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Data exploration is an informative search used by data consumers to form true analysis from the information gathered. Often, data is gathered in a non-rigid or controlled manner in large bulks. For true analysis, this unorganized bulk of data needs to be narrowed down. This is where data exploration is used to analyze the data and information from the data to form further analysis.
Data often converges in a central warehouse called a data warehouse. This data can come from various sources using various formats. Relevant data is needed for tasks such as statistical reporting, trend spotting and pattern spotting. Data exploration is the process of gathering such relevant data.
There are two main methodologies or techniques used to retrieve relevant data from large, unorganized pools. They are the manual and automatic methods. The manual method is another name for data exploration, while the automatic method is also known as data mining.
Some people believe these terms are synonymous, while others see a technical difference between them. Data mining generally refers to gathering relevant data from large databases. Data exploration, on the other hand, generally refers to a data user being able to find his or her way through large amounts of data in order to gather necessary information.