You can do many things in Azure Data Factory. I like to simplify it to two main tasks. You can copy data and you can transform data. Both of these tasks can be automated and scheduled.

Streaming media: Subscription-based streaming companies collect and process insights on customer behavior, which they may use to improve their recommendation algorithm.

Finance: Investment firms use the most up-to-date market data, which is collected and stored in real time, to efficiently manage portfolio risks.

Healthcare: Healthcare organizations rely on big data to improve the quality of care for patients. Hospitals use vast amounts of historical data to streamline patient pathways, resulting in better outcomes and reduced cost of care.

Omnichannel retailer: Retailers use data lakes to capture and consolidate data that's coming in from multiple touchpoints, including mobile, social, chat, word-of-mouth, and in person.

IoT: Hardware sensors generate enormous amounts of semi-structured to unstructured data on the surrounding physical world. Data lakes provide a central repository for this information to live in for future analysis.

Digital supply chain: Data lakes help manufacturers consolidate disparate warehousing data, including EDI systems, XML, and JSONs.

Sales: Data scientists and sales engineers often build predictive models to help determine customer behavior and reduce overall churn.