Arizona State University (ASU) hosted its eighth annual Data Conference, a two-day event that delved into data, analytics, and many of the tools and processes surrounding them. The theme — Navigating Data in an AI World — was brought to life through a series of keynotes and sessions that explored how the recent boom of generative artificial intelligence (AI) is impacting the decades-old world of data analysis and business intelligence (BI).
A look at the event by the numbers helps to showcase impact: more than 300 data specialists and experts from across the university joined more than 40 breakout sessions, featuring over 70 speakers.
Sessions included peer-to-peer learning from across the ASU data community, as well as insights from industry leaders and ASU vendor partners like AWS, Tableau, Snaplogic and more. Session titles included “Persistence, retention, and graduation,” “Double dipping dashboard using DARS data” and “Cracking the code of rankings data.”
Mike Sharkey, Executive Director of Data and Analysis at ASU Enterprise Technology has helped to coordinate the annual event. "At ASU, we're very federated, with data people all over campus; here they come together to learn new things and meet new people, and I feel like that is a secret weapon for ASU's data community," Sharkey shared.
Every day, ASU leverages countless data-informed insights about its community, environments and products to help make decisions around student success and other areas in support of the ASU charter. Over both days, attendees got to delve into practices and upskilling with various data tools and processes.
Keynote presenters provide wide-range of perspectives
The day one keynote address featured Educational Technology Consultant and Industry Analyst Phil Hill. Hill shared his unique insights on the future of AI and higher education, noting that the future of AI-related research and work is becoming more democratized.
In one such insight, he challenged the current view of generative AI relying solely on massive, public datasets. "We're getting into a world where there's a lot more fine-tuning of public data in conjunction with privately held data,” Hill asserted.
Additionally, he emphasized that as AI becomes more integrated into business intelligence workflows, it becomes increasingly essential to establish robust data governance practices to protect sensitive information and mitigate potential biases, within higher education and without.
"We need to be looking about where things are going, and I think we're getting into a new wave of AI – we need to be dealing with what are the implications beyond just large language models like ChatGPT," Hill said.
The second day’s keynote featured a fireside chat between ASU experts, including ASU Enterprise Partners’ Chief Data Officer Erin Barringer-Sterner and Enterprise Technology's Executive Director of AI Acceleration Elizabeth Reilley. Both have robust backgrounds in data analysis, AI and business intelligence, and their discussion shed light on the intersection of where these fields intersect.
When it comes to leveraging AI, the speakers stressed the importance of human oversight and critical thinking. AI models are only as good as the data they are trained on, and it is crucial to ensure that the data is accurate, unbiased and relevant. Human analysts can provide the context and domain expertise needed to interpret AI-generated insights and make informed decisions.
"The fundamentals of what we do, the foundation on which all of our work currently sits, I don't think that is going to change,” Barringer-Sterner asserted. “So all of that hard work that we put into cleansing our data, preparing our data, understanding our data and documenting our data is not going to be any different."
