Understanding Data Understanding: A Framework to Navigate the Intricacies of Data Analytics

TL;DR

This paper proposes a comprehensive framework for understanding data in analytics, emphasizing five key dimensions to help organizations better interpret complex datasets and improve insights.

Contribution

It introduces a systematic framework with five dimensions of data understanding, filling a gap in existing literature by providing structured guidance.

Findings

Identified five key dimensions of data understanding.

Synthesized current knowledge into a cohesive framework.

Guides organizations in navigating complex data for insights.

Abstract

As organizations face the challenges of processing exponentially growing data volumes, their reliance on analytics to unlock value from this data has intensified. However, the intricacies of big data, such as its extensive feature sets, pose significant challenges. A crucial step in leveraging this data for insightful analysis is an in-depth understanding of both the data and its domain. Yet, existing literature presents a fragmented picture of what comprises an effective understanding of data and domain, varying significantly in depth and focus. To address this research gap, we conduct a systematic literature review, aiming to delineate the dimensions of data understanding. We identify five dimensions: Foundations, Collection & Selection, Contextualization & Integration, Exploration & Discovery, and Insights. These dimensions collectively form a comprehensive framework for data understanding, providing guidance for organizations seeking meaningful insights from complex datasets. This study synthesizes the current state of knowledge and lays the groundwork for further exploration.