* [Topics](/en/topics "Topics") * [Artificial intelligence](/en/topics/ai "Artificial intelligence") * What is predictive analytics? What is predictive analytics? ============================= Updated January 22, 2024•*3*-minute read Copy URL Jump to section --------------- OverviewPredictive analytics techniquesImprove IT performanceWhy Red Hat? Overview -------- Predictive analytics describes a method of analyzing current and historical data to make predictions about future events. Predictive analytics uses techniques such as machine learning, statistical modeling, and data mining to help organizations identify trends, behaviors, future outcomes, and business opportunities. Predictive analytics techniques ------------------------------- Predictive analytics work by training a model to predict values for new data, based on an input set of variables. The model then identifies relationships and patterns among the variables and provides a score based on what it was trained to look for. That score can be used as business intelligence to [assess the risk](/en/topics/management/what-is-risk-management) or potential benefits of a set of conditions. It's used to determine the probability that something will happen. Predictive analytics can be applied to both structured and unstructured data. Data mining, which is the process of discovering patterns, trends, and behaviors in large data sets, helps to prepare data from multiple sources, such as a data warehouse or [data lake](/en/topics/data-storage/what-is-a-data-lake), for analysis. Once the data is ready for analysis, predictive modeling is the process of creating and testing a predictive analytics model. Once a model has been trained and evaluated, it can be reused in the future to answer new questions about similar data. Common predictive modeling techniques are regression techniques, machine learning techniques, decision trees, and neural networks, but there are many other options. ### Regression models Regression models use mathematical equations to determine the relationship between variables. Linear regression models return continuous outcomes with infinite possibilities (like potential real estate values using a known cost-per-square-foot), whereas logistic regression models return limited numbers of possibilities (such as whether a specific home in that neighborhood will sell above or below a certain price). Regression models are often used by banking and other financial institutions to determine credit risk or detect credit card fraud, forecast market trends, and predict the impact of new [financial service](/en/solutions/financial-services) regulations. ### Decision trees Decision trees are another popular predictive analytics technique that identifies how 1 decision leads to the next decision. A decision tree approach can be applied to machine learning models, which determine a series of "if this, then that" conditions based on a list of sequential and hierarchical questions that lead to a result based on the input data. This model’s branching format can also show all the possible outcomes of a decision by representing how each decision can lead to particular outcomes. ### Machine learning Machine learning is a continuation of predictive analytics. Predictive analytics often relies on data scientists or analysts to create the models, but machine learning algorithms—used in artificial intelligence and deep learning software like IBM’s Watson—are self-learning. They improve and evolve as they process data, without needing constant reprogramming. #### Improve AI/ML application management Get expert perspectives on how to simplify the deployment and lifecycle management of Artificial Intelligence/Machine Learning (AI/ML) applications so you can build, collaborate, and share ML models and AI apps faster with this webinar series. ### Neural networks Neural networks are advanced analytics techniques used to determine the accuracy of information gained from regression models and decision trees. Neural networking identifies nonlinear similarities between disparate data, and is particularly useful when knowing the scope of what may happen is more important than understanding why those possibilities might occur. Red Hat resources ----------------- [Keep reading](/en/resources "Keep reading") Improve IT performance with predictive analytics ------------------------------------------------ Predictive analytics can help IT performance by identifying risks or informing you of potential problems with your [IT infrastructure](/en/topics/cloud-computing/what-is-it-infrastructure). Instead of waiting for an error report—perhaps citing an equipment failure—ops teams can use predictive analytics to proactively find and address problems before they affect your environment, which can also save your organization time and money. You can also use predictive analytics to prioritize areas of focus, filtering out noise and homing in on what most needs your attention. Create risk assessments, prevent security issues, avoid unplanned downtime by looking for anything unusual on a network, and identify potential vulnerabilities by examining all the actions happening in real time. [Automation](/en/topics/automation) tools can be used as a companion to predictive analytics to remediate identified issues or implement changes based on predicted outcomes. [Learn more about IT automation](/en/topics/automation/what-is-it-automation "article | What's IT automation?") Why Red Hat? ------------ Red Hat gives you the predictive analytics and automation tools you need to identify insights about your IT infrastructure and automate remediation, along with solutions, [services](/en/services), and [training](/en/services/training-and-certification) to support your business as you focus on innovation and moving forward. Included with all Red Hat subscriptions, [Red Hat® Lightspeed,](/en/lightspeed) formerly Red Hat Insights, is our as-a-service predictive analytics tool. [Use it with Red Hat Ansible® Automation Platform](/en/technologies/management/ansible/automation-dashboard-analytics) to trigger automatic remediation through [Event-Driven Ansible](/en/technologies/management/ansible/event-driven-ansible). Find (and fix) [Red Hat Enterprise Linux®](/en/technologies/linux-platforms/enterprise-linux) bugs from your browser. View your entire hybrid cloud's health on [console.redhat.com](https://console.redhat.com). And use Red Hat Lightspeed's telemetry to flag critical issues that could lead to failed [Red Hat OpenShift®](/en/technologies/cloud-computing/openshift) cluster states. [Explore predictive analytics with Red Hat](/en/lightspeed "product | red hat insights") The official Red Hat blog ------------------------- Get the latest information about our ecosystem of customers, partners, and communities. [Keep reading](/en/blog "The official Red Hat blog") All Red Hat product trials -------------------------- Our no-cost product trials help you gain hands-on experience, prepare for a certification, or assess if a product is right for your organization. [Keep reading](/en/products/trials "All Red Hat product trials") Keep reading ------------ ### What is Model-as-a-Service? Model-as-a-Service (MaaS) is an approach to delivering AI models as shared resources, allowing users within an organization to access them on demand. [Read the article](/en/topics/ai/what-is-models-as-a-service "article | What is Model-as-a-Service?") ### What is vLLM? vLLM is a collection of open source code that helps language models perform calculations more efficiently. [Read the article](/en/topics/ai/what-is-vllm "What is vLLM?") ### What is generative AI? Generative AI is a kind of artificial intelligence technology that relies on deep learning models trained on large data sets to create new content. [Read the article](/en/topics/ai/what-is-generative-ai "article | what is generative ai?") 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