Everpure unveils data primacy framework to fix AI's broken foundation with governance-first approach

Enterprise Data Cloud blueprint powers AI data strategy

Everpure launches Enterprise Data Cloud blueprint to guide AI data strategy Fragmented data, siloed infrastructure and reactive portfolios are problems that every enterprise has to deal with, and fixing them efficiently requires a completely new operating model -- one that the Enterprise Data Cloud is designed to deliver. That's the basis for Everpure's new Success Blueprint, according to Stephanie Richardson (pictured), vice president of product marketing at Everpure Inc. The framework spans ten capability areas throughout the three dimensions of agility, cyber resilience and scalability. It's designed to help organizations assess where they stand today and identify their vulnerabilities while charting a prescriptive path toward a unified, governed and autonomous data environment. "Implementing technology is part of the solution, but it's not the only thing," Richardson said. "It really requires you to refactor your environment and start with a fundamentally different data strategy and a fundamentally different approach to your technology." Richardson spoke with theCUBE's Christophe Bertrand and Alison Kosik at Pure Accelerate 2026 event, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed how the Enterprise Data Cloud blueprint works in practice and why a structured maturity model is the perfect mechanism for calibrating infrastructure, security and data leaders. (* Disclosure below.) Enterprise Data Cloud blueprint turns data strategy into measurable business outcomes The blueprint delivers in three forms: an assessment that benchmarks current maturity and reveals priority areas, self-service guides that walk organizations through prescriptive steps at each maturity level and facilitated workshops where Everpure experts work alongside customer and partner teams to coordinate on a shared data strategy before purchase decisions are made. "Book a blueprint workshop with your team," Richardson said. "It's about getting your team aligned on what the vision is of what we're trying to build, and that's often the hardest part." Richardson described a concrete progression using operational efficiency as an example. At lower maturity levels, teams spend time on manual provisioning tasks that they've repeated for years. As organizations move up the curve, those tasks become automated, then self-optimized through policy-driven workload rebalancing. Ultimately, it reaches a state where the environment runs autonomously against preset SLAs. Every step delivers quantitative value, and organizations don't need to reach the top of the maturity curve before seeing returns. Everpure has attached specific business outcome metrics to each capability area in the blueprint, tracking results across efficiency gains, power reduction and reduced audit times to build the evidence base for AI return on investment over time. "Every step of the way, you're going to see value," Richardson said. "What you don't want to do is make any decisions that you're going to have to refactor again in a couple of years." Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of the Pure Accelerate 2026 event: (* Disclosure: TheCUBE is a paid media partner for the Pure Accelerate event. Neither Everpure, the sponsor of theCUBE's event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Everpure CEO - 'AI makes data primacy necessary, but the organizational challenge needs a senior leader driving it'

Today Everpure outlined its data primacy thesis - introducing products that include Data Intelligence, Data Stream, and the evolving Intelligent Control Plane - and the architectural argument that 50 years of application-centric enterprise IT must now invert. This morning's CEO Q&A provided us with an opportunity to really stress test this thesis and get an understanding from the vendor's top boss about what it takes for enterprises to bring data to the fore and push applications downstream - and to get a clearer view of why the adoption of AI will make data primacy work this time around. Responding to a question from diginomica, Charlie Giancarlo cut through the product announcements and highlighted a point that will really resonate with enterprise software buyers: data primacy is arguably more of a political problem than a technology problem. But more on that shortly. Breaking point - why AI changes the calculus Firstly, Giancarlo's architectural argument against the application-centric model has been building for some time. The problem is not simply that data is siloed - that is a fact of enterprise life going back decades. The new element is that AI, and specifically agentic AI, makes incoherent data actively dangerous rather than merely inconvenient. Every SaaS vendor and every AI provider wants a copy of enterprise data, which is not only untenable, but is downright risky. Every copy drifts from the moment it is created and where a human analyst can use judgement to reconcile inconsistencies between versions, an agent will act on what it is given. Asked during the Q&A whether "data primacy" is really a repackaging of every previous data-centric wave - master data management, data lakes, et al. Giancarlo said: I think we've just reached a breaking point, and maybe AI is that breaking point... You have the SaaS players saying, 'for my AI agent to be more useful to you, you've got to get me a copy of your other data.' How many times are you going to replicate your data in all these different places? The other change is this concept of creating a shared context layer across all of your data, which is also new. We've reached a breaking point on the existing way of doing things, and we have some new technology. He was also clear about what separates the model from ETL - which creates a new copy of data for a specific purpose, analytically or otherwise. What Everpure is describing is metadata that maps how different repositories relate to one another, without requiring the data to move. The difference here is important and it is the answer to the "why do I need another copy?" objection that has tripped up every previous data integration effort. The political and organizational challenge However, technical capability and organizational willingness are two different things. I asked Giancarlo to address the organizational reality, where the shift to data primacy requires enterprises to treat data as a centrally governed shared resource, rather than something owned by the application teams and business units that have controlled it for decades. That is the precondition that has defeated every serious previous centralization effort - not the technology, but the change management. One thing that has remained constant in my nearly two decades of covering enterprise technology: and that is people and organizations are stubborn and unwilling to change. A technology and architectural shift can make complete sense, but people will continue to guard their empires, protect their jobs, and have vastly competing agendas. Is the technology this time actually lowering that political barrier to change? Giancarlo's response was that for this to work, it requires organization-wide investment. He said: You can't solve the problem by just having one group involved - just finance, or just the business group, or just sales. One of the reasons there's app proliferation is that a lot of these apps reflect workflows in those individual organizations that make it very comfortable for them. Now the considerations around the core elements of those workflows, which exist in each of the different functional organizations, have to come together and come to agreement. Everpure is undergoing this change itself, shifting towards data primacy, and so Giancarlo could talk about what it takes at his own company to drive the change. What Giancarlo said should be considered closely by any enterprise reading the news coming out of Everpure this week and considering an inversion of its enterprise stack: When we started this programme about a year and a half ago, we called it Mercury. It's for internal IT. I have attended a coordination meeting every single week. I've had to personally drive it. The CEO of Everpure has been personally chairing weekly coordination meetings for 18 months to make data primacy work inside his own organization. He expects the full journey to take around two and a half years in total. It does not have to be the CEO in every organization - he was clear about that - but it does require a heavyweight: It doesn't have to be a CEO - it has to be a senior leader. It's probably going to be the Chief Data Officer in a company who has to drive this. We've also used third-party consultants, because you need expertise in certain areas that you may not have resident in your own company. His defense against the comparison with previous failed centralization waves was to compare data primacy to a major enterprise application roll-out. He said: Is that really so different from the first implementation of Salesforce, or Workday, or ServiceNow, or the continuing development of ServiceNow within an organization? Not really. It is a reasonable comparison as far as it goes. Those implementations required executive sponsorship, cross-functional alignment and multi-year budgets. But the outcome of those programmes was a running application doing something immediately visible and measurable. What data primacy delivers is infrastructure for future applications and agents - a harder thing to build sustained organizational momentum around across two and a half years, particularly when the executive driving it moves on or priorities shift under quarterly pressure. Giancarlo pointed to the large banks and certain healthcare organizations as the furthest advanced - in part because M&A forces continuous data reintegration on them anyway, and they have been working toward authoritative systems of record for years without calling it data primacy. Those customers, he suggested, will recognize the architecture Everpure is now naming. But for the rest of the enterprise market, the system integrators are the route to scale - which is a significant bet on a partner and consulting ecosystem that is still being built around capabilities. Channel opportunity - but different for different partners And on that point, the channel is going to be key to the success of this. And it's something, I'd suggest, Everpure is going to have to focus on heavily. It faces the same challenges it has selling to buyers - different conversations, different personas, different outcomes - as it does with the channel. Selling flash arrays is a whole different ball game to selling an enterprise-wide data platform. For system integrators the data primacy argument is a different proposition entirely. Giancarlo said: For them this is like manna falling from heaven. Any new opportunity at a re-architecture is automatic strategic work as well as implementation work. We believe where the IT world needs to go is towards a data primacy model where enterprises control their own data and their own shared context for those sources of truth. Then agents of any type - an application, a SaaS service, a workflow, or a literal AI agent - can work on those sources of truth. That opens up huge opportunity for the channel, because all of that requires a lot of expertise, a lot of help and a lot of services. So we believe it's a great opportunity for the channel. He also put some internal scale on the fragmentation problem. Everpure is approximately a $4 billion company with "over 750 applications, and every one of them has its own version of the data and its own version of what that data means." If the company making the argument for data primacy is dealing with that internally, the implication for larger enterprises - and system integrators in turn - needs little elaboration. My Take The candor in this morning's Q&A from Giancarlo was very much welcome. Everpure is not shying away from the work ahead of it - and it isn't burying its head in the sand about the challenges. A CEO who tells you his own programme has needed weekly personal attention for 18 months and will take another year to complete is not overselling. He is telling you what it actually costs, and that matters in an industry that has a well-established habit of making enterprise transformation sound easier than it is. The data primacy argument is a coherent and compelling one, as I said yesterday. App sprawl has fragmented enterprise data, AI punishes that fragmentation more severely than anything before it, and someone needs to build the neutral layer that lets enterprises own the meaning of their data rather than ceding it piecemeal to every SaaS vendor and AI platform. Everpure has a version of that argument, real product behind it, and an astute read on the vendor lock-in fear that is clearly (once again) facing buyers right now. However, it's clear that this is a multi-year organizational change programe that the technology enables but does not substitute for. The question for Everpure is whether it can build the partner and consulting ecosystem quickly enough, and demonstrate integration depth on a recently completed acquisition, before the better-resourced data platform vendors close the same gap from the other side. I will be following up on those points with a deeper analysis, following a number of conversations with other executives, later this week.

