We’re living in an era of information overload, with 1.7 megabytes of data generated every second, for every person on Earth. By 2025, it’s estimated that this number will reach 463 exabytes of data, a metric few of us have heard of, or could fully comprehend even if we had. The information constantly being generated by the digital world is so complex and vast that traditional methods of data processing and analysis are no longer fit for purpose, prompting the demand for skilled data science professionals, a field expected to rise 15% by 2029.
With such enormous amounts of data comes new and exciting possibilities. As Robert Kiyosaki once said, the best way to predict the future is to look at the past, which is the aim of the branch of data science known as predictive analytics. This practice uses historical data to create mathematical models capturing important trends, which is then applied to current data to predict an outcome and suggest actions. Traditional methods of predictive data science relied heavily on advanced statistical methods such as plotting data and searching for correlation and regression. Today’s predictive analytics, however, utilises artificial intelligence and machine learning to predict behaviour in unprecedented ways. Every time you’re given an insurance quote, or Spotify magically creates a brilliant playlist ‘just for you’, these are examples of machine learning taking place over a large data stream. In this blog post, we’ll take a look at three fields in which data science is predicting the future each and every day.
Predictive analytics in healthcare is hardly new. in 1845, John Snow catalogued outbreaks of cholera in London to accurately identify its mode of communication. This enabled him to track the source of outbreaks to one particular water pump, helping to stop the spread in its tracks. Although since then our methods have become infinitely more sophisticated, our aims remain the same: the suppression and prevention of disease. For example, when the Covid-19 pandemic struck, Johnson & Johnson used a global surveillance dashboard that pulled in data at a national and local level, tracking the disease on an hourly basis. This tool served as an incredible source of information which enabled health teams to make data-driven decisions on when and how to act, as well as forecast hotspots and pick out which areas may be suitable to find test candidates for new vaccines. Additionally, predictive analytics has been of vital importance in determining which patients are most at risk of contracting the virus, as well as experiencing more severe outcomes. For example, a team of scientists from Mount Sinai created a predictive analytics model that centred around three clinical features: minimum oxygen saturation, type of patient encounter, and age. Incredibly, the results indicated that these three features can classify with a high degree of accuracy which Covid-19 patients are likely to live or die. Predictive insights can also be incredibly valuable within the ICU, when lives can depend on a timely response. As patients’ vital signs are continuously monitored and analysed, predictive algorithms help to identify those with the highest probability of requiring intervention within a given timeframe. This allows healthcare staff to proactively intervene based on only the subtlest signs of deterioration in the patient’s condition. Similarly, predictive analytics can provide a fairly accurate estimation of whether particular patients will survive and recover, or succumb to their illness. In the case of diseases such as cancer, it can also tell us what the likelihood is of recurrence based on factors such as age, weight, and lifestyle.
2. Social media and entertainment
Think about the last time you ordered a book, a hat, or even a mop on Amazon. You’ll immediately receive recommendations as to what else you should buy. Similarly, Netflix will happily nudge you towards other titles it thinks you will enjoy based on your previous viewing history – often with remarkable accuracy. Amazon, which has been pioneering this form of data analytics for an incredible 20 years, harnesses a very advanced form of this technique: every visitor to the Amazon website sees it differently, because it’s totally personalised to their interests. Basically, it’s like walking into a supermarket and seeing all your favourite foods right there in front of you! And Amazon doesn’t just use your purchase data, it’s also using the data of other customers who bought the same products as you, providing “frequently bought together” listings. Put simply, Amazon can predict what you’d like to buy before you can. Similarly, apps like Facebook or Instagram hold an incredible amount of personal information on each user, right down to their interests and relationship status. The accuracy with which Facebook can predict ‘people you may know’ is frankly frightening, and involves a complex academic field known as network science. One of the most intriguing aspects of this is that Facebook’s social networks can not only suggest who you may know now, but can predict who you’re likely to know in the future, along with places you’re likely to visit. However, the ethics of Facebook’s data collection methods has often been called into question, with many considering them to be a stark invasion of privacy.
Thinking of booking your next sojourn in the sun? Data science may well be responsible for deciding where you go next. The vast majority of online travel outlets provide recommendations in a similar manner to Netflix or Amazon; tailored suggestions based on your recent searches and bookings, along with those of people considered to match your demographics and activity. As an example, let’s say you’re searching on Expedia for flights to Paris, they’ll also recommend accommodation options. As you move around the internet, you’ll probably find similar advertisements on Google’s display network suggesting other destinations that people within the same audience segment as you have later gone on to book. The travel industry also makes use of smart tools which monitor flight fares and hotel prices, sending out timely alerts on good deals. For instance, Skyscanner searches millions of flights, hotels, and car hire deals, letting thousands of users every day know when certain destinations are especially affordable. Digital travel booking websites are also able to make use of historical data stretching back years to create self-learning algorithms that can predict future price movements based on factors such as demand and seasonal trends. As an example, if flights from London to New York increased in price over Christmas for the past few years, the algorithm will prompt users to book their flight early to save money. However, data science in the travel industry isn’t just being used to help us book our next holiday. Data scientists can also utilise Big Data to predict the busiest travel times to help optimise routes, quickly figuring out the fastest and most cost-effective option. This is incredibly handy for companies like Uber, and can help save thousands of litres of fuel, as well as time and money. Furthermore, data analytics is transforming the way airlines operate, enabling them to optimise airspace and plan flight paths. They also make use of built-in machine learning algorithms to collect and analyse flight data throughout each route, recording information on altitudes, weather, weight, and other important factors. Each of these helps to estimate the optimum amount of fuel that should be used on future flights.
Predictive analytics provides a unique opportunity to identify future trends, offering valuable insights and solutions along with optimal times to implement them; after all, in the words of Andrew McAffee, the world is one big data problem. Just as electricity provided the power behind the Industrial Revolution, it’s quite clear that we’re living through a new revolution powered by information. Data is driving business insights, allowing us to enjoy better entertainment than ever before, helping to save the planet – and could even save your life one day.
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