The concept of Big Data is often the exclusive realm of big companies and international conglomerates, but it has such a huge impact on everyday people in ways that many don’t even realize.
From the most basic suggestion engines based on a few tags, through to the fertility of somebody who is trying to conceive a child, data is playing a significant part in everybody’s lives.
So how exactly is it affecting the lives of people in the following areas?
Health & Fitness
Fitness monitors are not new, having been publicly available in some form or another for the past 10 years. What we are seeing through the increased use of Big Data though, is that the suggestions of what individuals should be doing to improve or track differently are having a profound difference. Where trackers could see how many steps had been taken, modern trackers can note anything from the pace to gradient, giving a far more accurate picture of fitness and how to improve it.
The biggest impact may well come from how the healthcare industry uses data and how individuals will benefit from this in significant ways. Through pooling anonymized data and creating a vast database of case studies, it becomes possible to analyze millions of records to identify the best solutions for the individual patient. Through pooling this kind of data it is also possible to identify how diseases and viruses spread and what the most effective way to halt them may be.
One of the biggest personal impacts that Big Data can have is on the conception of a child. With many couples struggling to conceive within 12 months, apps like Kindara are hoping to help through asking women to input information such as details of their cycles, fitness activities, moods and temperature. This information is then analyzed and recommendations are given to help improve fertility. This kind of information can be invaluable for couples looking to start a family.
Even the simplest forms of travel today are powered by Big Data, so just moving from one place to another often comes down to how you and your apps are using data. Take Citymapper, one of the most popular transit apps in the world. The app takes GPS data of where you are, location data for public transport within a certain proximity and also the current state of them. So if a train line has delays, the app will know and be able to redirect you to another route that can get you there faster.
Also by simply using public transport in many cities, people don’t realise that big data is helping to make sure that they are getting to their destinations effectively and safely. Sensors throughout many networks pinpoint potential safety hazards allowing technicians to be directed to the correct place and solve the issue quickly and efficiently.
When we watch Netflix, choosing a programme from the 36,000+ on there would be almost impossible unless there were some kind of personalized suggestions to point us in the right direction. This is exactly what happens, with suggestion engines looking at what you have previously watched and liked, then showing you similar programmes to help you choose.
It is not only in the watching of programmes either, it is also in the creation of some of the best loved shows that networks make. House of Cards, for instance, was created because data showed that people liked Kevin Spacey and political dramas. Therefore the reimagining of a 1990s BBC miniseries was created and became one of the best loved shows of the last 5 years.
Companies like Amazon and Google have created entire businesses around their use of analytics to both predict what people may want to buy and to make it as simple as possible for consumers to do so. They do this through suggestion engines based on previous purchase history which is perhaps the most basic form of data use in shopping and one that people are more than aware of.
Footfall analytics are now having a significant impact on how brick and mortar shops are both laid out and operated too. Through knowing the amount of people who have come through their doors whilst also knowing the sales done by these shops compared to the numbers of people arriving, means that they can see how effective their campaigns and shop layouts are.
It is also personalizing shopping for many people, with custom offers and codes given to certain people based on their previous behavior. If somebody were to buy frequently from one site, then stopped, data would tell the marketing team to offer significant discounts to that person in order to get them back as a customer. Without this kind of data the company may lose these customers forever and it also allows the customer to receive specialist treatment to bring them back into the fold.