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 6. Provide guardrails Before we can make data available for novel uses—to satisfy curiosity, so to speak—we must put guardrails around it for privacy and confidentiality. Data- savvy enterprises practice "privacy by design," deliberately establishing safeguards based on planning and foresight. They gain speed and flexibility down the road by making sure that they have already considered what needs protection and have set up automated ways to protect it. In fact, the recent European Union General Data Protection Regulation (GDPR) requires privacy by design. The cloud provides many tools for setting up automated access controls and does so at a granular level that lets you give faculty and staff access to precisely the data they should have access to. There are ways to track the provenance and validity of the data, to encrypt or obscure it, and to restrict access on a field-by-field basis or record-by-record basis. Amazon Macie even uses ML to identify which data in your data lake is personally identifiable information (PII) and track how it is used. Or you can choose to manage data only at an aggregated level or with information masked or anonymized. The flexibility is there; each data-informed institution must make responsible decisions about privacy given the type of data they steward. Many other challenges arise in using the vast amounts of data that the institution has available. It is often a challenge to accurately connect data from different IT systems pertaining to a single individual, especially in countries like the US that do not have a single national identification system. Data can be inaccurate not only because of mistakes made in data entry but also because of limitations in the IT systems that collect the data. For example, there are IT systems that only allow for a surname and a given name, which imposes inaccuracy for people who have more than two names 10 . Regardless, the goal of a data-informed institution is to make data available to drive rigorous and accurate decision-making and continuous innovation. It requires collecting and storing data for flexible use later, making it and the right tools available without friction to those who will use them, ensuring privacy and confidentiality by design, cultivating the skills to make valid inferences, and solving the data hygiene problems that can lead to poorly informed decisions. This is what it means to bring agility to data. 10. For some great stories about IT systems insensitive to real-world scenarios, consult Gojko Adzic's Humans vs Computers. In other words, you can specify which students' data a faculty or staff member has access to and which pieces of data associated with those students they can view. 13

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