@article {1143,
	title = {Data Science as an Innovation Challenge: From Big Data to Value Proposition},
	journal = {Technology Innovation Management Review},
	volume = {8},
	year = {2018},
	month = {03/2018},
	pages = {16-25},
	publisher = {Talent First Network},
	address = {Ottawa},
	abstract = {Analyzing {\textquotedblleft}big data{\textquotedblright} holds huge potential for generating business value. The ongoing advancement of tools and technology over recent years has created a new ecosystem full of opportunities for data-driven innovation. However, as the amount of available data rises to new heights, so too does complexity. Organizations are challenged to create the right contexts, by shaping interfaces and processes, and by asking the right questions to guide the data analysis. Lifting the innovation potential requires teaming and focus to efficiently assign available resources to the most promising initiatives. With reference to the innovation process, this article will concentrate on establishing a process for analytics projects from first ideas to realization (in most cases: a running application). The question we tackle is: what can the practical discourse on big data and analytics learn from innovation management? The insights presented in this article are built on our practical experiences in working with various clients. We will classify analytics projects as well as discuss common innovation barriers along this process. },
	keywords = {analytics, big data, digital innovation, idea generation, innovation process},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1143},
	url = {http://timreview.ca/article/1143},
	author = {Victoria Kayser and Bastian Nehrke and Damir Zubovic}
}
