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 Fast feedback Feedback, in this sense, does not mean asking students or employees whether they like a new feature or IT application. More commonly, data-savvy institutions use quantitative feedback—the kind of feedback that is gathered by watching how students and employees actually act—or by monitoring educational trends or other metrics. For example, organizations often improve the usability of their websites through A/B testing; that is, by trying two variations on a piece of the design (usually one variation is the current, status quo version, and the other is a new piece of design they are considering introducing). They show some users version A and some version B. They collect data on the users' activity and analyze it in relation to the outcomes they care about. If they want to decide whether to make a button green or red to maximize the number of times it is clicked, they can show some users a green version and some a red one and see which gets more clicks. Expedia and Netflix are examples of companies that routinely do A/B testing, drawing on large amounts of data from a data warehouse in the cloud 11 . The powerful approach of learning and adjusting through feedback goes far beyond just A/B user interface testing. Ideas for new student services or programs for non-traditional or adult learners, for example, can be tested by creating a "minimum viable product," the smallest and simplest version of the service the institution can use to gather information on whether the service will be successful or what needs to be changed to make it so. Diversity-enhancing strategies, new learning modalities, technology alternatives—all of these can be tested through trial and measurement to reduce uncertainty. And the key to doing so is gathering data and making it available for analysis. The technique of using minimum viable products and fast feedback is described in Eric Ries's book The Lean Startup. According to Ries, at any given moment, an organization holds two hypotheses: a value hypothesis, about how their proposed product will create value for customers, and a growth hypothesis, about how the organization will be able to "grow its market"—that is, get people to use the product. The minimum viable product is the smallest product that will give the startup information to confirm or refute these hypotheses, at which point it can make changes and re-test them with the market. This set of practices does not just apply to startups or to new product development. It has become central to the way organizations, including large enterprises in both the commercial and public sectors, achieve business agility by changing course based on their learnings. If an institution is thinking of developing a new IT system for use by its own faculty and staff, it presumably has a hypothesis about how that IT system will deliver the business outcomes that are proposed in its business case. That hypothesis should be tested, and changes should be made based on what the data shows. As a result, agile practice requires data. To learn and adapt, an institution has to collect data on the impact of its new initiatives and use it to inform those initiatives. Agility further requires that the institution continually sense changes in its business environment, so it can respond appropriately to maximize its mission outcomes. A data-informed institution not only brings agility to its data but also uses data to support its agility. 15 11.

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