We have read many times that we live in an ocean of data. The advance of information technology and the arrival of the internet meant not only that we could store ever more data, but that it was being generated ever faster. One consequence is that data can “go off” very quickly, so we have to analyse it and extract its value practically in real time.
Against that background, big data has for some time been establishing itself as the set of technologies revolutionising every sector at once, and showing us almost daily what it can do to extract knowledge and predictions.
Using mass data and artificial intelligence properly matters particularly for monitoring and tracking progress towards the Sustainable Development Goals (SDGs) set out in the 2030 Agenda, since it allows governments and institutions to adopt measures and take decisions on the basis of data.
Even so, achieving that requires equitable access to information while protecting people’s privacy, as the UN itself points out.
Some examples of these uses include developing smart cities, where knowing in real time how residents and vehicles move helps design mobility and urban planning policies that are more sustainable and kinder to the environment. Citizens themselves can also benefit, through initiatives such as open data, where government institutions share data on public matters like pollution levels, use of health resources, mobility and so on.
As we said, though, not every city or country has the same resources or knowledge to make full use of what data could offer them. It was for that reason that the United Nations launched Global Pulse in 2009, harnessing the capabilities of big data and AI for development, humanitarian action and peace, with various initiatives and laboratories across Asia and Africa.
Some organisations have joined this commitment to sustainable development too. The GSMA — the body representing mobile operators and related companies — launched the “AI for Impact Toolkit”, a set of tools that use anonymised mobile operator data to develop solutions for responding to humanitarian crises, natural disasters and health problems.
The extraordinary situation of the pandemic has demonstrated in particular how much potential mass data holds for improving people’s health and preventing disease. The Johns Hopkins Center for Systems Science and Engineering, for instance, created an interactive map gathering real-time data on coronavirus infections and cases worldwide. Its purpose is to offer reliable, real-time figures and to counter misinformation, allowing users to explore where and when outbreaks occurred, as well as how many patients recovered and how many died.
Health and healthcare is in fact one of the sectors where this set of technologies can reach the greatest potential and value. Not for nothing is the volume of data an average hospital can generate estimated at more than 600 TB of information. Which brings to mind one of the famous four Vs of big data: sheer volume.
So we receive news almost daily demonstrating how capable algorithms are of producing more precise diagnoses and more accurate predictions than doctors themselves. It is no surprise, then, that technology giants such as IBM with its Watson, Google and Apple are devoting their analytics technologies to improving health and treating disease.
Nor are companies blind to the usefulness of these technologies in CSR. There are numerous examples along those lines:
- Greater transparency on environmental and social matters. It is worth recalling that in 2014 the European Parliament approved the Non-Financial Reporting Directive, with the aim of improving the transparency of certain organisations on social and environmental issues. Big data may be an opportunity for companies to move towards active rather than passive transparency about their policies and initiatives — by making the data accessible and reusable by stakeholders, for instance, so as to create knowledge that is both available and permanent. It can also become a brand asset that improves consumer trust, since sharing such data means that in theory there is no room for deception.
- Sustainable, environmentally respectful production. Industry 4.0, which combines big data, AI and the Internet of Things, makes real-time data on energy consumption and productivity available. Companies can compare that data against historical models or even “digital twin” simulations. Cross-referencing it with external data — from the electricity grid, from transport or from environmental factors — can also help improve energy efficiency, optimise resources and reduce equipment obsolescence, and ultimately cut a company’s carbon footprint.
- Developing “healthy companies”. Some companies have begun offering their workers wearable devices that collect health and activity data. Analysing it makes it possible to detect avoidable occupational risks, to predict absences, and to analyse habits and promote healthy lifestyles — to the benefit of workers and of the company itself.
Dr Alejandro García García, lecturer on the Master’s in Marketing Management and International Trade, specialist in analytics, big data and business intelligence.
