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 Agility for data – six steps To achieve institutional agility, we'll need to be poised to respond to unexpected changes in the social and educational environments, and we'll need to create innovations that are truly novel—and so, we will need to be able to put our data to work in ways we don't necessarily anticipate when we collect it. Our challenges: • Our data is probably locked away in transactional, relational databases; off- the-shelf student information, learning management, enrollment, classroom management, survey data, and other IT systems; and siloed in ways that make it inaccessible to different parts of our organization. • We may not have the right analytical tools, or they may not be available to the right people at the right times. • Our models for security and privacy are ad hoc, as we perhaps never contemplated using the data for exploration. Most likely, we are fostering privacy simply by making the data as inaccessible as possible. Our goals: • Maximize the data's availability, subject to guardrails for privacy and confidentiality. • Foster transparency across the institution by breaking down information silos. • Offer faculty and staff the appropriate tools to explore the data in unplanned ways and in ways that take advantage of the latest advances in analytics. • And be sure to have the expertise to interpret the data, both rigorously and creatively. In "Analytics without Limits: FINRA's Scalable and Secure Big Data Architecture," 8 John Brady, the chief information security officer (CISO) of the Financial Industry Regulatory Authority (FINRA), frames these objectives elegantly by saying that he wants to lower the cost of curiosity. He refers to cost in its widest sense, including the time it takes to draw inferences from the data and the risk in making it available. FINRA's business is to explore the 37 billion or more transactions that take place in the financial markets every day, looking for patterns of fraud. Since they don't always know in advance what a pattern of fraud looks like, they must rely on the expertise of their analysts to spot suspicious behavior. Their task is all about curiosity: They want their analysts to examine data with inquisitiveness as to what patterns appear and why. The task of their IT organization is to reduce the cost of that curiosity and the effort that an analyst has to exert to explore a hunch. 7 8.

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