What is Predictive Analytics?
Predictive Analytics is a term being increasingly used in sectors from healthcare to marketing but few people understand what it means or how it works. Predictive analytics refers to the use of statistics and modeling techniques to make predictions about future outcomes and performance by looking at current and historical trends in data to determine the probability of potential scenarios occurring in the future. For example, past records and present data of a region's weather condition can give meteorologists information to forecast future weather. By applying the concept of prediction, there is a fundamental shift in focus from ‘descriptive analytics’ – understanding and articulating what happened in the past and why – to using the available data to anticipate what will happen in the future. This powerful set of statistical tools provides organisations with the advantage of determining potential outcomes and using this information to plan action and allocate resources preemptively.
Evolution of Predictive Analytics
Predicting the future has been a constant preoccupation of humans. Throughout the millennia we have developed a variety of tools, heuristics and superstitions designed to create the feeling of control of things we cannot anticipate or comprehend and the confidence to act. These tools have evolved over time to become more sophisticated and objective by relying on empirical data.
The early statisticians used pen and paper to draw insights from historical data, with one of the earliest adopters being Lloyd’s of London in the 17th century who utilised a predictive model to calculate the risk of ship voyages from records of past journeys to ensure profitable investment. Fastforward to the present day and an increasing digital world, these predictions are no longer made with pen and paper and unreliable data in handwritten ledgers, but huge, vast and complex data sets that are growing exponentially and continuously curated to fit specific needs.
AI can use these data sets to generate predictions across any number of contexts, from what product to suggest someone on Amazon to identfying pedestrians crossing the road with self-driving cars.
An important thing to realise is that every prediction is based on a probability; so even when a prediction fails to transpire, AI uses this ‘failure’ to learn from the experience and inform future judgements and decisions. In the context of AI and machine learning, prediction is all about data.
The rise of AI
In the 1950s, the concept of Artificial Intelligence (AI) was introduced to the world as the science and engineering of making intelligent machines by mimicking the problem-solving and decision-making capabilities of the humans to perform complex tasks. AI does this by analysing massive amounts of historical data to “learn” patterns in the data set and generalising these learnings to new data, enabling ‘intelligent’ automated action. Statisticians realised AI had the potential to push predictive analytics to another level and overcome much of the tedious and tiring search for patterns within data. AI is a tool that naturally exceled at this task and would do much of the work at a speed and accuracy humans are incapable of. Humans would only need to prepare the data, calibrate the algorithm and examine the results.
Examples of Predictive Analytics performed by AI
There are a variety of use-cases of AI-driven predictive analytics tools across industries. Here are some of the main ones you should know about.
Predictive analytics in retail is commonly applied to support the customer journey and experience by using AI to segment customers, anticipate purchase needs, recommend products or services, and tailor promotions or offers. For example, based on a customer’s purchasing history, online shopping sites can predict the customers future preference and make timely recommendations. Predictive analytics can also be used after a purchase to reinforce the customer’s buying decision, decrease returns and enhance customer service.
In healthcare, AI can be used to replicate expert human clinician judgement and decision-making, overcoming limitations such as fatigue, staff shortages, and backlogs. AI can produce predictions - such as a patient diagnosis - within a few seconds and requiring zero human intervention. Another use-case is predicting outcomes such as hospital readmission rates to responses to medications, from determining a disease’s likelihood to predicting infections. Backed with enough data and advanced algorithms and hardware, AI-driven predictive analytics can identify risky medical conditions ahead of time and provide clinicians with the insights to triage patients and tailor treatments.
Sporting organizations are huge sources of data. This ranges from sales data to data on team and athlete performance, from training, sleep and nutrition plans to performance on game day. With the help of AI and predictive analytics, this data can be used to personalise training plans, predict potential injuries and competition tactics – or even sell more tickets. players. One of the most popular models is Bing Predicts, a forecasting framework by Microsoft's Bing internet searcher. It utilizes measurements and web-based media feed to make its evaluations.
Weather forecast and Environment
AI is used to forecast global weather with speed and accuracy. A recent study shows AI can identify extreme weather 2–6 weeks into the future. Accurately predicting violent weather events with a longer lead time gives organisations like public health and water management more time to prepare ahead. AI will also play a key role in predicting global environmental change. The integration of AI-powered systems helps environmentalists foresee key issues such as food and water shortages, threats to sustainability decrease of biodiversity, climate change, and global warming.
The financial sector is the industry with one of the greatest adoptions of AI-driven predictive analytics. Financial organisations use AI to predict stock prices, inform lending decisions and improve risk management. Read this article to learn more about how AI is used in finance.
Predictive analytics has evolved greatly since its inception, but the objective remains the same: to utilize data to better anticipate future events, reduce risk, reduce costs and make better decisions. As AI continues to mature, predictive analytics will no doubt become increasingly more sophisticated and powerful.
In recent years, the key trend in analytics is increased accessibility: predictive modeling is now within reach of everybody even without statistical training. With the right business objective and data, there is no reason your business can’t benefit from the game-changing power of predictive analytics