Artificial Intelligence (AI) in Predictive Analytics

What is AI-based Predictive Analytics?

Predictive Analytics is the process of using data analysis to make predictions about future trends and behavior based on past events. In predictive analysis, AI technology is employed in the form of machine learning algorithms and structures that enable the system to gain knowledge from the provided information. Most models are created using previously obtained data in order to look at relationships and other patterns that can help to forecast specific results.

How do you perform Predictive Analytics using AI?

The combination of AI and Predictive Analytics creates a system that predicts future outcomes that are effective and accurate. Following is the process of how this integration works −

• Collecting and Preparing Data − Historical datasets should be gathered from various sources that are relevant to your problem statement and combined into a large dataset. The next step would be preparing data, which includes identifying missing values, errors, handling inconsistencies, formatting and structuring.
• Model Development and Training − After the preprocessing data, the next step would be building and designing the model. The collected historical data is fed to the developed model to identify patterns and relations. The type of model you choose will have a huge impact on its ability to learn and the final outcome.
• Validating and Testing − Once trained, the model has to be tested to check its efficiency and performance. This process involves providing the model with new, unseen data that hasn't been used in the training process.
• Deploying − After validating the model, the next is using the model in a productive environment to make real-time predictions.
• Continuous Learning − AI-based Predictive analytic models are dynamic, i.e., they are designed to learn and adapt to new data. Further, the model can be fine-tuned and trained on the new dataset to improve efficiency and accuracy.

Advantages of AI-based Predictive Analytics

AI-based predictive analytics offers multiple benefits which include −

• Enhanced Decision-Making − Using AI algorithms, you can identify relations and patterns. This will reduce manual work and enhance strategy planning.
• Improved efficiency − AI algorithms can analyze large datasets much faster than manual analysts, leading to increased efficiency and accuracy.
• Risk Management − AI predictive analytics can identify risks and issues before hand, which allows you to take measures. This can be especially valuable in weather forecasting, finance, and healthcare.
• Ability to Personalize − In sectors like e-commerce and retail, AI based predictive analytics can be used to customize product recommendations by predicting what they might purchase based on the past behavior with the website.

Challenges in AI-based Predictive Analytics

Along with benefits there are quite a few challenges when AI combines with predictive analytics. Some of them include −

• Quality and Quantity of Data − The performance and accuracy of AI prediction models depend on the quality and quantity of the data gathered. Only good quality and large datasets can yield accurate outcomes.
• Interpreting Model − Some AI models and algorithms can be complex, making it difficult to understand the logic behind their decision.
• Ethical considerations and Privacy − There have to be some regulations and ethical standards especially while working with sensitive data to maintain trust.
• Integrating Model − Integration of AI into existing systems is quite challenging, especially for organizations with developed IT infrastructure.