Everpure & WWT on building data-ready AI infrastructure

Everpure and WWT say data-ready AI infrastructure starts with clean, governed data Getting to production-ready AI requires much more than fast storage because enterprises need data-ready AI infrastructure that's built on clean, governed and well-understood data before any meaningful deployment can scale. It's a shift that's redefining what partners bring to the table, according to Hope Galley (pictured, right), vice president of Americas partner sales at Everpure, and Justin Field (left), technical solutions architect at World Wide Technology, the latter of which was recently named Everpure's global partner of the year. Both said the conversation is moving decisively away from speeds and feeds toward business outcomes and cross-functional selling into the C-suite. "Every CIO or CEO knows that they should be in AI," Galley said. "But what does that mean? What business case? What can AI solve for them? The more that you have a consultative approach, those are the ones who are going to win." Galley and Field spoke with theCUBE's Christophe Bertrand and Alison Kosik at Pure Accelerate 2026 event, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed what data-ready AI infrastructure requires and how the partner model is transforming to meet it. (* Disclosure below.) Data-ready AI infrastructure demands clarity before deployment Field said that the biggest shift he sees in customer conversations is the move from raw performance benchmarks to data preparation, making sure the underlying data is clean and curated before any AI investment is made. WWT's AI proving grounds and advanced technology centers exist specifically to let customers validate infrastructure decisions at scale before committing, which removes the risk of large investments that underdeliver. "A lot of those talks have switched over to just the data preparation, and is the data even clean," Field said. "No matter what you buy, it won't give you good value if your data isn't curated and contextualized and ready." The newly announced Everpure data intelligence capabilities address that challenge directly, providing partners and customers with documented visibility into what data exists and how many copies are in play, Galley noted. This is foundational for both compliance and data-ready AI infrastructure. Everpure's Evergreen//One consumption model adds another layer of flexibility, letting customers scale storage commitments in line with AI project timelines, instead of being constrained by supply chain uncertainties. Galley said that partners who lean into consultative services and cross-functional selling are the ones pulling ahead in the current market. "Clarity is a big thing around AI right now," Galley said. "Customers are saying: 'How are you going to help me with AI and give me the facts behind that on how you're going to help?' The more that you have a consultative approach, those are the ones who are going to win." Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of the Pure Accelerate 2026 event: (* Disclosure: TheCUBE is a paid media partner for the Pure Accelerate event. Neither Everpure, the sponsor of theCUBE's event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

