What is the Role of Exploratory Data Analysis (EDA) in RCA?
Exploratory Data Analysis (EDA) is an essential early step in the Root Cause Analysis (RCA) process. EDA serves as the foundation that guides more in-depth analysis by helping frame the right questions.
Defining EDA:
EDA is a technique used to understand and summarize the main characteristics of a dataset, often visually.
It's a flexible, creative process that allows analysts to approach data with an open mind, looking for patterns, relationships, anomalies, or any notable insights that could guide further investigation or hypothesis generation.
EDA in the Context of RCA:
In Root Cause Analysis, EDA is typically the starting point. Before diving into specific hypothesized causes or employing statistical tests, EDA provides a broad overview of the data and helps identify any obvious issues or unexpected patterns that might indicate underlying problems.
It enables analysts to familiarize themselves with the data, understand its structure, and begin to conceptualize the potential root causes.
Example of EDA in Internet Businesses:
Consider an online retail company noticing an unusual drop in sales. An analyst might begin with EDA by visualizing sales data across various dimensions such as time, product categories, and customer segments.
By using different data visualization techniques, the analyst might discover that the drop is particularly pronounced in a specific category or time frame. This insight directs further analysis more effectively by focusing on particular aspects or anomalies discovered during the exploratory phase.
Difference Between EDA and Later Techniques in RCA:
While EDA is about open-ended exploration and visualization to understand the data's structure and main characteristics, later techniques in RCA are more focused and hypothesis-driven.
After EDA suggests where the problems might lie, subsequent RCA methods involve targeted investigations into those specific areas, often employing statistical tests or more sophisticated data modeling techniques to confirm or disprove hypotheses about the root causes.
Essentially, EDA sets the stage for these more focused inquiries by narrowing down the scope and guiding the direction of the deeper analysis.
Takeaway:
EDA is a critical first step in the RCA process, providing the initial insights and direction necessary for effective and efficient analysis.
As we progress through this skilleton, we'll build on the foundation provided by EDA to go into specific RCA techniques that address the potential root causes suggested by the initial exploratory work.