Data is more valuable than gold. You may have heard this statement before, and by now, it is somewhat of a cliché. However, if the trillion-dollar value of many companies is anything to go by, this adage might be correct. Here’s the thing. Data can do so many great things for business. This is why predictive analytics is now a keyword in the parlance of so many hiring managers.
Full Stack Development with Specializations
Code Institute recently announced the introduction of a choice of Specializations to our Full Stack Software Development programme. This means that after students have learned the skills of full-stack development, they can then choose to specialize in one of three in-demand areas to complete the programme. The areas in which a student can now specialize are:
- Predictive Analytics
- Advanced Front End Development with React.js
We’ll cover these three topics in a series of blogs, focusing now on Predictive Analytics.
What is Predictive Analytics?
Predictive analytics uses a mix of machine learning, statistical algorithms and historical data to predict outcomes. A myriad of companies worldwide uses it to predict risk, assess markets, predict outcomes, and much more.
Why specialize in predictive analytics?
Specializing in predictive analytics is a massive advantage for employees. Why? Well, in its Future of Jobs Report 2020, the World Economic Forum reckons that by 2025, on average, 82% of the companies surveyed within the following industries will have adopted big data analytics: Agriculture, Food & Beverage; Automotive; Consumer; Digital Communications & Information Technolgy; Education; Energy Utilities & Technologies; Financial Services; Government & Public Sector; Healthcare; Manufacturing; Mining & Metals; Oli & Gas; Professional Services; Transportation & Storage. What these companies want to do is make this big data work for them – which is why they need people with predictive analytics skills.
Software developers specialising in this field are in huge demand as companies are now using their data and statistical algorithms through machine learning techniques to predict the future effectively. Using historical data, companies can use predictive analytics to predict a myriad of things.
Predictive Analytics allows businesses to detect fraud and even prevent further criminal behaviour. It can enable marketing departments to optimise their campaigns based on their target audiences’ data. It lets them predict the next item that customers may want to purchase or view. Also, it can allow workplaces to forecast inventories based on previous interactions. Most importantly, predictive analytics can help a company to reduce risk.
Today, we’re going to break down why predictive analytics is a fantastic skill to specialize in. We’ll look at some examples of its uses in various industries.
Let’s look at online retailers (e-tailers, if you will). I often use Amazon as an example. It amazes me how well they use predictive analytics. Somehow, they always seem to know what I want to buy next. Using the data they’ve already collected on my previous purchase, they often tease me with my next purchase. The same can be said for Spotify. Don’t you love how they can make a solid recommendation as to what you should listen to next? This magic is created by software developers who understand how to use predictive analytics.
Companies use predictive analytics to reduce risk. Let’s take a financial institution as an example. Imagine I ask for a loan from a bank. However, historically, I’ve regularly been late with payments, or I’ve missed them altogether. Based on my previous data, a bank can then examine whether or not I’m a good candidate for a loan – thus avoiding risk. This is just an example – I’m sure I have an excellent credit score!
One of the main ways in which predictive analytics is used in the finance industry is for fraud prevention. When it comes to organised crime, fraud is a major concern for financial institutions. In Deloitte’s paper, “Tipping the triangle: Predictive analytics to mitigate empty envelope fraud”, they tell us that “as the volume of data grows and the industry focuses more closely on detection, analytics has evolved to provide proactive, real-time insights into fraud behaviours and activities”.
Predictive analytics is now present in all industries. However, one that should not surprise us is healthcare. The health industry now uses data to both improve patient care and reduce healthcare costs. According to ArborMetrix, predictive analytics can leverage AI and machine learning to examine previous patient data. It then has the ability to forecast disease risks for individual patients.
Data in healthcare has gone so far that it can forecast how patients will react to different treatments and devices. ArborMetrix tells us that it can even predict a “patient’s risk of developing a specific disease, and their prognosis for a given condition”.
It should come as no surprise to anyone that the hospitality industry widely uses predictive analytics. The logic behind why it’s used in hospitality seems pretty straightforward. It’s all about supply and demand. Think about it. I’ll use the months of July and August of any year as an example. Ever notice that it’s more expensive when you book a hotel for these months than booking a hotel in November? Or, have you ever noticed that when a big event or sporting occasion is happening in a particular city, the accommodation price shoots up during that period? Annoying, right?
Well, that’s business, and that’s hoteliers using the available data to capitalise on the supply and demands of that time. They know that this is when their accommodation will be in most demand. It’s worth noting that weather data can also change the price that you pay for a hotel.
On the other hand, the hospitality industry can also use predictive analytics to ensure a more personalised experience for their returning customers. For example, they can use data from your previous stay to understand your likes and dislikes. Of course, if a hotel has a good marketing team, they will be able to examine data from that guest based on any previous interactions that they’ve had with the weekly newsletter, etc.
Like with hospitality, we know that events and the time of year often affect what we pay to fly. Often, peak or off-peak can mean the difference of hundreds of euros or pounds. However, predictive analytics is used in many other ways within the airline industry.
Apart from the obvious above, airliners often use predictive analytics for the routes they take. For example, knowing the weather forecast is imperative for the airline industry. Using predictive analytics that reads the weather correctly can alter routes to avoid hazardous weather systems. It can save fuel and more. Analysing data taken from weather forecasts also assists airliners in predicting how the weather impacts what’s going on on the ground, thus allowing them to predict congestions during taxi time, etc. You can read more about the impact of predictive analytics in the aviation industry in this piece on AviationPros.
6. Reducing risk
Another way a company can reduce risk with predictive analytics is by collecting insights and data from the news. This is called media intelligence. For example, in March 2021, the EverGiven container ship blocked the Suez Canal. As a result, billions of dollars worth of deliveries were delayed and among those were large oil shipments.
Media intelligence allowed the oil industry to stay up to date on the movement of shipments and the impact these delays had on their business. Companies monitored the Suez blockage’s real-time effect on oil value and how it changed due to the delayed delivery. It allowed the monitoring of stock markets based on the daily rising costs of the crisis. It offered deep insights relative to the situation – which allowed predictive analytics within companies to reduce their risk of revenue loss.
Developers who have predictive analytics skills are in huge demand.
As so many companies are now using artificial intelligence and collecting data, they are now figuring out how best to use their stored information. As already mentioned, predictive analytics allows companies to use their data to predict future outcomes. Therefore, predictive analytics can increase its bottom line by predicting what will happen based on its collected data.
Predictive analytics is now used in pretty much all industries. What I’ve written here highlights just a few examples of its importance.
Learning Predictive Analytics with Code Institute
If you choose to specialize in predictive analytics on our programme, you will get hands-on experience in creating predictive systems which can help businesses in decision making and time management. You will learn how to create a real-time industrial scaled predictive analytics system that uses machine learning models, to predict risks related to business ideas and actions.
- Map Business Requirements to Machine Learning problems.
- Obtain actionable insights using Data Analysis and manipulation.
- Create intelligent systems using Machine Learning.
- Represent data stories via Data Visualization.
Get in touch
If you’ve been contemplating a career change to programming, there is a huge demand for developers with this skill. Learn more about applying to our Full Stack Software Development Programme with Specializations, by scheduling a call with an education advisor today.