Big data analytics is the proverbial catnip for businesses looking for a competitive edge over their rivals, and it is easy to see why.
The ability to convert the primordial soup that is raw data into actual actionable information is instrumental in helping businesses distinguish themselves from their peers. Businesses can then act upon these insights to improve their efficiency, customer service, and profit margins.
But this technology is capable of so much more than just growing profit margins and increasing market share. Analytics can be applied to any data set, no matter how large, and no matter the topic. Healthcare is one such sector that is benefiting from the ability to analyse terabytes of historical and live data, which has led to a much worthier application of analytics – saving people’s lives.
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 the non-profit organisation PATH has contributed to save 6.2 million lives, by using innovations in technology and cross-sector partnerships that help develop powerful tools and strategies. It is making a difference on a massive scale.
Malaria remains endemic in Africa. The World Health Organisation reported approximately 216 million malaria cases in 2016, with young children and pregnant women particularly vulnerable to the disease. Malaria still takes the life of a child in Africa every two minutes.
A new PATH project is making great progress to defeat the disease by putting data at the heart of its work. “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. This ties into the already existing wider “Elimination 8” campaign involving eight countries from southern Africa looking to eliminate Malaria by 2030.
The technology implemented by PATH has transformed the efficiency and response times of Zambia’s National Malaria Elimination Center. The project engages with technology providers Alteryx, Mapbox, DataBlick, Twilio, DigitalGlobe, Slalom and Exasol, in order to track and map the spread of the disease and empowers coordinators to allocate resources across the country accordingly. It ultimately prevents outbreaks from cascading.
The tech community at large is occasionally guilty of equating things like incremental advances in datacentre energy saving methods with historical improvements to global wellbeing. But data-centric methodologies can – and should – be used to help people live better, healthier lives.
Another satellite data project was carried out by researchers from Stanford in 2016, mapping poverty rates in sub-Saharan Africa. Static high-resolution satellite imagery is an unconventional data source, but has the advantage of being readily available. This is incredibly useful as data availability is often the first stumbling block to overcome in any project. By identifying light sources in night photographs and cross referencing it with higher-resolution daytime imagery, the researcher’s machine learning algorithm learned to pick out things that were recognizable to humans, such as urban areas, farmland and roads. In this way they were able to map distribution of poverty and development in a way that outperformed existing ‘on the ground’ approaches.
This kind of analysis of static data points is all well and good for long term government planning, but more up to date data is needed for more mercurial and thorny issues, such as assisting migrants fleeing war zones. Using live mapping methods to find the most travelled routes and cross referencing them with the conditions inside the country enables aid organisations anticipate peaks and dips in movements. In the Syrian aid effort, the aid process is digital, with refugees registered with biometric verification, smartcard-based aid, smart device data collection, mobile communications and telemedicine.
Even without high tech options like satellite imaging or biometric verification, there are instances of organisations using voluntary crowdsourced data to overcome the availability problem, such as with Wildlife Preservation Canada. They launched a website in collaboration with several partners in 2014, which allows ‘citizen scientists’ to help their researchers to locate populations of declining bee species, assess changes in population size and distribution, and track invasive species throughout North America, producing vital information that will help shape their targeted recovery work.
Vital actionable information can even be gleamed from seemingly unimportant datasets. In the UK, we have been able to infer where diabetes is the most prevalent, or where antibiotics are being overused by tracking local prescribing rates of the drugs by GPs and Hospitals. In this way, we can begin to paint a picture of where the fights against diseases is successful, and where needs more assistance.
There are of course barriers that stop non-profit organisations from using and analysing their data – most notably the lack of skills and resources. But there are an increasing number of initiatives like the Open Knowledge Foundation which run courses to help people gain data skills, or independent analytics companies willing to commit a portion of their time and expertise to worthwhile projects
We have seen real time analytics power huge advances in many industries behind the scenes, from optimising online gaming to keeping the rails stocked in fashion shops, but when it delivers actionable insights in ways that save people’s lives and improve the quality of them, it’s a humbling reminder that data analytics can serve a bigger cause.