Predictive analytics is the process of using data to predict future outcomes. This process uses data analysis, machine learning, artificial intelligence, and statistical models to find patterns that can predict future behavior. Organizations can predict trends and behaviors with great precision from seconds, days, or years ago using historical and current data.

In this content, we'll explore how Google Cloud data analytics, machine learning, and AI solutions can help your business run smoother and faster with predictive analytics.

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How does predictive analytics work?

Data scientists use predictive models to determine correlations between different elements in selected datasets. Once the data collection process is complete, a statistical model is formulated, trained, and modified to generate estimates.

The workflow for building predictive analytical frameworks follows five basic steps:

1. Identify the problem: It starts with a forecast, a good thesis and a set of requirements. For example, can a predictive analytical model detect fraud? Can the holiday determine the optimal inventory levels for the shopping season? Can it determine potential flood levels from severe weather? An obvious problem to solve will help determine which predictive analytical method should be used.

2. Collect and organize data: An organization may have decades of data to tap into, or a continuous stream of data from customer interactions. Data flows need to be defined before predictive analytical models can be developed, and then the datasets can be organized in a repository such as a data warehouse like BigQuery.

3. Pre-process the data: Raw data is only useful to a limited extent on its own. To prepare data for predictive analytical models, it must be cleaned to eliminate anomalies, missing data points, or outliers.

4. Develop predictive models: Data scientists have a variety of tools and techniques to develop predictive models depending on the problem to be solved and the structure of the dataset. Machine learning, regression models, and decision trees are some of the most common types of predictive models.

5. Verify and distribute the results: Check the correctness of the model and adjust accordingly. Once acceptable results are achieved, make them available to stakeholders through an app, website or data dashboard.

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Uses and examples of predictive analytics

Predictive analytics can be used to streamline operations, increase revenue, and reduce risk for virtually any business or industry, including banking, retail, utilities, public sector, healthcare, and manufacturing. Here are some usage examples, including data lake analytics;

Fraud detection

Predictive analytics examines all actions in a company's network in real time to detect anomalies that point to fraud and other vulnerabilities.

Conversion and purchase forecast

Companies can take actions such as retargeting online ads to visitors with data that predicts a higher likelihood of conversions and purchases.

Risk mitigation

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

Operational improvement

Companies use predictive analytical models to make inventory estimates, manage resources, and work 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.

Maintenance forecast

Organizations use data to predict when routine equipment maintenance will be needed and can then plan for it before a problem or malfunction occurs.

Contact us to find out how Google Cloud data analytics, machine learning and AI solutions can help your business run smoother and faster with predictive analytics.