tele-visits. These and other data are published every year by the research team from this Observatory.

Second, the School of Management (SOM) of Politecnico di Milano is contributing to an H2020 pan-European research project – named NESTORE – aimed at developing an artificial intelligence-enabled virtual coach to help European citizens aged 65+ in their healthy ageing. The virtual coach can provide users with personalized pathways to healthy ageing that cover physical activity, nutrition, cognitive capabilities and social interaction. At present, the coach is under validation (phase 2) in three pilot countries – Italy, Spain and the Netherlands – with promising results in terms of engagement and acceptability. The SOM is chairing the development of the exploitation strategy of the solutions developed within the research project – such as a virtual coach, an App, a smartwatch to collect data, a tangible interface, a chatbot, and a series of games. At the beginning, NESTORE will adopt a direct-to-consumer business model, but with the ambition of becoming a digital therapy within two years – after a phase 3 validation study – and being approved and reimbursed by the national healthcare systems. Our data show that one citizen aged 65+ out of three is interested in virtual coaches because they are searching for 24/7 support for their healthy ageing.

Finally, the MIP Politecnico Graduate School of Business has launched in September 2020 the first edition of the Executive Master in Innovation Management in Healthcare. Multi-disciplinary professionals from hospitals and vendors from the MedTech industry are learning how to disrupt the current paradigms of health care delivery and accelerate the transition toward innovative socio-technical configurations of Connect Care.

There is an ancient Chinese curse which says “May we live in interesting times.” In this light, the School of Management of Politecnico di Milano is fully committed to making available its distinctive competencies to sustain the healthy ageing of citizens 65+, to allow them to live longer and healthier lives in interesting times.