Abstract
We now have all the tools in place to optimize one or more data-to-learning-to-action processes and, more specifically, to maximize the value of learning that is associated with data-to-learning-to-action processes. So, in this chapter, we’ll work through some common patterns of learning constraints and potential solutions that are applicable to a wide variety of organizations and functional areas. The solutions invariably rely on people-, process-, or technology-based capabilities and, most typically, combinations of the three. We’ll work through these examples by traversing backward along the chain from the targeted decision, which is the sequence that is most appropriate to be applied in any real-world setting. And, of course, that targeted decision should be one that has been determined to have significant leverage on a value driver for the organization. We will spend time on each element of the data-to-learning-to-action chain and discuss some of the common constraints that are associated with each element and some typical potential solutions to these constraints. These examples will hopefully resonate with some of the data-to-learning-to-action processes in your own organization and help jumpstart your analysis.
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Notes
- 1.
For techniques to overcome common cognitive barriers to individual and group decision making, see again: Bang, Dan, and Chris Frith, “Making better decisions in groups”, Royal Society Open Science, August 2017. http://rsos.royalsocietypublishing.org/content/4/8/170193
- 2.
Andreessen, Mark, “Why Software Is Eating the World”, Wall Street Journal, August 20, 2011, https://www.wsj.com/articles/SB10001424053111903480904576512250915629460 ; Simonite, Tom, “Nvidia CEO: Software Is Eating the World, but AI Is Going to Eat Software”, MIT Technology Review, May 12, 2017, https://www.technologyreview.com/s/607831/nvidia-ceo-software-is-eating-the-world-but-ai-is-going-to-eat-software/
- 3.
Silver, David et al., “Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm”, Cornell University Library Arxiv.org, December 5, 2017. https://arxiv.org/pdf/1712.01815.pdf
- 4.
This is the approach of the self-learning AlphaZero system , for example, that recently defeated the best directly programmed systems: “AlphaZero compensates for the lower number of evaluations by using its deep neural network to focus much more selectively on the most promising variations—arguably a more ‘human-like’ approach to search . . .” Silver, David et al., “Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm”, Cornell University Library Arxiv.org, December 5, 2017. https://arxiv.org/pdf/1712.01815.pdf
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© 2018 Steven Flinn
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Flinn, S. (2018). Patterns of Learning Constraints and Solutions. In: Optimizing Data-to-Learning-to-Action. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-3531-7_9
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DOI: https://doi.org/10.1007/978-1-4842-3531-7_9
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