Data-to-Value: An Evaluation-First Methodology for Natural Language Projects

• URL: http://arxiv.org/abs/2201.07725v1
• Date: Wed, 19 Jan 2022 17:04:52 GMT
• Title: Data-to-Value: An Evaluation-First Methodology for Natural Language Projects
• Authors: Jochen L. Leidner
• Abstract summary: "Data to Value" (D2V) is a new methodology for big data text analytics projects. It is guided by a detailed catalog of questions in order to avoid a disconnect between big data text analytics project team and the topic.
• Score: 3.9378507882929554
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Big data, i.e. collecting, storing and processing of data at scale, has recently been possible due to the arrival of clusters of commodity computers powered by application-level distributed parallel operating systems like HDFS/Hadoop/Spark, and such infrastructures have revolutionized data mining at scale. For data mining project to succeed more consistently, some methodologies were developed (e.g. CRISP-DM, SEMMA, KDD), but these do not account for (1) very large scales of processing, (2) dealing with textual (unstructured) data (i.e. Natural Language Processing (NLP, "text analytics"), and (3) non-technical considerations (e.g. legal, ethical, project managerial aspects). To address these shortcomings, a new methodology, called "Data to Value" (D2V), is introduced, which is guided by a detailed catalog of questions in order to avoid a disconnect of big data text analytics project team with the topic when facing rather abstract box-and-arrow diagrams commonly associated with methodologies.

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