Creating Value from Data

The Data Science Lab of Politecnico di Milano works on data sciences projects with special attention to application-oriented problems. These can be physical industry-oriented business challenges like industry 4.0, which relate to industrial plant monitoring and anomaly detections, or smaller scale specific applications, such as studying brand reputation online.  

In addition, the lab has a share of problems coming from industrial companies via joint projects or consultancy activities, researching techniques and methods for optimising language models. In a recent Large-Scale Analysis of Online Conversation about vaccines before COVID-19, Brambilla investigated the impact of disinformation on vaccination with his team. This research found a correlation between the dynamics of social media shares on fake, unreliable, partial, or biased news about vaccination and vaccination trends. The researchers also try to detect how to tackle fake accounts, fake bots, and social media bots that generate misinformation. 

Most of the projects the lab conducts come from European or national public institutions or research grants. For example, Brambilla's team is a research partner within the European research consortium PERISCOPE, running from November 2020 until October 2023, which investigates the socio-economic and behavioural impacts of the COVID-19 pandemic.

Small companies have difficulties keeping the pace of Data Science developments. This is what Brambilla calls the Data Divide. ‘It is the modern evolution of the digital divide. Data divide is about who has the data and who doesn't, who can or cannot access or process it. This divide is critical, even at a societal and economic level. It will impact a lot, like market models and so on. A large quantity of data within large companies such as Amazon creates huge potential business opportunities.  

This is challenging as small businesses may not be able to keep up with this pace and assuming that smaller businesses have the power to leverage this significantly, is too optimistic. In the long run, the quantitative trend may kill all the small competitors.’

The challenge for small businesses is at the strategic level. ‘Small businesses could try to benefit as much as possible by using some existing platforms applied to their small amount of data and see what they can get out of it.  

Another way to go could be to build a federation of small businesses. So, the data is not just collected by one hairdresser or one bakery but within a network of small businesses. This network shares information for optimising the quality of life and business in the cities.’ 

A third option Brambilla suggests is creating alliances between small and big entities to counteract the dynamics of big companies pushing small companies out of business. Building mechanisms for creating partnerships or cooperation between small and big entities could work if both get benefits. For instance, Italy installed a regulation for large malls to be closed on the weekends. Consequently, people somehow rediscovered the advantage of small shops in the city centres. An alliance between big and small businesses could leverage a combined data-driven valuation. It is impossible to compete in terms of pricing with the big players. You need to use the value you have.’ 

That brings up another option: working more closely together in the value chain. ‘One could try to integrate the provider and clients in one value chain. Small stores buy things from big warehouses or even directly from the producer. A pasta producer or car producer might be interested in building a stronger alliance with their sales channel and maybe deliver some type of data-driven platforms along the pipeline.’ 

All companies should rethink their strategies. Staying ignorant of the impact of data science on your business is not an option. ‘You become marginal on the market. You must be ready for a change. If you want to scale up your business, you need a data-driven mindset focused on the business objective. This could be “I want to become more cost effective” or “I want to become more efficient in reaching out to our customers”.  

The EIT Digital course Data Science for Business Innovation has this very objective, to demonstrate how to implement change in your data strategy to meet your business and customer needs.’