Amassing, organizing, analyzing, and reaching conclusions about data is a crucial function of businesses, organizations, governmental agencies, professionals, researchers, academics, and other individuals and entities. Collectively, these functions and actions typically are referred to as “data analysis.” A key component of comprehensive data analysis is a process known as data exploration.
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Data Exploration Basics
Data exploration is the initial step in comprehensive data analysis. Data exploration usually involves the identification and summarization of the main characteristics of a particular data set. A dataset is a collection of related information composed of separate data elements. The components of a dataset can be organized or manipulated into a usable informational unit, either manually or through the use of specialized computer software.
Historically, data exploration was undertaken visually, according to Forbes magazine. In more recent times, specialized computer software is available that assists in organizing datasets in a cogent, usable fashion.
At this juncture in the 21st century, data exploration is undertaken using automated as well as manual activities. Automated and manual activities utilized in the data exploration process wholly are interrelated.
Examples of Automated Data Exploration
The automated component of data exploration can include profiling data. This process aids in placing raw data into a more structured form as a precursor to a manual examination and consideration of that information.
Importance of “Data Drilldown”
The automated element of data exploration typically is followed by what oftentimes is called “data drilldown.” Data drilldown is a manual process that is utilized to identify any patterns or anomalies identified through the automated component of data exploration.
Data drilldown involves viewing the raw data after the conclusion of the automated process. Data drilldown may also necessitate the use of spreadsheets to consider raw data in something or a more organized manner. In addition, manual scripting and specific queries into the data may be necessary as part of the effort to identify patterns or anomalies.
The final element of comprehensive data exploration is christened data refinement. This involves what people involved in the process commonly called “pruning” or “refining” data. Unusable elements of collected data are removed from the aggregate, an endeavor called “data cleansing. The poorly formatted date is refashioned. Finally, relevant relationships across datasets are defined.
Who Does Data Exploration?
Historically, what is comprised of what is known as data exploration today was a prime focus for statisticians. In this day and age, data exploration is more widely undertaken. Data exploration is the work of such professionals as data analysts and data scientists. The data scientist represents a relatively new professional designation. Data scientists tend to be found in this day and age in larger companies and other types of organizations, including governmental agencies and some nonprofit entities.
In the final analysis, the various elements of data mining are designed to create a meaningful, understandable, usable mental model. It also is to achieve a suitable definition of basic metadata, which includes structure, relationships, and statistics. In layperson’s terms, the ultimate objective of data exploration, and the application of its component parts is to make once disparate datasets truly usable.