Harnessing Big Data in the Animal Welfare Industry: Utilizing Data Science to Improve Regulatory Oversight of Commercial Dog Breeding

Clinton Ross Mauck; Jyothi Vinnakota Robertson; Marjorie Robin Vincent

doi:10.56771/jsmcah.v2.65

Authors

Clinton Ross Mauck JVR Shelter Strategies, Belmont, CA, United States https://orcid.org/0009-0005-9476-9041
Jyothi Vinnakota Robertson JVR Shelter Strategies, Belmont, CA, United States https://orcid.org/0009-0008-7479-9034
Marjorie Robin Vincent JVR Shelter Strategies, Belmont, CA, United States https://orcid.org/0009-0003-4494-2198

DOI:

https://doi.org/10.56771/jsmcah.v2.65

Keywords:

Commercial Dog Breeding, USDA Inspection Compliance, Canine Welfare, Big Data, Data Science

Abstract

Introduction: In the age of Big Data, the animal welfare industry stands to benefit from data-driven decision making, particularly in commercial dog breeding. Despite its potential, many organizations and regulatory bodies, such as the United States Department of Agriculture (USDA), face significant challenges in organizing and using it effectively. The existing challenges limit the extent to which the vast amount of data collected by the USDA can be used to improve regulatory oversight and promote animal welfare. This study explored the potential of leveraging publicly-available inspection report data to inform animal welfare standards and identify areas of improvement.

Methods: We formulated an innovative approach for extracting, cleaning, and structuring data from the Public Search Tool (PST) database. Our approach involved the use of customized web-scraping tools and data manipulation techniques, including automatic data retrieval, transformation of inspection reports into a text-friendly format, and pattern recognition for collating pertinent data elements. We conducted descriptive statistical analyses on the assembled dataset to set the stage for a comprehensive exploration of inspection reports from Class ‘A’ commercial dog breeding facilities.

Results: Our study produced an extensive dataset detailing compliance with animal welfare standards at Class ‘A’ commercial dog breeding facilities across the United States from 2014 to 2023. Preliminary analysis revealed prevalent areas of non-compliance, such as inadequate veterinary care and substandard housing conditions. The dataset facilitated a deep analysis of animal welfare practices within the commercial dog breeding industry, providing insights across geographical locations and facility sizes.

Conclusion: Our study underscores the potential of harnessing Big Data to inform regulatory decisions and improve animal welfare within commercial dog breeding. It introduces a method to transform publicly available data into an accessible format. This allows us to go beyond anecdotal evidence into comprehensive assessments, facilitating constructive dialogue and effective policy-making. Further research leveraging advancements is recommended to deepen insights and encourage collaborative efforts to elevate animal welfare standards.

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