Insights and Surprises from Data Universe 2024
It’s never been a better time to jump into the vastness of the data universe and take full advantage of every mission to learn, connect, and share. With different events and conferences popping up, knowing which conference to attend can sometimes be overwhelming. Luckily, my friends at Dataops.live invited me to the inaugural Data Universe conference in New York City. No surprise, the event’s central theme revolved around Artificial Intelligence (AI), but several other sessions and speakers did wow and surprise me and offered their perspectives on the data landscape today and into the future.
What is Data Universe?
Think of Data Universe as a series of TED talks about data, AI, governance, strategy, architecture, and engineering topics. The sessions included lectures, fireside chats, and panels with audience input via a survey tool. I found the content rich with facts relevant to today’s data landscape and challenges, and many of the speakers peppered in their own stories and experiences that tied back to the topic.
The absence of demonstrations stood out most to me in the sessions. There were no tooling demos or walk-throughs of functionality to showcase how a tool could support the speaker’s topic. For example, one of my sessions discussed invoking an “AI Operating System” to support an organization’s growth toward using AI at scale. In my mind, I was thinking about the importance of data lineage in tracking the sources of data AI uses; however, there was no such tie-back or demo that conveyed the importance of lineage.
In contrast to the lack of demos, there was a sponsor exhibition in the center of the session hall where technology and consulting businesses showcased their products and services. The dialogues I had with the independent vendors were lively, with discussions swirling around practical, real-world AI applications and the changing landscape of IT in general. And, not surprisingly — I ran out of business cards here.
Who attended Data Universe?
A mix of business and technology professionals was present at Data Universe. I met people from various industries, including life sciences, manufacturing, financial services, and retail. Most participants engaged in discussions around AI’s prevalence in the data space and the ethical guardrails we need in industry and society to ensure its safe usage.
I also met a wide range of data professionals – from heads of AI/ML at AWS to software engineers at Netflix — all interested in sharing their experiences with AI and how seamless it can be to infuse AI into daily development work. Their stories of tinkering with AI services to optimize repetitive tasks and free up time to focus on larger, more strategic efforts inspired me.
What were the sessions about?
Most major conference events like this have separate tracks to organize topics, and Data Universe was no exception. There were six tracks — Business Strategy & Transformation, Engineering & Infrastructure, Modern Architectures & Data Products & AI, Emerging Tech & Society & Ethics, Analytics & Intelligence, and Governance & Security/Privacy. I bounced around to different track sessions throughout both days and found value in each.
One of the most impactful sessions I attended was delivered by Harini Gopalakrishnan, the Field CTO of Life Sciences at Snowflake. Her session, “Driving an effective data strategy: How do we make data FAIR?” discussed the FAIR concept and its application to reducing biases when collecting clinical data.
FAIR is an acronym for Findable, Accessible, Interoperable, and Reusable. In this session, Gopalakrishnan told several stories about how biases appear in clinical trial data. By using the FAIR framework to evaluate data acquisition efforts, her teams made data acquisition more equitable across gender populations, which added to a much richer dataset to make clinical decisions. Topics like these excite me about how we can better manage data for the greater good.
What surprised me most about the sessions was how they were delivered. Data Universe was in one convention hall at North Javits Center in New York, and each session shared the same space. Session participants had to wear a headset to hear the presenter and not be distracted from the other sessions. Personally, I get distracted when there’s activity around me, so I appreciated the headsets. Minor, yes, but it is worth noting this for future events.
Other Notable Highlights
There’s a unique “surprise” element to technology conferences when you see special vendors that shake up the landscape. I had quite an in-depth conversation with the team at Women In Data about a few topics ranging from attracting female talent to supporting an inclusive culture in the age of remote work. When I started in this industry over 20 years ago, organizations like this were small and relatively unknown. However, that’s changed entirely with more people gravitating toward data professions and the heavy demand for talent in the industry.
In my talks with the Women In Data representatives, I quickly learned that there’s no magic trick to including more women in the data profession. Instead, we must support groups like this and remember to be influential allies in our daily work. I’m grateful for the opportunity to keep learning from groups like this, and found their presence at Data Universe dynamic and relevant.
Final Impressions
Heading into this conference for the first time, I kept an open mind as I didn’t know what to expect. I’m glad I did! The sessions were rich, and the discussions were lively. Many people were eager to meet and discuss anything related to AI, modern data management, governance, and ethics; where else can you find all that in one place? While I wish there were more demos in the sessions and the sessions themselves were in dedicated conference halls, perhaps we’ll see enhancements next year. All in all, a good show!
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