Design Thinking Humanizes Data Science
The infographic above is very cool and depicts a very interesting proposition. I love it. However, my concern with the proposition is that while these technology combinations could be quite powerful, the Internet of Things, Human-Machine Interfaces, Cyber physical systems and Artificial Intelligence are only enabling technologies, that is, they only give someone or something the means to do something. You still need someone or something to actually do something; to decide what to do, when to do it, where to do it, with whom to do it, how to do it, the required items to do it, etc.
There is a H-U-G-E difference between enabling and doing. For example, I can enable you with an individualized diet and fitness plan that will improve your life, but the subsequent improvement in your life won’t happen if you are not doing it.
“Do. Or do not. There is no enabling” to paraphrase Jedi Master Yoda.
These technology combinations are missing the glue to bind them together to ensure business success, and customer and marketplace differentiation, whereby the glue is:
Data Sciencethat seeks the variables and metrics that yield better predictors (patterns, trends, relationships, associations) of performance and operations.
Design Thinking that uncovers the analytical heuristics buried in the organization’s tribal knowledge and drives the subsequent adoption of the analytics.
Economics that identifies and captures the sources of customer, operational and market value (wealth) creation.
At the end of the day, the organizations that win will be ones that learn how to leverage Data Science, Design Thinking and Economics to drive the business and operational focus, and align, adopt and monetize these enabling technology combinations.
Designing Thinking and AI Research: McKinsey Meets Accenture
McKinsey (“ Business Value of Design ”) and Accenture (“ How AI Boosts Industry Profits Innovation ”) recently released separate research that shows how organizations that adopt AI and Design Thinking out-perform those that do not (see Figure 2).
We find that organizations that blendDesign Thinking with Data Science are having more success – not only in the quality and accuracy of their analytic models, but equally important in the subsequent organizational alignment around and adoption of the analytic results. Both Design Thinking and Data Science discover – versus define – the criteria for success; Data Science discovers the criteria for success buried in the data (codifying trends, patterns, and relationships) while Design Thinking discovers the criteria for success buried in the human interactions (using personas, journey maps, and storyboards).
Common characteristics between successful Design Thinking and Data Science execution include:
Open culture for sharing and standing on the shoulders of others
Inclusive culture where “All ideas are worthy of consideration”
Learning culture through experimentation (and failure)
Willingness of culture to unlearn old methods that have been held as the gospel truth
Maybe the most important cultural similarity is the ability to embrace the mentality that if you don’t have enough “might” ideas, you’ll never have any “breakthrough” ideas.