"Data primacy" - Everpure's pitch to invert the app-centric enterprise

In Las Vegas this week, ahead of the main-stage keynotes, Everpure took press and analysts through announcements that aim to turn its February's rebrand into a longer-term product strategy. When Pure Storage became Everpure, I wrote that dropping "storage" was a coherent extension of the company's recent ambitions (away from storage-focused, towards data management) - but this week the company decided to center its strategy around a one core architectural idea : data primacy. In short, separating enterprise applications from enterprise data and making data the core value holder in the enterprise. In a blog timed to the news, Chairman and CEO Charles Giancarlo argues that every computing revolution is, at root, architectural - and that AI is now inverting a hierarchy that has been evident in enterprises for multiple decades: it's no longer applications first, data second. Giancarlo wrote: Everyone talks about the importance of data. Now it's time to put data first. Everything else will follow. What's shipping To understand what Giancarlo is proposing here, we first need to outline what's being announced this week. First up is Everpure Data Intelligence - the productized form of the 1touch acquisition flagged alongside February's rebrand, now generally available. It discovers structured and unstructured data wherever it sits, including inside databases such as SQL Server and Oracle; scans for sensitive information like PII and PHI and tracks lineage; and maps raw data to its business meaning to build what Everpure calls a semantics knowledge graph. Crucially, it is meant to work across an entire estate, not just Everpure's own arrays. Ashish Gupta, former CEO of 1touch and now General Manager for Data Management, outlined the impact the platform can have on how customers work with their data: At one of the large credit card companies, the use case was how they respond to DSAR requests - data subject access requests... it took about 21 person-hours to respond to any one. When you have over 7,000 requests coming in on a daily basis, that cost is astronomical... it enabled this customer to take 30 seconds, instead of 21 hours. Everpure Data Stream, also available now, takes that classified data and prepares it for AI, calculating the vector embeddings that feed retrieval and generation - cutting raw data preparation, the company says, from months to minutes. It is built on NVIDIA's AI Data Platform reference design and runs on FlashBlade, scaling to FlashBlade//EXA for GPU-cloud workloads. The Enterprise Data Cloud picks up updates too: Evergreen//One Overdrive (Q3), absorbing traffic spikes of up to 25 per cent above baseline without a permanent upgrade; agentic additions to the Intelligent Control Plane through the year; and an EDC Success Blueprint to guide the transition. Back to the inversion argument These product announcements are fine in isolation, but it's important to understand them within the context of how Everpure sees future AI enterprises being structured. Giancarlo's blog argues that application sprawl fragmented the meaning of enterprise data, and AI makes that fragmentation untenable. He wrote: In the app-centric world, 'customer' in a CRM means something different than 'customer' in a billing system; 'asset' in an ERP is not the same object as 'asset' in a supply chain platform. Multiply this across hundreds of applications and you get a system where cross-domain meaning has to be reconstructed every time it's needed for a new workflow, usually through another data copy, another spreadsheet, another pipeline, and another meeting to reconcile all of it. This creates errors that often need manual fixes. The use of AI and analytics only exacerbates the problem. AI and AI agentic systems will only be as good as the data they feed on. And AI and analytics based on inconsistent data produces inconsistent results. In business, accounting, and R&D, 95% accuracy is just not good enough. Data must be consistent and accurate. Chief Technology Officer Rob Lee, said on stage this week that for years we talked about software eating the world - but AI flips this to data eating the world - with governance and architecture taking the front seat while applications move to the back. He pointed to how this has already begun on the analytics side, and now needs to reach the transactional side. Lee said: You've started, over the last three to five years... to see the initial phases of this move towards data primacy, focused around analytics, not on the transactional side, with things like open table formats - Iceberg, Parquet... we see a future where the entire transactional side of the house needs to follow along. Everpure argues that whilst the likes of Snowflake and Databricks are working on this, aimed at analytics; Everpure wants to be the neutral, automated layer beneath transactional data. An IDC survey, commissioned by Everpure, says 94 per cent of IT leaders put data quality as the determining factor in AI success. And our own November 2025 report, from conversations with technology leaders, found that while 93 per cent now use AI, only just over a fifth report success above 80 per cent - and the blockers were poor data quality and disconnected systems, not the technology. Arguing against vendor lock-in Prakash Darji, General Manager for Digital Experience, wasn't shy about naming application vendors as attempting to 'garden-wall' their environments to protect against a data-primacy architecture. I'd argue his comments are probably a bit on the enthusiastic side - but the sentiment will ring true for customers who are thinking about how they open up their application environments for agentic AI, where the benefits truly lie in working horizontally across enterprise data. Darji said: SAP says do all your stuff in SAP. Salesforce says do it in Agentforce. Databricks says bring it into Databricks. Snowflake says do it over here. Palantir says do it in mine. Can everyone be right? If everyone's right, you're going to be copying all of your enterprise data into everyone's technology stack - for vendor lock-in. His framing borrowed from the early days of APIs - application APIs begat API gateways once people decided they would not rewrite every integration into a separate proprietary thing. The same, he argued, is now happening with data. He added: Every vendor has almost taken a self-serving view - 'Of course I need this capability, but your first step is to give me all your data.' We're not saying that. Our goal is to allow you to operate with your data, to own your control and semantics, and to solve this problem heterogeneously across the customer's entire landscape. CTO Rob Lee added: This whole approach the industry seems to be taking - 'send me all your data and I've got you' - is going to be a very challenging one. It is a shrewd line, because it speaks to a fear we are hearing across the diginomica network - buyers wary of consolidating their AI future onto one vendor's stack. A neutral layer that lets data stay where it lives is exactly what a nervous buyer wants to hear and Everpure is selling reassurance as much as architecture. What it means for the application You cannot make this argument without mentioning the dreaded SaaSpocalypse debate - the contested idea that AI hollows out the traditional SaaS application. Everpure is being pretty explicit about this. It's essentially saying that if semantics and governance move down to the data, and applications become workflows reading from and writing to a shared system of record, the application is no longer where the value accrues. That is the territory Blue Yonder's Duncan Angove staked out at ICON with "the agent is the app", and it rhymes with the case Celonis President Carsten Thoma made to me about operational context surviving platform disruption. Our own network tells a more mixed story than the headlines - in our February micro-pulse, over three quarters of CIOs had not cancelled a single SaaS renewal because of AI, and as many expected SaaS spend to rise this year as to hold flat. No reckoning, then. But one respondent caught the drift - they increasingly think of SaaS "as the repository for data rather than the place all work is done." That is data primacy, near enough, in a practitioner's own words. To his credit, Darji did not oversell how quickly any of this arrives. Drawing on his years building financial applications on SAP's HANA database before joining Everpure, he conceded the hard workflows move last. He said: The simple applications that are SaaS today will probably move towards this schema-first, plug-and-play model, and the more complicated workflows will take longer. The harder problems are the governance, the lineage, the SEC compliance. Just like we started... on a point of view that there's no place for disk and we were on a journey to make the all-flash data centre real - we're probably starting this journey now... to say data primacy is another horizon we want to reach. I appreciate the honesty, but it's hard to ignore that "horizon" carries some weight in this. Everpure isn't afraid to take a position on where it thinks enterprises will be in five, ten, 15 years time - as it did with flash. And it's arguably taking a longer term strategy here than most, which is a commendable approach in an industry driven by quarterly pressure. My take Data primacy and the neutral-layer pitch are compelling. Seeing demos from Everpure showing how its platform can recognize connected data across multiple systems and data formats, and connect them to create something governable and useful is impressive. For decades enterprises have been talking about one source of data truth - remember master data management and ever expanding data lakes?? - but it has largely fallen flat because of the investment in human intervention required to make it anything remotely workable. Silos persist. However, agentic AI may be the technology that finally breaks through in some meaningful way. That said, the SaaS giants are not standing still - they are building their own data layers, with incumbency, budgets and a decade of relationships. Persuading a CIO that the real system of record now lives a layer below the one they have spent years and a fortune on, owned by their perceived storage vendor, is a horizontal sell cutting across every data owner in the building - that's a new sales challenge for Everpure. The vendor has the right argument, in my view, but I don't see this being a quick win. This will take time and a combination of competition amongst buyers forcing ongoing change, as well as growing frustration with application providers only taking them 50% of the way to real AI value. However, it's true that app sprawl fragmented enterprise data and AI punishes that fragmentation. And someone - or some people - will build the layer that fixes it. Everpure has a more coherent version of the argument than most, with real product behind it and a smart read on buyers' fear of lock-in. I'm going to be pushing Everpure throughout the week here in Las Vegas about what this means in practice, how buyers can realistically get there, what challenges lie ahead, and how it plans to convince the enterprise it is the vendor to take this on. Stay tuned to diginomica for more in depth analysis from the event.

