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Clive Higgins

Clive Higgins
Clive is a chemist turned business guy, educated at Brunel University in London and Harvard. He has spent the last 20+ years helping startups and industry giants to identify market opportunities, develop new products, grow their organizations and return enviable profits. He's built and managed some of the biggest brands in the scientific software space, from Chemdraw and E-Notebook to SampleManager and Spotfire.
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Recent Posts

Innovation Management: The Value of Seeing What You Have

If your job is to get your company, team, or community to innovate, you know how organizational forces can make it hard to even try something new. Visualizing the resources available is an effective first step in overcoming some of those organizational forces. Simply being able to see, and show, what you have allows you to make a compelling case for marshaling resources and even spark some initial interactions in that direction.

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The True Meaning of Catalyst, Crescendo, and Adaptation

People sometimes ask me what our company’s name means and why we chose it. The explanation often leads to discussions about similar but different terms. So I thought I’d use this blog post to explain, hopefully illuminate and while I’m at it, to correct some usage that’s bugged me for some time. Actually, let’s start right there.

I'm not sure where accuracy becomes pedantry, but there are two words - catalyst and crescendo - that instantly make my ears prick up when I hear them, only because I've heard them used incorrectly for long. One is from science and one from music, two things I tend to obsess about.

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The Data Scientific Method

The Oxford English Dictionary defines the scientific method as "a method or procedure that has characterized natural science since the 17th century, consisting in systematic observation, measurement, and experiment, and the formulation, testing, and modification of hypotheses." With more scientists today than ever, the scientific method is alive and well, and generating more data than ever. This explosion of data has brought about the field of data science and an associated plethora of analytics tools. Controversially, some have claimed, such as in this Wired magazine article, that data science is so powerful that it has made the scientific method obsolete. Google's founding philosophy is that “we don't know why this page is better than that one. If the statistics of incoming links say it is, that's good enough.” The implication is that with enough data, people will no longer need to know why something happens, it just does, and that’s good enough. Is it, really?

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