There are numerous business applications of data science technologies and without getting into specifics, on a broad level, the applications can be classified into 5 categories – applications to improve products, understand customers, understand business, understand risks and build new business models.
Here is an attempt to explain each of these categories.
Improve products and services
Data science technologies are used to understand the user behavior when they use digital products. This helps product designers to make their products better, and to get the right product market fit. A product or a service can deliver great value to the users by simply understanding their past behavior, and using it as a basis to provide various recommendations. Data science can bring efficiency to services and manage customer expectations.
Improve customer satisfaction
Data science technologies are used to understand the demographics of customers and to get a complete view of customers. This helps in serving customers better, just by knowing them well. It also helps in directing the marketing efforts in the right direction.
Understand the business
Data science technologies are used to understand various key performance metrics that are key to the business, like process bottlenecks, operational efficiencies etc.
Data science technologies can help in estimating resource requirements, managing inventory and enabling digital businesses thereby reducing the operational costs of the companies.
Data science can help companies to detect frauds that can potentially damage their business, affect customer experience, cause harm to society in general, cause monetary loss to the company, demoralize employees etc.
Data science technologies can be used to identify suspicious incidents that can raise an alarm. This can be identifying fraudulent activities by customers or employees or other signals that can indicate potential risks to the business, like non compliance to regulatory requirements etc.
Build new business models
Organizations now have access to a lot more data, providing them insights that were not available before. This can potentially lead to building new business models for their customers, which can not only improve their existing services, but also provide new services to their customers, and possibly generate new streams of revenue from them.