You can read the original article (in Spanish) on El Mundo’s website here.
P. CLEMENTE
Reducing contamination, easing energy poverty, improving citizen services, and maximizing educational potential are just some of the things data analytics has achieved.
It’s all about Big Data – and lots of Big Data. There’s nearly endless talk about its advantages and how the intelligent information we extract from the billions of data points we have at our fingertips will become the fuel, the weapon, or the key strategy for the world of the future. Now, there’s research about Big Data’s potential to help improve basic social services and do things like ease energy poverty, increase the quality of citizen services, and help education reach its full potential. And those are just a few examples.
In a similar vein, Bismart, a company specialized in Big Data and Artificial Intelligence, has focused on the problems related to contamination and poor air quality. And data analysis can achieve a lot here. This summer, the company announced its partnership with Libelium for a project in which they installed sensors with the ability to measure certain contaminants and levels of dangerous gases. Cross-analyzing this information with weather predictions, traffic data, economic activity, demographic shifts, and Bismart’s algorithms creates a predictive model that could stop contamination in its tracks.
“If the government has a prediction of the levels of contamination over the next few days, they could suggest lots of things – reduce traffic, re-route trucks at certain times or locations, encourage the use of public transportation, evaluate emergency services and roadside assistance, and so on. You could even think about a tool for citizens that would alert them to avoid specific areas,” explained the company’s CEO, Albert Isern.
At the moment, they have a prototype of the project installed in Brussels. But they have blind faith that it will prove useful, as this isn’t the first time they’ve done something like this.
In June, the same company presented Grand Management, a tool to plan public budgets in a preventative manner rather than reactive one. It does this based on economic data and predictive algorithms created by the company, which let you see whether there is enough money for each service, with an objective way to justify budget increases and predict by group or area social services’ future needs. For Bismart, it’s essentially a basic tool to combat energy poverty.
The important thing is that they’re not alone in this fight. Telefonica’s division specialized in Big Data, LUCA, is working on achieving the same thing using what they call Big Data for Social Good. The directors of this initiative say that customer data can be used to measure the economic impact of earthquakes and work with satellite images to indicate areas at risk of flooding. They can also use search engines to monitor the spread of dengue, and cell phone data to analyze levels of illiteracy in areas that are difficult to access.
Closer to home, the Center of Excellence (Eurecat’s Centro de Excelencia, CoE) of Big Data in Barcelona worked with the Catalan government and the company Atento to launch a project this summer that used advanced data analytics techniques and Big Data for citizen assistance, specifically to improve the quality and efficiency of the 012 emergency phone assistance service.
Another similar project was carried out in the Citizens’ Service Bureau at the Sant Cugat del Valles City Council. Right now, explained Circe Serra, CoE’s project manager, they have detected behaviors and types, but the goal is to obtain a predictive model. The organization has the same goal holds for their project to improve tourism management in the neighborhoods surrounding the Sagrada Familia.
And while we’re talking about how Big Data can improve social wellbeing in cities, it’s important to mention what’s going on in classrooms, too. The Catalan government’s education inspector, Neus Lorenzo, explained that in her department they look at Big Data trends to study teaching policies. In education, she explains, this technology is currently applied in two ways.
The first way is as a learning tool to provide personalized education. “In these platforms, the machine corrects and records the children’s learning processes, creates profiles, and proposes paces of study for each them,” she explained. “Based on that, you can group, compare data – even on an international level – and suggest personalized models.” Not just that, the information you extract from the PISA tests can be used to create textbooks. Secondly, Big Data can be used to measure people’s interactions on social media, a phenomenon that has generated quite distinct learning habits.
“The advantage of these technologies is that they’re automatic. They let you do things much more quickly and they minimize errors as much as possible. But at the same time, those are the risks: automatization means that the criteria are quantitative and ethics and reflection are forgotten, so we’re in the hands of a very dangerous artificial mechanism,” said Lorenzo.
That’s because the biggest advantage of Big Data in education is achieved when these platforms that collect student data converge, such as algorithms that recognize faces or emotions. These algorithms know how a student feels and what they do and don’t understand. Reports combine this information and then establish categories of students. And, to finish off the process, decisions about whether a student passes that year or what they should learn are made by a robot. The dystopian future that Neus Lorenzo presents would be ruled by a cult-like technological system that puts people into boxes and limits their learning to what some robot considers they should learn based on their abilities and social context.
“Technology will make us better in lots of things, but there’s a lot of risk,” added the educational inspector. “The main thing is to be aware of all of this, because right now there’s no training about these two big challenges –relational communication and the capacity for voluntary control that the machine has.”
“Anything related to technology has had and will have good and bad applications,” commented Circe Serra on a similar note. “But when it’s applied well, Big Data can help a lot.”
In fact, she continued, there are already projects underway to develop technology that will prevent algorithms from being discriminatory.
What needs to be explained, she concluded, is that we need to work with the data we have and aggregate them. She added, decisively, “And that’s when Big Data works.”
