A system is a combination of elements that interact in specific ways to create specific behavior. Systems thinking focuses on how the individual components of a complex system interact with each other rather than studying the components of the system separately. A few examples of complex systems are corporations, weather patterns, macroeconomics, and politics. In all these examples, many factors are interacting with each other, and analysis of data is necessary to see the bigger picture. Data science is one of the tools that can be used for systems thinking because of its ability to extract patterns from the behavior of the system. A few characteristics of the system are mentioned below.
Combination of several components
A component can not be considered a system individually. A complex system always consists of many components and interacting factors.
Interconnect between components
The components of the system are always in interaction with each other. If we eliminate interactions from the system, we have only a set, not a system. That is where the difference between a group and a team comes.
The behavior of the system depends on the interaction of all components of the system together and not the components of the system separately.
A system border is a relative concept and not absolute. That means humans define system borders. A person can define the border of its system as its own state or political border, while another person can consider her family as her system border.
The goal of the system provides criteria to analyze the success or failure of the system. For example, when leaders and policymakers of a country talk about economic, a goal should be defined.
Open and close systems
If the system is interacting with other systems, it is an open system. A close system does not have interaction with other systems.