The devil is in the data in the digital era. And the key to unlock the power of data is Data Analytics. We know enough about the pitfalls or the dark side of indiscriminate data mining. But, what if this power is harnessed responsibly for the greater human good? This is possible through responsible Data Analytics that can solve several social problems.

Data Analytics during World War I
Looking back, data analytics was deployed during World War I by British and US aero-defence scientists to analyze and protect warplanes from damage during combat.

The British and US militia gathered data of all damaged aircraft, identified holes and concluded that the front and the tail wings endured maximum damage. A statistican was engaged to analyze and design shields around the front and tail ends. However, the problem persisted.

Using the “Missing-Sampling information” technique the statistican then inferred that all parts of the aircraft can be exposed to firing. He concluded that maximum damage to the warplane could however be avoided if the shield was built around the central area. The solution was implemented andmore combat aircrafts returned with very minor damages.

For a better tomorrow today
Cut to today. Think of how healthcare or social mobility processes could be simplified through responsible data analytics? Technology specialists could design models/bots that can analyze your historical medical data stored securely on cloud.The bots analyze and suggest if a hospital visit and admission is necessary before you visited a doctor.

This calls for accurate data collection and analytics. Here are the steps to achieve accuracy in data analytics and its application through real-life use cases:

Sufficient data is a must and needs validation – For Society
In the social scenario, if you are a data scientist or a technologist developing data-driven models do remember that data validation is necessary to create effective social models. Firstly, available data should be sufficient and gathered from accurate sources. It should then be validated against a set of parameters that addresses a social problem. Then, there is data organization before the actual data analytics can begin. The insights thus derived, guide the development of the right model.

'Helping pregnant women' use case

India has high maternal mortality rate either due to delayed admission/treatment of pregnant mothers or improper pre-natal care. Real-time, data-driven model is seemingly a way out of this social morass.

Data analytics can be harnessed to create pre-natal healthcare and child birthing models to reduce maternal mortality rates.

An instance here is a company that is engaged in developing solutions for pregnant women to get them to the hospital when their labour pain sets in.

Now, this is the most critical time during pregnancy. This solution aims to predict the correct time of admission of pregnant mothers to hospitals. For this, they used custom-made patches that the pregnant women wore on their stomachs to monitor heart-rate of the babies and the other vitals. This data is passed on to a medical professional at the nearest hospital through a digital app.

This model obtains data through the patch and transfers it to the central database where data analytics is done. The appropriate insights are shared with the pregnant mothers as well as the doctors for effective monitoring.

Addressing child mortality
Another social instance is the Tamil Nadu government’s initiative to invite data scientists for analyzing child mortality data and designing solutions to solve it on a war footing. Work on this project has been happening, but much depends on the government’s willingness and mechanism of sharing quality data. These are primary concerns when algorithms are being built.If not analyzed properly, the predictions and hence the models can be flawed.

Responsible data analytics has immense power to solve the problems of humankind and our planet. How and why we use it will determine whether it’s our boon or bane.