When Data Becomes the Mission
Why state and local government, cities, and research universities are reorganizing infrastructure around data itself If you remember one thing from this article: infrastructure used to organize around applications. Increasingly, now it organizes around data. If you spend enough time around enterprise infrastructure, you start to notice something about how conversations begin. Someone asks about storage. Not in a philosophical way. In a practical way. How much capacity do we have left? What’s the refresh cycle? Is this staying on premises or moving to cloud? What’s the backup strategy? For years, that framing made perfect sense. Infrastructure was the foundation, and the job of infrastructure teams was to keep the lights on and the foundation solid. But lately, in conversations with customers across state and local government, municipalities, cities, and universities, something feels different. Because eventually someone says something like this: “We have this data… but we can’t actually use it.” And that is when the real conversation begins. Why the public sector reveals the truth about data There’s a perspective I heard recently that stuck with me. The public sector isn’t a niche market. It’s a microcosm of the entire enterprise technology world. At first that sounds counterintuitive. The stereotype is that government IT has been quietly living under a rock since the previous century, next to a beige server and a stack of COBOL manuals. But if you look closely, the opposite is true. State agencies, cities, and research institutions operate in environments that combine nearly every architectural challenge the private sector faces — all at once. Massive datasets Highly distributed users Strict security requirements Long retention policies Global collaboration And an absolute requirement that systems remain available when people need them most. In other words, the public sector experiences the full spectrum of data challenges simultaneously. If you want to stress-test a data architecture, put it inside government. Think about it. A state government may run thousands of systems across dozens of agencies, each serving different missions but increasingly sharing the same underlying data. A city manages infrastructure at the physical edge of society — traffic, water, SCADA, emergency services — where real-time decisions depend on accurate information. Universities generate some of the largest research datasets on earth while collaborating across institutions and countries. Each of these environments demands something slightly different from infrastructure. But they all demand the same thing from data: Security. Integrity. Mobility. Context. Availability. And when those requirements collide in one environment, something interesting happens. The solutions that work there tend to work everywhere. A laboratory for the modern data enterprise This is why many technology leaders quietly view the public sector as something more than a vertical market. It’s a laboratory for enterprise-scale data architecture. If a platform can operate in a world where: sensitive personal data must remain protected • systems span thousands of locations • regulatory oversight is constant • and uptime has real public consequences …then that architecture will almost certainly succeed in commercial environments. Banks, manufacturers, healthcare providers, and global enterprises face the same challenges. Just rarely all at once. Government simply compresses those problems into a single environment. Solve the data problem for government, and you solve it for the enterprise. That’s one reason the shift toward data-centric platforms is becoming so important. When organizations treat infrastructure as a place to store files, they solve only a small part of the problem. But when they treat data as the central operational asset — something that must be understood, governed, protected, and made usable across environments — the architecture begins to look very different. And the public sector, with all its complexity, becomes the place where those architectures are tested first. Which brings us back to the shift we’re seeing across the industry. Because once you start looking at infrastructure through the lens of data itself, something else becomes obvious. The center of gravity has moved. When multiple systems depend on the same dataset, the data becomes part of the operating foundation. And once that happens, moving it — or even restructuring it — becomes dramatically harder. Which brings us to the concept that explains a lot of what is happening right now. The quiet physics of data gravity The first time I heard the term “data gravity” wasn’t in a conference keynote or a vendor presentation. It was in 2015, when a recruiter from a startup called DataGravity (now Anomalo) reached out and asked if I would be interested in interviewing. At the time, the idea sounded fascinating — and slightly theoretical. The company was built around the premise that data itself was becoming the most valuable asset in the data center, and that infrastructure needed to understand the content, context, and behavior of data, not just store it. The name alone hinted at something deeper: the idea that as datasets grow, they start exerting