At Everpure’s Accelerate 2026 conference in Las Vegas last week, data intelligence and shifting from application-centric systems to data-centric governance and intelligence were key talking points throughout.
In an interview, Shawn Hansen, VP Platforms, Everpure, emphasized the importance of the channel ecosystem to Everpure’s strategic shift and the need for intelligence.
Everpure’s strategic shift follows partner opportunity in infrastructure
Hansen explained that the company’s rebrand from Pure Storage to Everpure represented a broader strategic evolution rather than just a new name. Everpure is moving beyond traditional storage toward helping customers manage, understand, and derive value from their data.
“It’s a pretty major strategic shift. Most of the partners I speak with, they don’t want to have as big a role in the infrastructure as they can,” Hansen notes. “As it goes from managing the containers to selling the oil inside the container and extracting the value, the partner becomes a more advantaged thing outside.”
With the proliferation of AI across a number of sectors, enterprise data growth has been substantial.
Hansen spoke about duplicate data, security concerns, and the challenge of identifying which data is truly valuable. Newer development approaches, like vibe coding, can contribute to rapid data expansion across organizations.
“Data is the new oil,” Hansen added. “The container’s super important, we still care about that, but helping appear inside of it or extract the value out of it. Especially as data continues to grow and large swatches of data keep piling on top.”
Building a platform-agnostic data intelligence layer
When discussing the technical challenges behind the company’s data intelligence platform, Hansen said that the efforts build on years of work and the 1touch acquisition.
Customer feedback revealed that data challenges extend beyond a single platform, prompting a strategy focused on delivering intelligence across diverse data environments rather than solely within Everpure’s ecosystem.
Hansen spoke about how AI is reshaping the way organizations interact with data. AI increasingly serves as a bridge across systems instead of operating within isolated applications.
Semantic knowledge graphs also help establish relationships between data sources, allowing AI to understand context and meaning across organizational silos.
“AI kind of bypasses all the application silos. Every application creates its own world, but AI has a bridge across all of these things,” said Hansen. “It has to try to understand the relationships between the data and provide answers.”
The partner role in building data intelligence
Hansen also notes that data intelligence creates significant opportunities for channel partners.
Partners can help customers identify valuable data, automate processes, govern information, and prepare for AI initiatives, rather than focusing primarily on infrastructure and storage. This enables partners to become strategic advisors.
“This gives partners more opportunities to talk with customers about what’s important in that data,” said Hansen. “No longer is storage just a matter of the container. How do you look inside it, attract value, and make sure it’s being used the right way?”
Governance challenges in the age of AI
Further, governance concerns as AI agents gain greater autonomy highlight the importance of trust, data access controls, and identity management.
Hansen compares traditional role-based access systems with more sophisticated, attribute-based approaches that can better support AI-driven environments.
“The ability to trust the data and understand who gets access to the data becomes more important,” said Hansen. “There’s a transition from role-based access because more sophisticated environments require multiple factors and attributes that determine access.”
Why the new demand for data is a broader industry shift
Going forward, Hansen described a potential customer challenge: organizations often do not know where all their data resides or which sources they can trust.
He explains that enterprises need an independent intelligence layer that allows data to remain where it is while still providing governance, trust, and visibility across the organization.
“We’re not ready for this world. Our data is located in different places and created by thousands of different sources,” said Hansen. “How do we ensure there’s one single source of control? There has to be another new layer of intelligence that allows organizations to become aligned.”
Ultimately, Hansen sees the most important industry transition as moving from application-centric computing to data-centric computing.
Rather than allowing applications to own and control data in isolated silos, organizations should maintain centralized ownership and governance of their information assets.
“The world is transitioning from application-centricity to data primacy,” Hansen says. “Instead of applications controlling the data and working in 20, 40, or 100 silos that don’t talk to each other, the organization owns and controls the centralized source of data independently.”