In 2017, Mario Lebar, CIO at the University of Manitoba, set out to develop an AI chatbot using IBM Watson technology in the hopes of providing best-in-class self-services for information seekers across campus. But the project did not go according to plan. A production-ready chatbot never materialized—but there was a silver lining to this perceived failure. The project exposed the true state of Manitoba’s data, which was fragmented across campus in organizational silos. But this realization kickstarted efforts to clean up and organize the university’s data.
Mario recently shared this story with fellow data and technology leaders at EAB’s Digital Transformation Symposium. In this post, I’ll discuss some of the lessons Mario and his team learned about risk, failure, and data’s role in organizational transformation.
Build organizational data capabilities incrementally to enable organizational transformation
Having arrived at Manitoba from the private sector in 2012, Mario estimated that the higher education industry is 10 to 15 years behind in effectively utilizing data and analytics. While institutions often spend large sums on data and analytics tools and technology, they have historically neglected to implement requisite data governance and infrastructure. Colleges and universities are often enamored by and dive headfirst into flashy big data and digital transformation projects that end up overwhelming capacity and demanding a growing slice of campus resources. On the other hand, taking a focused and incremental approach to building data capabilities on campus can be the onset of transformational change.
In Mario’s words, you need to build your organization’s data capabilities with future growth and digital transformation in mind:
In other words, by taking the incremental steps often incorrectly perceived as banal by leadership (e.g., establishing data hygiene and governance on campus), instead of blindly starting a bevy of digital transformation projects, you put in place the right Lego blocks that allow campus to grow further, faster. For Manitoba, the disappointment of a failed AI chatbot project was the urgency driver for data maturity—so that leaders would be prepared to take advantage of future transformational big data and AI opportunities.
Take transformation risks—and don’t be afraid of failure
Manitoba’s example is not meant to dissuade institutions from pursuing ambitious digital transformation projects, but to emphasize how important it is to have your data environment in order. In Manitoba’s case, the journey (and failure) to actualize their larger digital transformation project helped them identify deficiencies and subsequently build their data capabilities—setting the stage for future projects to run more smoothly.
Lead student-centric innovation on your campus
How will you innovate to ensure your campus emerges from the pandemic stronger than before? Watch this webinar to learn from colleges and universities leading the way with fast-paced innovation cycles.