Predictive analytics

What is predictive analytics?

Predictive analytics is a subset of advanced analytics that employs statistical modeling, data mining techniques, and machine learning to forecast future events based on previous data. Companies use predictive analytics to detect trends in data to identify dangers and opportunities.

Predictive analytics is commonly related with big data and data science. Data from transactional databases, equipment log files, pictures, video, sensors, and other sources abound in today’s businesses. Deep learning and machine learning algorithms are used by data scientists to detect patterns and anticipate future occurrences in order to get insights from this data. Examples include linear and nonlinear regression, neural networks, support vector machines, and decision trees. Learnings from predictive analytics may then be used to prescriptive analytics to drive actions based on predictive insights.



Tasks in data science and data engineering should be automated. In real time, train, test, and deploy models across various corporate applications. Increase the availability of common data science capabilities in hybrid and multicloud systems.


Make use of ready-made apps and trained models. You may use cutting-edge tools to enable data science and business teams cooperate for model building.


Use a consolidated platform to handle the full data science lifecycle. Standardization of development and deployment processes is required. Throughout the organization, provide a single framework for data governance and security.

Use cases


Machine learning and quantitative technologies are utilized in financial services to estimate credit risk and detect fraud.


The use of a specific network of apps and software to record and manage your company’s business activities, such as project management or budgeting, is known as process automation.

Human Capital (HR)

HR teams utilize predictive analytics to find and hire individuals, assess labor markets, and estimate an employee’s performance level.

Sales and marketing

Throughout the customer lifetime, predictive analytics may be employed in marketing campaigns and cross-sell techniques.


Retailers utilize predictive analytics to develop product suggestions, anticipate sales, assess markets, and manage seasonal inventories.

The supply chain

Predictive analytics is used by businesses to enhance inventory management, allowing them to fulfill demand while lowering stock.