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 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.
Related products and services
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
AI Platform
lets you build complex models more quickly for a lower
cost. For novices,
AI building blocks
let you quickly add AI to your services.
