Tag: eMagazine

“The challenge of pursuing impact in research”: now online the new issue of SOMeMagazine

Posted on | by Beatrice Grillo | Leave a Comment on “The challenge of pursuing impact in research”: now online the new issue of SOMeMagazine

SOMe Issue #8 has been released.

In this issue we discuss the impact of research and the challenge of defining and measuring it.
Stefano Magistretti and Federico Caniato explain how our School is engaged in building an “impact culture” to be encouraged and sustained over time, also using an assessment framework to evaluate the impact of the our research.

To report some impact cases, Enrico Cagno, Giulia Felice and Lucia Tajoli tell the fundamental role of academic research in supporting the green transition in emerging countries, while Diletta Di Marco shows how citizens can contribute in evaluating the social impact of scientific research, choosing whether or not to support a project. Finally Angelo Cavallo talks about the new space-based technologies that bring opportunities for innovation and sustainability and imply new business models.

In our “Stories” we feature the impact of Covid-19 on the life of working women and some projects promoting sustainability in fashion and corporate behaviors.

 

 

To read SOMe’s #8 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”
  • #7 “From data science to data culture: the emergence of analytics-powered managers”

The challenge of pursuing impact in research

Posted on | by Beatrice Grillo | Leave a Comment on The challenge of pursuing impact in research

Conversation with:
Federico Caniato, Full Professor of Supply Chain & Procurement Management at School of Management, Politecnico di Milano
Stefano Magistretti, Assistant Professor of Agile Innovation at School of Management, Politecnico di Milano

 

Universities are increasingly engaged in demonstrating the impact of their research. What is the impact of research? 

The impact of research is crucial not only for the Politecnico di Milano, but for the entire Italian university system, and more in general for universities worldwide. It is not easy to define what research impact is. We can say that the impact of research encompasses all the results, implications, and consequences resulting from scientific research activities aimed at generating knowledge, but they are also expected to provide concrete benefits. In our school, we have defined research impact in three progressive levels of maturity: dissemination, adoption, and benefits. Dissemination is the spread of the results and findings among the relevant stakeholders, adoption is the use of the research results by the stakeholders, and benefits are the consequences of this adoption.

Why is impact so important for research?

Research is often accused of being self-referential, i.e. ‘speaking’ only to members of the academic community without providing a significant contribution to society at large. Instead, research can have a much broader and more significant impact than expected. Therefore, it is crucial to illustrate such impacts to a broader audience, requiring researchers to learn to assess and share the value of their work with multiple stakeholders.

What is the approach to impact assessment in the School of Management?

In 2017, we started a journey in the School of Management to develop a culture of research impact assessment. This journey saw a reflection on the assessment framework, the development of a method, and the collection and analysis of the research impact assessments. We started by combing the literature for impact assessments, interviewing experts, and interacting with our international advisory board to define our framework. The framework comprises the three levels of maturity (i.e. dissemination, adoption, and benefits) and five stakeholder domains (i.e. institutions, enterprises, students and faculty, citizens, and the academic community). The second step was the adoption of the framework. This initially began in 2019 with a set of 16 pilot projects, which then extended to a more extensive set of projects (42 in 2020; 43 in 2021).

The conventional idea of ‘impact’ makes sense in a linear model: changes or discoveries in science and research are expected to cause changes in society, but impact assessment frameworks are usually far more complex, can you tell us why?

The research impact assessment is more complex because the impact is not linear. Some elements impact one stakeholder, causing indirect effects on other stakeholders. For example, research results adopted by public institutions may benefit citizens, or the results disseminated to students may be adopted later, when the students are professionals within companies. Thus, the impact network is intertwined. Seeing the link among the domains and level of maturity, and how an initiative might influence other areas of impact requires a framework that tries to bring everything together. Let’s take an example. If you publish an academic paper, there is diffusion within the academic community, but if you share it in class, there is also an impact on students; if you use it in corporate education, that novel piece of research can become the seed for a potential company project. So from a single action — dissemination of research among the academic community — you might have an impact over multiple stakeholders on different levels.

How much of this impact analysis must be made ex ante, while planning the activity, and how much ex post?

The impact assessment is a helpful tool in every moment of a research project. We saw colleagues adopting it when writing proposals for an EU project or internal research initiative. This is because the impact is both ex-ante and ex-post. The most important thing is to envision potential impact ex-ante, which helps to set the expectations and objective of the project. Ex-post assessment instead aims to measure the results obtained in terms of impact, monitor the results of the planned activities, and demonstrate the actual achievements. Thus, there is not just a single moment for impact analysis; it is always a good to measure it before, during, and after the research initiative.