Data centric model pivot drives Everpure's brand evolution

Everpure shifts from hardware to data-centric model as governance emerges as the key to AI returns Enterprises racing to deploy artificial intelligence are discovering that the bottleneck is not compute or models, but rather the failure to adopt a data centric model to resolve unmanaged, ungoverned data sitting across silos that cannot be classified, accessed, or trusted at production speed. That pressure is precisely what drove Everpure Inc.'s rebranding from Pure Storage and its pivot toward a data centric model built around intelligence and governance. The company is no longer positioning itself as a storage provider that happens to run AI workloads -- it is staking out the full data layer, according to Lynn Lucas (pictured, left), chief marketing officer of Everpure Inc. "The business strategy to move and expand into data management, and now Data Intelligence, we felt might not be as resonant with a company name with storage in it for the new audience that we are expanding into," Lucas said. "The newer folks that we are addressing, chief data officers, chief AI officers ... really felt like storage in our name might be limiting." Lucas and Phil Goodwin (right), research vice president of multicloud data management and protection at IDC Corp., spoke with theCUBE's Christophe Bertrand and Alison Kosik at Pure Accelerate 2026, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed the company's rationale for rebranding, Everpure's shift toward a data centric model, and the governance gap that prevents enterprises from realizing AI return on investment. (* Disclosure below.) Data centric model and governance as the missing AI unlock The rebrand reflects a real market shift that IDC research is tracking closely. Enterprise AI failures are rooted in data dysfunction, not model limitations, Goodwin noted. "This pivot to more of a data centric model rather than hardware-centric, I think, is very well timed," Goodwin said. "The research that we've done has shown that the real barriers to AI success start with governance. In fact, that's the number one reason that AI projects fail. The second one is data access -- the silos, the inability to bring in the data." About 54% of AI projects never make it to production, representing zero return on investment for companies that have already committed significant capital, Goodwin noted. The answer is not simply pooling data -- it is governing what goes where. Enterprises that treat governance as an afterthought, rather than a foundational control layer, are the ones left with failed pilots and mounting costs. "Not all data belongs in every learning module," Goodwin said. "You really need to be able to differentiate what data needs to go where, how it needs to be protected, how it needs to be governed, maybe it's sovereign data ... it's really that data control layer that's critical in an AI environment." Everpure Data Intelligence, built on the company's acquisition of 1touch.io Inc., is designed to deliver that control regardless of where data lives -- on-premises, in the cloud, or outside of Everpure's own arrays entirely, Lucas noted. The vision is a kind of navigation system for enterprise data: not just mapping where data sits, but surfacing the context that makes it actionable and safe for AI. "You need AI-ready data with your AI-ready infrastructure in order to get the business outcomes for your organization," Lucas said. "Make sure you are working on both in order to deliver to the business what you need." Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of Pure Accelerate 2026 event: (* Disclosure: TheCUBE is a paid media partner for Pure Accelerate 2026. Sponsors of theCUBE's event coverage do not have editorial control over content on theCUBE or SiliconANGLE.)

AI-ready data foundations fuel enterprise AI

Data context and governance are the missing ingredients keeping enterprise AI from scaling The next phase of enterprise AI is shifting focus from models to the data that fuels them, with organizations increasingly investing in AI-ready data foundations. As regulatory requirements grow and data environments become more complex, companies are prioritizing data intelligence strategies that provide the visibility, context and governance needed to scale AI responsibly. AI is transforming how work gets done, with intelligent agents increasingly able to make decisions and carry out tasks independently. To unlock that potential, organizations must understand their business processes and apply the right intelligence and context, enabling faster execution while freeing up resources for innovation, according to Ashish Gupta (pictured), chief executive officer, president and chairman and board member of 1touch.io Inc. "I feel AI is going to be very, very productive for everyone in the market," Gupta said. "But it's going to be very productive only if you've got the right data context driving that accuracy." Gupta spoke with theCUBE's Christophe Bertrand and co-host Alison Kosik at the Pure Accelerate 2026 event, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed the growing importance of data context for AI accuracy and the role of governance in building AI-ready data foundations. (* Disclosure below.) AI-ready data foundations in focus Many organizations struggle to achieve AI-ready data, which is one reason AI projects often fail to move beyond the pilot stage. Success requires four things working together: a clear vision of what AI should do, the right data to make it accurate, cultural adoption across the organization and continuous monitoring to ensure the system keeps learning, Gupta noted. "The first thing is that you need to understand what you want to let AI do," Gupta said. "From that second perspective comes around is, what is the data that is going to make it more accurate in doing what it needs to do?" When those elements are missing -- or when AI costs aren't actively managed -- the effort quickly becomes unmanageable, Gupta noted. "You need to continue to make sure that it's accurate and learning as it moves along," Gupta said. "When you take all of these things together, in addition to the fact that costs can burgeon if you don't do it correctly, it becomes quite a large task if it's not done in a coordinated manner." Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of the Pure Accelerate 2026 event: (* Disclosure: TheCUBE is a paid media partner for the Pure Accelerate event. Neither Everpure, the sponsor of theCUBE's event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Everpure Unveils Data-Primacy Architecture for the AI Era

