Data exploration is a very common phrase in the data management and data science worlds. It plays a vital role in data analysis. This article delves deeper into the topic of data exploration. It focuses on the term’s definition, exploration cycle, benefits, and use cases.
Data Exploration Defined
So, exactly what is data exploration? Data exploration is the first stage of the overall data analysis process. Before any data can become meaningful information, it first needs to be deconstructed. Data exploration is a mainstay in the fields of data science and statistics. But with today’s increasing adoption of digital transformation, the essence of exploration methods spans several other industries and disciplines. For instance, in master data management, data professionals use both manual and automated tools to understand the nature of reference data better.
Manual data exploration involves the use of scripts for raw data analysis. Data professionals can also filter data into spreadsheets using manual exploration tools. One major tool is Microsoft Excel. With Excel’s several tools, data professionals can sort out raw data, define categories and determine variables between those categories.
Businesses can also use several open-source automated tools to enhance exploration. Other proprietary data exploration resources include BI tools, data visualization software, data preparation vendors, etc. The most common computer languages for writing many of these data exploration platforms are Python and the R project for statistical computing.
Data exploration can also use a combination of manual and automated tools to create comprehensive data visualization boards. These boards make it easier for data professionals to explore and investigate large data sets before ticking them off for analysis.
How it Works
The typical data exploration process follows three steps. The first step aims at understanding the data variables at hand. This step scans data catalogs for missing and incomplete data. Just a quick look at a column name might be enough to determine its content type as well as any missing links.
The second step in the data exploration process is to detect data anomalies, also known as outliers. There are several types but notable among them are the update, deletion, and insertion ones. They’re inconsistencies that can render a database redundant. Only when data professionals rid their data systems of anomalies can the final step of data exploration occur.
The data exploration process ends by examining patterns and relationships among a business’s data variables. Data professionals use this stage to narrow down on the exact data needed for further analysis.
Why it’s Important
Data exploration is sometimes referred to as exploratory data analysis. Generally, data exploration helps data professionals to avoid making less reliable assumptions about a data set. With data exploration, they can learn about a dataset’s characteristics before delving into the analysis. One vital aspect for many data professionals is data quality. Preserving quality in a business’s data system is often a primary focus at all data management stages.
Data exploration is one of the best ways to ensure these quality checks at the earliest stage of a data cycle. It helps to anticipate unexpected discoveries in a data set. By deploying exploration tools like Bokeh, data professionals can get a bird’ eye view of a data set. This position gives them more room to pick out anomalies before a data set is cleared for analysis.
Data exploration also affords data professionals a deeper understanding of what unique data characteristics are at hand before delving into full analytics.
What Industries Use It
The following industries use this approach to data management:
- Engineering
- Corporate governance through the use of business intelligence
- Education
- Banking and securities
- Research and development
- Healthcare providers (especially in the communication of COVID-19 data)
