What Is Predictive Analytics?

What is a predictive analytics model?

A predictive analytics model is a mathematical model that data science engineers build to answer questions related to "events of interest" such as the prediction of the occurrence of an event in the future.

Different types of models exist (statistical, machine learning) can be used for predictive analytics projects.

The process of creating a predictive analytics model includes running algorithms, such as "time series" algorithms for making time-based predictions and "association" algorithms for identifying recurring patterns in large transactional data sets.

How do organizations use predictive analytics?

Predictive analytics projects vary widely, based on the business objectives for using data insights about the future. For example, companies in many industries, such as financial services and energy, predictive maintenance, use predictive analytics solutions for everything from forecasting product demand to estimating equipment maintenance needs.

Whatever the project type, the core reason that organizations use predictive analytics is to enable more proactive behavior. 

For instance, organizations could use insights from predictive analytics to prevent problems that might cause disruption or compromise profitability. Or they could use insights to take advantage of emerging trends and events that may give them competitive advantage.

Here's an example of how a business could use predictive analytics to improve its everyday business operations: 

A beverage manufacturer's data science team creates a predictive analytics model to determine when trucks are most likely to arrive at the warehouse to pick up a shipment. Knowing when products should be packaged and ready for loading creates more operational efficiency.

The manufacturer might also provide information about ideal truck arrival times and estimated wait times to its logistics partners, to help improve efficiency further in the supply chain.

What are other types of advanced analytics?

Diagnostic analytics, prescriptive analytics, and operational analytics are other examples of advanced analytics methods. A brief explanation of each follows.

Diagnostic analytics

Diagnostic analytics is a reactive form of data analysis. This form of analytics is based on descriptive analytics, an analytics process that examines data to determine changes that occur in a specified period.

To perform diagnostic analytics, analysts use a combination of techniques, such as data mining and data correlation, to understand why an event, trend, or relationship occurred. 

Organizations often use diagnostic analytics tools and processes to identify issues undermining device or system performance. Such tools also help cybersecurity teams to identify the root causes of data breaches.

Prescriptive analytics

This emerging area of advanced analytics, which includes the use of predictive analytics, is meant to help organizations answer two questions: "What should we do next? And then, what will happen?"

Prescriptive analytics goes beyond making predictions by suggesting actions to take and the potential outcomes of those actions. Many companies in the energy sector use prescriptive analytics to help improve operational safety. Self-driving cars also use prescriptive analytics.

Operational analytics

Operational analytics, sometimes referred to as continuous analytics, can be used to improve an organization's operations. While not a new discipline, it's a rapidly evolving area of analytics that is becoming more prognostic.

As organizations become more digitized, they're able to expand their use of operational analytics. That's because they now can access more of their data, including from the cloud, more easily and in real time. 

Using advanced tools, they can analyze the data quickly to get insights that they can put into action almost immediately—or, with some solutions, even automatically.

An example of operational analytics is collecting and analyzing various metrics from a service delivery chain, such as network, application performance, and associated dependency metrics—proactively from multiple vantage points to quickly troubleshoot network services issues or optimize web application experiences for the distributed workforce.