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The Data-Informed Institution

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Content elements: › How education is using data for digital transformation › The mission and business value of data › Data, adaptability, and agility › Agility for data - 6 steps › How can we use data to bring adaptability to our institution? › In closing › About the author 3. Make it available The next step in bringing agility to data is to make it available—when and where it is useful. (Note that I didn't say when and where it is needed. I'm talking about agility and innovation here.) The model that is often used today is one of self- service provisioning. When an analyst at FINRA is curious, he or she can spin up a set of tools and a subset of the data to analyze without having to request and wait for someone else to provide it. The resulting freedom lets the analyst pursue a train of thought, a "flow," rather than proceeding in a stop-start way that destroys creativity—or, you could say, that increases the cost of curiosity. The cloud is an important enabler for this, as it allows new work environments to be provisioned, used, and then discarded when no longer needed. It also makes it easy to put guardrails in place to protect privacy (more on this below). Much of the data held by higher education institutions is locked away in student information and learning management systems. The data is available for reporting within those systems—in the ways that the software vendor makes it available. To make the data truly agile, and thereby to use it as an asset for adaptability, the first step is often to move it into a data lake where it can be combined with data from other systems and made available for a broader set of flexible analytic tools, like machine learning. The University of Maryville, a four-year institution in the US, wanted to use its data to improve student outcomes but found that "in higher education data largely remains siloed into enterprise IT systems or departments. Academic or learning data and student information, such as student profiles, course completion, housing, and financial aid information, largely remains separated." They solved this by setting up a data lake on AWS and filling it with data from their siloed IT systems. In doing so, they were able to design a model that could automatically identify students who haven't activated their Maryville accounts—necessary for beginning coursework—and trigger a text reminder. The Department of Education in Western Australia brought together data on 320,000 students across 800 schools to analyze and serve their needs for information and communication technology devices. 10

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