New Everpure Data Intelligence enables discovery, context, and governance at the source -- turning fragmented enterprise data into an automated, AI-ready foundation. Everpure today announced new capabilities to help businesses securely fast-track enterprise AI initiatives while maintaining visibility and control over all their data. Anchored by the introduction of Everpure Data Intelligence (formerly 1touch.io) and new updates to the Enterprise Data Cloud, these advancements will help organizations turn fragmented enterprise data into trusted, AI-ready intelligence. Historically, enterprise IT relied on an application-centric model that trapped critical data and context inside application silos for specific functions like sales, finance, or logistics. Today, this app-fragmented approach acts as an operations bottleneck -- triggering massive data sprawl, blind spots, and costly replication of untrusted data. To unlock the value of AI, businesses must shift to a data-centric model. "AI completely upends the traditional IT hierarchy; enterprises that do not shift from app-centricity to data primacy will fall behind," said Charles Giancarlo, Chairman and CEO of Everpure. "Because data is a company's primary asset, embedding context, semantics and governance directly at the data layer is the right way to reduce data fragmentation created by the growth of apps and AI agents. Enterprises need to consolidate their fragmented enterprise data footprint into a real-time corpus of trusted intelligence." In the data primacy model, information is liberated from individual applications to become a shared and governed system of record. Data needs to be self-describing and carry its own meaning and logic wherever it goes. Furthermore, governance is embedded at the data layer, ensuring lifecycle management and privacy rules are permanently attached to the information rather than policed by outside software. Apps and agents read from, and can contribute to, the data in the system of record, but do not own it. Delivering Data Intelligence for the AI-Era Everpure Data Intelligence discovers, classifies, and contextualizes enterprise information at its source. It works on all data, inclusive of the Everpure Platform, public clouds, SaaS applications, and third-party storage. Instead of trapping meaning inside rigid application or hardware silos, Data Intelligence contextualizes definitions and connections in enterprise wide data while adding safety rules and contextual relationships necessary for AI. For enterprises deploying AI agents, feeding them accurate and relevant data maximizes response accuracy while drastically reducing the context windows and token costs. Available now, Data Intelligence delivers three core capabilities across an enterprise's entire estate: * Universal Discovery: Offers visibility into structured and unstructured data regardless of storage format, including major databases like SQL Server and Oracle, showing exactly where critical application data resides * Automated Governance: Automatically scans the entire system to identify sensitive info (like PII and PHI) and track lineage, providing a complete map of the data landscape to ensure secure compliance. * AI-Ready Context: Maps raw data directly to its real-world business definition, creating a semantics knowledge graph. This enables modern AI agents to instantly understand, query, and safely act on information across the entire enterprise. "Enterprises are spending millions on advanced AI models and compute, but their underlying infrastructure is starving those systems with disconnected data," said Matt Kimball, VP & Principal Analyst, Moor Insights & Strategy. "The biggest bottleneck to AI adoption right now isn't the software, it's the plumbing. Putting data at the absolute center of the enterprise strategy is exactly how IT leaders can rein in runaway operational costs and accelerate rollouts." Expanding the Enterprise Data Cloud Architecture Because Data Intelligence works seamlessly with the Everpure Platform, it further extends the value of the Enterprise Data Cloud architecture. Organizations can now apply context-aware intelligence directly to how information is stored, operated, and protected across their entire estate. Everpure is introducing updates to its Unified Data Plane to deliver a shared foundation across the entire enterprise that eliminates performance silos, maximizes resource efficiency, and brings cloud-like scaling directly into physical data architecture. A key development is Evergreen//One Overdrive, available in Q3 2026, which provides a temporary, cloud-like performance boost for on-premises storage to seamlessly absorb traffic spikes up to 25% above baseline without requiring permanent subscription upgrades. Sitting above the Unified Data Plane, the Intelligent Control Plane embeds AI directly into daily operations -- turning manual, reactive storage administration into a self-optimizing, secure environment. By utilizing natural language orchestration and predictive behavioral analysis, these tools abstract operational complexity: * Workload Rebalance & Mobility: Powered by the infrastructure layer, this feature automatically moves active workloads across the fleet without causing downtime, optimizing capacity distribution and guaranteeing steady application performance. (Available Q4 2026) * Copilot Workflow Execution: Allows storage administrators to use natural language to plan, validate, and trigger secure, end-to-end operations across the entire global infrastructure estate. (Available Q2 2026) * Enhanced Cyber Anomaly Detection: Monitors telemetry across the entire environment to spot coordinated, suspicious login patterns or behavioral drifts that individual storage arrays might miss in isolation. (Available Q2 2026) * Fusion Compliance & Agentic Triage: Automatically detects hardware or software configuration drift to enforce global corporate governance while leveraging agentic AI to suggest root causes for immediate technical remediation. (Available Q4 2026) To help organizations transition to a data-centric architecture, Everpure is introducing the EDC Success Blueprint. This step-by-step roadmap provides a proven methodology for building and scaling an enterprise data cloud. It begins with a practical readiness assessment to identify immediate infrastructure and security risks, then maps out a clear path across 10 operational pillars to transition environments from manual management to a highly automated, efficient architecture. Designed to continuously evolve alongside technology, the EDC delivers a unified, secure data fabric that shows exactly where data lives, how it connects, and what it means. This completely transforms how the entire environment operates: governance policies protect data based on its inherent meaning rather than its application silo, AI workflows optimize around shared enterprise context, and infrastructure automatically adapts to real-world usage behavior.

