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
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
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:
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.
Saving Over 75 Hours Day with Automated Forecasting
- Data Prep and Analytics
- Data Science and Machine Learning
- Process Automation
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- Data Prep and Analytics
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