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"The animal matchmaking program isn't just for gorillas, and it takes some things into consideration that probably aren't on Tinder's radar. It scores every animal on a variety of traits (and when we say "every" animal, we mean there's an entry for each flamingo in each American zoo), including social skills, age, experience, family history, and interpersonal relationships. Oh, and genetic diversity. Animals with rare genes are more valuable to breeding programs because their offspring will introduce more genetic diversity into the dating pool."

"The mapping information could ostensibly tell third-parties when the owners are most often home, the shape of rooms, the size of houses, and even the frequency of cleanings. Gizmodo imagines that a room's shape could be valuable to Amazon as information on the acoustics of a room containing an Alexa device, informing both volume and directional settings so that its broadcasting and microphone can work properly. "

"Insurance is just one area which could benefit from mining patient information in order to acquire the best business outcomes - although at the detriment of the person attempting to get insurance. After all, why would a company agree to hand out a policy to a person whose data suggests has a high risk of a heart attack?"

"Now, the number of combinations of these columns grows exponentially with the number of columns. So if you have many, many columns-and we do in modern databases-you'll get up into millions and millions of attributes for each person.

Now, if I start allowing myself to look at all of the combinations of these features-if you live in Beijing, and you ride bike to work, and you work in a certain job, and are a certain age-what's the probability you will have a certain disease or you will like my advertisement? Now I'm getting combinations of millions of attributes, and the number of such combinations is exponential; it gets to be the size of the number of atoms in the universe."

"Going from small data analytics to Big Data analytics or to predictive and prescriptive analytics is trickier. Expanding in both dimensions is human capital intensive, requiring talented data scientists. A McKinsey report (2011) estimates that by 2018, there will be a shortage of 140,000 to 190,000 workers with "deep analytical" experience and a further 1.5 million data-literate managers in the US. Technology giants such as Google, Facebook and Amazon, and large investment banks and top hedge funds can afford such employees, however even now the competition is fierce, as is evidenced by the ongoing talent war in Silicon Valley. The data scientist is indeed a sexy job in the 21st century."

"While 77% of shoppers in a recent study by Opinion Lab said in-store tracking is unacceptable, 61% also responded that they expect to be compensated with cost-saving discounts if they're tracked.

How stores sell in-store tracking to shoppers and highlight its benefits is where one of the next major battles of retail will be fought, Kilcourse says. "

"The most well-documented of these deals with the hash function used to "anonymize" the license and medallion numbers. A bit of lateral thinking from one civic hacker and the data was completely de-anonymized. This data can now be used to calculate, for example, any driver's annual income. More disquieting, though, in my opinion, is the privacy risk to passengers. With only a small amount of auxiliary knowledge, using this dataset an attacker could identify where an individual went, how much they paid, weekly habits, etc. I will demonstrate how easy this is to do in the following section."

"Big data is the advance in data management technology, which allows for an increase in the scale and manipulation of a company's data. It allows companies to know more about their customers, products and their own infrastructure. More recently, people have become increasingly focused on the monetization of that data."

"Twitter claims a 90 percent accuracy rate for the clever techniques it uses to learn the gender of any given user. Glenn Fleishman reports on a the company's disconcerting new analytics tools, the research behind them, and how large a pinch of salt they come with."

"From a computer science perspective, learning the behaviour of a single user is tough. This is the small data problem; unlike big data, where patterns and trends easily emerge, individual human beings can be unpredictable and can change behaviour, which is not helpful for pattern-hunting algorithms."

"The reason is that routine big-data analytical techniques can now effectively manufacture personal data that is not protected by any of the measures we've used up to now. A well-known illustration of this is the way Target, an American retail chain, creatively collated scattered pieces of data about individuals' changes in shopping habits to predict the delivery date of pregnant shoppers - so that they could then be targeted with relevant advertisements."