Autonomous infrastructure unlocks live data for AI agents

Autonomous infrastructure breaks data silos to accelerate enterprise AI Data intelligence is becoming the next battleground for enterprise AI and autonomous infrastructure as companies discover that copying information into dashboards and data lakes is too slow for agentic workloads. The shift is forcing IT teams to rethink architectures built for applications first and data second. That debate is also reshaping how infrastructure companies frame their AI strategies, as recent SiliconANGLE coverage of Pure Storage shows. Breaking application silos and turning scattered repositories into live context for AI is now a core goal, according to Chadd Kenney (pictured), vice president of product management at Everpure Inc. "If you were able to break down those silos, take the context and share it across each one of these applications and then later build a system of record with all of that data consolidated, AI agents now could actually be running on top of real-time data versus this latent copy," Kenney said. "If they only have access to Salesforce data, they would have to infer what the costs are and maybe just make up what would be profitable or not. If they understood what suppliers were, what the costs were and also what the total product cost was, they could actually infer what a profitable order is and make that workflow work." Kenney spoke with theCUBE's Christophe Bertrand and Alison Kosik at Pure Accelerate 2026, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed how data intelligence, autonomous infrastructure and governance could make enterprise AI more practical in production. (* Disclosure below.) Data intelligence becomes the live layer for AI and autonomous infrastructure The next step requires a broader infrastructure reset. Rather than ask customers to centralize everything first, Pure Storage is using discovery and classification across on-prem, cloud and software-as-a-service repositories, often tied to a configuration management database, to map where data lives. That approach extends the reach of its FlashArray and FlashBlade platforms from storage into data intelligence. "We typically integrate with a CMDB like in ServiceNow, and it shows you each of your data endpoints," Kenney said. "It spins up containers, interrogates the data and then brings it back to contextualize it and classify it. This knowledge map is what AI agents actually need to infer data across a wide swath of data." That, in turn, shifts the IT mandate from constant operational firefighting to policy-driven automation. Kenney noted that autonomous compliance and performance management can reduce the time teams spend chasing outages, giving them more room to focus on governance, privacy and how controls eventually follow data instead of individual systems. "From the bottom up, they built an autonomous infrastructure," he said. "Beyond that, there's an understanding of the data and they're getting full use of it. If three years from now people don't take advantage of this innovation, they're going to still be stuck managing infrastructure and not actually understanding their data yet." Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of Pure Accelerate 2026: (* Disclosure: TheCUBE is a paid media partner for the Pure Accelerate event. Neither Everpure, the sponsor of theCUBE's event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Everpure Announces Data Stream to Expand AI-Ready Data Offerings

New capability provides definitive implementation path for production AI, allowing customers to make their data AI-ready for natural language, search, and analysis of unstructured data. Everpure today announced the availability of Everpure Data Stream, which brings advanced AI capabilities directly to enterprise data where it already lives. It enhances Everpure's comprehensive enterprise strategy to deliver AI-ready data. As organizations increasingly deploy AI agents to automate complex tasks, they are constrained by significant challenges. These include ingesting enterprise data for AI use, maintaining strict data security and governance, and building infrastructure capable of scaling to meet the demands of AI. Everpure Data Stream addresses these friction points by reducing raw data preparation from months to minutes, enforcing stream-level access controls that keep information securely within the corporate network, and providing a scale-out architecture that lets storage and compute scale independently to match changing model requirements. "We are undergoing a massive capital supercycle in AI, where the defining factor between industry icons and those who disappear is the ability to adapt," said Robert Lee, Chief Technology Officer, Everpure. "The winning AI architecture requires a unified platform that allows businesses to start small with immediate use cases and seamlessly scale to exabyte capacity. Everpure solves this challenge by delivering a trusted, secure, and high-performance data pipeline that accelerates time-to-results for an enterprise's data." Bridging Data Readiness and Production AI To be truly ready for AI, data must be classified, curated, cleaned, secured and scaled. Everpure enables these outcomes end-to-end on the AI path. Classify, contextualize, and govern your data Enterprise data is heavily fragmented across SaaS, cloud, on-premises, and mainframe environments, turning data integration and migration into a costly barrier to secure, accurate AI system deployment. Everpure Data Intelligence (formerly 1touch) solves this by discovering, classifying, and contextualizing enterprise information at its source to map data dependencies into a data relationship graph. This universal data intelligence layer makes data understanding and relationships available via APIs and the Model Context Protocol (MCP), delivering highly relevant, accurate data inputs to AI models. Additionally, as AI systems and agents interact directly with data, Data Intelligence provides attribute-based access controls with security and governance policies -- ensuring rigorous compliance across all AI workloads to guarantee enterprise-grade trust. Convert raw data for AI use The availability of Everpure Data Stream accelerates enterprise AI projects by removing cost and complexity barriers. Everpure Data Stream extends upon the NVIDIA AI Data Platform reference design to deliver the foundation enterprises need to seamlessly convert unstructured data into real-time AI results. By replacing manual data ingestion and manipulation with a GPU accelerated pipeline from ingestion to inference, Data Stream helps organizations see results faster. "Building the next generation of AI factories requires a data architecture that seamlessly bridges secure, governed enterprise data with accelerated computing," said Jason Hardy, Vice President of Storage Technology at NVIDIA. "Everpure's integration with the NVIDIA AI Data Platform provides the infrastructure foundation organizations need to scale from AI experimentation to full-production intelligence." Everpure is also developing next generation AI solutions with NVIDIA STX, a modular foundation for AI-native storage using NVIDIA Vera and the NVIDIA BlueField-4 STX storage processor. This collaboration focuses on bringing acceleration, security and intelligent data services closer to enterprise data as organizations deploy agentic AI at scale. Scale performance across all workloads Fragmented storage pipelines starve AI compute clusters, stalling training and inference. To prevent this, FlashBlade delivers ultra-low latency and leverages KV Cache Accelerator to optimize memory efficiency during inference. Powered by the non-disruptive Evergreen architecture, organizations can start with FlashBlade//S and scale seamlessly to FlashBlade//EXA for GPU-cloud scale, effortlessly expanding from data stream to large AI factories. Finally, Portworx provides the container platform needed to seamlessly deploy, manage, and run these AI pipelines from the edge to the core data center. "Idle GPUs are economically destructive," said Sabur Mian, CEO and Founder of STN. "We standardized on Everpure FlashBlade because it eliminates the data bottlenecks that typically stall massive AI workloads. FlashBlade//EXA allows us to scale thousands of GPUs with up to 800 Gbps of throughput per node while maintaining rock-solid latency, driving up to 20% performance improvement for our customers." By delivering on these capabilities within a single, cohesive architecture, Everpure eliminates the temptation to build yet another wave of fragmented data silos. A new, commissioned IDC Global AI Readiness Survey reveals that 94% of IT leaders identify data quality as the absolute determining factor in AI success. Everpure's unified foundation gives enterprises the long-term flexibility and agility required to adapt as the broader AI landscape continues to evolve.

