Skip to main content

It is called the ‘fall forward’ effect. Much like “The Wave” at a sporting event, participants lean far forward on their chairs in near unison. The entire room shifts at once to identify themselves within the organization’s social graph, each employee seeking to understand their level of connectivity and importance relative to their colleagues.

This was a real moment of truth for F5, the Fortune 500 company focused on making training more relevant and nimble. With 25,000 employees operating in a rapidly changing industry, the Talent & Education Development team needed to address their ever-changing business landscape.

One individuals’ network map or social graph before the designers and data scientists layered on the actions taken by each participant and group.

To achieve their goal, they sought to better their High Potential (HiPo) employee programs, in hopes of improving the impact of each employee. This proved to be a greater challenge than they imagined, despite overwhelming support from senior leadership. F5 experimented with educational apps, network analysis, crowdsourcing technologies, manual group creation, and external design thinking consultancies. Over the years they dabbled in a multitude of innovative solutions with varying degrees of success. But nothing truly helped to improve and sustain critical measures across the board.

Eventually, one of the program leaders came across our data science approach. Leveraging network science, big data, machine learning, and artificial intelligence algorithms, program participants could be understood within the context of teamwork, collaboration, and connectivity. From inception, the data science approach proved to be a better fit for F5 because it complemented their big data and results-driven culture. While past vendors had been employed for specific analysis, none had delivered or answered the ‘now what’ once the data had been analyzed. Using our process, the data science outputs were then co-designed around the practical solutions needed for easy implementation. In addition to creating high-performance groups, the data science solution identified each person’s networking journey, digital profiles, and Personal Power Scores(tm), enabling participants to operate in an agile manner across their networks.

As a large, conservative organization though, F5 approached data science integration through a slow, controlled implementation process. Highlighting the importance of networks and data for future training and success, the data science solution was first introduced during a normal employee training event. With interest peeked, attendees saw a personalized real time network analysis for each employee involved in the session (and the aforementioned ‘fall forward’ event).

Further distilling this output, trainees were intentionally networked with their peers in successive learning rounds. During these sessions, data was paired with specific content or educational modules to enhance effectiveness and long-term potential. Next, leveraging their data, connections, relationships and learnings, each employee had the opportunity to develop their strategic network plan to personalize the enhancement of their career.

As a result, all metrics across the board for the corporate and development training programs improved by double digits year-over-year. Based on the overall quality of class programming to the applicability of learnings in the workplace, the data science approach was considered a great success and continues to be expanded across the organization.

As the original class continues to progress within F5, some of the classmates were given access to the data science solution. These employees were tracked as they continued through their training curriculum. This ensured that they were making the right connections to drive success for themselves and the organization.

Curious to know more about how we did it? You can check out some of our product use cases, or get in touch with us to find out which one(s) could be right for you and your organization.

Leave a Reply