Data Exploration

What Is Data Exploration?

Exploration, one of the first steps in data preparation, is a way to get to know data before working with it. Through survey and investigation, large datasets are readied for deeper, more structured analysis. Exploratory Data Analysis (EDA) is similar but uses statistical graphics and other data visualization methods.‍

Why Is Data Exploration Important?

Exploration allows for deeper understanding of a dataset, making it easier to navigate and use the data later. The better an analyst knows the data they’re working with, the better their analysis will be. Successful exploration begins with an open mind, reveals new paths for discovery, and helps to identify and refine future analytics questions and problems.

How Data Exploration Works

Data without a question is simply information. Asking a question of data turns it into an answer. Data with the right questions and exploration can provide a deeper understanding of how things work and even enable predictive abilities.

R and Python are the most common languages used for exploration; the former works best for statistical learning while the latter lends itself well to machine learning. Coding is not necessary for data exploration through no-code platforms.

The exploration process is also increasingly important to working with Geographic Information Systems (GIS) since so much of today’s data is location-enriched.

Data exploration typically follows three steps:

Understand the Variables: The basis for any data analysis begins with an understanding of variables. A quick read of column names is a good place to start. A closer look at data catalogues, field descriptions, and metadata can offer insight into to what each field represents and help discover missing or incomplete data.

Detect Any Outliers: Outliers or anomalies can derail an analysis and distort the reality of a dataset, so it’s important to identify them early on. Data visualization, numerical methods, interquartile ranges, and hypothesis testing are the most common ways of detecting outliers. A boxplot, histogram, or scatterplot, for example, makes it easy to spot points far outside the standard range, while a z-score informs how far from the mean a data point is. Once found, an analyst can investigate, adjust, omit, or ignore the outliers. No matter the choice, the decision should be noted in the analysis.

Examine Patterns and Relationships: Plotting a dataset in a variety of ways makes it easier to identify and examine the patterns and relationships among variables. For example, a business exploring data from multiple stores may have information on location, population, temperature, and per capita income. To estimate sales for a new location, they need to decide which variables to include in their predictive model.

The Future of Data Exploration

The analytic process used to be the exclusive realm of engineers who wrote code to extract and explore data. That’s not the case anymore. Today, analytics automation puts analytics in the hands of everyone. It allows companies to better work with their two greatest assets: their data and their people. The access afforded by APA allows employees to focus on finding relationships and patterns rather than wrangling data.

Getting Started With Data Exploration

Technology has transformed a typically time-consuming, complicated process into one that’s streamlined, accessible, and auditable. The Alteryx Analytics Automation Platform was designed with end-to-end analytics in mind and allows companies to quickly aggregate data, spot trends and patterns, understand variables, detect outliers, and explore relationships within a dataset in a no-code platform.