Big data analytics is the proverbial catnip for businesses looking for a competitive edge in the market, and it is easy to see why. The ability to provide more granular analysis of large data sets is helping businesses distinguish themselves from their peers, by providing actionable insights faster than ever before. Businesses can then act upon these insights to improve their efficiency, customer service, and profit margins.
But it is not just about helping to grow profit margins and increase market share. Analytics can be applied to any data set, no matter how large, and you can discover insights to help improve many data-rich applications from smart cities to healthcare. One could argue that it is in the latter where data analytics is providing the ultimate benefits, by helping to save lives.
PATH is a non-profit organisation that uses innovations in technology to save lives and improve the health of those in need, especially young women and children. It tackles a wide range of health issues through entrepreneurial cross-sector partnerships that help develop powerful tools and strategies that can make a difference on a massive scale.
With nearly half of the world’s population at risk of malaria, organisations worldwide are continuously looking for new ways to combat this global health issue. Over the past 15 years PATH has contributed through its various programmes to saving 6.2 million lives, but despite this malaria is still endemic in Africa; the World Health Organisation reported approximately 212 million malaria cases in 2015, with young children and pregnant women particularly vulnerable to the disease. Malaria takes the life of a child in Africa every two minutes.
Thankfully, a new PATH project is making great progress to defeat the disease by putting data centre-stage. “Visualise No Malaria” is working with the Zambian government to harness big data analytics and the cloud with the aim of eradicating the disease from Zambia by 2020.
The technology has transformed the efficiency and response times of Zambia’s National Malaria Elimination Center. Coordinators once relied upon reports from health centres and health visitors on bicycles to target the deployment of its limited resources: insecticide-treated bed nets, indoor residual spray, rapid malaria tests and drugs. Now, the project led by PATH and data visualisation software provider Tableau engages a number of different technology providers including EXASOL to quickly analyse data and produce maps that track how the disease spreads. This means coordinators can allocate resources across the country accordingly and prevent outbreaks.
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The data visualisation works by mapping geospatial data, using data such as elevation and slope, combined with hydrological features such as topographic wetness and stream power, which PATH’s scientists use to create a very precise, accurate map of water courses and therefore where mosquitos are likely to breed. They are working to combine this with meteorological models of precipitation and temperature and data about disease outbreaks to better hone future analysis and allow health officials in Zambia to send resources to areas with the highest probability of malaria outbreak.
We are delighted that our partner Tableau has involved EXASOL in this project, in which our role is to provide the computational power of our fast analytic database through Amazon Web Services. Through PATH, health professionals are able to perform highly complex queries of not just “big data” but “massive data”, at speeds that enable almost fast rendering of maps and dashboards for interactive analysis. This allows the data to be understood and actioned immediately in the field.
This PATH-Tableau project is the first time that cloud-powered big data analytics has been combined with the know-how to tackle malaria in this innovative way, and if “Visualise No Malaria” proves successful in eradicating the disease from Zambia within the next three years, it will provide a blueprint for other countries in sub-Saharan Africa.
It’s tantalising to consider the possibility that other fast-spreading illnesses could be tackled by looking at how data has been used to help combat malaria. This means that, should another health epidemic similar to the bird flu and Ebola outbreaks of recent years occur, we would be able to analyse those datasets and use visualisations to predict how it will spread and how fast, and then draw up plans to target treatment and contain the outbreak.
We have seen real time analytics power huge advances in many industries, from optimising online gaming to keeping the rails stocked in fashion shops, but when it delivers actionable insights in time to prevent loss of life, it’s a humbling reminder that data analytics can serve a bigger cause.
