# Understanding Explorations An Exploration is a query interface that will empower your data consumers to ask questions by themselves and get their answers. Properly designing Explorations is a key pillar of offering a "self service" BI experience to your company. Explorations contains a collection of dimensions and metrics, coming from multiple tables that were created in the "modelization layer" of your Data Stack. Such collections should be fined tuned to contains the proper fields and metric definitions that data consumer need in order to get answers. A proper setup of well designed Exploration will have the following benefits: ### For Data Consumers: * **No insight lag**: Being able to get answers fast without having to write complicated SQL queries on the fly * **Work on cleaned/consolidated data**: Getting access to a consolidated repository of cleaned dimensions and metrics that are safe to use * **Consistency**: Getting the same results as the rest of the team and not having to troubleshoot difference in KPIs with co-workers * **Empowerment**: Being able to go beyond "dashboards" to answer complicated questions without having to open a ticket to the data team ### For Data Providers/Builders: * **Governance**: By defining in a single place the definition of metrics, you don't allow every Data Consumer to invent a new way of calculating important KPIs, lowering data inconsistencies and improving organisation trust. * **Low maintenance**: Having a single place to build / update metrics. A single metric definition change will update all the dashboards/integrations that are using it. * **Less low value work**: Data Consumers can run their own extract / simple enquiries by themselves, freeing time for the more value added analysis. * **Abstraction layer**: Exploration are living above data tables but are not exposing them directly, you can change everything in your Data Warehouse without impacting any Data Consumer as long as you update your Exploration to use the newly created tables. * **Avoid invalid SQL queries being used**: Exploration are generating a safe SQL that doesn't contains errors that SQL novice can make easily which results in invalid numbers, like [SQL Fanout](/misc/sql-fanout.md) As you see, properly designing Explorations is a high stake topic. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://help.whaly.io/core-concepts/explorations/understanding-explorations.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.