It is important to obtain a clear understanding of the context of data and how it relates to improvement processes. When developing analytics around quality or performance improvement, required information includes: What does the data represent? What process, workflow, outcome, or structural component does the data correspond to? How is the data stored? What kind of storage is the data in (such as an enterprise data warehouse, Access software computer database, file cabinet), how is the data physically stored, and how might that storage format constrict what can be done with the data? Also, how good is the quality of the data; are there missing values that might bias analysis, and are there invalid entries that need to be cleaned and/or addressed? What is the data type? Regardless of how data might be physically store in a database, what kind of data do the values represent in “real life”? What can logically be done with the data? Given the type of data and how it is stored, what kind of database and mathematical operations can be performed on the data in meaningful ways? How can the data be turned into useful information? How can the data drive decision making and enable leaders and quality stakeholders to take appropriate and necessary action? Consider a current quality improvement issue at your place of employment, and answer the above questions. You may need to consult with other people in your organization in order to get the information you need.