Data primacy could build reliable AI infrastructure

Data primacy puts Everpure at the center of enterprise AI: theCUBE's Pure Accelerate 2026 keynote analysis As artificial intelligence transforms the enterprise, the old model of managing data in application-controlled silos is breaking down. The companies that will win the AI era are those that embrace data primacy by treating data itself, rather than the applications sitting on top of it, as the primary asset. That shift was on full display at Pure Accelerate 2026, where Everpure Inc., the company formerly known as Pure Storage, made clear that its ambitions now extend well beyond flash storage into intelligent data management. The central thesis of the conference, according to Christophe Bertrand (pictured, left), principal analyst for cyber resiliency and data management at theCUBE Research, is that data must become universal, contextual and governed to serve AI at scale. "This is not about storage anymore. It's about data," Bertrand said. "And that was very clear in the CEO's address. There's a new data dynamic -- which makes data sort of primary -- and they call it data primacy. I think it's a very good term, where data is really central to everything." Bertrand and co-host Alison Kosik (right) conducted a keynote analysis at Pure Accelerate 2026, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed Everpure's architectural pivot, the company's business momentum, and what data primacy means for enterprises pursuing AI outcomes. (* Disclosure below.) Data primacy and the end of application-centric architectures Everpure's keynote landed against a backdrop of strong business fundamentals. CEO Charlie Giancarlo highlighted first-quarter revenue of $1 billion, annual recurring revenue of $2.04 billion, and a net promoter score of 84 across nearly 15,000 customers, Bertrand noted. But the strategic story carried more weight than the financial results. The core problem Everpure is targeting is the fragmentation created when every application vendor pulled data into its own silo -- leaving enterprises unable to build a coherent picture of what their data actually means across systems, Bertrand explained. "In the age of AI, you can't go talk to 25 different systems to figure out what a customer means," he said. "You want to have that universal truth about the actual data and relate it back to the business. So in the age of AI, it becomes very, very critical to have that." The company's answer spans three layers: a unified data plane with improvements to the FlashArray, an intelligent control plane with automation and mobility capabilities, and the universal data intelligence layer -- enabled by its acquisition of 1touch -- to provide discovery, context and governance across datasets. Without all three in place, AI models risk consuming bad, incomplete, or non-compliant data, Bertrand noted. "You can't have access to enough of the data you need for the actual results you wanna get out of your AI endeavors," he said. "It's really having that data primacy at the center of the conversation -- not just storage of data anymore, not just this application versus that application." Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of Pure Accelerate 2026: (* Disclosure: TheCUBE is a paid media partner for Pure Accelerate 2026. Sponsors of theCUBE's event coverage do not have editorial control over content on theCUBE or SiliconANGLE.)

Everpure Unveils Data-Primacy Architecture for the AI Era

Everpure announced new capabilities to help businesses securely fast-track enterprise AI initiatives while maintaining visibility and control over all their data. Anchored by the introduction of Everpure Data Intelligence (formerly 1touch.io) and new updates to the Enterprise Data Cloud, these advancements will help organizations turn fragmented enterprise data into trusted, AI-ready intelligence. Historically, enterprise IT relied on an application-centric model that trapped critical data and context inside application silos for specific functions like sales, finance, or logistics. Today, this app-fragmented approach acts as an operations bottleneck?triggering massive data sprawl, blind spots, and costly replication of untrusted data. To unlock the value of AI, businesses must shift to a data-centric model. In the data primacy model, information is liberated from individual applications to become a shared and governed system of record. Data needs to be self-describing and carry its own meaning and logic wherever it goes. Furthermore, governance is embedded at the data layer, ensuring lifecycle management and privacy rules are permanently attached to the information rather than policed by outside software. Apps and agents read from, and can contribute to, the data in the system of record, but do not own it. Everpure Data Intelligence discovers, classifies, and contextualizes enterprise information at its source. It works on all data, inclusive of the Everpure Platform, public clouds, SaaS applications, and third-party storage. Instead of trapping meaning inside rigid application or hardware silos, Data Intelligence contextualizes definitions and connections in enterprise-wide data while adding safety rules and contextual relationships necessary for AI. For enterprises deploying AI agents, feeding them accurate and relevant data maximizes response accuracy while drastically reducing the context windows and token costs. Available now, Data Intelligence delivers three core capabilities across an enterprise's entire estate: Universal Discovery: Offers visibility into structured and unstructured data regardless of storage format, including major databases like SQL Server and Oracle, showing exactly where critical application data resides; Automated Governance: Automatically scans the entire system to identify sensitive info (like PII and PHI) and track lineage, providing a complete map of the data landscape to ensure secure compliance. AI-Ready Context: Maps raw data directly to its real-world business definition, creating a semantics knowledge graph. This enables modern AI agents to instantly understand, query, and safely act on information across the entire enterprise. Because Data Intelligence works seamlessly with the Everpure Platform, it further extends the value of the Enterprise Data Cloud architecture. Organizations can now apply context-aware intelligence directly to how information is stored, operated, and protected across their entire estate. Everpure is introducing updates to its Unified Data Plane to deliver a shared foundation across the entire enterprise that eliminates performance silos, maximizes resource efficiency, and brings cloud-like scaling directly into physical data architecture. A key development is Evergreen//One Overdrive, available in third 2026, which provides a temporary, cloud-like performance boost for on-premises storage to seamlessly absorb traffic spikes up to 25% above baseline without requiring permanent subscription upgrades. Sitting above the Unified Data Plane, the Intelligent Control Plane embeds AI directly into daily operations?turning manual, reactive storage administration into a self-optimizing, secure environment. By utilizing natural language orchestration and predictive behavioral analysis, these tools abstract operational complexity: Workload Rebalance & Mobility: Powered by the infrastructure layer, this feature automatically moves active workloads across the fleet without causing downtime, optimizing capacity distribution and guaranteeing steady application performance. (Available fourth quarter 2026); Copilot Workflow Execution: Allows storage administrators to use natural language to plan, validate, and trigger secure, end-to-end operations across the entire global infrastructure estate. (Available second quarter 2026); Enhanced Cyber Anomaly Detection: Monitors telemetry across the entire environment to spot coordinated, suspicious login patterns or behavioral drifts that individual storage arrays might miss in isolation. (Available second quarter 2026); Fusion Compliance & Agentic Triage: Automatically detects hardware or software configuration drift to enforce global corporate governance while leveraging agentic AI to suggest root causes for immediate technical remediation. (Available fourth quarter 2026). To help organizations transition to a data-centric architecture, Everpure is introducing the EDC Success Blueprint. This step-by-step roadmap provides a proven methodology for building and scaling an enterprise data cloud. It begins with a practical readiness assessment to identify immediate infrastructure and security risks, then maps out a clear path across 10 operational pillars to transition environments from manual management to a highly automated, efficient architecture. Designed to continuously evolve alongside technology, the EDC delivers a unified, secure data fabric that shows exactly where data lives, how it connects, and what it means. This completely transforms how the entire environment operates: governance policies protect data based on its inherent meaning rather than its application silo, AI workflows optimize around shared enterprise context, and infrastructure automatically adapts to real-world usage behavior.

