Minimalist Description: Data science is about refining data to help make wise decisions. Data science and simulation science are like the right hand and the left hand of decision support. In fact, they both have a Control component because at the highest level of refinement, data science and simulation science use the same tools. But data science is data-driven. Instead of using notional representations of the real world to make decisions, data science supports decisions through direct input from the real world. There are three different kinds of refinement methods in data science, each of which transforms things at one level in the knowledge hierarchy into things a level higher up:
  • Data Fusion—Makes data relevant by transforming it into information. The things we can measure and the things we want to know are usually different. Data fusion turns measurements into models of useful variables.
  • Analytics—Transform information into knowledge that answers questions crucial to the enterprise. Analytics interpret the information and make sense of it.
  • Control—Defines decisions and courses of action. Control tells us what to do to be effective.
Data fusion, analytics, and control are closely related, but in most businesses, they have different stakeholders. The executives sit at the top of the pyramid, and so they are more interested in control. The information workers are toward the bottom of the pyramid, and so they are more interested in data fusion. Without the taxonomy we've shown here, the enterprise looks too complicated, and whole stakeholder groups are likely to be overlooked. If you work with S3, many of our questions will be about what kind of data science problems you want to solve and which levels in the knowledge hierarchy are involved in each problem. Answers to those questions help us identify the stakeholders. Once we know who they are, we have a variety of proven tools capture their individual needs. Then, and only then, does it make sense to do the heavy math.


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