a kind of gravitational pull on the systems around them. Back then, it felt like an interesting concept. Today it feels like a description of reality. The term “data gravity” itself was introduced by Dave McCrory back in 2010, and it turns out to be a remarkably accurate way to describe modern infrastructure. Dave McCrory Blog The idea is simple. As datasets grow, they become harder to move. More applications depend on them. More workflows connect to them. More policies govern them. Eventually, the architecture starts organizing around the data itself. Not because someone designed it that way. Because the physics of large systems leave you very little choice. Imagine trying to relocate a state Medicaid dataset that has been integrated with multiple benefit programs, identity verification systems, and fraud detection tools. Technically possible? Sure. Operationally trivial? Not even close. The larger and more interconnected the dataset becomes, the stronger its gravitational pull. Compute moves closer to the data. Applications move closer to the data. Infrastructure reorganizes around the data. This is why organizations that once talked primarily about storage capacity are now talking about data platforms. The center of gravity moved. When data stops being passive The moment data becomes operational, everything changes. For years, most organizations treated data as something that accumulated quietly inside systems. Applications produced it. Storage kept it safe. Backups made sure it could be restored. But that model starts to break down when the data itself becomes part of real-time decision making. You can see this most clearly in environments that generate enormous volumes of information. Cities now run infrastructure that continuously streams telemetry — traffic sensors, utility meters, environmental monitors, emergency response platforms. A water meter that once reported usage once a month might now generate thousands of readings per year. A traffic system that once relied on static timing can adapt dynamically to real-time conditions. Each improvement creates more data. More importantly, it creates operational dependence on that data. Universities experience the same phenomenon in a different form. Research environments produce extraordinary datasets across genomics, climate science, and artificial intelligence. Sequencing a single human genome generates roughly 100 gigabytes of raw data, and large research programs may create terabytes or petabytes of new information every week. In those environments the challenge isn’t just storing data. It’s feeding it fast enough to the systems that depend on it. Modern research clusters and GPU environments can process enormous volumes of information, but only if the underlying data pipeline keeps up. When storage cannot deliver data fast enough, expensive compute resources sit idle and discovery slows down. And that reveals an important truth about modern infrastructure. When systems depend on data in real time, the question stops being where the infrastructure lives. The question becomes whether the data is available, trustworthy, and recoverable. That distinction also explains why ransomware has become so disruptive to public institutions. Attackers understand that the real leverage is not the servers or the network. It’s the data. When access to data disappears, the services built on top of it disappear as well. Which brings us back to the deeper shift happening across the industry. If data has become this central to operations, services, and discovery, then managing it as a passive byproduct of infrastructure is no longer enough. Infrastructure alone is no longer the strategic layer. The strategic layer is the data itself. Organizations still need performance, availability, and resilience. Those fundamentals have not changed. What has changed is the expectation that infrastructure should also help organizations understand, govern, protect, and use their data more effectively. That is a very different problem than simply storing it. And it is the reason the conversation is evolving from storage management to data management platforms. The real punch line Public sector organizations didn’t set out to become data enterprises. Over time the data accumulated. Then the dependencies formed. And eventually everything started orbiting the datasets that mattered most. Data has gravity. Data has risk. Data has power. Infrastructure still matters. But increasingly, the real mission is something else entirely. The mission is the data. Appreciate you reading. Dmitry Gorbatov © 2025 Dmitry Gorbatov | #dmitrywashere
FlashCrew: Glasgow
https://experience.purestorage.com/flashcrewglasgow We welcome you to the last installation of ‘FlashCrew’ Glasgow, as we embrace the rebrand and pivot to [Ever]Pure User Groups in the future. Forget the full-day slog. Join us at the Radisson Blu Hotel, Glasgow on 21 May 2026 from 12:00 (BST) where we've distilled the most critical industry insights into a high-impact afternoon designed to give you a competitive edge. Join us for a session packed with expert-led deep dives, real-world case studies, and high-calibre networking.Catching up
