What is Predictive Analytics?
What is predictive analytics? It is a set of techniques that uncover patterns in data to anticipate future outcomes. Historical and current data is projected forward to provide foresight that allows an organization to be proactive.
Why is Predictive Analytics Important?
Predictive analytics allows organizations to make more informed decisions and to be less reactive. The future can be unpredictable, so forecasts with a reasonable degree of accuracy take some of the risks out of decision-making.
What is Predictive Analytics – Examples
To understand what is predictive analytics, it is helpful to review examples in various industries.
Retail
By analyzing the shopping history of a store, it is possible to anticipate future stock requirements and adjust replenishment accordingly. Variables such as seasonality and changes in local climate can be factored in to adjust store orders.
Marketing departments can also use predictive analytics to better target segments so email communications, ads and offers customers receive remain relevant and aligned with their interests.
Telecommunications
It’s important for mobile operators to keep existing customers, as winning new ones is expensive. By analyzing customer behavior, operators can better predict and manage potential customer churn, enabling sales to reach out with timely offers to increase retention.
Finance
Lenders need to control risk when lending. Credit rating agencies can use predictive analytics to calculate the risk of loan applicants and then apply appropriate pricing based on risk.
Insurance
Predictive analytics can assess the risk of insuring an individual or business based on a variety of factors such as location, previous claims, and credit score. This helps insurers create risk-adjusted premiums when quoting for insurance policies.
Travel
Using predictive analytics, airlines are increasingly able to determine the best way to price tickets to maximize sales and revenue. Prices are continually adjusted as conditions change.
Manufacturing
Manufacturing is a very constrained process with limits on available inventory storage, lead times for transportation and productivity. Manufacturers use predictive analytics to predict demand and set production levels to meet expected demand. Adjustments are made continuously to ensure the right manufacturing, warehouse and logistics capacities are available.
Cash Flow Management
Predictive analytics can assist in better managing business cash flow. More accurate cash flow forecasts can help ensure that incoming revenue is balanced with operating expenses to maintain healthy working capital levels.
Benefits of Predictive Analytics
When understanding what is predictive analytics, it is important to note that foresight helps businesses make better decisions. Predictive analytics helps to balance risks through better insight. Organizations can make operational decisions more confidently when a thorough analysis of empirical data backs them.
Organizations can analyze and executed new business that are likely to have the best return, which in turn can lead to improved revenue.
Predictive analytics help keep customers engaged and satisfied, increasing retention rates.
Getting Started With Predictive Analytics
Listed below are steps that organizations can follow to get started on their predictive analytics journey:
- The first step in any predictive analytics project is to decide what questions need to be answered.
- Next, gather the historical and real-time data needed to train your machine learning model. The data will often come from multiple sources.
- You must transform, filter, format, and cleanse data for analysis.
- You will need a scalable data platform to store data for analysis.
- Select the appropriate analytics techniques to apply for the analysis.
- Test the results and tune the analytics model to improve accuracy.
- Visualize results to improve the communication of insights to support the business initiative.
Actian and the Data Intelligence Platform
Actian Data Intelligence Platform is purpose-built to help organizations unify, manage, and understand their data across hybrid environments. It brings together metadata management, governance, lineage, quality monitoring, and automation in a single platform. This enables teams to see where data comes from, how it’s used, and whether it meets internal and external requirements.
Through its centralized interface, Actian supports real-time insight into data structures and flows, making it easier to apply policies, resolve issues, and collaborate across departments. The platform also helps connect data to business context, enabling teams to use data more effectively and responsibly. Actian’s platform is designed to scale with evolving data ecosystems, supporting consistent, intelligent, and secure data use across the enterprise. Request your personalized demo.
FAQ
Predictive analytics is the process of using historical data, statistical algorithms, and machine learning techniques to forecast future outcomes. It helps organizations anticipate trends, identify risks, and make proactive, data-driven decisions.
Predictive analytics is important because it enables organizations to anticipate future scenarios and make informed strategic choices. By analyzing patterns in historical data, businesses can optimize operations, improve customer retention, and identify new growth opportunities.
Predictive analytics works by collecting and preparing data, applying statistical and machine learning models, and using these models to predict future events or behaviors. The process often includes data cleansing, feature engineering, model training, and validation to ensure accurate and reliable predictions.
Common applications include demand forecasting, fraud detection, customer churn prediction, preventive maintenance, risk management, and marketing optimization. Organizations use predictive analytics to stay ahead of market changes and improve decision-making.
Popular tools and technologies include Python, R, SAS, and platforms like TensorFlow, Scikit-learn, and Actian. These tools support data processing, model building, and deployment, allowing users to perform accurate predictive modeling at scale.
Actian enables predictive analytics through advanced data integration, high-performance processing, and seamless connectivity across cloud and on-premise environments. By delivering clean, reliable, and accessible data, Actian helps organizations build and deploy predictive models with confidence and precision.
