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      Reducing data silos created through reporting proliferation from individualized dashboards using NLP methodologies

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            Abstract

            Google’s SAS team is responsible (in part) forproviding actionable insightsto teams of Account Strategists (AS).In some cases AS’ createdata silos and individualized dashboards, leading to fragmentation of insights and confusions around core metrics. To reduce this fagmentation and to create a single source of truth, the CMU team recommeded GoogleCommencetier-based migration of the custom-dashboardsto Connect Sales and Discovery platforms utilizing topic modeling and survey analyses results. The eventual future state that SAS team should strive for is democratizing reporting use cases via aSelf Service Data Mart, a platform that is based on a single, well-managed data repository.

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            Author and article information

            Journal
            ScienceOpen Posters
            ScienceOpen
            10 July 2022
            Affiliations
            [1 ] Heinz College, Carnegie Mellon University
            [2 ] Carnegie Mellon University
            Author notes
            Author information
            https://orcid.org/0000-0001-7034-3648
            Article
            10.14293/S2199-1006.1.SOR-.PPE4AH2.v1
            db888175-0af3-4b1a-8ed2-00ef91df7fcb

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

            History
            : 10 July 2022

            The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
            Databases,Applied computer science,Applied probability & Statistics,Information systems & theory,Data mining statistics,Machine learning,Data analysis
            Natural Language Processing,Data Mining,Machine Learning,Google,Ad Tech,Carnegie Mellon University,Topic Modeling

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