In this article, originally published on Government Technology Insider, the Defense Acquisition University (DAU), a leader in data literacy education, discusses the importance of reskilling federal agency workers in data science. As we begin to emerge from a pandemic that has pushed government and industry to advance ten years in a matter of months, data has taken a larger role in driving better and faster decision-making in federal agencies. The DAU’s mission is to offer deliver continuous learning and support tailored to the needs of the Defense Acquisition Workforce.
Data literacy is an essential skill for federal agency workers today. When data literacy is taught within an agency, they’re able to better use data science for faster and more accurate decision-making, build experiences powered by machine learning (ML), and automate costly human workflows. And with the shortage of data scientists reskilling agency workers, via online learning, to be data literate is more pressing now than ever before.
The importance of reskilling agency workers online and building a stronger, more data literate workforcewas part of the discussion during a recent webinar, “Crash Course on Digital Transformation and Reskilling for Federal Agencies.” Speakers included David Pearson, Center Director for Engineering and Technology, Defense Acquisition University (DAU) and Emily Sands, Vice President, Data Science, Coursera. During the webinar both Pearson and Sands mentioned the learning landscape, how it’s been impacted since COVID-19, and how higher education institutions — like the DAU – are teaching important concepts like data literacy in virtual settings.
According to the DAU, the Department of Defense (DoD) has long been recognized as an innovator and adopter of data science. But, in order to earn that recognition, it required a longstanding commitment to data literacy. And, this commitment to teach and reiterate the importance of data literacy has been a primary mission of the DAU.
“Data drives decision-making and that’s critically important going forward,” explained Pearson. “We see an expanded role of data across the DoD which is using data for competitive advantage. That advantage expands in two ways. First to the warfighters, so they can make better decisions in battle and, second, to the supporters of the warfighters, those behind the scenes that also need to make important decisions fast.” Pearson’s job is to support an increased use of data by first creating a foundation of a workforce that grasps and fully embraces data as a way to solve problems.
“There are many skills required by the workforce today. But a prerequisite is to make a cultural change. Promoting data literacy across the workforce and trying to find out what data literacy means and what skills are needed to demonstrate it, that’s something I’ve wrestled with and it’s something I try to build out in training products for the workforce,” explained Pearson. “For example, does everyone really need to be able to program in Python to be data literate? No. However, I do think that everyone needs a basic appreciation of statistics in order to interpret data and really use it to support decision-making.”
Remediating data literacy in the way that Pearson does require, first and foremost, a learning partner that helps identify knowledge gaps. The partner must also work to reskill and upskill the workforce by getting current staff the key technical skills needed to succeed in data excellence. And training the workforce to acquire these skills, during the pandemic, calls for virtual learning that’s seamless and efficient.
Federal agencies, including the DoD, understand the benefits of data science and the value it brings to the mission. They know that data literacy supports faster and more accurate decision-making. They also understand that in order to put data to work, their workforce must be data literate. When data literacy is woven into the fabric of an agency, and initiatives built with data science emerge as a result, agencies like the DoD are better prepared and missions are more successful.
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This article was originally published on Government Technology Insider on February 25, 2021.