Automated Business Goal Extraction from E-mail Repositories to Bootstrap Business Understanding

Marco Spruit, Marcin Kais and Vincent Menger
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Marco Spruit: Leiden University Medical Center (LUMC), Campus The Hague, Leiden University, Turfmarkt 99, 2511 DC The Hague, The Netherlands
Marcin Kais: Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC Utrecht, The Netherlands
Vincent Menger: Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC Utrecht, The Netherlands

Future Internet, 2021, vol. 13, issue 10, 1-11

Abstract: The Cross-Industry Standard Process for Data Mining (CRISP-DM), despite being the most popular data mining process for more than two decades, is known to leave those organizations lacking operational data mining experience puzzled and unable to start their data mining projects. This is especially apparent in the first phase of Business Understanding, at the conclusion of which, the data mining goals of the project at hand should be specified, which arguably requires at least a conceptual understanding of the knowledge discovery process. We propose to bridge this knowledge gap from a Data Science perspective by applying Natural Language Processing techniques (NLP) to the organizations’ e-mail exchange repositories to extract explicitly stated business goals from the conversations, thus bootstrapping the Business Understanding phase of CRISP-DM. Our NLP-Automated Method for Business Understanding (NAMBU) generates a list of business goals which can subsequently be used for further specification of data mining goals. The validation of the results on the basis of comparison to the results of manual business goal extraction from the Enron corpus demonstrates the usefulness of our NAMBU method when applied to large datasets.

Keywords: CRISP-DM; business understanding; e-mail data understanding; business goal generation; natural language processing; knowledge discovery (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
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