How Your Personal Data Helps to Achieve Sustainable Development Goals: MyData Conference 2022

This year, the annual MyData Conference took place in Helsinki, Finland. As Public Sector Data and AI Offer Leader, I quite felt home in this city. For a start, organizations like Helsinki Region Infoshare publish datasets for the common good, such as the water posts that can be freely used to fill water bottles. These are becoming an increasingly important way to cope with climate change. I saw at least one person benefitting from this data on the 22^nd of July.

The Digital Social Contract

In recent years, we have seen the emergence of mission economies that share a purpose: sustainability. Another thing they share is their approach to tackle their goals, which involves the utilization of data and AI. Together, they are giving rise to a digital social contract that is powering the creation of augmented public services.

In all cases, data and AI are enabling the public sector to achieve its missions with more pace, efficiency, and security. The end-to-end automation of case management and document processing results in intelligent administration. The implementation of large-scale automation fosters engagement, as liberated citizens can interact with public servants and processes around the clock. Moreover, the level of security and service is markedly improved, with automation powering real-time threat, incident, and anomaly detection. When taken together, data and AI generates insight that can be leveraged to feed a better decision-making process – from understanding a situation to suggesting next-best actions.

Relieving instead of replacing

It is important to remember that data and AI are tools, just like a carpenter’s electric saw. They make the work easier, add precision, and improve efficiency. But they are only as good as the operators – that’s us. An augmented public service should be the best of both worlds, with humans making the decisions based on the information gathered by these tools. It’s also worth pointing out that the human operator makes AI ethical, a quality it is unable to achieve on its own. Only by integrating technology with human management can we revolutionize the following four playing fields:

• Intelligent Automation of Administration
• Interactions between the citizen and servant
• Anomaly detection and identification
• Improved decision-making processes

AI for Good

This emphasis on the ethical use of AI underpins the AI for Good initiative, supported by the United Nation’s International Telecommunications Union (ITU). Like everyone else at Capgemini, I am extremely proud of our involvement in this movement. All members are committed to ensuring data and AI are used to active the following Sustainable Development Goals (SDGs):

• Education: Decent work and economic growth, quality education, and good health and wellbeing.
• The Environment: Life on land, clean water and sanitation, and climate action.
• Nourishment: Zero hunger, no poverty, and good health and wellbeing.
• Heath: clean water and sanitation, good health and wellbeing, and reduced inequalities.
• Peace and Information: Peace, justice, and strong institutions combined with quality education.
• Justice: Peace, justice, and strong institutions.

Capgemini’s Collaborative Data Ecosystems: Sharing is caring

When it comes to some of our greatest challenges, there really are some very simple solutions. The French journalist, Hervé Kempf, said it best:

“Consume less, share better”

This sentiment typifies the ethos of collaborative data ecosystems, which are partnerships between stakeholders to share and manage relevant data and insights. Operating on the assumption that the collective is stronger that the individual, these ecosystems create value for all the participants, value that they couldn’t generate on their own. But organizations to must have both a vision and a strategy that enables effective execution.

Strategy and Business Model

Organizations should follow the five-step plan of action:

Additionally, advisory services and industry specific point of views can accelerate collaboration.

Implementation of Collaborative Data Ecosystems

It is vital organizations establish data collaboration platforms and implement security and privacy protocols. Emerging technologies, such as federated learning and data mesh, shall be combined by strong data engineering platforms and the relevant privacy set-ups: Homomorphic Encryption and differential privacy are two examples of how data exchange can occur in a safe way.

Furthermore, Capgemini provides organizations with several accelerators that make this possible. Our 890 and Industrialized Data and AI Engineering Acceleration (IDEA) offers are invaluable tools when establishing data collaboration ecosystems.

Your Data for Good: the quest for SDGs

Collaborative data ecosystems can have a profound effect on cost efficiency, omnichannel empowerment, insight multiplication, use case enablement, and citizen engagement. When these five factors are driven by data, they can transform environments and territories, education and work, information, justice and safety, food and agriculture, and healthcare. As you may have noticed, these are some of the UN’s more notable sustainable development goals.

To give you just one example, let’s take a look at healthcare supply chain management:

In 2018, each hospital in the United States spent an average of $11.9 million on medical and surgical supplies. This accounts for up to one-third of total operating expenses for some. Despite this, improving supply chain and inventory management is often not considered a high priority for hospitals; providers tend to focus more on the processes surrounding direct patient care. Yet, having these supplies is necessary for delivering high-quality care. Due to the Covid-19 pandemic, improving agility and resilience to demand and supply-side shocks has become even more critical. As a result, hospital managers are increasingly looking for ways to leverage data and technology to gain insights into inventory, pricing, lead times, and demand trends.

The proof is in the pudding

To give you some idea of how effectively collaborative data systems meet sustainable development goals, allow me to give you a couple of examples from the projects I’ve been involved with at Capgemini.

Federated Learning

The Federated Learning platform, developed by Capgemini, makes it possible for hospitals to share trained Artificial Intelligence (AI) models to create a global model that outperforms local versions. Three Spanish hospitals were able to dramatically improve both the speed and accuracy of COVID-19 screening. This was achieved by aggregating the clinical experience of each hospital to develop automated medical diagnosis models.

OnDijon: the smart city

Dijon was struggling with operations and ambitions, siloes, and the lack for a big picture. Capgemini and Bouygues worked with the Dijon metropole to develop an Artificial Intelligence (AI) platform that connected the control center with every machine, scanner, and citizen. This smart city initiative made use of open data, utilizing citizens in the process of creating new public services that unleash the potential of real-time information, smart mobility, and traffic-based lighting. The result was a 40% cost reduction for services through improved responsiveness to citizen activity. The city also expects to see energy savings of 65% over the next 12 years.

The rise of the smart citizen at the heart of data collaboration

Before bringing this analysis of data enabled sustainable development goals to a close, let’s return to the heart of the subject. It is easy to forget the human element in all this talk of technology, data, and goals. But human centricity is what this all about. It’s extremely satisfying to know that data is being used to the betterment of civilization. However, it doesn’t make much difference if engagement is low. That’s why it is worth remembering that real change in the world will only happen when the overriding majority of citizens are both informed and motivated. In short, we need a everyone to become smart citizens.

We are well on our way, but here are three guiding ideas to keep in mind:

1. The smart citizen shall be involved in data projects from the beginning
2. The citizenship shall be nurtured over time
3. The citizen shall move from a consuming to a producing role in data

I’m sure that you’ve got the message by now – each and every one of us is directly involved in the quest for the value of data. So, let’s go out there and embrace our part!