Hey all! It's been a while since I've posted here and I feel compelled to reach out to see what everyone is working on. Like all of us, I've been pulled in many different directions lately (power, cooling, security camera's), and it has made me appreciate that managing our Everpure environment allows me cycles to focus elsewhere. Current storage related projects are, Cloudsnap: working with the Everpure support team to get cloudsnap working so that we can investigate long term backups to our Flashblades or S3 in the cloud. Integration with CyberArk: Again, working with the Everpure support team to enable privileged users with rotating passwords to work with our Everpure management environment. Pureprotect: Chad Montieth and Suresh Madhu have been instrumental in our testing and development of a case to possibly replace SRM for DR failover and testing. Don't forget about Accelerate June 16th - 18th in Las Vegas. This is a worthwhile event that provides free training classes and certification tests. Jason Finley and I from SEHP get to attend this year. register here Begin Registration - Pure Accelerate 2026 What are you working on? Share with the group any success or challenges. Keep an eye on the community page next week for an update from Nick Fritsch. Happy Easter all! - Charlie
Ask Us Everything: Everpure & Databases - From Firefighting to Forward Thinking
Databases aren’t going anywhere—in fact, they’re becoming more important than ever. In this Ask Us Everything session, Don Poorman sat down with Everpure database experts Anthony Nocentino and Ryan Arsenault to talk all things structured data. And while AI continues to dominate headlines, one theme came through clearly: AI doesn’t replace databases—it depends on them. If you’re running Oracle, SQL Server, SAP, or anything mission-critical, here’s what stood out.Pure Certifications
Hey gang, If any of you currently hold a Flash Array certification there is an alternative to retaking the test to renew your cert. The Continuing Pure Education (CPE) program takes into account learning activities and community engagement and contribution hours to renew your FA certification. I just successfully renewed my Flash Array Storage Professional cert by tracking my activities. Below are the details I received from Pure. Customers can earn 1 CPE credit per hour of session attendance at Accelerate, for a maximum of 10 CPEs total (i.e., up to 10 hours of sessions). Sessions must be attended live. I would go ahead and add all the sessions you attended at Accelerate to the CPE_Submission form. Associate-level certifications will auto-renew as long as there is at least one active higher-level certification (e.g., Data Storage Associate will auto-renew anytime a Professional-level cert is renewed). All certifications other than the Data Storage Associate should be renewed separately. At this time, the CPE program only applies to FlashArray-based exams. Non- FA exams may be renewed by retaking the respective test every three years. You should be able to get the CPE submission form from your account team. Once complete email your recertification Log to peak-education@purestorage.com for formal processing.
Big news with Nokia and Pure
Nokia has selected Pure Storage to power the high‑performance, all‑flash data layer for its telco cloud on Red Hat OpenShift, enabling secure, scalable CNFs from edge to core. This is very big news as Pure continues to grow its global footprint in the telecom industry, helping telcos across multiple use cases: RAN modernization (5G/6G), AI, telco clouds, autonomous networks, OSS/BSS and lots more. Read all about the Nokia partnership in the Pure Storage blog: https://blog.purestorage.com/news-events/nokia-pure-storage-telco-red-hat/
Ask Us Everything: Pure Storage + Nutanix — What the Community Really Wanted to Know
The January Ask Us Everything (AUE) session tackled one of the hottest topics in infrastructure right now: what Pure Storage and Nutanix are doing together—and what that means for our customers. Judging by the volume and depth of questions, it’s clear that many of you are actively evaluating next-generation virtualization options and want real answers, not marketing slides. With Cody Hosterman (Sr Director Product Management, Pure Storage), Thomas Brown (Field CTO, Nutanix), myself - Joe Houghes (Field Solutions Architect, Pure Storage), and our host Don Poorman (Technical Evangelist, Pure Storage), the conversation went deep into architecture, migration realities, and the practical problems this joint solution is designed to solve. Here are the biggest takeaways from what attendees asked—and what they learned. This is joint engineering, not just “interoperability” One of the most important clarifications came early: this isn’t a case of “here’s a LUN, good luck.” Nutanix has natively integrated Pure Storage FlashArray APIs directly into the Nutanix stack. That means: No plugins to install No bolt-on frameworks to manage No separate operational silos In Prism, the Nutanix management plane, Pure Storage behaves like a first-class storage backend. Snapshots, protection, provisioning, and automation are driven from Nutanix, while Pure Storage delivers its strengths—performance, data reduction, SafeMode, and simplicity—under the covers. NVMe/TCP support is a deliberate, forward-looking choice Several attendees asked why