Like artificial intelligence, predictive analytics is a well-established concept. It has been around for a long period of time now. Like artificial intelligence, it has recently become more and more important due to the need to understand large amounts of data generated by enterprises.
What is predictive analytics?
Predictive analytics comes under the sector of advanced analytics which is utilized to make predictions about unknown future events. Predictive analytics is the use of artificial intelligence, machine learning technology, statistical modelling, data analysis, data mining to predict likely outcomes.
Predictive analysis analyses patterns in the data to determine whether these patterns are likely to reappear, so that companies and investors can adjust where they use resources to take advantage of potential future events.
Predictive analytics is used in following sectors:
Why to use predictive analytics?
Predictive analytics relies on historical data to make predictions. Professionals use machine learning techniques and statistical algorithms to analyse past events and generate probabilities and predictions for future systems. Almost everyone is affected by predictive analytics and can benefit from it.
As long as there is data, predictive analysis is very valuable. Enterprise use cases include:
- Manufacturing-optimizing production processes, improving maintenance plans, planning inventory, etc.
- Healthcare- pharmaceutical market analysis and clinical trials.
- Marketing- Most ecommerce websites use predictive modelling.
- Fraud detection- Using analytics in fraud detection can help create a smooth experience for retail users.Other use cases of predictive analysis are Risk management, Cybersecurity and Operations
Trends in predictive analytics
- Fast-growing data sets-
- Ease of use
- Adaptive learning
Simulation and Predictive Analytics
Simulation and predictive analysis are linked because they both require models. Simulation models the behaviour of the system, while predictive analytics uses models to obtain information about the future.
In predictive analysis, you can use decision trees to model simple systems. For large data sets and complex systems, regression or neural network-based machine learning may be a better choice. The decision tree will indicate whether something will happen based on the input. In contrast, a machine learning-based system can specify a value, such as when to schedule maintenance. Another difference between these two methods is their data requirements. Modelers can create decision trees from limited historical data, whereas machine learning requires a large amount of training data. For machine learning, this data usually comes from historical data sets or continuous comments, but simulation models can also be used for synthesis.
Simulation can help when the system is not easy to describe mathematically or the historical data is insufficient to train or test machine learning techniques. Simulation does not represent the entire system as a statistical algorithm or generates a fixed data set, but captures the characteristics and relationships of system components to provide a dynamic model. When you run the simulation, the behaviour of the system will appear, and the data generated by the simulation varies according to its configuration. Simulation helps predictive analysis in two ways:
For machine learning, when data is scarce, perhaps due to cost or risk, simulation models can provide synthetic training data.
For complex systems, it is usually easier to describe the components of the system and their relationships than to describe the behavior of the complete system.
Mego uses predictive analytics to predict future outcomes and evaluate future strategies. If you’re planning to implement Predictive Analytics in your organization or want to learn more about the topic, feel free to get in touch with one of our data analytics experts. To know more visit www.m76analytics.com