What Educators Can Learn From House of Cards

Big Data

What Educators Can Learn From House of Cards

By Scott Morgan     Mar 2, 2015

What Educators Can Learn From House of Cards

In 2011, Netflix made a reported $100 million bet and bought two full seasons of House of Cards, sight unseen.

While Netflix executives hadn’t seen the show, what they had seen was data. Tons of data. Data from 29 million customers about what they wanted to watch. For Netflix, buying House of Cards felt less like a gamble and more like a strategic decision backed by big data. And it paid off.

With stories like this, it’s no wonder that Harvard Business Review is calling “data scientist” the “sexiest job of the 21st century.”

As almost every aspect of our lives becomes affected by the exponential growth in data, the clamor for talented people who can interpret data and use it to inform key decisions will only get louder.

In my view, education is the most promising industry for current and future analysts and data scientists to make a meaningful difference in our communities, our nation and our world.

While we’ve seen bright spots of progress, on the whole, we need the massive, $600 billion K-12 public education system to take a quantum leap forward. To do that, we need to harness the power of data.

First, we need a lot more analysts and data scientists in education.

A 2014 study of the public school sector forecasts a gap of nearly 5,000 new mid- to senior-level (non-teaching) leaders in data/analytics in the nation’s 50 largest cities by 2023. This talent gap is expected to increase as the demand for new, innovative schools grows. What’s more, of the total projected gap of 32,000 jobs across 18 different sectors, data/analytics roles represent the single largest gap by nearly 25 percent.

There are nearly 50 million K-12 students in the U.S.’s public schools, and we have a ton of data about them: enrollment, attendance, test, demographic, instructional and much more. To make what we know meaningful in how we serve students we need leaders who can wade through oceans of information, identify what we should be looking at and why, and recommend what we should do next based on solid analysis.

The analysts and data scientists who’ll succeed in these critical roles aren’t just number crunchers and quant jocks; they’re powerful storytellers who can bring valuable insights to schools and classrooms so that educators can make data-informed choices and unleash the full potential of each and every student.

Second, we need to speak clearly about data.

For a lot of us in education like me, who started teaching 15 or 20 years ago, “data” used to sound like it belonged somewhere very far away from our students and schools. But we are increasingly aware that information is imperative to know if we’re getting our work right – if students are learning and thriving – and data tells us where we stand and where we can improve.

For education’s millions of stakeholders – students, parents, educators, community members, legislators, investors, and more – to see data’s value, we must speak clearly.

Recently, Kati Haycock, President of The Education Trust, spoke with Education Pioneers’ Analyst Fellows about how she presents data. For one, she provides context. She tells people why the information matters.

For another, she uses language that stakeholders can understand. For instance, when possible, she talks about moving students by a certain number of grade levels, rather than using measures of growth that don’t resonate with parents, board members, and community members.

Third, we need to shift the culture around using data in education.

While we may not all start creating pivot tables tomorrow, we can respect the value of data and use it to inform important decisions.

Embracing data is a big cultural shift in the education sector. In our field, intuition and feeling are often highly prized. (Education isn’t alone here; just look at the 83 percent of mergers and acquisitions that take place in corporate America that don’t create shareholder value; or how talent scouts operated in baseball before the Moneyball revolution pioneered by Oakland A’s general manager Billy Beane used data in novel ways to find undervalued players.)

Human relationships are fundamental to teaching and learning. To be successful, the teaching/learning equation should contain sizable doses of intuition, feeling, compassion, empathy, patience and more. We can’t lose sight of the human element. Research shows that students perform better when they have meaningful relationships with caring adults who know them well and believe in them.

But we also need to know if our students are learning. The trouble with intuition and gut feeling is that they’re not scalable to ensure that the 50 million kids who are counting on us get the quality education that they need. We need data to point us in the right direction and make sure we are constantly improving for all students.

Building robust data cultures in schools and education organizations is about more than bringing in a subset of people with analytical skills. It’s also about the people who see the beauty in numbers and data helping others to do the same. And it’s about organizations helping their teams to build new skills, to learn to see the importance of data, and to analyze key information before making decisions. It’s about culture building.

What’s Next

Education is and will remain a public service, powered by people.

There is no silver bullet to solve education’s complex challenges. We can’t expect the effective use of data to drive transformational change alone. Data has limits.

But that said, information is changing the world.

To take the quantum leap that our education system needs to provide every student in our country with an outstanding education, we must embrace what will help us get there and use every resource available to us – and big data is a big one.

We have 50 million reasons why we need to get this work right, right now. Educating our young people is no $100 million gamble.

This work has much, much higher stakes.

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