Data is present everywhere, Data and Analytics teams at companies gather data from various sources. It might be Web Analytics, Application Databases, Customer Feedback forms, BI platforms and many more.
For example, every time we rate an Amazon purchase and give a review, we generate 2 types of data: one is the rating which is categorical data, and the review text which will help in text analytics, sentiment analysis and other NLP tasks.
Similarly, say we use a food delivery service like Zomato, we are generating a vast amount of data. This would include location data, price, our food interests, payment habits (card, Netbanking, cash on delivery etc). All this data is hugely beneficial to companies. With all the data companies possess, it comes down to effective implementation. A real-life example would be how Walmart stocks up before any probable natural calamity. Before Hurricane Frances in 2004, Walmart executives wanted to find out what types of items they should keep in bulk. Their data teams looked through past data of purchases to find out what sort of items would sell more.
It turned out that Strawberry pop tarts and Beer are the most purchased food items in sich items. After storms, often there is a chance of power cuts and gas line disturbances for long times, Strawberry pop tarts are hugely popular as they don’t need any heating and last a long time. And beer for just chilling off. Walmart kept Floridians happy by keeping an ample supply of Pop-tarts and beer and they themselves made good profits. UPS is one of the biggest package delivery companies in the world. UPS also leverages data to optimize package transport. Their Network Planning Tools (NPT) uses Machine Learning to solve logistics and transportation challenges, like how packages should be routed, planned and when to be delivered. The AI also suggests routes on its own. The platform reportedly has saved millions of dollars for UPS.