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Research Article

Data quilting: Art and science of analyzing disparate data

& | (Reviewing editor)
Article: 1629095 | Received 07 Sep 2018, Published online: 23 Jun 2019
 

Abstract

Motivated by incongruences between today’s complex data, problems and requirements and available methodological frameworks, we propose data quilting as a means of combining and presenting the analysis of multiple types of data to create a single cohesive deliverable. We introduce data quilting as a new analysis methodology that combines both art and science to address a research problem. Using a three-layer approach and drawing on the comparable and parallel process of quilting, we introduce and describe each layer: backing, batting and top. The backing of the data quilt is the research problem and method, which supports the upper layers. The batting of the data quilt is the data and data analysis, which adds depth and dimension to the data quilt. Finally, the top layer of the data quilt is the presentation, visualization and storytelling, which pieces together the results into a single, cohesive deliverable. For illustrative purposes, we demonstrate a data quilt analysis using a real-world example concerning identity theft.

PUBLIC INTEREST STATEMENT

Data quilting, the new data analytics methodology introduced in this research, proposes a framework for the analysis of both structured and unstructured data. The data quilting methodology relies on the parallel process of quilt creation to simplify an otherwise complicated process into three quilt layers: backing, which establishes the research problem and design; batting, which includes the data and analysis; and top, which creates the data quilting deliverable through storytelling, reporting and visualization. Data quilting provides a flexible methodology that can be used by individuals and organizations to guide the data analytics process and create a meaningful deliverable to address their business and research problems.

Cover image

Source: Author.

Notes

1. CitiBank Identity Theft Commercial- Darrel P.: https://youtu.be/Iy5jiYWuNKo.

3. YouTube search interest collected using Google Interest search capabilities. Geographic information for specific video views is not available to the public.

Additional information

Funding

The authors received no direct funding for this research.

Notes on contributors

Murugan Anandarajan

Murugan Anandarajan is a Professor of MIS at Drexel University. His current research interests lie in the intersections of areas Crime, IoT, and Analytics. His work has been published in journals such as Decision Sciences, Journal of MIS, and Journal of International Business Studies. He co-authored eight books, including Internet and Workplace Transformation (2006) and its follow up volume, The Internet of People, Things and Services (2018). He has been awarded over $2.5 million in research grants from various government agencies including the National Science Foundation, U.S. Department of Justice, National Institute of Justice, and the State of PA.

Chelsey Hill

Chelsey Hill is an Assistant Professor of Business Analytics in the Information Management and Business Analytics Department of the Feliciano School of Business at Montclair State University. She holds a BA in Political Science from The College of New Jersey, an MS in Business Intelligence from Saint Joseph’s University and a PhD in Business Administration with a concentration in Decision Sciences from Drexel University. Her research interests include consumer product recalls, online consumer reviews, safety and security, public policy and humanitarian operations.