Data interpretation is the decision-making process that follows the analysis of collected data and drives future action. It is often the final stage of decision-making strategies for business processes and operations or the creation of public policies and programs.
Data interpretation relies on data gathered during the collection period to make informed decisions about how to proceed. The process also uses the information garnered during initial analysis to come to a conclusion about what changes, if any, are important enough to pursue for future action. The typical process revision or policymaking process begins with intentional gathering of data, from sources already in place or by creating new sources, and continues into an analysis phase where analysts sort through the information in search of patterns that can lead to an informed decision.
The interpretation of data can vary wildly between experts, even when the experts view exactly the same data and analysis, because data interpretation relies heavily on the personal and professional experience of the person making the recommendations for change. Not all experts may place the same weight on the same points of analysis, resulting in highly disparate opinions on the effectiveness of specific proposed strategies that come out of the data interpretation process.