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The Scary Examples Of Big Data Analytics You Should Be Aware Of

Scary big data examples

We’re used to discussing how you can benefit from Big Data in daily business, for example by using corporate dashboards to visualize data or making the right strategic business decisions based on tracking the right sales KPIs. Now let’s take a different point of view and examine some real-world big data examples to see how businesses can benefit from us or rather the Big Data we leave behind. Every time we browse the Internet, be that on a computer, smartphone, tablet or any wearable device we leave a digital trail. This digital footprint can be either passive or active, depending on whether the owner agrees to his data being collected. In case of active digital footprints, when a user releases his personal data deliberately, for example on social media or by means of a sports tracker, this doesn’t cause so much controversy. But what if the user didn’t want to share his information and still his data finds its way to unwelcome parties? In today’s post we will take a look on big data analytics examples where it’s us who serve as guinea pigs.


Most people are aware that Google collects their browsing history and creates their ads profile to target them with matching advertisements. The search engine can guess your age, sex and interests based on your online behavior. You can also easily recall where you’ve travelled, especially if you have an Android phone – be sure Google recorded it. The intelligent personal assistant tool developed by the Silicon Valley based giant – Google Now – proactively delivers information that the user will find useful (based on his search habits, check-ins, past Google calendar events etc.). If you use Gmail, Google Now can pull out information from e-mails and update you on hotel reservations, flight information and package tracking. More obvious features include movie or concert suggestions based on your interests and location, stock and news cards, weather for upcoming travel destinations, friends’ birthday reminders or monthly summaries of walking/biking activity. Other examples of intelligent personal assistants include Apple’s Siri and Microsoft’s Cortana. Their providers can use and share your personal information with subsidiaries, licensees or partners – as it’s vaguely defined in the terms of use of such tools and may in reality include thousands of different organizations.


Facebook is another company that performs Big Data analytics on its users’ and it’s not that much of a mystery. Studies have proved that simply by analyzing ‘likes’ of over 58,000 volunteers, scientists were able to accurately predict users’ personal attributes such as sexual orientation, personality traits, intelligence, happiness or use of addictive substances. Thanks to such detailed profiles Facebook can customize the ads you see on your interface, whereas different organizations, e.g. governments can identify your political views and refine their campaigns to target you better. What is more, financial lending companies have found that social connections on Facebook can be a good indicator of a person’s creditworthiness. One such company, Lenddo, checks if you’re befriended with someone who didn’t pay back on time to Lenddo and if the answer is yes, you will have problems with getting a loan. Another example is the German company called Kreditech which uses up to 8,000 data points when assessing an application for a loan, including data from Facebook, eBay or Amazon accounts.

Banks and supermarkets

This Big Data examples show how we are targeted as customers. Regardless of whether you use online banking or not, every month you receive a detailed statement of your spending. The information includes date of purchase, the address and name of the retailer and the amount of purchase, and makes up a perfect database for data analysts. The analysis of shopping behavior by credit card issuers is such a common practice, that U.S. government have decided to exert control over it by means of credit card reform law signed by President Barack Obama in 2009. The regulators want to check whether profiling based on credit card histories may negatively affect low-income users and lead to redlining – the practice of targeting certain groups of people with inferior insurance or lending treatment. The same applies for loyalty cards at supermarkets used to track and predict customer spending trends. Big retailers like Tesco collect a huge amount of data on their shoppers to create demographic profiles and study consumers’ behavior. Past purchasing patterns are used as a basis for offering additional products and using targeted advertising online. However, there are certain examples of credit/loyalty cards surveillance that do it justice. For instance, when used to detect frauds on the basis of unusual shopping habits or identify “risky” card users who tend to overdraw their credit card limits or not pay their bills. Moreover, law enforcement agencies are allowed to use this data to determine the location of a suspect or track terrorist activity.

Media providers

Big data analytics examples from online media providers are intended to improve our user experience. Netflix, the global provider of streaming movies and TV series, has as many versions as the number of its users. You customize it at the point of setting up a new account by choosing movies, series and genres you like to watch and then the algorithm will generate further suggestions and most probably won’t miss. Netflix keeps track of not only what we watch but also of how we do it or what device do we use for it – laptop, TV, tablet or smartphone. The information whether the user has watched the whole episode or few episodes in a row, at what point he paused, fast-forwarded or stopped watching is also valuable as it helps to create better, or rather more profitable, series.

Another provider that boasts personalized offer is Spotify. With its function ‘Discover Weekly’ it provides users with weekly music recommendations. Every Monday users receive playlists with 2 hours of music that is supposed to match their personal taste and usually it does. What’s the secret behind it? Spotify discovered that the traditional recommendations algorithm is too perfect as it provides us with obvious and boring suggestions. Consequently, Spotify decided to hybridize the foreseeability of the algorithm with the unpredictability of Spotify users. It analyzes thousands of playlists generated by its users and if some of the playlists are very similar, the system will suggest us songs that were chosen by people who like very similar music to us – regardless whether the music genres or the locations overlap. It turns out that our preferences and tastes are not as exceptional as we think.

Our list of Big Data analytics examples where it’s us who is being analyzed is by far not complete as it contains only the examples that are commonly known to the general public. We didn’t want to intimidate you or make you feel overwhelmed but this list has probably made your hair stand on end anyway. It’s up to us, as consumers, to be aware of the privacy issues that are a natural consequence of more and more data being collected.

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