Is the impact ‘native’ or built over time? Do we need our PhD candidates to be ‘natural-born impacters’ or is it an orientation that can be encouraged and sustained over time?

The impact culture is not native. It is something that PhD candidates and researchers in general should be trained in. Indeed, some impacts are easy to design and achieve, but impacts of a higher level are more challenging and require careful consideration, so it is important to build impact over time. Indeed, it is difficult to gain everything with a single new research programme. As for PhD candidates, it is probably something that we should share with them and encourage them to reflect on. This is something we started at the last AiIG (Associazione Italiana Ingegneria Gestionali) Summer School held by the Politecnico di Bari in September 2021, where we shared the framework with more than 50 Italian PhD candidates and asked them to apply it to their PhD research. The PhD candidates were positively surprised about the unexpected outcomes of this assessment exercise. Disseminating the culture of research impact assessment is something we need to do at every level.

 

 

Transition to green technologies in emerging countries: how research can help in directing resources

Posted on | by Beatrice Grillo | Leave a Comment on Transition to green technologies in emerging countries: how research can help in directing resources

Selecting the geographical areas and green technologies for successful funding of sustainable economic growth is a difficult task particularly in emerging countries. Academic research is fundamental in providing tools to support public and private institutions in this task.

 

Enrico Cagno, Full Professor in Industrial Systems Engineering at School of Management, Politecnico di Milano
Giulia Felice, Associate Professor in Economics at School of Management, Politecnico di Milano
Lucia Tajoli, Full Professor in Economics at School of Management, Politecnico di Milano

Recently, the COVID crisis brought to the public eye the extent to which research is in many ways fundamental for the survival of the community. This was extremely evident for disciplines with a direct and recognized impact on human lives and development. Still, the direct and indirect impact of academic research in many other areas and disciplines might be considerable for the well-being of people and the evolution of societies along many dimensions.

An important case, particularly relevant in the current economic phase, is the role of academic research in providing analyses and methodologies that can support private and public institutions in appropriately conveying and using resources in countries, regions, sectors to foster an equitable and sustainable economic growth.

A pertinent example regards the resources to support the transition of countries to green technologies. Climate finance has a fundamental role in tackling climate change and in promoting environmentally sustainable growth in transition and developing economies. A precondition to succeed is the ability to select those countries where the support to green investment does not crowd out private investment, but instead opens room for its expansion, in line with the existing market potential. Several banks and institutions operate with this aim and, as is well known, a large part of the funding in the Next Generation EU is devoted to the European Green deal. The Glasgow Cop26 Summit has once again simultaneously highlighted the unavoidable global dimension of the green transition and the asymmetric position of developing and mature economies due to their different stage of development.

An important issue in maintaining the different approaches of developing and mature economies towards green technologies is that in many cases it is not easy to support green transition in developing countries because of a lack of adequate information on the access and opportunities provided by the technologies. Funding could be misallocated, that is to say, it could be conveyed where it crows out private investment, or where there is no potential for the investment in the new technology to diffuse after initial support. This is where research becomes useful. Methodologies and tools can be developed supporting institutions in the selection of areas and technologies for successful funding.

In this context and to this aim, research at SOM can contribute to developing a conceptual framework and providing methodologies to obtain an overall evaluation of the readiness of countries, regions or sectors to adopt green technologies, ranking countries or areas in terms of their exposure to these technologies. In a recent project developed for the European Bank for Reconstruction and Development (EBRD), the ultimate aim was to capture the extent to which targeted countries could benefit from funding green technologies, in particular those developing and emerging countries for which data on the diffusion of green technologies are scarce or not available. The creation and use of a technology by a country or a firm is the pre-requisite for its diffusion and eventually adoption. Therefore, in order to benefit from the promotion of green investment, the target country should already have an adequate level and mix of use and production of the green technology. This mix depends on the overall economic situation and level of development of the country, as indicated, for instance, by income per capita, installed production capacity, and the level of technology in closed products. There is no specific universally accepted definition or measurement of the diffusion of a technology. International trade of products embodying a specific technology reveals the presence of that technology in the trading countries. Therefore, trade is often used in the economic literature to track technology diffusion. The advantages of using trade data and advanced methodologies to elaborate them are that they are reliable and available for the majority of countries at a very refined product category level and for a long time span.

