Predictive Analytics is a form of data analysis that incorporates strategies created by Artificial Intelligence (AI). The process aims to predict future behavior based on past performances and results using Machine Learning (ML) that can apply to various industries, including healthcare, finance, and geology. 

By incorporating  Deep Learning (DL) into the process, Predictive Analytics can interpret complex patterns, making nuanced predictions that would take humans an immense amount of devoted effort. So, what is Predictive Analytics and how is AI shaping it into the perfect business application?

Understanding Predictive Analytics

Predictive analytics is a form of advanced analytics that leverages statistical algorithms to interpret historical and current data that helps form accurate predictions on future activity, trends, and behavior. 

Similar to other AI models, the process begins by feeding a machine with either real-time data or data being curated by developers. The machine then runs the data through statistical algorithms including linear regression and neural networks to create assumptions on the given information. 

Finance tends to be an obvious example of how predictive analytics can be applied, helping to map out risk and investment strategies, but there are many applications in other fields like healthcare where doctors and nurses can use predictive analysis to monitor and track certain diseases. 

Predictive Analytics can also help in manufacturing, helping managers to oversee assembly lines. Many factories already incorporate industrial robotics, and predictive analytics can help these facilities anticipate and prepare for upcoming maintenance issues to keep production levels consistent. 

The Rise of AI in Predictive Analytics

AI and Predictive Analytics play a crucial role in the larger field of Data Analysis by using Deep Learning to create analytical models. In traditional statistical methods, scaling can become an issue, especially with the rise of big data and the massive amount of information ready for processing. Fortunately, AI and Predictive Analytics are improving this issue in many ways:

• Improved Accuracy: AI predictions can improve over time through self-evaluation loops.

• Efficiency: Businesses can receive real-time insights from AI systems that are capable of processing massive quantities of data. 

• Complexity: Many AI models are powerful enough to analyze the smallest details of complex patterns that break traditional statistical models. 

• Automation: AI automation routines can help free up time for data analysts to focus on other, more important tasks.

• Handling Unstructured Data: Incomplete data can be a struggle for many traditional models, but AI can recreate missing information as an integer to complete its algorithms. 

How AI Enhances Predictive Analytics

Looking closely, there are many ways that AI is being used to improve Predictive Analytics:

• Improved Data Preprocessing: AI-driven data cleaning is an integral step in data preprocessing that can complete and repair damaged or missing data. 

• Advanced Predictive Modeling Techniques: AI models can rearrange unstructured data into more interpretable forms for processing. 

• Real-time Predictions: Traditional statistical models generally require data collection and analysis to be separate whereas AI models can run both steps in real-time. 

• Increased Accuracy and Precision: Deep Learning models are specifically adept at interpreting subtle patterns and trends that are easy to overlook. Likewise, they can be trained to spot false negatives.  

• Personalization and Customer Segmentation: Customer data can also be analyzed by AI, helping businesses craft targeted marketing campaigns. 

Challenges in AI-Powered Predictive Analytics

Over the past decade, big data has become a larger ethical issue for lawyers to debate. Data from traffic and user engagement is easing to gather but has become a growing concern for consumers who do not want their private information shared. 

With AI, the problem becomes two-fold with companies not only selling private data but also using it to train their AI models. This has a lot of obvious risks attached to it including data leaks and the potential for AI to share private information with the wrong users. 

The issue is even more complex when we consider that many Deep Learning models operate within a “black box” that cannot explain its decision-making process without Explainable AI. By feeding customer data into these training models, not only is consumer information at risk of being leaked, but it isn’t entirely understood how it is being used by machines either. 

Industry Applications for Predictive Analytics

Predictive analytics has many applications across many industries:

• Marketing: AI is revolutionizing marketing with its ability to interpret user data. This is leading to more targeted marketing campaigns and strategies that can identify and segment niche consumer groups. 

• Logistics: Predictive Analytics is improving supply chain management by analyzing seasonal trends and economic factors to determine the most efficient shipping routes and storage. 

• E-Commerce: AI models can use customer data to provide personalized product recommendations while also helping business owners prepare inventory for seasonal volatility. 

The Future of Predictive Analytics

As AI continues to advance, Predictive Analytics will have many applications within emerging technology. The Internet of Things (IoT) is creating a new way for humans to interact with the digital world and can generate insights into how we’re using consumer technology. 

However, the ability of Predictive Analytics to overstep their boundaries is also cause for concern. Although much of the data generated contains valuable insights, it comes at the cost of risking the consumer and will become a growing legal debate for lawyers to argue.