Predictive analytics is one of the most commonly used AI technologies, with applications in a range of industries. What is predictive analytics, and how does it work?
In simple terms, predictive analytics uses data to predict future outcomes. For example, predictive analytics can be used by financial institutions to predict whether a transaction is fraudulent. The technology can also be used by manufacturers to predict future consumer demand.
How the technology achieves accuracy in these predictions is where predictive analytics gets really interesting.
It all starts with data. Predictive analytics technologies use data from multiple sources. In fact, the best predictive analytics tools use as many relevant data sources as possible. At its most basic form, the system can then use statistical algorithms to make predictions based on the data it has assessed.
The AI Supercharge
AI takes predictive analytics to a whole new level. It does this in multiple ways, but two of the most common are machine learning and real-time modelling.
Machine learning technologies improve the accuracy of predictions as the system learns and improves with the more data it assesses. Machine learning also helps to maintain accuracy in changing market conditions and when new trends emerge.
Real-time modelling involves the use of sensors on physical elements and the real-time capturing and processing of data. This technology enables the creation of digital twins – virtual models of products, systems, or processes that are kept up to date with real-time data.
With real-time modelling, predictive analytics solutions can move beyond making predictions based on historical data to instead make predictions using real-time information. This progression in the technology not only improves accuracy, but also improves relevancy.
Today’s Use Cases for Predictive Analytics
The benefits of predictive analytics depend on the industry and the application. The technology can increase productivity, for example, or it can increase sales, achieve efficiency savings, reduce business and financial risk, optimise supply chain management, and more.
Below are examples of how some industries are making use of the technology and how they are benefitting.
Predictive Analytics in Manufacturing
Predictive analytics is used by manufacturing businesses to anticipate customer demand so they can ensure sufficient supply. The technology can also be used to manage the supply chain, predicting where bottlenecks or raw material shortages can occur.
Predictive analytics also has applications in equipment maintenance. Manufacturers constantly work to minimize machine downtime, both planned and unplanned, to maximize output and operational efficiency. With predictive analytics, equipment failures can be anticipated before they occur, enabling the efficient scheduling of maintenance work.
Oil and Gas
The energy sector also uses predictive analytics in a similar way to manufacturing organizations – to predict when parts may fail for the efficient planning of maintenance.
Predictive analytics can also be used to predict future safety risks and the reliability of safety systems. The technology also has applications for predicting future resource requirements as well as to forecast future demand and prices.
Financial Services and Banking
In the finance sector, predictive analytics is used to predict fraud and to assess the institution’s credit risk exposure. The latter can be particularly beneficial as economies change and other factors influence the ability of people and organisations to repay debts.
The financial sector also uses predictive analytics to forecast market trends, consumer demand for products, and more.
The Future of Predictive Analytics
The power and potential of predictive analytics are already being realised by companies in a range of industries as well as other organisations and governments. However, one of the most exciting things about predictive analytics is the fact the technology is still very young, so it will continue to improve and offer even more value.
If a predictive analytics algorithm was to predict the future of predictive analytics technologies, the outlook would be very positive indeed.