Everpure accelerates AI workloads with Data Stream and unveils data-primacy architectural vision

Everpure accelerates AI workloads with Data Stream and unveils data-primacy architectural vision Big-data storage company Everpure Inc., formerly known as Pure Storage, is rethinking enterprise data architectures to facilitate better access and scalability for artificial intelligence workloads. At its annual customer conference, Pure Accelerate, the company today announced the immediate availability of Everpure Data Stream, which helps to transform raw, unstructured data so enterprises can feed it into AI models more easily. Alongside Data Stream, the company unveiled Everpure Data Intelligence to simplify data discovery and classification, plus significant performance upgrades to its Enterprise Data Cloud platform. The announcements signal a fundamental shift in strategy for Everpure, which is urging customers to abandon their old application-centric data architectures in favor of a new "data primacy" model. Everpure Chairman and Chief Executive Charles Giancarlo introduced the new offerings in a blog post, where he explained that the biggest hurdle for enterprise AI is no longer the models, but the complexity of the data pipelines needed to feed them with information. He cites a recent survey by International Data Corp., which found that 94% of information technology leaders consider data quality to be the ultimate factor when it comes to getting value from AI. With Everpure Data Stream, the company is tackling the problem of data access with a new engine that automates raw data preparation in order to reduce ingestion times from months to a matter of minutes. At the same time, the new platform will enforce access controls at the data stream level to ensure proprietary data remains secure. The company explained that Data Stream builds on Nvidia Corp.'s AI Data Platform reference design, replacing traditional and heavily manual data ingestion processes with a graphics processing unit-accelerated pipeline that extends all the way to the inference stage. Giancarlo said this architecture allows both compute and storage to scale independently of one another, ensuring that GPU clusters will never be starved of data, solving a key problem around compute efficiency. It's enabled by Everpure's FlashBlade storage systems, including FlashBlade//S and FlashBlade/EXA, which Giancarlo said can push through data at an extremely rapid throughput of 800 gigabytes per node, eliminating GPU idling once and for all. The move is aligned with a broader storage industry shift, with all of the major players looking to focus more on the data and less about where the bits live, Steve McDowell of NAND Research told SiliconANGLE. He explained that NetApp Inc.'s Intelligent Data Infrastructure and Dell's AI Data Platform are earlier examples of this trend, which is all about ensuring AI gets enough data to keep up its relentless pace of work. "Pure put a big stake in the ground with its rebrand earlier this year," McDowell said. "Today's announcements are the first real products aligned with its new mission. Everpure Chief Technology Officer Robert Lee said future enterprise data architectures will require a unified platform that enables businesses to begin with smaller projects before scaling up to handle exabytes of data. "Everpure solves this challenge by delivering a trusted, secure and high-performance data pipeline that accelerates time-to-results for an enterprise's data," he said. The data-primacy architecture Pipeline performance is only one part of the equation when it comes to streamlining AI data access, which is why Everpure also debuted a new technology called Everpure Data Intelligence. It's based on the capabilities of a company called 1touch.io Inc., which was acquired just over three months ago when Everpure announced its rebrand. 1touch.io sold a platform called Kontxtual, which is used to scan enterprise's on-premises data infrastructures and cloud environments to create a comprehensive inventory of their data assets, along with descriptions of what each one is. The Everpure Data Intelligence layer is meant to address the semantic fragmentation of enterprise data by discovering, classifying and contextualizing both structured and unstructured information at its source. It works by mapping dependencies into a universal semantic knowledge graph, which autonomous AI agents can then access via the Model Context Protocol to obtain real-time context about whatever it is they're working on. According to Giancarlo, this has the effect of minimizing AI tokens costs while reducing compliance risks, because it also applies automated governance to protect sensitive assets such as personally identifiable information. McDowell believes that Everpure Data Intelligence is going to be the most impactful of today's announcements. "It instantly gives the company data awareness capabilities, along with the automated data ingest provided by Data Stream, that equal or exceed the capabilities of its rivals," he explained. "1touch.io was a strong acquisition by Everpure and it's yielding immediate returns. Data Intelligence puts the company on a solid foundation for sovereign AI data needs, while also adding the contextual detail that's so important for agentic AI workflows." Everpure announced a series of updates to the Enterprise Data Cloud platform too. For instance, it's getting a boost with Evergreen//One Overdrive, which will launch in the third quarter and provide a 25% performance boost for on-premises storage during traffic spikes. There's also a new Intelligent Control Plane that enables natural language copilot workflow execution, better cyber anomaly detection and automatic workload rebalancing. When this launches later in the year, it will enable companies to replace reactive storage management to a self-optimized system, the company said. Last but not least, it introduced support for native virtual machines on Microsoft Azure. The latter is an especially interesting move by Everpure, because it appears to be taking a much more measured approach than one of its chief competitors, NetApp, which has built up a strong presence in cloud storage due to the availability of its ONTAP storage service on public cloud platforms, McDowell said. "The Azure-native VMs allow Everpure's hybrid cloud customers to move data-heavy workloads to Azure without refactoring the underlying storage, eliminating the pain of migrating data," the analyst explained. "It's a powerful capability for those enterprises that need it." Giancarlo said the emergence of AI underscores his belief that software applications should not be the center of gravity of IT architectures. Most companies have embraced an application-centric design that has resulted in massive app sprawl with siloed definitions of data and slow and costly extraction process that lead to inferior AI performance. "AI completely upends the traditional IT hierarchy; enterprises that do not shift from app-centricity to data primacy will fall behind," he argued. "Because data is a company's primary asset, embedding context, semantics and governance directly at the data layer is the right way to reduce data fragmentation." Giancarlo believes that the transition to a data-primacy framework should become a major priority for enterprises. "A company's data is now a growing corpus of intelligence whose strategic value far outweighs the applications that created it," he added. "Everyone talks about the importance of data. Now it's time to put data first, and everything else will follow."