In the worlds of business and technology, pundits are keen to sing the praises of AI technology and its benefits, often without going into much detail on the actual applications of AI. Rather, AI is often used as a catch-all term to describe a wide range of functions encompassing machine learning, automation, analytics, and more.
Oftentimes, when discussing the successful application of artificial intelligence in business contexts, the actual task being discussed is so-called predictive analytics. In a nutshell, predictive analytics describes AI-powered solutions that analyze existing data to accurately predict future outcomes. Read on to find out everything you need to know about predictive analytics and what it can mean for your business.
What is Predictive Analytics?
As mentioned, predictive analytics is a term that describes the use of machine learning to predict future outcomes. It does this by analyzing troves of historical data to make identify patterns and make confident predictions on what will happen next. It is one step ahead of traditional modeling in that it tends to use ‘big data’ processes, using sophisticated algorithms and truly massive datasets to make more informed predictions than humans could ever make.
Predictive Analytics Use Cases
To better understand what predictive analytics entails, we’ve broken down some of the main business use cases for this emerging technology.
Professional sportsbook services
The global sports betting industry is a massive, multi-billion-dollar a year enterprise. In order to help customers make more informed betting choices on say, horse races, platforms are making use of AI analytics for predicting horse race winners. AI solutions such as Beth.bet can automatically analyze vast troves of data on jockeys, horses, and track conditions to instantly provide predictions on who will win upcoming races, allowing consumers to choose their bets accordingly.
Financial services
Predictive analytics has been the bread and butter of financial services for decades now. In more recent years, AI analytics tools are being used to predict the direction of the stock market, using historical data to project patterns on asset prices and commodities. In addition, such analytical tools are increasingly being used in customer-facing areas such as lending, where AI can help calculate a person’s risk profile and set interest rates accordingly.
Manufacturing automation
In manufacturing, margins are often razor-thin. That’s why many of the world’s top manufacturers are embracing predictive analytics to help them trim the fat and make operations more streamlined. Cat manufacturers use analytics to predict demand and reduce excess production. Chemicals manufacturers use analytics to predict preventative maintenance schedules and reduce costly downtime. The list goes on.
Energy production and consumption
Being able to accurately predict energy consumption needs is not just important for businesses and consumers, but also the health of the planet. That is why predictive analytics is increasingly used to forecast energy production throughout the year, to ensure that only the strictly necessary amount of energy is consumed. This saves time, money, energy, and carbon emissions.
Predictive analytics helps us to move beyond human limitations to better understand trends and patterns and thus make more confident predictions for the future. It’s not alchemy, but it is rapidly becoming an implacable asset in a wide variety of contexts.