80%+ of companies don’t have a plan for Gen AI use cases — insights from 700+ data leaders
80%+ of companies don’t have a plan for Gen AI use cases — insights from 700+ data leaders
"Hot off the press" survey results from last week's Snowflake Summit
I spent last week in the heart of San Francisco at Snowflake Summit 2024. With a packed house of over 20,000 attendees across 400 sessions, it was an incredible week full of insights and learnings.
At the Summit, we surveyed over 700 data leaders to get the latest scoop on what’s happening in the data world. They shared their top priorities, current AI journeys, and much more. In this issue of Metadata Weekly, I’m breaking down these “hot off the press” survey results with my co-author Austin Kronz, Director of Data Strategy at Atlan.
✨ Navigating the future of data analytics: insights from Snowflake Summit 2024
51% of data leaders are focused on improving data governance
As organizations strive to harness the full potential of their data, several key priorities have emerged for the remainder of 2024. The survey revealed a clear focus on improving their data and preparing for AI use cases.
51% of leaders are focusing on data governance initiatives, followed closely by data quality (48%) and AI readiness (39%). This combination makes sense, given the importance of well-governed and high-quality data for AI projects. Lower priorities were cost optimization (31%) and cloud migration (25%), indicating that many data teams are already working with modern infrastructure.
54% of data leaders struggle to drive adoption of data governance
While the focus on data governance is evident, it’s far from an easy task. It fails for a few big reasons that I’ve talked about before — its rigidity, siloed or closed-off nature, and lack of a common language.
More than half of data leaders (54%) highlighted the difficulty of getting end users to actually adopt and own data governance initiatives. Managing growing diversity (39%) and getting buy-in (32%) are also significant challenges, followed by the rigidity of existing governance frameworks (22%).
Data leaders are most excited about Gen AI, followed by data products
The survey highlighted several trends generating buzz and excitement within the data community. These trends are set to shape the future of data analytics, offering new opportunities and capabilities.
Generative AI stands out with 66% of respondents excited about its potential to create new content and automate solutions. Data products, which treat data as reusable and integrated assets, also caught leaders’ eyes with 40% saying they’re excited about that trend. Active governance and active metadata are also garnering attention, while data contracts are less exciting for data leaders.
80%+ of leaders do not yet have a detailed plan for Gen AI use cases
GenAI is all the rage nowadays, so we wanted to learn about data leaders’ AI journeys — specifically with Snowflake Cortex, of course, since they were attending the Snowflake Summit.
Most organizations are in the early stages of their GenAI journeys, with 42% of leaders saying that they’re still researching GenAI use cases. Some organizations (13%) have set their goals but lack detailed plans, whereas 9% have both goals and plans in place. Early adopters, representing just 7% of organizations, have already implemented GenAI use cases.
Identifying and prioritizing impactful Gen AI use cases is the biggest challenge, followed by data governance
Data leaders everywhere are struggling with the details of prioritizing and implementing AI. Having a plan for GenAI use cases is one thing, but making it a reality is an entirely different issue.
54% of data leaders said that identifying and prioritizing impactful use cases is a major hurdle, while 42% struggle with establishing the right governance frameworks. Other challenges include ensuring that they have adequate resources (32%) and creating trustworthy, AI-ready data and metadata (24%).
📚 More from my reading list
The danger zone in data science by Duncan Gilchrist
What 10 years at Uber, Meta and startups taught me about data analytics by Torsten Walbaum
What we’ve learned from a year of building with LLMs by Eugene Yan, Bryan Bischof, and others
You’re collecting too much data by Alan Au
How to get more out of your startup’s data strategy by Solmaz Shahalizadeh
How to build terrible AI systems on the Vanishing Gradients podcast
Understanding business needs - staying relevant as a data team by SeattleDataGuy
7 data modeling concepts you must know by Madison Mae
What the AI boom is getting wrong (and right), according to Hugging Face’s head of global policy by Russell Brandom
Top links from last week:
The ABCs of data products by Yordan Ivanov
Mapping the mind of a large language model by Anthropic
What is it like to dislike data? by Michiko Wolcott