What happens during the Data Requirements stage?

During the Data Requirements stage, data scientists focus on identifying the necessary data content, formats, and sources needed for initial data collection. This stage is crucial because it lays the groundwork for the data collection process by determining what types of data are required to address the specific business problem or research question at hand. Understanding the data needed in terms of its content—such as variables, metrics, and attributes—along with the formats—like structured or unstructured data—is essential for ensuring that the data collected will effectively inform and support the analytical model to be developed later on.

Identifying sources where this data can be obtained is equally important, as it influences the quality and reliability of the data. This meticulous preparation helps prevent issues later in the project, such as data gaps or incompatibilities that could hinder analysis or lead to misleading conclusions. Without clearly defining these requirements upfront, the subsequent steps in the data pipeline would be less efficient and can result in wasted resources or missed insights.