Successful Data Science Is a Communication Challenge
Currently, we experience a growing number of highly sophisticated digital services in virtually every domain of our lives. Tightly coupled to this observation is the appearance of Big Data and consequently the need for Data Science. When trying to transform data into value, communication is key. However, communication can easily get ambiguous and may threat success by misunderstandings. Thus, this article reviews the (communication) model of Data Science and maps the ten V’s of Big Data to this model. Finally, we propose four top skills that each and every data science group needs to have to operate successfully.
- 1.M. B. Kinshuk Mishra, Personalization at Spotify using Cassandra, https://labs.spotify.com/2015/01/09/personalization-at-spotify-using-cassandra/: Spotify Labs, 2015.Google Scholar
- 3.C. S. Jeff Magnusson, Talk “Watching Pigs Fly with the Netflix Hadoop Toolkit” at Hadoop Summit (June 27, 2013), 2013.Google Scholar
- 4.D. Laney, 3D Data Management: Controlling Data Volume, Velocity, and Variety, http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf: META Group, 2001.Google Scholar
- 5.K. D. Borne, Top 10 Big Data Challenges – A Serious Look at 10 Big Data V’s, https://www.mapr.com/blog/top-10-big-data-challenges-serious-look-10-big-data-vs, 2014.Google Scholar