What Is Predictive Analytics?

What is predictive analytics?

Predictive analytics is the use of historical data, statistical algorithms, predictive modeling, and big data machine learning techniques to help organizations predict future outcomes more accurately, plan for unknown events, and discover opportunities in future activities.

As a branch of data science for business, the growth of predictive and augmented analytics coincides with that of big data systems, where larger, broader pools of data enable increased data mining activities to provide predictive insights. Advancements in big data machine learning have also helped expand predictive analytics capabilities.

Learn how Google Cloud data analytics, machine learning, and artificial intelligence solutions can help your business run smoother and faster with predictive analytics.

Predictive analytics defined

Predictive analytics includes a variety of statistical techniques that analyze current and historical facts to make predictions about the future. With the help of sophisticated tools, as well as big data machine learning and AI models, organizations can use historic and current data to reliably forecast trends and behaviors seconds, days, or years into the future with a great deal of precision.

Experienced data scientists use predictive models to identify correlations between different elements in selected datasets. Once data collection is complete, a statistical model is formulated, trained, and modified to generate accurate predictions.

What is predictive analytics used for?

Predictive analytics can be used to streamline operations, boost revenue, and mitigate risk for almost any business or industry, including banking, retail, utilities, public sector, healthcare, and manufacturing. Sometimes augmented analytics are used, which uses big data machine learning. Here are some more use case examples, including data lake analytics.

Fraud detection

Predictive analytics examines all actions on a company’s network in real time to pinpoint abnormalities that indicate fraud and other vulnerabilities.

Operations improvement

Companies use predictive analytics models to forecast inventory, manage resources, and operate more efficiently.

Customer segmentation

By dividing a customer base into specific groups, marketers can use predictive analytics to make forward-looking decisions to tailor content to unique audiences.

Conversion and purchase prediction

Companies can take actions, like retargeting online ads to visitors, with data that predicts a greater likelihood of conversion and purchase intent.

Risk reduction

Credit scores, insurance claims, and debt collections all use predictive analytics to assess and determine the likelihood of future defaults.

Predictive maintenance

Organizations use data to predict when routine equipment maintenance will be required and can then schedule it before a problem or malfunction arises.

Google Cloud AI and machine learning products and services provide the foundation for predictive analytics. Available tools and models enable organizations to use data insights to go beyond knowing what happened in the past and toward offering a best assessment of what could happen in the future.

For experts, the Vertex AI lets you build complex models more quickly for a lower cost. For novices, AI building blocks let you quickly add AI to your services.