Querying and mining

Historically, the information technology group or department has been beseeched by a variety of business users to produce and provide reports showing information stored in databases and systems that are of interest.

These ad hoc reporting requests have evolved into requests for on-demand raw data extracts (rather than formatted or pretty printed reports) so that business users could then import the extracted data into a tool such as MS Excel (or others), where they could then perform their own formatting and reporting, or perform further analysis and modeling. In today's world, business users demand more self-service (even mobile) abilities to meet their organization's (or an individual's) analytical and reporting needs, expecting to have access to the updated raw data stores, directly or through smaller, focus-oriented data pools.

Creating ad hoc reports and performing extracts based on specific on-demand needs or providing self-service access to data falls solely to the role of the organization's data developer. However, take note that a data scientist will want to periodically perform his or her own querying and extracting—usually as part of a project they are working on. They may use these query results to determine the viability and availability of the data they need or as part of the process to create a sampling or population for specific statistical projects. This form of querying may be considered to be a form of data mining and goes much deeper into the data than queries might. This work effort is typically performed by a data scientist rather than a data developer.