@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}
}
@article {1141,
	title = {Editorial: Insights (March 2018)},
	journal = {Technology Innovation Management Review},
	volume = {8},
	year = {2018},
	month = {03/2018},
	pages = {3-4},
	publisher = {Talent First Network},
	address = {Ottawa},
	keywords = {analytics, big data, business models, closed innovation, ecosystems, emerging economies, innovation, internationalization, Internet of Things, Open innovation, technology startups, value creation, value propositions},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1141},
	url = {http://timreview.ca/article/1141},
	author = {Chris McPhee}
}
@article {1082,
	title = {Using Artificial Intelligence and Web Media Data to Evaluate the Growth Potential of Companies in Emerging Industry Sectors},
	journal = {Technology Innovation Management Review},
	volume = {7},
	year = {2017},
	month = {06/2017},
	pages = {25-37},
	publisher = {Talent First Network},
	address = {Ottawa},
	abstract = {In this article, we describe our efforts to adapt and validate a web search and analytics tool {\textendash} the Gnowit Cognitive Insight Engine {\textendash} to evaluate the growth and competitive potential of new technology startups and existing firms in the newly emerging precision medicine sector. The results are based on two different search ontologies and two different samples of firms. The first sample includes established drug companies operating in the precision medicine field and was used to estimate the relationship between the firms{\textquoteright} innovativeness and the extent of online discussions focusing on their potential growth. The second sample includes new technology firms in the same sector. The firms in the second sample were used as test cases to determine whether their growth-related web search scores would relate to the degree of their innovativeness. The second part of the study applied the same methodology to the real-time monitoring of the firms{\textquoteright} competitive actions. In our findings, we see that our methodology reveals a moderate degree of correlation between the Insight Engine{\textquoteright}s algorithmically computed relevance scores and independent measures of innovation potential. The existence of such correlations invites future work in attempting to analyze company growth potential using techniques founded in web content scraping, natural language processing, and machine learning.},
	keywords = {analytics, artificial intelligence, business intelligence, entrepreneurship, online textual data, precision medicine sector, startup growth potential},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1082},
	url = {http://timreview.ca/article/1082},
	author = {Andrew Droll and Shahzad Khan and Ehsanullah Ekhlas and Stoyan Tanev}
}
@article {785,
	title = {TIM Lecture Series {\textendash} The Business of Cybersecurity},
	journal = {Technology Innovation Management Review},
	volume = {4},
	year = {2014},
	month = {04/2014},
	pages = {53-57},
	publisher = {Talent First Network},
	address = {Ottawa},
	keywords = {analytics, banking, cybersecurity, hacking, incident response, information security, intelligence, targets, threats},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/785},
	url = {http://timreview.ca/article/785},
	author = {David Grau and Charles Kennedy}
}
@article {813,
	title = {TIM Lecture Series {\textendash} The Laboratory for Analytic Sciences: Developing the Art and Science of Analysis},
	journal = {Technology Innovation Management Review},
	volume = {4},
	year = {2014},
	month = {07/2014},
	pages = {52-54},
	publisher = {Talent First Network},
	address = {Ottawa},
	keywords = {analysis, analytics, big data, collaboration, cybersecurity, framework, innovation, instrumentation, monitoring, prediction, strategy},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/813},
	url = {http://timreview.ca/article/813},
	author = {J. David Harris}
}
