Discussion of “Experiences with big data: Accounts from a data scientist’s perspective”

Abstract

In modern production, bad quality does not happen often enough to provide a meaningful comparison with good quality; simulation is a useful tool to generate artificial data. Every data scientist should develop branch relevant domain knowledge. They should have a clear view what, and how, data is recorded and archived. Quite often the given analytical task is not the only one or the real one. It is helpful to reflect on the task and to take into account the background and the business goals behind it. Communication is the key issue for good data science.

Keywords:

• big data
• data science
• data quality
• Industry 4.0
• practitioner experiences

This article responds to:

Experiences with big data: Accounts from a data scientist’s perspective

Additional information

Notes on contributors

Andrea Ahlemeyer-Stubbe

Andrea Ahlemeyer‐Stubbe is Director of Strategical Analytics at servicepro‐Agentur für Dialogmarketing und Verkaufsförderung GmbH, Munich, Germany. Upon receiving her Master’s degree in Statistics from the University of Dortmund, Andrea formed a consulting firm, offering customized professional services to her clients. She now leads servicepro’s analytics team, working on international projects for well‐known brands in Europe, United States, and China, drawing on the wealth of experience gained from her 20 years in the industry, specifically in the areas of data mining, data warehousing, database marketing, CRM, big data, and social CRM. She is co-author (with Shirley Coleman) of Monetising Data – How to Uplift Your Business and A Practical Guide to Data Mining for Business and Industry as well as a frequent lecturer at several universities and speaker at professional conferences. She writes for special interest magazines as well as marketing and management publications. From 2007 to 2009, she was President of ENBIS (European Network for Business and Industrial Statistics).