Cities are using technology to make smarter decisions about delivering social services
By Albert Isern on February 10, 2017
Filed under Microsoft CityNext
Today’s post was written by Albert Isern, Chief Executive Officer of Bismart, a Barcelona-based company that received the award for Best Innovation and Technology Solution at the 2016 IoT Solutions World Congress.
Bismart is a big data company and a Microsoft CityNext partner based in Barcelona, the capital of Catalonia and the second-largest city in Spain. At Bismart, we specialize in solutions that transform data into actionable insights, to help public and private organizations improve their decision making. One of our most exciting projects right now is a solution based on big data that uses the Internet of Things (IoT), business intelligence, and machine learning to help local governments create smarter cities by managing their budgets more strategically. With our solution, governments can assess the current and future challenges faced by specific neighborhoods and population groups. Armed with that information, they can direct their social services spending to proactively assist their most vulnerable citizens or those with the greatest need.
Cities can use smart solutions to make data-based decisions
Traditionally, governments respond in a reactive way to the social needs of the communities they serve. Help arrives only after someone applies for assistance, the outcry from social agencies becomes loud enough to draw official attention, or, in the worst cases, after tragedy occurs. Unfortunately, many of those in need, such as the elderly, may not know that help is available or how to access it. Meanwhile, cities lack the kind of data-driven insight that could allow them to anticipate when and where help will be needed most, and what kind of help to provide. Bismart is working with local governments to find a better way, starting with a pilot project we have initiated with the government of Catalonia in Spain.
Using our smart solution, cities can aggregate data from many different sources, collecting information about demographics, housing, healthcare, economic activity, available social services, electricity and water supplies, waste management, public transportation, and other services. City governments can then apply predictive models to gain a clearer understanding of what their citizens and communities require—such as an area that may need more public transportation to serve the needs of a rapidly aging population. Instead of making decisions by looking in the rearview mirror to see what happened in the past, cities can start predicting what’s coming next. This will allow them to intervene earlier, address growing needs before they become crises, and improve the quality of life for all their citizens.
Why Microsoft technology was the right choice
When we set out to build our smart social data aggregation solution for social services planning, we relied on Microsoft technology, specifically Azure, Power BI, and Cortana Intelligence Suite. Microsoft offered scalable tools and an integrated end-to-end solution, and using platform as a service (PaaS) solutions gave us tremendous flexibility.
Power BI provides advanced data analytics while Cortana Intelligence Suite offers predictive functionality with the Azure Machine Learning solution and the ability to incorporate R code, an open-source solution that has become a de facto standard in the development of predictive solutions. Azure is also a highly secure platform, something that is essential for a solution of this kind.
How governments can use big data to solve big problems
Analyzing and correlating huge quantities of data to help local governments make highly informed decisions about where to apply their resources is a revolutionary approach to solving many of the toughest problems that our cities and society experience—yet one that is greatly needed.
Helping to ensure the well-being of all their citizens is one of the leading challenges facing cities today. Many factors affect quality of life, from poverty and isolation to mental and physical health. As a group, elderly people are particularly vulnerable and in need of assistance.
People today are living longer than ever before, due to unprecedented medical advances and improved standards of living. Yet, the well‑being of the elderly is lower than that of the population overall as a result of poorer health, reduced economic status, and loneliness.
Take the example of an elderly woman named Maria. She is in her eighties and lives alone in a big city like Barcelona. She wants to remain independent, so she lives in an apartment in the center of the city. She gets a pension of €800 per month. She walks with difficulty because of a hip operation that wasn’t entirely successful, but she tries to follow the advice of her doctor by leaving home once a day to purchase her daily meal and get some fresh air. She receives a monthly visit from the social assistant at the nearest primary health center to see whether the apartment is in good condition and to make sure she is taking her medications.
Barcelona has approximately 30,000 women like Maria. We know they exist, we even know where they live, but we know very little about how they live because we have no way to track their behavior. We cannot answer the most basic questions about the elderly residents in Barcelona, or New York, or Mumbai, or any other city—questions that are vital for assessing their needs and assuring their well-being and quality of life.
Fortunately, technology now gives us the ability to monitor people’s lives without invading their privacy. Our Smart Social Aggregated Data solution would allow governments, for the first time, to foresee social needs and correctly allocate resources based on real data and the lives of real people.
What’s next for Bismart?
This is an exciting era. We are on the cusp of a technological revolution that will change the course of human history forever. The possibilities that big data and machine learning provide are almost limitless. There are many huge problems throughout society that we are looking to tackle with our solutions, yet we know that even the biggest of those problems are reflected in individual lives. We never lose sight of that.
On the same day that my colleagues and I were receiving an innovation award for this solution at the 2016 IoT Solutions World Congress, an elderly woman died in Barcelona due to lack of social services. Unable to afford the cost of electricity, she had been using candles to light her small apartment. One night after she fell asleep, a candle tipped over, her bed caught fire, and she died. No one in city government knew she wasn’t receiving the help she needed. This is exactly the kind of problem we’re trying to help governments solve.