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Journal Article: Big Data in Innovation Management

ITONICS

In this research project, ITONICS is actively involved in the development of an automated environmental scanning system for SMEs. The subsequent case study uses two sets of data to show how the environmental scanning system can be applied to find emerging trends.

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Journal Article: Big Data in Innovation Management

ITONICS

In this research project, ITONICS is actively involved in the development of an automated environmental scanning system for SMEs. The subsequent case study uses two sets of data to show how the environmental scanning system can be applied to find emerging trends.

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etventure drives big data analytics for big corporations

etventure

Due to the fast-paced digitalization of the last decades, big companies are confronted with ever-larger amounts of data. At the same time Big data solutions like, for instance, predictive analytics and data modelling can help organisations in making better decisions and identifying new opportunities.

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Insights from Innovation Roundtable Summit 2019

ITONICS

Read more about what role Big Data and Machine Learning play in Innovation Management. This employee innovation program – Kickbox , engaged over 1000 international partners, who came up with various creative projects, such as apps, Adobe product enhancements, etc. Employees are CEOs of Their Ideas.

Agile 47
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Qmarkets Spring Product Release 2019: Get to the Cutting Edge of Innovation Management…

Qmarkets

With Big Data, Machine Learning, and a more engaging user experience than ever before, Qmarkets’ latest product release delivers a set of advancements which push forward the frontier of innovation management , and help you drive more bottom-line value from your project. Enhanced Social Engagement.

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Responsible Artificial Intelligence

Norbert Bol

The rapid growth of AI today is possible due to the increased computing power, the availability of big data and the ongoing learning of AI itself as it never sleeps. Clarke (2019) describes 10 themes for responsible AI, which are: (1) Assess Positive and Negative Impacts and Implications. (2) 2) Complement Humans. (3)

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Responsible Artificial Intelligence

Norbert Bol

The rapid growth of AI today is possible due to the increased computing power, the availability of big data and the ongoing learning of AI itself as it never sleeps. Clarke (2019) describes 10 themes for responsible AI, which are: (1) Assess Positive and Negative Impacts and Implications. (2) 2) Complement Humans. (3)