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What are predictive analytics?
Predictive analytics are an analytics method that analyze current and historical data to make predictions about future events. Predictive analytics use analytics techniques such as machine learning, statistical modeling, and data mining to help organizations identify trends, behaviors, future outcomes, and business opportunities.
Why use predictive analytics?
With the Internet of Things (IoT), organizations have access to and are collecting more data than ever. Predictive analytics are a helpful tool to interpret and use big data for business insights and decision making.
Because you need large quantities of data in order to identify patterns and trends—as well as having sufficient data to make informed decisions—big data is at the core of predictive analytics.
Many different predictive analytics tools and software are available, making it possible for business users and analysts to build models that obtain insights. The predictive analytics software that is best for your organization will depend on your specific use cases and goals.
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 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, 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 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 regulations.
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 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.
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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.
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. 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 create risk assessments, prevent security issues, and avoid unplanned downtime by looking for anything unusual on a network and identifying potential vulnerabilities by examining all of the actions that are happening in real time.
Automation tools can be used as a companion to predictive analytics to remediate identified issues or implement changes based on predicted outcomes.
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, and training to support your business as you focus on innovation and moving forward.
Red Hat® Insights combines predictive analytics with prescriptive analytics to give you step-by-step remediation guidance across physical, virtual, container, private, and public cloud environments. Your business saves time and money addressing problems before they affect your environment by proactively identifying and remediating risks across your Red Hat infrastructure.
With real-time views into hybrid environments, you are informed about potential issues and provided with remediation steps to fix them before they become problems. Automate remediation in real time by using Red Hat® Ansible® Automation Platform Playbooks along with Insights.
Automate your build, deployment, and management processes to be consistent, lightweight, and repeatable.
Proactively identify and remediate threats to security, performance, availability, and stability.