Why K-12 Education Needs Data Strategists

This post was originally published by Miriam Greenberg on LinkedIn.

The incredibly fast moving world of information – the growth, collection, management, and analysis of lots and lots of information – presents the K-12 sector with an opportunity to dramatically improve student outcomes. In education, we are sitting on the same kind of explosion of information that is occurring in every other sector.  If appropriately “cracked” and “mined” for understanding and insight about what kids need, what is working, and where resources are being wasted, data has the power to radically improve how we deliver education and manage resources in the sector. Yet “data” and “evidence” are still modern-day bogeymen. Those words ring alarms for student and teacher privacy, rather than student and teacher growth.

It shouldn’t be this way.

At the Strategic Data Project, we have been focused on the question of harnessing “big data” in education for the last eight years. In fact, our stated mission is to transform the use of data in education to improve student achievement.  In pursuit of this mission, we started with the notion that districts and states needed more analytic capacity – both in terms of numbers of people as well as the skills brought to bear – to respond to and act upon big data.  We launched a Fellowship designed to recruit and support analytic experts in districts and state education agencies.  Soon into our efforts, however, it became clear that hiring more analysts into education, as traditionally defined, is not actually the solution.  We realized we were actually in the middle of creating a new profession.

Today, we squarely claim that SDP is in the business of establishing and supporting the new profession of data strategists in K-12 education.  What are data strategists?  For starters, they aren’t just analysts.  Our SDP Fellows are analytic entrepreneurs who are deeply curious about the data (structured and unstructured) they have to work with and about creative approaches for analyzing it.  They ask questions like:  how do college enrollment rates differ, by high school, when holding constant student achievement upon 9th grade entry? Or by course-taking patterns?  Note how different this is from examining the average college enrollment rates in a district, even when disaggregating by what was once NCLB subgroups.  The analysis described here creates finer visibility into where there is success, where there is variation, and where interventions are needed.  Another question might be to examine the relationship between bus drop-off times, school breakfasts served, and student outcomes and behavioral indicators for students eligible to receive these meals.  Or, whether a district is retaining effective teachers at greater rates than ineffective teachers, and how that varies by school, grade level or subject matter.

Data strategists love datasets, they know programming languages – or, as we data geeks like to call it “code” – and they apply the knowledge they learn from their rigorous analytics to decisions and policies that impact the future.  They are less interested in accurately reporting the past within the context of a fixed set of rules and constraints.  In truth, that type of work (predominant in education today), is more akin to accounting – which delivers important assurances that public resources are being used appropriately, but tells us little about how to make decisions or allocate resources differently.   Data strategists are more like portfolio managers, using data to make informed predictions about the future and using their analyses to continually monitor whether intended results are being achieved.  They are not limited by data on high-stakes assessment results alone.  They seek “big data” that are holistic, complex, and informative.

The priority deadline for districts, states and education agencies interested in enrolling SDP Fellows is fast-approaching.  We hope agencies will join us in diagnosing the current state of their data-use and building the capacity for meaningful data strategy, rather than mere number crunching.

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