How Big Data Analytics is Used in Everyday Life Around Us

In recent times, there has been a significant increase in the application of big data analytics in everyday life. Data has been associated with profit maximization techniques (used by e-commerce sites and targeted ads), data leaks and privacy issues.
However, data is not all bad. Some issues are highlighted to create a bad name for data science but here I would like to talk about the helpful side of data which we encounter in everyday life.
Healthcare
Healthcare has progressed over the years helping people live a longer life, all this is thanks to the amount of big data we have been collecting and experimenting with. We have been able to create self-learning healthcare programs, which are able to work on data of individual patients: along with gender, age, weight, and medical history, also lifestyle, habits, preferences and we will be able to provide a personalized recommendation about adjustments that will be most beneficial.
Today most people are looking to buy fitness trackers and download health apps – these are helping people lead an active life, eat healthier and control their weight – and this is only the beginning. These devices are actively monitoring heart rate, sleeping patterns and other vital signs that can be used to serve other healthcare purposes and predict overall public health sentiment. With so much data, we might be able to prevent an epidemic before it taking place.
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Logistics
Thanks to data, virtually everything in our environment is running smoothly. Improved logistics is not always visible to the public, but the impact is immense.
Airlines are able to schedule flights, predict delays based on weather data and estimate the demand for seats required based on seasonal fluctuations, competition analysis, latest societal trends or events. Also, they are able to accurately predict the number of planes required in the future.
Delivery companies such as DHL, FedEx, etc use big data science to improve their delivery times, leading to higher operational efficiency. You get correct delivery estimates even when you are ordering from another country – this is impossible without processing and analyzing large volumes of data for best solutions.

Face Recognition
Facial recognition algorithms were discovered a decade ago, but they would mistake the human face for all sorts of things – animals, some graffiti etc. Now with more and more data fueling it, the algorithms have been learning and now it is close to perfect.
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Today iPhone X has come up with a face unlock feature wherein it can even recognize twins. It enables Facebook to give you suggestions on tagging friends, it activates goofy filters in Snapchat, Instagram. Going forward, facial recognition can be a powerful tool of law enforcement.
Self-Driving Cars
Driverless cars considered a dream earlier is only possible today because of the vast amounts of big data we can process. It is estimated that one driverless car produces close to 1 GB of data per second, which equals petabytes of data a year, that too from one vehicle.
Apart from the sensors that collect and process data real-time (radar, video cameras, GPS, ultrasonic sensors, etc.), self-driving cars also use data from other cars. It helps them to build an up-to-date roadmap and navigation is through all these data sources. It is similar to how we use Google Maps to navigate our way through the least congested or fastest route. Then there is machine learning that helps cars to predict a critical situation based on the data it collects. This is the reason why every driverless car company is letting their cars explore the streets of the world before getting into mass production.
If you look around you will find more such examples of big data analytics in everyday life.