"Business model" is used to mean "a mechanism for getting customers to pay money" in a narrow range, and "a mechanism for getting customers to pay money and a mechanism to support that mechanism" in a wide range. It has been.
Generally speaking, it is said from the perspective of "the deciding factor for a successful business."
From a Data Science perspective like this site, business models are very important in a different sense than what is commonly said.
It might be, "In a business setting, data science and business models are two sides of the same coin ."
At the top level of Data Management , data input / output is grasped and managed for the business flow (business transfer) with the granularity (level) of the business model.
In Cause and Effect Analysis for Problem solving and task achievement , the business model is a direct cause, an indirect cause, a cause of the cause, and so on. The deeper you understand the business model behind you, the easier it will be to think about fundamental measures.
The distribution of data that is the subject of data analysis is like the variability and fluctuations that the business model causes.
Data science technologies such as Machine Learning and IoT can be important points in establishing a business model. For example, some business models use a Recommendation System , while others use data collected by sensors and cameras.
Next Analysis of congestion (queues)Tweet