Final answer:
In my previous roles, I routinely transacted with an e-commerce platform and a banking system. The data captured by these systems can be used for business intelligence and analytics purposes such as customer segmentation, sales forecasting, fraud detection, and credit risk assessment.
Step-by-step explanation:
As a graduate student at Yale, I build data streams to address the impacts of climate change. In my previous role at Pew Research Center, I managed research projects involving data mining and machine learning. One example of an OLTP (front end system of an organization) that I routinely transacted with is an e-commerce platform, where I made purchases and managed my account.
The types of business intelligence and analytics that might use the data from an e-commerce platform include customer segmentation, sales forecasting, and product recommendation systems. By analyzing data such as customer demographics, purchase history, and website interactions, businesses can gain insights to optimize their marketing strategies, personalize customer experiences, and forecast demand.
Another example of an OLTP system is a banking system, where I routinely transact with my accounts and perform financial transactions. The data captured by the banking system can be used for various business intelligence and analytics purposes, such as fraud detection, credit risk assessment, and customer behavior analysis. By analyzing transactional data, banks can identify suspicious activities, assess creditworthiness, and understand customer preferences.