By now we are all in agreement: The business of data is changing. Business users are more empowered to work with data; IT is becoming less about control and more about enablement. New data science job descriptions—such as the data scientist—are springing up as companies everywhere look for the right people with the right skill sets to squeeze more value from their data. Data itself is getting bigger, hardware more economical, and analytical software more “self-service.” We’ve embraced the paradigm shift from traditional BI to iterative data discovery. It’s a new era.
Naturally, these changes have had a significant effect on how people work within the data science landscape whether they are executives, data scientists, power users, or analysts. However, it’s not just the current workforce that has gotten a facelift. After all, there are a lot of skills available and a very big toolbox to choose from. Adding to that, over the past few years, we’ve been reminded that data workers are in high demand, and we’ve seen firsthand how limited the current supply is. There’s the familiar U.S. Bureau of Labor Statistics estimate that expects 1.4 million computer science jobs by 2020. Another recent stat from McKinsey Global Institute estimates that there will be 140,000 to 180,000 unfilled data scientist positions in the market next year. So, we are faced with two challenges: First, we need more capable data people, and second, we need them to have deeper, more dynamic skillsets. This means we have to start thinking about cultivating talent—rather than recruiting it—and training an incoming workforce isn’t something that industry can do alone (although with specialized software training programs, MOOCs, and a vast variety of conferences, we are trying our best). To enact lasting change and a sustainable funnel of competent data workers suited to the new era of the data industry, we need to move further down the pipeline to that place where we all discovered we wanted to be data people in the first place: the classroom.
To accomplish this, the academic community has been tasked with developing new educational programs that can develop the skills and education needed by incoming data science professionals (not to mention generating enrollment in these programs). These university information science programs—called business intelligence, business analytics, data science, professional business science, or the dozen or so other terms used by academia—are only just beginning to be sorted out. However, they are growing exponentially across the country, and so far enrollment is promising.
Different universities are taking varying approaches to structuring a new kind of data science education. Some are developing entirely new pedagogy focused on the fluid and dynamic fields of data science. Others are reshaping existing curricula by unifying across academic silos to integrate disciplines of study, particularly among business and IT domains. Still others are forming academic alliance programs to give students learning experiences with contemporary industry tools and creating projects that expose students to analytical problems within real-world business context. Nevertheless, all universities are listening to campus recruiters, who are clearly saying that we need people with more data skills and knowledge, and they’re working hard to fill that gap.
Over the past few years, the number of new business analytics program offerings has significantly increased, as evidenced by a searchable dashboard of data science programs available at https://public.tableau.com/profile/lindyryan#!/vizhome/USBusinessAnalytics
DegreeProgramsJan2017/Dashboard1. In 2010, there were a total of 131 confirmed, full-time BI/BA university degree programs, including 47 undergraduate-level programs. Today, that number has nearly tripled and continues to rise with new and improved programs at the undergraduate, graduate, and certificate levels—both on and off campus—springing up at accredited institutions across the country. So, while we might not have access to all this new data talent yet, if academia has anything to say about it, help is on the way.