Today’s businesses collect more data than they think — from customer behavior to supply chain statistics. Predictive analytics software crunches that information to surface actionable insights.
This business intelligence approach has many uses, including targeting online ads, identifying fraud, predicting when a machine will fail and more. It’s even used to minimize the impact of natural disasters.
Quantum Computing Advancements
When it comes to predictive analytics, quantum computing is advancing exponentially and promises to bring new power. It will enable faster and more accurate data processing, which can lead to better business decisions.
Many business leaders are already looking forward to the time when quantum computers will be widely available. This technology will make it possible to solve more complex problems, such as determining the best routes for global supply chains or optimizing traffic patterns for self-driving cars.
It may also speed up the research and development of drugs. It currently takes pharmaceutical companies 10 years and billions of dollars to discover new drugs, but quantum computing could cut that time significantly. It would also help researchers to scan large databases of substances more quickly and identify potential candidates for further testing. It would allow computational chemists to simulate in silico how drug components interact and work, which could lead to breakthroughs in new medicines.
Predictive analytics tools scan a massive volume of data and look for patterns that can help predict future trends. These tools can help businesses save money, optimize resources, and serve customers better.
Modern companies across numerous industries can leverage predictive analytics to reduce future risk, boost sales and customer satisfaction, and streamline operations. For example, Rolls-Royce uses predictive modeling to reduce carbon emissions from its aircraft engines and optimize maintenance to keep them flying longer. Meanwhile, the District of Columbia Water and Sewer Authority uses a predictive model to locate leaks in small sewer pipes and save millions in lost revenue.
The key is to find the right model for your business needs. Popular predictive modeling techniques include decision trees and regression, both of which are widely supported by predictive analytics platforms. Decision-making models use a tree-shaped diagram to determine a course of action and show statistical probability, while regression techniques are designed to identify the relationships between factors such as demographics and product features.
Machine learning is the algorithm-based approach that informs predictive analytics. It uses historical data to predict future trends and events that can help businesses formulate and make impactful strategic decisions. Predictive analytics tools like regression analysis, decision trees, clustering algorithms, neural networks and autoencoders are designed to help businesses get the most out of their data.
For companies that are overflowing with data but struggling to turn it into actionable insights, predictive analytics and machine learning can be a game-changer. But it is important to note that predictive models will only work if they have high-quality data. If data is not centralised, unified and structured properly, these tools will be unable to deliver the results that businesses need.
Artificial intelligence, or AI, is the application of advanced analysis and logic to interpret events, support decisions, automate actions, and even learn and act independently. AI techniques can be used to inform predictive analytics for business insights, as well as to detect fraud and keep IT environments secure.
Many businesses are using predictive analytics to optimize sales and supply chain operations. For example, PepsiCo uses predictive analytics tools to know when stores will run out of stock so they can reorder. Hospitals like Kaiser Permanente and NorthShore University HealthSystem use predictive models to identify chest pain patients who may not need to go to the intensive care unit.
There are limitless ways to apply predictive analytics, ranging from predicting consumer behavior to improving production processes. For example, machine learning algorithms can help find hidden relationships in sales data. For instance, if customers who bought products A and B also purchased product D, the program can then systematically suggest to those customers that they buy the same combination in the future.