Educating the Next Analytics “Bilinguals”
A leading business–university partnership is setting standards for graduates fluent in data science and analytics.
Every organization needs bilinguals to serve as translators. After all, being able to speak a language other than your native tongue is crucial if you want to understand, learn about, collaborate, or trade with the vast majority of humans who might not naturally grasp the meaning of your words. And it is common to hear laments that American students aren’t acquiring the language skills necessary to thrive in an age of globalization. The Modern Language Association reported that in 2013, only about 1.52 million American students were studying a foreign language — down from its peak in 2000.
But as I’ve noted in this blog, being bilingual isn’t just a matter of native English speakers learning how to conjugate verbs in French or Spanish. Rather, it’s important that businesses cultivate talent that can simultaneously speak the language of advanced data analysis and nuts-and-bolts business operations. As data analysis becomes a more prevalent and powerful lever for strategy and growth, organizations increasingly need bilinguals to form the bridge between the work of advanced data scientists and business decision makers. Without this translation function, the investments companies are making in data and analytics are unlikely to yield maximum results.
Organizations increasingly need bilinguals to form the bridge between the work of advanced data scientists and business decision makers.
Obvious? Perhaps. But it isn’t clear how to cultivate bilinguals. What skills and experiences do they need and what programs and curricula should colleges and universities develop to help them grow? This is a challenge that the Business Higher-Education Forum (BHEF) is taking on. The group, which brings together CEOs, university and college presidents, and other leaders, has started an initiative to define and implement the courses of study required to provide today’s graduates the data science and analytics (DSA) skills they need to be successful — and that organizations badly need to remain competitive. And I’ve had the privilege of helping to facilitate this initiative.
To begin addressing this goal, BHEF convened a workshop last fall at the New York Federal Reserve to map national core DSA competencies that would be needed in all business sectors. Attendees included representatives from leading companies, such as IBM, Cisco, NBC Universal, Boeing, and McGraw Hill Financial; from New York City-based universities, such as NYU, CUNY, and Barnard; and other stakeholders, including the Business Roundtable, SAS, and Wiley Publishing.
The workshop touched on implications for community colleges and advanced degrees, but the focus was on the four-year bachelor’s degree. And in the course of a half-day session, a remarkable consensus began to emerge around four key points.
First, there was widespread agreement that although the particular needs of different business domains (healthcare and health policy, financial services, retail, etc.) vary widely, a common core of knowledge applies across the board.
Second, the broad competency areas that make up this common core are also fairly well understood. At a high level, these fall into five broad buckets.
- Data literacy: understanding the various data types, attributes, sources, and potential business value
- Data preparation: ways to create, collect, extract, and harmonize data along with some basic programming skills
- Data analytics: statistics, modeling, visualization, and data mining (structured and unstructured)
- Data governance and ethics: an awareness of security and privacy issues, data as an asset, and relevant regulatory and legal issues
- Data communication: the ability to use visualization tools and translate analytic outputs for non-technical audiences.
For each of these broad competencies, the group emphasized the value of hands-on exposure to data sets and analytical tools.
Third, beyond technical knowledge, there is a need to emphasize “softer” core skills and behaviors. For example, workshop participants talked about the value of teamwork, a healthy skepticism of standard approaches, and a willingness to adopt an agile, “fast failure” mind-set.
Fourth, all agreed that learning opportunities in DSA must be integrated into courses throughout the undergraduate curricula, beginning in the first year, in both two- and four-year institutions.
Even more valuable than answering these key questions, the workshop pointed toward new and intriguing ones, such as how to turn these competency definitions into specific academic programs and course content. Should universities create minors in data science and analytics that students could take as a companion to majors in economics, management, public health, and other data-rich fields? Should there be certificate programs in specific competencies such as analytics? What is the right balance between awareness of concepts and practical experience working with real data in a business environment? What are the best roles for education content creators and software providers? What can be done to promote data science and analytics to a more diverse set of students than we see in today’s graduates?
As the head of a DSA team within the walls of a leading strategy consultancy, my role was to keep the group grounded in the realities of day-to-day practitioners. And I was pleasantly surprised by the enthusiasm of the academic and business communities, by their recognition of the complexities and opportunities inherent in this burgeoning field, and by their willingness to work together toward a common goal. And needless to say, answering these crucial questions will take a lot of time, investment, and trial and error. Watch this space for updates as we continue to work on answers in the months to come.