Data exploration is the process of examining raw data to observe its characteristics and patterns, and identify relationships between different variables. It helps to expose the structure of the dataset, the presence of outliers, and the distribution of data values, revealing patterns in the data that enable data analysts to gain insight into the data before it is ported into a database or data warehouse. Data exploration is inherently visual, encouraging users to explore data in any visualization, democratizing access to data and providing governed self-service analytics. It's an essential step in gaining a broader understanding of the data's usefulness to the business and speeding up time to answers. Data exploration techniques include visual data exploration, data notebook exploration, central tendency measures, variance analysis, and data exploration tools such as Microsoft Excel, Google Looker, Mode Analytics, Power BI, Python Libraries, Qlik, and Tableau.