![]() For some special cases, we may outsource the process to take advantage of greater expertise-to get ink out of the white shirt or to care for the cashmere sweater that needs extra special handling at a dry cleaner, for instance. We buy a spiffy washer and dryer (capital investment) we add consumable expenses in soap, bleach, spot remover, and fabric softener and then we supply our own labor and expertise from hamper to hanger. ![]() Many of us choose to manage this process in-house. We make decisions for inputs of capital and labor, investments of time and supervision, and allowances for scheduling and workflow. Some of us may even be meticulous with beautifully arranged closets. We just want clothes that are clean, folded, pressed, and hung up or put away. Clothes LaundryĪt home, most of us rarely consider the routine, tacit assumptions that shape our process choices for laundry. Finally, the perceived quality of an outcome is deeply influenced by the context in which it is used. What do our unanalyzed terabytes of 1's and 0's have in common with dirty socks and soiled blouses? They all involve a cost of transforming from a prior, undesired state to a future, desired state in a process that requires a mix of capital, labor, and expertise and that must be performed under time and quality constraints. Add to that many new high-volume sources of data from social media or geolocation tracking, and that gap between real or perceived internal inabilities and an institution's urgent needs has driven many to seek external help-to send the data laundry out to others-in hopes of a quick win by accelerating information insights. We have an abundance of primary data from our many systems of record (e.g., Student Information Systems, Finance, Learning Management) spanning a decade or more, yet few of us have made the substantial investments internally to repurpose our data for new insights. Our institutions are often quite data-rich and insight-poor. Some of the thirst may also emerge when sales pitches assert cloud-based services as a quick "magic bullet" to solve local information needs. ![]() They sometimes seek reports or real-time dashboards with newly tailored information, but increasingly they expect analysis, prediction, and benchmarking based on historical data to inform the future. The thirst for insightful information comes from students, faculty, department chairs, deans, provosts, presidents, boards, and others who seek a rapid means to more effectively manage their investments in the academy. These motivating forces are pervasive across higher education for institutions of all sizes and missions, both public and private. 2 A second force is the growing belief that computational analyses of big data from colleges and universities will yield formerly unknown insights regarding new efficiencies and effectiveness in the competitive market. Greater transparency of information also fuels increased shopping among colleges and universities by students, parents, donors, and state legislatures as they make choices for educational investments. First, market forces are changing the economics of higher education as financing a college education becomes more of a private good and less of a subsidized public good. 1Īt least two forces are motivating an accelerating demand for this process. I am not referring to data laundering, which seeks to obscure, remove, or fabricate the provenance of illegally obtained data for nefarious purposes. It is a mostly unseen, antecedent process that unlocks data's value and insights for the needs of decision makers. Who is or should be doing an institution's data laundry? By data laundry, I am referring to the legitimate process of transforming and repurposing abundant data into timely, insightful, and relevant information for another context. Similarly, the data at our institutions is piling up and taking on a new urgency for the challenges ahead. Some of us dispense with the chore altogether and outsource it to a dry cleaner or hire help. Sometimes we let the clothes pile up, and other times the laundry becomes urgent. Sorting, washing, drying, folding, ironing, buying soap, and repairing the washer are often viewed as a necessary distraction from how we would prefer to spend our time. We all like to wear clean clothes, but few of us enjoy the chore of doing the laundry.
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