Big Data In Real Life

Big Data is a never ending, never wilting sector of technology that amazes everyone with its capabilities. But still some of us do not completely understand its usefulness.

Let us look at a real life problem to understand the usefulness of Big Data.

McDonald’s serves both cold drinks and hot coffee along with its burgers all day long. But let’s say they realize that the hot coffee doesn’t have enough demand and their raw material which they fill in the coffee machine in the morning is wasted at the end of the day every day.

Now, McDonald’s wants to figure out how they can reduce the losses based on when their customers actually buy hot coffee.

We will assume that the coffee machine of McDonald’s needs to be filled in the morning for coffee to be available throughout the day and you cannot use it as an instant coffee machine. This is for simplicity.

This is a real life example of data analytics. McDonald’s can do two things in this case:

1. Based on the sales figures, they can figure out how much coffee is actually consumed daily. Based on the average consumption of coffee daily, they can then put in the raw materials in that quantity only and reduce the wastage of unused raw materials. This requires you to analyze the data of the sales of hot coffee in the McDonald’s outlet and then figure out how much coffee is sold every day. What are the trends in the sales – are their days or times when the sales go up or down, are there huge spikes or a normal distribution of consumption, etc. Once you have the answers to all these questions, you can tell McDonald’s how much coffee they need to put in the machine every morning.

2. But let’s say the coffee machine uses a lot of electricity and just saving the costs of wastage of raw materials is not enough. In order to make a significant saving, you need to also switch off the coffee machine when the demand is low. Now, based on the time of sale, you need to figure out at what time of the say the sales of hot coffee go up, are these patterns consistent over long periods of time, is there some correlation between the time of the day and the consumption, etc. This will require you to do a statistical analysis of the data based on which McDonald’s will decide what time to run the coffee machines at.

McDonald’s saves thousands of dollars by reducing wastage and optimizing supply in line with the demand. A fairly simple application of Big Data.

Let us cite a few more brief example from our everyday lives. 

1) Every time you log on to Google, Facebook and see ads, they are based on your preferences, browsing history, FB likes/groups, what your friends liked and so on.

– Profiling models and Ad Targeting

2) Every time you try to buy an air ticket online, the prices vary on the basis of the route, demand, expected last-minute demand, how early you book and so on.

– Revenue Management

3) Every time you log on to e-retail sites and look at a product, you’ll start getting recommendations for other products also considered by other visitors.  If you end up buying something, you’ll get recommendations for other products that bundle with it.

E.g. Buy a phone and it will recommend a case or glass protector

– Recommendation Engines

4) If you make ISD calls or STD calls in India, you might get a recommendation for a STD/ISD package.  The idea being to convert what is unguaranteed future income.

Look around and you’ll see the results of Big Data at work!

One more striking example is how the Afghanistan Conflict was better understood by NYU students.

“Drew Conway was a Ph.D. student at New York University who also ran the popular, data-centric Zero Intelligence Agents blog. He analyzed several terabytes worth of Wikileaks data to determine key trends around U.S. and coalition troop activity in Afghanistan. Conway used the R statistics language first to sort the overall flow of information in the five Afghanistan regions, categorized by type of activity: enemy, neutral, ally, and then to identify key patterns from the data. His findings gave credence to a number of popular theories on troop activity there–that there were seasonal spikes in conflict with the Taliban and most coalition activity stemmed from the “Ring Road” that surrounds the capital, Kabul, to name a few.

Through this work, Conway helped the public glean additional insight into the state of affairs for American troops in Afghanistan and the high degree of combat they experienced there.”

Big Data can be applied to any field and can be utilized in many ways. You need to have the vision and the expertise.