The article outlines a workflow for exploring new data in Elasticsearch by refining mappings, a process crucial for optimizing data indexing and querying. It describes a six-step iterative workflow involving extracting JSON documents, indexing them, and refining automatically generated mappings to improve data exploration. The importance of understanding Elasticsearch's dynamic mapping feature is highlighted, as it provides initial defaults that can be fine-tuned for better performance. The article also addresses a common issue with default mappings, where fields like "tags" are analyzed in ways that may not align with user expectations, and offers solutions such as updating the mapping to treat certain fields as keywords. The author’s personal experience with this workflow is mentioned, emphasizing the time-saving benefits of using tools to facilitate quicker mapping refinements and experimentation without impacting production environments.