How to Design an Efficient EDA Study

The inherent nature of exploratory studies ensures that both the initial inquiries and the research methodology are more open and adaptable than those typically found in the conventional confirmatory analysis phase, without compromising on effectiveness and precision.

Interested in learning how to craft an efficient exploratory data analysis study? Keep reading for insights.

The Exploratory Question

As previously discussed, the most significant impact of an exploratory study is realized when it precedes a confirmatory study.

Owing to its cyclical nature, an exploratory study is essential for:

• Generating more effective hypotheses to be validated later.
• Enhancing our understanding following a prior confirmatory study.

Regardless of the objective, it's crucial to articulate what I term an "exploratory question."

An exploratory question mirrors the formal hypothesis of a confirmatory study but lacks the same degree of specificity and detail.

For instance, from the formal hypothesis:

“To assess the role of inducible nitric oxide synthase in surgical pain.”

An analogous exploratory question might be:

“What are the key biochemical compounds involved in surgical pain?”

Noticeably, exploratory questions explore the relationships among complex, multidimensional data groups, where the most pertinent elements remain unidentified.

Our extensive experience across numerous scientific projects has enabled us to identify the five most common types of exploratory questions posed by researchers and data scientists.

Data as the Foundation

Although pre-existing knowledge is valuable in designing an exploratory study, data always forms the cornerstone of our work.

The next step in an exploratory study is to pinpoint the most suitable data sources and datasets to address the posed exploratory question.

These datasets must meet several fundamental criteria:

• Relevance: Data should be directly related to the exploratory question.
• Reliability: Data should be derived from appropriate capture and storage processes for the research study.
• Suitability: Data should encompass a broad range of dimensions (variables) and records potentially related to the topic under investigation.
• Automation: Data should be primed for processing with automatic data exploration tools.

Potential data sources include:

• Open-access databases: Offering valuable and dependable information on various topics, these databases can save resources and effort, despite possibly lacking completeness.
• Previous studies: Researchers or their colleagues may already possess relevant data from earlier investigations, offering a closer look at the current problem, albeit often in smaller volumes.
• Preliminary stages of the current study: If other sources are unavailable, consider designing the project to include an initial phase for collecting generic data related to the exploratory question.

Letting Your Data Unfold its Story

With the data and question in hand, why not allow the data to unfold its story?

Using AutoDiscovery, this process is streamlined into three stages:

• Consolidate: Merge datasets from various sources into a single dataset to uncover inter-source relationships.
• Discover: Command the software to identify, filter, and prioritize all relevant relationships to the exploratory question, involving the pertinent variables.
• Explore: The discovery process produces a visual narrative of the relationships, which must then be interpreted to gain insights into the exploratory question.

In the next and final installment of this series, I'll detail how to fully leverage the insights gained from the exploration.

Are you ready to explore with your data? You might be surprised by what you can discover today…