Following this approach, researchers at SOM used official and public trade data of “green goods” (as defined by the World Trade Organization and the OECD) covering all countries to assess potential diffusion and adoption of “green” technologies, by building a set of indicators to gauge market maturity and production capacity of a country for a given product. Based on these indicators, a sequence of steps was developed to identify the opportunity for successful actions. The methodology was then discussed and improved throughout the implementation of the project with the EBRD experts that were going to use it, and then validated with the country’s experts on the actual diffusion of the products analysed in terms of demand and production capacity.

The EBRD will use the methodology described above as a tool to select the potential targets of the funding, that is to say, the couple country-technology. The methodology is easily replicable on publicly available data and therefore suitable for orienting the institution in its choices. The EBRD is owned by about seventy countries from five continents, as well as the European Union and the European Investment Bank. This implies that its activities impact a large population, of firms, which will be financially supported by EBRD to adopt/produce green technologies, and of citizens who will benefit through sustainable growth and higher quality of life thanks to the firms’ adoption of green technologies.

The project could potentially affect several Sustainable and Development Goals (Health and Well Being, Clean Water and Sanitation, Affordable and Clean Energy, Sustainable Cities and Communities, Responsible Consumption and Production, Climate Action) to the extent that should support the diffusion of green technologies and goods in developing and emerging countries.

 

Space Economy: towards a new frontier for innovation and sustainability

Posted on | by Beatrice Grillo | Leave a Comment on Space Economy: towards a new frontier for innovation and sustainability

Space and digital technologies combined represent a powerful force enabling cross-sector innovation towards making our world more sustainable. However, technological opportunities are mere fertile ground, which to yield fruit needs managerial and enterprising strategies for the strategic renewal of established organisations and for the creation and growth of innovative startups. 

 

Angelo Cavallo, Assistant Professor in Strategy & Entrepreneurship at School of Management, Politecnico di Milano

Space Economy is a phenomenon at the frontier of innovation and sustainability which materialises in the combination of spatial and digital technologies for developing business opportunities that give many businesses, in different sectors, the possibility to increase their competitiveness on a global scale through innovation on all levels – from product/service, to processes, right down the overall business model.

The economic value generated by the combined use of space and digital technologies was estimated at about 371 billion dollars in 2021 (Satellite Industry Association). However, the value of the Space Economy goes beyond market estimates and stands out for the opportunity to innovate in many fields and at the same time help make our planet more sustainable through the integration of terrestrial and satellite data, at the foundation of new space-based services. Using high resolution global maps of land coverage, climatologists can develop climate models and understand how the climate is evolving on the earth’s surface. Multispectral images and radar, combined with machine learning and deep learning techniques, means it is possible today to create predictive deforestation models. Timely and constant monitoring of forests is essential to the implementation of conservation policies. Another field of application for satellite data is the monitoring of pollution.  A now well-known case regards the monitoring of pollution levels during the lockdown period resulting from the Covid-19 pandemic. To date, a large number of these analyses are conducted using data from terrestrial sensors, spread right throughout Europe. Satellite technologies are complementary and useful in areas where there are no terrestrial sensors.

An increasing number of academics include the combination of digital–space technologies among the drivers that can help to achieve the Sustainable Development Goals (SDGs), a tool adopted globally to steer economic and social activities towards the attainment of sustainability goals.
For example, space-based services contribute to the SDG 7 “Affordable and Clean Energy” which sets out to guarantee access to energy for a much vaster pool of users and can be promoted through the remote monitoring systems of plants in places where weather conditions and other natural phenomena can cause major damage to infrastructure and where maintenance can be difficult.

The development of a space economy market and of space-based solutions depends however on the structuring and exploration of new business models, retracing the entire value chain, from which services can be developed for those who create new infrastructures right down to the end-users of those services, making their operations more efficient and/or create new products. Innovating traditional business models and moving towards a platformization, servitization and open innovation model is fundamental to make sure new space-based services have a large-scale economic, environmental and social impact.

Citizens know better?

Posted on | by Beatrice Grillo | Leave a Comment on Citizens know better?
A team of scientists asked citizens to evaluate social impact and select which research to support. Here’s what they found.

 

Diletta Di Marco, PhD Student in Management Engineering – Innovation and Public Policy 

Science strives to improve the conditions of humanity and nature. But it is not always clear how to identify the research that serves the most pressing needs. For a long time, the direction of science has been chosen by professional scientists alone, through peer reviews, but new initiatives of participated democracy are trying to second the desire of citizens to take an active role in important decisions about science. For example, a Danish local government has asked citizens to choose which medical research projects should be funded by voting online.[1] Also, the Canadian Fathom Fund has chosen to top up funding to scientists that display their project on crowdfunding platforms and collect at least 25% of their budgeted costs online.[2] 

In a world facing unprecedented social, environmental, and economic challenges, the main idea of these initiatives is to involve those most affected by the problems and their consequences – the citizens themselves.

