The predictive “cause and effect” in system dynamics can include nonlinear cause and effect models, and develop nonlinear business problem-solving scenarios.
The better you can understand the interrelationships of issues and interactions of different situations, the more comprehensive solutions will emerge, without causing further problems.
A correlation between two variables does not necessarily imply that one causes the other: Organizations and societies are complex, it’s critical to understand relationships between possible causes or interrelationships between different problems. (Depth + Breadth) of Understanding = Probability for complex problem-solving.
Observe deeper about the root causes, ask questions about each independent variable, and the samples you collect. Only after extensive observation, can you gather adequate data, and only after studying critical variables, can you understand correlation between them, clarify the logic of cause-effect, in order to generate better solutions.
As correlation analysis could trigger a chain of associations, so figure out interrelationships of people and things: Associate all related data to capture insight, associate business processes with enterprise architecture; associate information with people and process; associate change with visions and purpose; associate business strategy, and decision management; associate talent with growing opportunities.
For every complex situation, you see it in another context, or you might see the influences of another domain colliding with the expected domain. Clarify the organization’s vision and incentive appreciation. From correlation analysis to insight abstracting, think holistically and figure out better solutions synthetically.
It is easy and even tempting to come to premature conclusions based upon the preliminary appearance of a correlation: Every initiative is to solve problems large or small, identify the root causes, and solve it thoroughly. You may get the correlation but if you fail to find the causation, your initiatives fail. If you begin with a question, there's usually an idea of what plan of action you will take depending on the answer. The problems usually have many causes and can be very complex, you can't figure out the exact cause and effect so the solutions are vague.
Always feel humble to accumulate fresh knowledge, collect feedback selectively. Work on discovering unexpected nuggets only when there are no big questions needing answers. The unexpected nuggets too often have taken a lot of effort just to get the attention they merit because decision makers or action takers are usually too focused on current objectives to grasp the value of an unexpected nugget. Set your own principles and practices for doing correlation analysis effectively.
To deal with unprecedented uncertainty and complexity, data based cause-effect analysis provides partial fact, but even critically, follows a more humanistic and holistic approach. The predictive “cause and effect” in system dynamics can include nonlinear cause and effect models, and develop nonlinear business problem-solving scenarios.
A correlation between two variables does not necessarily imply that one causes the other: Organizations and societies are complex, it’s critical to understand relationships between possible causes or interrelationships between different problems. (Depth + Breadth) of Understanding = Probability for complex problem-solving.
Observe deeper about the root causes, ask questions about each independent variable, and the samples you collect. Only after extensive observation, can you gather adequate data, and only after studying critical variables, can you understand correlation between them, clarify the logic of cause-effect, in order to generate better solutions.
As correlation analysis could trigger a chain of associations, so figure out interrelationships of people and things: Associate all related data to capture insight, associate business processes with enterprise architecture; associate information with people and process; associate change with visions and purpose; associate business strategy, and decision management; associate talent with growing opportunities.
For every complex situation, you see it in another context, or you might see the influences of another domain colliding with the expected domain. Clarify the organization’s vision and incentive appreciation. From correlation analysis to insight abstracting, think holistically and figure out better solutions synthetically.
Always feel humble to accumulate fresh knowledge, collect feedback selectively. Work on discovering unexpected nuggets only when there are no big questions needing answers. The unexpected nuggets too often have taken a lot of effort just to get the attention they merit because decision makers or action takers are usually too focused on current objectives to grasp the value of an unexpected nugget. Set your own principles and practices for doing correlation analysis effectively.
To deal with unprecedented uncertainty and complexity, data based cause-effect analysis provides partial fact, but even critically, follows a more humanistic and holistic approach. The predictive “cause and effect” in system dynamics can include nonlinear cause and effect models, and develop nonlinear business problem-solving scenarios.
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