Five ways Data Science can predict the future

Data Science has been a hot topic within the technology world in 2019, with the job title of Data Scientist being named the number one best job in America for the past three consecutive years. Given that companies need to use their data to improve services, boost productivity or drive sales, it is likely that we will see Data Science ingrained in everything we do in the future. And that is the key word — future. In comparison to Data Analytics where Big Data is used to look at how performance has been in the past, Data Scientists use Big Data to predict all eventual outcomes that could impact an industry.

Data Scientists at the cutting edge of their sector working within AI & Machine Learning can include the information gathered from Big Data sets to create models that can automatically learn from their mistakes.

Data Science has impacted almost every industry — from healthcare, where models can predict patient outcomes and side effects, to traffic, where algorithms help predict when rush hours will be and when best to travel at weekends. Of course, no algorithm or model is perfect; you can never truly predict a person's behaviour and allowances need to be made for real life. But still, in its best form, Data Science can be used to make recommendations based on real life data to help predict certain outcomes.

Here are 5 industries where we can see Data Science at work:

1. Travel

Driving, flying or even cycling, travel is a necessity to pretty much every person. So, imagine if there was a model that could contribute to lowering emissions, shortening travel time and saving money. This is where Data Science comes in. Data Scientists can use Big Data to predict busy travel times in order to optimise routes — figuring out the quickest and most cost-effective option. This may sound simple and a minor change, but when taken into account for the thousands of trips taken across the world, this could help to save hundreds of litres of fuel as well as ultimately saving businesses and people time.

How Uber uses Data Science to hit the road

Uber Eats' main goal is to deliver food — and hot! How can Data Science help to achieve what seems like such a simple goal? With the help of machine learning, meteorologists and statistical modelling, staff at Uber can predict all possible variables — from traffic to bad weather — and how these will affect both travel and cooking times, leading to the delivery process being as efficient as possible.

• Machine learning models predict traffic conditions in real time
• Meteorological data factors in weather disruptions to delivery routes
• Statistical modelling estimates cooking times and handover windows
• All variables are combined to minimise delivery time and maximise customer satisfaction

Happy Customers = Bigger Business.

2. Sport

Statistics have always been a massive part of the sporting industry — from how to play, how to win and how to pick teams. In fact, BusinessWire has found that the global sports analytics market is expected to reach a revenue of $4.5 billion by 2024. So, with the rise in Data Science it is only natural that we would see this used in sport.

How Liverpool nearly won the 2019 Premier League using Data Science

Liverpool have placed themselves at the cutting edge of Data Analysis and even have a dedicated Head of Research — Ian Graham. Ian is well known for using Data Science to beat wealthy football teams in securing great, undervalued players. Football is a chaotic game which can make it difficult to analyse who is performing well and likely to score. However, he created a model to figure out how every run, pass and attempt at scoring influenced the chances of winning the game. Liverpool now uses this model to recruit players and to develop game-winning strategies.

• Data models identify undervalued players other clubs overlook
• Every pass, run and shot is assigned a probability weighting for impact on match outcomes
• Insights feed directly into recruitment decisions and tactical preparation

3. Government

Although many wouldn't think that the Government would class itself as 'online', the Government actually holds more information than Facebook and Google combined. Consider the information governments store: phone records, photo IDs, fingerprints, and information requested through warrants from companies such as Google. It is clear that there is a wealth of data that can be used to analyse future outcomes.

How Data Science helps the US Government detect tax evasion

It's estimated that tax evasion costs the US Government $458 billion a year. Unsurprising, then, that in an age of technology the Government is looking to update its fraud detection capabilities. To do this, the Government has taken full advantage of the data available and created detailed taxpayer profiles.

1. Data is pulled from social media, email analysis, electronic payment patterns and metadata
2. Data Science models generate predicted tax return figures for each taxpayer profile
3. Submitted returns are automatically compared against predicted figures
4. Any return that wildly exceeds expectations is flagged automatically to the audit team

4. E-Commerce

It's no secret that online shopping is a booming market. Even at a basic Data Analytics level, retailers can change web page layouts and pricing based on typical buyer profiles. The emergence of Data Science has given retailers the ability to change the way they advertise and increase the use of product placement to drive sales and website traffic.

How Instagram uses Data Science to tailor the shopping experience

Instagram is well known for its ability to push any product you may have been looking at online to the top of your news feed. Instagram is owned by Facebook, which holds detailed information on its users. Based on your likes, comments, personal information, web history and other apps, Data Scientists at Instagram create algorithms that predict the products you are most likely to buy — and push them to the top of your profile.

• User behaviour data including likes, comments and browsing history informs product recommendations
• Algorithms are continuously refined to improve purchase prediction accuracy
• With 500+ million worldwide profiles, the commercial impact at scale is enormous
• Data Science has even created entirely new job categories — such as paid Instagram influencers

5. Social Life

There are so many apps available for socialising — whether it be Facebook for connecting with old or new friends, Instagram to follow your favourite influencer, or Tinder to find the perfect date. The rise of social networks has completely changed the way people connect. The amount of personal data these apps hold makes it easy for Data Scientists to create algorithms that can suggest people you may know or your perfect match.

How Facebook uses Data Science to find "People You May Know"

When you log into Facebook you may notice "People You May Know" pop up and wonder where they've found these suggestions. When you sign up to Facebook, you give them permissions to search through your contacts — allowing Facebook to find those contacts already on the platform. Facebook also asks for personal information about where you went to school, where you live and more, giving their algorithms additional data to identify potential connections.

Using what they call Network Science, Facebook can use the structure of your existing network to predict not only who you might know now, but people you might also know in the future.

The Road Ahead for Data Science

Data Science is now part of everyday life — and it is only going to get more popular and more pivotal. The challenge facing the industry is ensuring that there are enough high-quality Data Scientists entering the field, and, more importantly, that those already in it can keep up with developments that are ongoing at a rapid pace.