Data science, advanced analytics and the advantages provided by open source technologies are critical for modern businesses to remain relevant and competitive in our rapidly evolving technology environment. Startups are disrupting a variety of industries by leveraging new technical and analytic techniques to change what customers expect from the industry as a whole. Many larger organizations can struggle with adopting new analytic approaches, and broad adoption is best achieved through a cultural shift towards leveraging analytics in every aspect of the business.
Companies, both big and small, are exponentially increasing their data science teams as a means to transform their business and drive technical change through data science across the enterprise. But simply hiring the data scientists is no guarantee of the paradigm shift most firms envision. The key to this crucial transition is data science literacy.
At S&P Global–financial services provider of credit ratings, data and analytics, research, and benchmarks–we pride ourselves on a long history of providing essential insights. The company’s roots trace back to 1860, when Henry Varnum Poor published an investor’s guide to the U.S. railroad industry that provided essential insights to help investors make smart investment decisions.
The company was born from smart, effective applications of data and analytics processes, which are now being supplemented with predictive models wherever we’re able; our core competency centers around our ability to be model-driven. Like many companies might realize, sustaining and growing on this approach means more than just the team of data scientists crunching numbers – to truly enable a cultural change, we need to arm employees with essential data science literacy.
First, we had to decide what mechanism(s) we’d use to educate 17,000 globally distributed employees on data science. We needed to figure out how to implement a training program that would scale in an interesting and interactive way. We also needed to think through the right set of resources and materials that would be valuable to a wide-ranging group of participants–educating those new to data science while also providing useful, hands-on experience for more advanced learners.
We decided to offer a hybrid approach to data science education, leveraging open source learning materials available in Massive Online Open Courses (MOOCs), and supplementing with additional internal resources to tie all of the course modules back to specific “familiar” applications. To facilitate this, we identified four primary components for the session:
In selecting the right course, we set out to find one that would provide enough breadth and depth to appeal to a broad base without sacrificing technical rigor. Believe it or not, though critical to the business, learning about data science might not be the top appeal for your average employee.
We aimed to identify a course that would align with employees’ job priorities, and one that we could incorporate domain and company specific information into. For the interactive sessions, we tapped members of the S&P Global Market Intelligence Data Science Department to facilitate discussions and showcase existing initiatives to demonstrate how data science techniques are currently being leveraged within the organization. We also wanted to identify a free and easy-to-use forum platform for participants to interact with the instructor and, more importantly, with other participants. And finally, we needed to make it technically simple for employees to set up data science environments and get technical support as needed.
The program in its first instance ran for 10 weeks, during which more than 130 employees spanning multiple global divisions participated. Engagement has been active throughout all 10 weeks, within every guided study session and the online forum.
We’ve been collecting feedback which has been quite positive. Here are some of my favorite anecdotes from employees that participated:
We still have a long way to go before all 17,000 S&P employees are “data science literate,” but we’re off to a running start and look forward to seeing how this program fosters an even more model-driven culture than we have today.