{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T17:56:37Z","timestamp":1772301397650,"version":"3.50.1"},"reference-count":21,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"National Science Foundation Operations Engineering","award":["CMMI-1935602"],"award-info":[{"award-number":["CMMI-1935602"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Big Data &amp; Society"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:p> The social services sector, comprised of a constellation of programs meeting critical human needs, lacks the resources and infrastructure to implement data science tools. As the use of data science continues to expand, it has been accompanied by a rise in interest and commitment to using these tools for social good. This commentary examines overlooked, and under-researched limitations of data science applications in the social sector\u2014the volume, quality, and context of the available data that currently exists in social service systems require unique considerations. We explore how the presence of small data within the social service contexts can result in extrapolation; if not properly considered, data science can negatively impact the organizations data scientists are trying to assist. We conclude by proposing three ways data scientists interested in working within the social services sector can enhance their contributions to the field: refining and leveraging available data, improving collaborations, and respecting data limitations. <\/jats:p>","DOI":"10.1177\/20539517231171051","type":"journal-article","created":{"date-parts":[[2023,5,2]],"date-time":"2023-05-02T05:52:00Z","timestamp":1683006720000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":13,"title":["Big ideas, small data: Opportunities and challenges for data science and the social services sector"],"prefix":"10.1177","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7934-5436","authenticated-orcid":false,"given":"Geri Louise","family":"Dimas","sequence":"first","affiliation":[{"name":"Data Science Program, Worcester Polytechnic Institute, Worcester, MA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0967-3960","authenticated-orcid":false,"given":"Lauri","family":"Goldkind","sequence":"additional","affiliation":[{"name":"Graduate School of Social Service, Fordham University, Pleasantville, NY, USA"}]},{"given":"Renata","family":"Konrad","sequence":"additional","affiliation":[{"name":"School of Business, Worcester Polytechnic Institute, Worcester, MA, USA"}]}],"member":"179","published-online":{"date-parts":[[2023,5,1]]},"reference":[{"key":"bibr1-20539517231171051","unstructured":"Abazajian K (2022) Why \u201cinnovation\u201d around houselessness fails so much. 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