Technologies such as artificial intelligence, big data, and data analytics are fast becoming the revolutionary tools to help society and governments in solving both global and highly localized problems. Rapid developments in these fields have now impacted most lives on the planet in some way. Examples include Netflix using data and machine learning to tailor movie recommendations, Palantir using data analytics to assist Nuclear Inspects in the recent Iranian Nuclear Deal, as well as semi-autonomous and self-driving cars hitting the roads.
Tools Are Stupid, Humans Tell Stories
Exploring tech blogs, following tech news, or listening to industry leaders, there are many debates about whether AI (powered by data) is going to overthrow humanity or how a significant amount of people will lose jobs thanks to the automation tools such as data and AI. It seems then that already many commentators have been lured into the impression that these tools are near perfect; efficient, smart, versatile, and independent. If they seem too perfect to be true, it is because they are. These tools are stupid, contextually speaking at least. The results received from data tools have to be interpreted by a human, and AI tools can perform excellently but generally only in a specific task operating within designed parameters. At some point society may eventually develop AI powerful or complex enough to negate the human, but that ‘some point’ may not be for a while. This is where the storytellers come in, also known as data scientists.
Data scientists are not just statisticians. Data science is an interdisciplinary area that combines mathematics (descriptive statistics), computer science (programming, computing), domain expertise (think of the medical knowledge needed to implement and the understand a computational mapping of genetic information), and social aptitude (especially communication). By combing these disciplines and using effective communication as the pipeline, data scientists can extract and tell the story that the data is trying to tell us. Thus, if the insights cannot be communicated properly, the highly complex analysis using a supercomputer and cutting edge algorithm that took three years to develop will be to no avail.
Palantir and the Iranian Deal
Returning to the case of Palantir, their technology was applied to a critical foreign policy and global security issue. They would flag connections between individuals, materials and, organisations for the IAEA (International Nuclear Agency) inspectors to investigate. In this context, it would be important that the IAEA and their team needed a proficient storyteller whether from the IAEA, third-party experts or from Palantir directly. Either way, the storyteller would have been an important part of utilising such a data tool and that means both reading the results of the data crunching and understanding the whole process beginning with data collection. In this case, the tool was being applied to not only help keep everyone safe from nuclear extinction in an almost literal sense but also to stabilise the region.
This case is certainly not your everyday example but it highlights the potential impact these tools can have on very high level and volatile situations such as in the sphere of foreign policy. This demonstrates that data literacy is arguably not just a necessary skill for leaders in the future but presents a big opportunity for leaders of all degrees; diplomats, politicians, ministers, doctors, managers, CEOs, and NGO personnel. In addition to using these tools to carry out their basic duties, these high-level decision makers, influencers, and shapers can apply data tools to make a powerful and positive impact to tackle issues both in and out of their own domains, from small local issues to large global ones. Data literacy at all levels of leadership adds benefit at all scales. This is not to say this does not come without its risks. Regulation is needed to ensure these tools are not used for bad intentions, nor are badly used. Regardless, this change is inevitable and while data scientists may not necessarily need to be leaders, leaders will almost certainly need to be data scientists…..at least to some extent.
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