While scientists, research organizations, and research funders are experimenting new ways of actively collaborating with citizens, one concern is that what constitutes a high social impact is problematic and subjective. Moreover, the mechanisms used to actively engage citizens in the agenda-setting process can create biases or grant undue influence to wealthy or powerful groups.

For all these reasons, assessing the impact of research is an exciting area for professional scientists, funding agencies and policymakers, who are keen to identify new criteria for judging the sustainability and value of research, in addition to traditional ones which are more centred around prerequisites like age, gender, previous experiences in research, and previous project experience in the same area of research.

In an attempt to investigate this important but under-explored area, a research team of our School of Management has studied how the public evaluates social impact and choses to grant or deny support to scientific research. The team consists of Chiara Franzoni and Diletta Di Marco from Politecnico di Milano, in collaboration with Henry Sauermann from ESMT Berlin.

The team selected four real research proposals that were actively raising funds on the platform Experiment.com. The projects were in very different domains, ranging from environmental studies on the diffusion of otters in Florida, to social studies on sexual orientation and pay-gaps, to curing Alzheimer’s disease, and Covid-19. They recruited more than 2,300 citizens on Amazon Mechanical Turk and asked for their assessment of one of the four projects in terms of the three criteria normally used in research evaluations: i) social impact, ii) scientific merit, and iii) team qualifications.
They then asked the citizens whether or not they had a direct interest or experience in the problem that the research was trying to solve (e.g. a family member affected by Alzheimer’s disease when evaluating a project studying a cure for Alzheimer’s), and finally elicited the citizens’ opinions on whether or not the project should be funded. They did so under two different voting mechanisms: i) as a simple free-of-charge recommendation to fund or not to fund the project (costless vote) and ii) as a small direct donation to the project (costly vote), which the evaluators could do by choosing not to cash in a $1 bonus given by the team. At the end of the day, the team then devoted the donated bonuses to real research projects.
They later analysed the responses with statistical and econometric modelling and with qualitative coding of the textual responses.

Their analyses showed three key results:

  1. Firstly, citizens placed a strong emphasis on social impact. They were more likely to support a project if they assessed social impact to be high, even if they assessed scientific merit or team qualifications to be low. A complementary analysis of opinions provided in the form of open-ended responses corroborated this view. Citizens tended to focus on the perceived importance of the problem (e.g. size of the affected population, problem severity) and paid less attention to the project’s ability to solve the problem.
  2. Secondly, the voting system adopted substantially affected the composition of those who voted. Costly voting shifted the crowd’s composition towards people with higher levels of education and income. This suggests that mechanisms that impose even a small personal cost trade off the intended benefits of inclusion and representativeness when involving citizens.
  3. Thirdly, citizens who had a personal interest in the problem addressed by the project were more likely to vote in favour of the project, irrespective of using a costless or costly voting mechanism. However, they did not seem to overestimate the project’s social impact expectations. This suggests that crowdsourcing may give more power to interest groups and members of the public with personal interests in the research. At the same time, even citizens with a personal interest in the project seemed to be able to provide unbiased assessments of social impact if asked to do so independently from expressing their support for the project itself.

The findings of this broad research project contribute to advancing the academic debate in different areas, like the management of online communities (by shedding light on the link between voting mechanisms and self-selection and the literature that compares crowd and expert contributions with science funding).
More importantly, they have an immediate practical use for policy makers, funding agencies and interest groups that strive to promote participated democracy.

Considering that traditional research grant mechanisms and review mechanisms focus on things that could go wrong and pay too little attention to potential gains, these results suggest that citizens’ evaluations of social impact are not necessarily “better“, but they may provide a different and potentially complementary perspective.

 

[1] https://www.sdu.dk/da/forskning/forskningsformidling/citizenscience/afviklede+cs-projekter/et+sundere+syddanmark Accessed November 15, 2021.

[2] https://fathom.fund/ Accessed November 15, 2021.

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

Posted on | by Beatrice Grillo | Leave a Comment on “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

Posted on | by Mirja Calgaro | Leave a Comment on 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

Posted on | by Beatrice Grillo | Leave a Comment on 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

Posted on | by Beatrice Grillo | Leave a Comment on 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

Posted on | by Beatrice Grillo | Leave a Comment on “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”