What Is Big Data? Examples of Big Data in Everyday Life

The term “Big Data” is often used without knowing its true meaning. Here’s what it means and what it represents in everyday life.

We hear more and more about big data, but those who use the term often do not fully understand its meaning. For this reason, it is legitimate to ask ourselves what it is, why it appears so frequently in online and offline publications, especially those of a marketing and IT nature, and what it means for big data to represent an evolution for different sectors.

To understand what Big Data is, it is not necessary to rattle off complex definitions, which would make Einstein himself pale, but it is enough to understand the profound meaning of the term and how its influence can be noticed in everyday life.

It is important to start by saying that the term “big data” itself is somewhat misleading. The term itself suggests the enormous amount of data available today across different sectors and automatically leads to the conclusion that the Big Data revolution refers to the opportunities available today to have so much information available to businesses.

This conclusion is only minimally true. Also, because the Also, there are sectors where data, although there are really large quantities, is not always available to everyone and, above all, is not always shared. In this aspect, which will be resumed at the end, the actual amount of data generated today is enormous from smartphones and credit card purchases to television, computer applications, smart city infrastructure, and sensors installed on buildings and public and private vehicles, from intelligent infrastructures in cities up to sensors mounted on buildings, on public and private means of transport, and so on.

Data is being generated in such an increasing flow that all the information accumulated over the past two years has reached the order of zettabytes  (10^21 bytes), setting a record for human civilization.

As we said, however, although there is an inexpressible quantity of data, the real revolution to which we refer when talking about Big Data is not the volume but the ability to use all this information to process, analyze, and find objective evidence on various issues.

What Does Big Data Really Mean? 

The Big Data revolution, in general, refers precisely to what can be done with all this amount of information, that is, to algorithms capable of dealing with so many variables in a short time and with few computational resources.

The difference is clear: until recently, a scientist analyzing a mountain of data that today we would call small or medium would have taken a long time and required mainframe computers worth over 2 million dollars. Today, a simple algorithm can process that same information in just a few hours, often using a basic laptop to access the analysis platform.

This is the Big Data Revolution. Big Data requires new ways of connecting information  and providing a visual approach to data, suggesting patterns and models of interpretation that were previously unimaginable.

And the best part is that Big Data isn’t just about IT. If information technology represents big data, then the necessary tools, such as cloud computing and search algorithms, are also part of big data, which is necessary and useful in different business sectors. Big data is necessary and useful across many different sectors, from cars to medicine, from commerce to astronomy, and from biology to pharmaceutical chemistry. Almost every sector that uses marketing and data analysis can be affected by the big data revolution. 

Examples Of Big Data In Everyday Life

And this revolution touches the lives of every single person without anyone noticing.

Here are some examples of what big data is capable of.

In marketing, Big Data is commonly used to build recommendation methods, like those used by Netflix and Amazon, which make purchase proposals based on one customer’s interests compared to millions of others.

All the data generated by a user’s online activity, from the previous purchases, and from the products they view, research, or evaluate allow the giants of commerce (electronic and otherwise) to suggest the most suitable products for the customer’s purposes; push him to buy for temporary need, permanently, or simple impulse.

Big Data also includes algorithms that can predict if a female shopper is pregnant by tracking her web searches and previously acquired items, such as lotions and so on. Once the situation is identified, that same user is offered special offers and coupons for related products.

With the help of big data, credit card companies themselves have identified unusual associations to assess a person’s financial risk. According to data mining research, the people who buy felt pads for furniture represent the best customers for the leasing institutions, because they are more careful and willing to pay their debts at the right time.

In the public sphere, there are many other types of big data applications:

• The deployment of police forces in areas and at times when crimes are more likely to occur.
• the study of the associations between air quality and health;
• genomic analysis to improve the drought resistance of rice crops;
• The creation of models to analyze data from living things in the life sciences, and so much more.

Big Data Challenges: Sharing Information

One significant real-world example of big data is monitoring and predicting disease activity using online search data. Google Flu Trends, launched in 2008, used a combination of Google search queries to monitor influenza activity in the US. Early analysis suggested that search data had the potential to deliver faster estimated results than traditional means of surveillance.

However, it also highlighted an important drawback of big data. The results of Google Flu Trends showed major errors in accuracy at certain times, such as the H1N1 epidemic in 2009 or the 2012–2013 flu season. Google discontinued the service in 2015.

We can see, however, that big data alone is not sufficient for accurate forecasting. Data quality, analysis, and the changes in the user behavior could potentially influence prediction accuracy. Scientists have continued to investigate various hybrid models using online data and traditional health information.

Another key problem is the exchange of data. There are many companies, research institutions, and organizations with relevant data sets that could be used for research and further investigation, but the access might be limited due to privacy, security, commercial, or ownership issues. Therefore, other data sets may not include this information.

This issue is especially relevant to domains like healthcare, where the combination of multiple sources of information can help researchers to discover patterns and make better predictions. However, this scenario also means that health data needs to be kept private and cannot be shared publicly.

Ultimately, Big Data is not just about the volume of data. Its value depends on our ability to organize, integrate, analyze, and interpret data in an ethically acceptable way. As organizations improve the ability to use high-quality data while safeguarding privacy and working through data-sharing issues, big data can unlock insights that add to what you could achieve with a standard set of data.