Predictive Analytics is a method or technique that uses data to model predictions about the potential future outcomes of your business. Unexpected analytics combined with advanced statistics and machine learning can model unknown future events using historical and current data. It is generally defined as learning from an organization’s past collective experience using data science and machine learning to make better decisions in the future.
Predictive Analytics allows organizations to predict customer behaviour and business outcomes using historical and real-time data to shape the future.
Predictive Analytics allows you to identify patterns in data to assess risks or opportunities for your business.
Main benefits of predictive analytics
Business managers constantly make decisions that affect every aspect of their business: operations, production, staffing, marketing and finance. Some decisions are made in day-to-day operations, some are strategic responses to competitive market movements, and some are long-term strategic decisions.
Businesses must ultimately compete on data pathways and data analytics. Analytics consists of 3 parts:
- Data exploration and visualization analysis.
- Data Science and Machine Learning.
Some business applications of predictive analytics
Predictive analysis allows businesses in different industries to gain opportunities by using past and present knowledge to predict what will happen in the future. Predictive analytics is often used to evaluate assets, execute predictive maintenance, and reduce machine downtime costs.
Predictive Analysis- How does it work?
Predictive Analytics relies on machine learning (ML). It combines statistics and computer science, which create models by processing data with algorithms. These models can detect trends and patterns in data, which are generally more advanced than just visual data search methods. Machine learning processes data through advanced algorithms and creates models to identify and solve the problem and make predictions using data from different sources.
Predictive analytics also relies on data science, a much broader concept than ML. Data science combines statistics, computer science and application-specific domain knowledge to solve the problem. Business Setting combines business data, processes, and domain expertise in machine learning to solve a business problem. It provides imaginative insights for decision-makers.