What are some of the tough problems you've solved using data in your daily life, with one or two examples as follows:

Life monitoring:

Wearing a device such as a health watch allows you to monitor daily activity and sleep. A great way to stay healthy and fit. Big data combined with technology can change our lifestyles and help us track our immunity ourselves to ensure we maintain healthy habits to fight the coronavirus pandemic.

Energy Consumption:

Big data combined with smart IoT devices allows smart meters to self-regulate energy consumption for efficient energy use. These smart meters are installed in neighborhoods to collect data from sensors throughout urban spaces. They can determine where the return and flow of energy is highest at any given time and redistribute it evenly throughout the grid, especially where it is most needed, to ensure that energy is distributed efficiently across a given network.

Logistics:

Big data can streamline logistics processes so that they run smoothly and within tight schedules. It is widely used in transportation to schedule flights, estimate seat demand based on seasonal fluctuations, conduct competitive analysis based on the latest social trends or events, and predict flight delays based on weather data. In addition, big data is deployed to accurately predict the number of aircraft needed in the future based on current usage and fleet deployment.

Healthcare:

Healthcare is another industry that generates large amounts of data on a daily basis. Big data reduces the cost of treatment because there are fewer opportunities to perform unnecessary diagnoses. It helps predict outbreaks of epidemics and helps determine what precautions can be taken to avoid preventable diseases by detecting them at an early stage. Evidence-based medicine can be provided to patients by identifying and prescribing the same drug after studying its previous results.

Music and entertainment:

OTT and music-on-demand platforms such as Beep and NetEase use big data to develop predictive machine-learning algorithms to analyze a user's music-entertainment preferences in order to recommend new shows and music that the algorithm thinks the user will like.