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Begin New Search Topic Science and Technology Political Geography Global Focus Content Type Journal Article Journal Fletcher Security Review Publication Year within 3 Years

1. "Is the Internet Trying to Kill Us?" And other Technology Security Unknowns In the New Roaring Twenties

Author:
Miles Taylor
Publication Date:
12-2021
Content Type:
Journal Article
Journal:
Fletcher Security Review
Institution:
The Fletcher School, Tufts University
Abstract:
Machines are poised to transform life as we know it... and America isn’t ready. A hundred years ago, machines remade the world. Society in the 1920s was transformed by a proliferation of cars, radios, movies, and airplanes, dramatically altering the way we lived, worked, and played. We did not know it then, but the sweeping technology revolution was poised to change the way we fought, too. The 1930s saw a revolution in military affairs as warfare became deadlier and faster-paced, a foreseeable consequence of the previous decade’s innovations.278 This nevertheless caught many nations by surprise, such as those affected by the German blitzkrieg. We are entering a New Roaring Twenties, and again we are unprepared for how it will affect national security in the decades to come. Make no mistake: today’s innovations will be tomorrow’s economic drivers, which is why we should be actively investing in the bleeding-edge tech of the future. But we must also be vigilant about how such developments will affect our security at home and reshape international conflict, or else risk a reordering of the global balance of power. This article previews several emerging technology dilemmas, what’s being done about them, and why—in some cases—it’s already too late.
Topic:
Security, Science and Technology, Cybersecurity, Internet, and Innovation
Political Geography:
Global Focus

2. Data Science Teams: Evolution of the Full-Contact Sport

Author:
Colleen McCue and Brandon Daigle
Publication Date:
12-2021
Content Type:
Journal Article
Journal:
Fletcher Security Review
Institution:
The Fletcher School, Tufts University
Abstract:
No one today questions whether data science can confer competitive advantage to organizations effectively adopting these capabilities. Considerable attention has been paid to technology, talent, and training as organizations attempt to build data science programs in an effort to convert aspiration to practice; moving from simply counting and reporting what happened to datainformed prediction and prescription. As understanding of the various and specific data science roles has evolved, talent development, management, and leadership are emerging as new frontiers. Organizations now work to create data science capacity where effective and responsible use of advanced analytics makes the whole truly more than a sum of the parts. Organizations increasingly recognize data science as a “must have” capability and are scrambling to recruit and hire data science talent. Data literacy programs are also proliferating given growing empirical research demonstrating the importance of upskilling across the organization in order to optimize data science talent and efforts. At the same time, the community is moving away from the data science “unicorn” concept in favor of a data science ecosystem. This is built through aggregation and assembly of complementary data science skill sets in support of meaningful, scalable, and sustainable breadth and depth of capacity. The challenge many organizations now face is how to organize and manage this disparate yet complementary talent? While the data science Venn Diagram has become ubiquitous, recent analysis suggests we are actually looking at something with far greater complexity and depth– more akin to a data science “tapestry” where unique data science capabilities and roles are intricately interwoven across the enterprise.297 Although aesthetically appealing, the tapestry construct is inherently static. Experience increasingly suggests that fixed data science resource allocation constructs are inflexible and even brittle; breaking in response to rapid evolution or change. Moreover, fixed or otherwise inflexible organizational constructs may have limited capacity to adjust on the fly to address unanticipated challenges and opportunity alike. What is needed, therefore, is data science capacity that is relatively fluid, agile and dynamic. It should be able to rapidly pivot in response to changing conditions, including unforeseen shortcomings or failures, while also setting the conditions to fully capitalize on unexpected windfalls in support of true competitive advantage. With this evolving understanding of high-performing data teams, we propose that analytically competitive organizations actually operationalize data science as something closer to a full-contact team sport where no one rides the bench.
Topic:
Science and Technology, Leadership, Data, and Management
Political Geography:
Global Focus