Data science is a tree to many branches, including data mining and data management. Over the years, these branches have evolved a great deal making the complexities in managing raw data sets more manageable for data scientists. Today, many people can use data visualization tools without having to deconstruct a single mathematical operation. However, it pays to have a basic understanding of data exploration and the several other stages that build up to make data analysis possible. This guide can be a great introduction to data exploration.
Data Exploration Defined
Data exploration is conceptualized as the first step in the data analysis process. Other experts also claim it to be the next step after data preparation has taken place. Both of them are part of the data mining process, which is the blueprint for all data management efforts. Whichever concept is more convincing, it’s essential to know the overarching goal of data exploration. Thus, to gain an in-depth understanding of an enterprise’s raw data sets.
This is what might draw the line between all the competing concepts. Data is a resource, and before anything, mining must occur. But in the data mining process, initial patterns can be confusing. Data exploration uses visualization tools to assess the characteristics of data and understandably reorganize the mined data for decision-makers. Key parameters often considered here are the structure of the data, variance in numeric values, presence of extremities, relationships between clusters, etc.
Usually, the most critical metrics for data scientists in data exploration are correlation, mean, and standard deviation. On this data discovery journey, a data analyst uses visualization to project the data into abstract images. The next step is to subject the results to different data exploration techniques, including pre-processing, modeling, and interpretation.
Some of these data exploration resources can be manual; others favor automation. The manual method involves the filtering and drilling of a data set into spreadsheets. This way, results can only be generated when a query is deployed. But the automatic method uses machine learning algorithms that can generate and even predict results.
How it Works
Data exploration works within a three-tier cycle. The first step is to understand the different variables at hand. The second is to detect any outliers, and then the exploration process can end by examining patterns and relationships.
Data Exploration Benefits
Helps Users Understand the Variables at Hand.
In statistics, a variable is an attribute of an object to be studied. Data makes it possible to score the value of a variable during data entry. In simple terms, data is just a specific measurement of a variable.
There’s a categorical variable that represents groupings and a quantitative variable that represents amounts. So, in data exploration, the first step is to scan through data catalogs, field descriptions, and metadata to determine the variables they contain.
This helps to identify missing values and or incomplete data.
It Helps Users Detect Outliers.
An outlier, also known as a data anomaly, refers to a data point, event, or observation that glides away from a data set’s normal behavior. Anomalies occur as a result of a database’s redundancy. They can also happen when the tables of a database are constructed poorly. Detecting anomalies is crucial for identifying fraud, network intrusions, and other data events that can make a data set vulnerable. It can also distort a data set. That’s why early detection is vital for further analysis.
It Helps Users Examine Patterns and Relationships
Data professionals use can narrow down the exact data needed for further analysis.
Some more specific benefits of data exploration in data analysis include the following:
- Ensures data accuracy
- Supports the generation of business-intelligent insights for decision-making
- Improves data quality early on in the data cycle
- Builds resilience and agility into data management efforts
- Improves data security and compliance