«Data science and business analytics: today companies can’t do without them»

Professor Carlo Vercellis, director of the executive programme in data science and business analytics, tells about the latest trends in the market of big data and makes an appeal: «External consultants are no longer enough. Organizations now need to integrate these positions internally»


Growth which for the last five years has been constantly in double digits, around 20%, investments that in Italy reached the value of 1.7 billion euros. The market of analytics, in other words the analysis of data, has come to a turning point. «But now it’s time to grow», says Professor Carlo Vercellis, professor of machine learning at Politecnico di Milano, director of its executive programme in data science and business analytics and scientific head of the Big Data & Business Analytics Observatory. «Large companies have gained familiarity with these tools, although up to now they have mainly relied on external consultants. It’s time to incorporate these figures within companies, even in SMEs. There are many challenges to be faced, just as many professional figures required and therefore job opportunities for those who want to work in this field».


Organization, management, process automation: the latest trends

There are two particular trends identified by Vercellis. «The first challenges are of an organizational and managerial nature, and involve the governance of the supply chain of data driven projects, that is those based on data: moving from experiments, which have become increasingly numerous and complex, to the pilot project, and then to the start of production and to deployment. The second challenge concerns business processes, that must be changed in a data driven perspective. We’re thinking about process automation, that is an automation of processes that substitute human activities with little value added through algorithms that allow software and robots to carry out a series of repetitive tasks. This allows to free up resources, human and material».


Lots of data, lots of algorithms: the need for functional awareness

However, data alone is not enough. You need to know how to question, read and interpret it, and for this there’s a need for specific skills: «We are submerged with data. The two main sources are social activities, that provide unstructured data, which cannot be reduced to tables of numbers; and the Internet of Things, or that network of objects, household appliances included, with smart features, which collect large amounts of more structured data», explains Vercellis. «To read them you need to know which analytical tools to use: we’re talking about algorithms, obviously, which while sharing basic settings are not all the same. According to the task to be carried out, one can be more suitable than another. For this reason, there’s the need for a professional with “functional awareness: experts capable of using data and business analytics tools, without having to be technicians. These are the professional roles that companies are starting to look for today, because little by little they are realizing that external consultants are not enough».


The job opportunities in the world of analytics

The professional profiles that fit this requirement are varied. «They go from business users, able to understand the logic and limits of these tools, to the translator, a bridge figure who knows the language of data science and business, and is able to facilitate communication between these two worlds. Professional roles today are increasingly technical: the data scientist, data engineer, business analytics data scientist solution architect».


The executive programme in data science and business analytics of MIP Politecnico di Milano aims to train professionals in the different areas needed: «It’s a course that begins in October, requires a commitment of two days a month and touches on all the issues tied to this subject», explains Vercellis. «It involves hands-on sessions and final project work in which students must apply notions learned to a problem, proposed by themselves or professors of the MIP faculty. The course is for individuals, who perhaps are looking to reskill, but I expect that above all it will be companies that take advantage of this opportunity: a great opportunity to train an internal resource able to manage the company’s needs, a task that an external consultant would never be able to carry out».

Machines? Smarter and smarter!

Exploring artificial intelligence and machine learning, technologies that bring accelerating change to our habits (and those of businesses)


Algorithms that can anticipate people’s tastes. Tests that can provide early diagnosis of a series of illnesses or predict which mechanical components are most likely to fail. Applications in a broad array of other fields, from manufacturing, marketing, and social media to voice recognition and self-driving cars. If the future is already here, this is partially thanks to artificial intelligence and one of its components: machine learning.
Machine learning is a discipline that develops algorithms to make machines intelligent, that is, able to learn from past experience and make decisions regarding the future,” explains Carlotta Orsenigo, Associate Professor of Computer Science at the Politecnico di Milano and expert in machine learning algorithms.
The advantages are enormous, also economically: more revenues at lower costs. Better forecasting of demand allows us, for example, to optimize stock management and offer better service to our customers.
Carlotta Orsenigo is also co-director of a master’s program in data science at the Politecnico di Milano School of Management, whose graduates may find work in the business sector. “The International Master’s Program in Business Analytics and Big Data is addressed to people who have a degree in science or economics and less than five years of work experience. The objective is to develop competencies in three different areas: technology, methodology, and business. The one-year program prepares students for a job market with a very high rate of placement.

Predicting demand

The key figure in machine learning is the data scientist, who analyzes data and develops algorithms that make it possible to use similar data as an effective prediction (and decision-making) tool and also interfaces with key company representatives (head of marketing or production, for example) on specific objectives.
Machine learning can be very useful in retail for analyzing and predicting demand for products and services. Based on what customers have bought in the past, predictions are made as to what they will buy in the future. Likewise, the algorithm can analyze an analogous customer pool, that is, one with characteristics similar to our own, to predict what our customers will choose” continues Orsenigo.
The other aspect of demand prediction are recommendations, i.e., the suggestions that big players such as Amazon or Netflix make to their customers (If you liked that film, you’ll also like this one! Are you looking for something to read? Readers with similar tastes also enjoyed this one!). The intelligent machine processes a huge quantity of data and extrapolates patterns and trends without any help from humans.

A host of applications

Another field of application is the manufacturing sector. In this case, the data to be analyzed are collected by the various sensors. Here we are getting into the Internet of Things (IoT). This makes it possible to identify potentially defective pieces in advance and prevent future failures.
Actually, the most important field of application of machine learning is medicine and medical science. “The analysis of genetic expression, for example, allows for the detection of patterns between healthy and unhealthy people and the design of targeted diagnostic tests” says Orsenigo.
Another very important area is voice recognition /vocal interfaces, as we have seen from the success of Alexa and similar virtual assistants. “Our generation still prefers the option of typing, but young people are increasingly used to interacting vocally with their devices.
And there are also chatbots, applications designed to simulate human conversation and learn from their interlocutor (tone of voice, topics of conversation, questions asked…) so they can provide increasingly well-targeted answers.
Not to mention self-driving cars
In a word, the future is still there to be written—sorry, coded.