McKinsey has partnered with the World Economic Forum to create an “Innovation Heat Map" to identify innovation clusters based on the analysis of the variables that drive innovation. The clusters are plotted on a classic McKinsey 2x2 that measures the size of a cluster on momentum versus diversity. I would encourage you to read the detailed analysis and look at the innovation heat map. The clusters are classified as:
While I applaud the efforts behind analyzing the vast amount of indicators to find patterns that explain certain macroeconomic innovation trends I disagree with the idea of measuring innovation based on number of patents. The effectiveness of our (US) patent system to measure innovation effectively across industries is questionable. How many of these patents are actually converted into real innovations? This problem is exasperated when we involve the patent systems of the countries across the globe.
This analysis assumes minimum infrastructure base as a qualifier to level the playing field. Though I do appreciate the intent of this assumption, this approach leaves out the countries who innovate despite of poor infrastructure such as India and China. An alternate approach could have been a weighted cluster that focuses on the efficiency of the clusters to demonstrate the untapped innovation potential due to lack of infrastructure.
These clusters provide an opportunity to explore some correlations. Hot Springs regions' growth is correlated to untapped natural resources and recent influx of skilled immigrants. This might explain Canada and Australia being Hot Spring regions due to their untapped natural resources and the immigration policies designed to attract highly skilled an well educated prospective immigrants.
To make this analysis even more compelling I would like to go back few years more than the current nine years of data and choose time-based visualization such as Gap Minder. This might reveal some interesting patterns about how the clusters grow from small to big and vice versa and change quadrants as the years go by. This visualization would not only allow to add filters but would also allow to track relative progress of a subset of clusters. We might also be to see how the clusters move from silent lake to dead pool since they cannot innovate themselves out of the current crisis e.g. auto and manufacturing regions in the US.
Dynamic oceans: Large and vibrant innovation ecosystems with continuous creation and destruction of new businesses. Leading innovators and primary sectors change organically as the hub frequently reinvents itself through significant breakthrough innovations.
Silent lakes: Slow-growing innovation ecosystems backed by a narrow range of very large established companies that operate in a handful of sectors. These clusters are frequently the source of a steady stream of “evolutionary” innovations and step-wise improvements.
Shrinking pools: Innovation hubs that are unable to broaden their areas of activity or increase their lists of innovators and so find themselves slowly migrating down the value chain, as their narrow sector becomes less innovation driven and increasingly commoditized.
Hot springs: A small and fast-growing hub that relies on a small number of companies to establish itself as a relevant world player in a narrow sector.
Silent lakes: Slow-growing innovation ecosystems backed by a narrow range of very large established companies that operate in a handful of sectors. These clusters are frequently the source of a steady stream of “evolutionary” innovations and step-wise improvements.
Shrinking pools: Innovation hubs that are unable to broaden their areas of activity or increase their lists of innovators and so find themselves slowly migrating down the value chain, as their narrow sector becomes less innovation driven and increasingly commoditized.
Hot springs: A small and fast-growing hub that relies on a small number of companies to establish itself as a relevant world player in a narrow sector.
While I applaud the efforts behind analyzing the vast amount of indicators to find patterns that explain certain macroeconomic innovation trends I disagree with the idea of measuring innovation based on number of patents. The effectiveness of our (US) patent system to measure innovation effectively across industries is questionable. How many of these patents are actually converted into real innovations? This problem is exasperated when we involve the patent systems of the countries across the globe.
This analysis assumes minimum infrastructure base as a qualifier to level the playing field. Though I do appreciate the intent of this assumption, this approach leaves out the countries who innovate despite of poor infrastructure such as India and China. An alternate approach could have been a weighted cluster that focuses on the efficiency of the clusters to demonstrate the untapped innovation potential due to lack of infrastructure.
These clusters provide an opportunity to explore some correlations. Hot Springs regions' growth is correlated to untapped natural resources and recent influx of skilled immigrants. This might explain Canada and Australia being Hot Spring regions due to their untapped natural resources and the immigration policies designed to attract highly skilled an well educated prospective immigrants.
To make this analysis even more compelling I would like to go back few years more than the current nine years of data and choose time-based visualization such as Gap Minder. This might reveal some interesting patterns about how the clusters grow from small to big and vice versa and change quadrants as the years go by. This visualization would not only allow to add filters but would also allow to track relative progress of a subset of clusters. We might also be to see how the clusters move from silent lake to dead pool since they cannot innovate themselves out of the current crisis e.g. auto and manufacturing regions in the US.
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