Fibre Channel or legacy protocols weren’t the focus. The answer: this solution is built for where infrastructure is going, not where it’s been. By standardizing on NVMe/TCP over Ethernet, Pure and Nutanix: Avoid decades of SCSI and FC tech debt Enable massive bandwidth scalability (100G, 400G, and beyond) Lay the groundwork for modern security features like TLS and in-band authentication This is a design meant to still make sense 10 years from now. Object-style vDisks eliminate old datastore limits A recurring “aha” moment came when attendees learned how vDisks are implemented. Instead of traditional filesystem-based datastores (with all their historical limits), each virtual disk maps directly to a Pure Storage volume. What that unlocks: Petabyte-scale virtual disks (no more 64TB ceilings) No datastore gymnastics to scale performance No artificial limits inherited from legacy file systems This felt especially relevant for customers running large databases, analytics platforms, or fast-growing enterprise apps. HCI isn’t going away—this complements it A key question from the audience: Does this replace Nutanix HCI? The answer was a clear no. Nutanix HCI still makes perfect sense for many workloads. But when customers: Need to scale storage independently of compute Have performance-heavy or capacity-dense workloads Want an “apples-to-apples” replacement for traditional VMware + external storage …Pure Storage + Nutanix provides a clean alternative without forcing architectural compromises. Migration is real, and the hard parts were addressed honestly Migration questions dominated the session—and the tone was refreshingly pragmatic. Attendees learned: Nutanix Move is fully supported and preserves Purity’s data reduction–which makes this a zero-cost migration in terms of storage capacity VMware NSX rules can be translated into Nutanix Flow during migration Backup tools (Veeam, Rubrik, Commvault, Cohesity, etc.) continue to work without re-engineering or changes in backup operations Most migration risk doesn’t lie in the hypervisor—it’s overlooked third-party dependencies The guidance was consistent: plan carefully, take stock of any dependencies, and don’t rush a wholesale cutover just to meet an artificial deadline. No user ever wants to be forced to do that. Operational simplicity is a major design goal A subtle but powerful theme emerged: you don’t need to tune this solution. VMware users often ask about “nerd knobs” and the need to tweak things to get them working right. In this solution, they’re mostly gone—and intentionally so. Best practices for queue depths, multipathing, performance tuning and more are already baked into the platform by the joint engineering teams. Improvements are managed through upgrades, eliminating the need for manual scripting or implementing performance tweaks for a "snowflake" deployment. The result of this best-of-breed, jointly-engineered solution is consistency, predictability, and easier support—especially during migrations–so that you can focus on the work that makes your business run. The roadmap is active—and community feedback matters This solution was not positioned as “done and dusted.” The GA release is the foundation, not the finish line. Capabilities like Kubernetes support, deeper snapshot orchestration, VDI validation, and migration optimizations are all on the roadmap. And importantly: your use cases drive priorities. And the Pure Storage Community is a great place to drop your feedback for the teams! Keep the conversation going This partnership sparked a lot of interest for a reason: it’s not just about changing hypervisors—it’s about modernizing how infrastructure works. If you missed the live session—or want to dive deeper—join the ongoing discussion in the Pure Storage Community: 👉 https://purecommunity.purestorage.com/discussions/virtualization/ask-us-everything-about-pure-storage--nutanix/3634 You’ll find Pure Storage and Nutanix experts answering follow-ups, clarifying edge cases, and sharing lessons learned from real deployments. While you’re there, be sure to check out past Ask Us Everything events—they’re packed with practical, practitioner-level insights.OT: The Architecture of Interoperability
In previous post, we explored the fundamental divide between Information Technology (IT) and Operational Technology (OT). We established that while IT manages data and applications, OT controls the physical heartbeat of our world from factory floors to water treatment plants. In this post we are diving deeper into the bridge that connects them: Interoperability. As Industry 4.0 and the Internet of Things (IoT) accelerate, the "air gap" that once separated these domains is evolving. For modern enterprises, the goal isn't just to have IT and OT coexist, but to have them communicate seamlessly. Whether the use-cases are security, real time quality control, or predictive maintenance, to name a few, this is why interoperability becomes the critical engine for operational excellence. The Interoperability Architecture Interoperability is more than just connecting cables; it’s about creating a unified architecture where data flows securely between the shop floor and the “top floor”. In legacy environments, OT systems (like SCADA and PLCs) often run on isolated, proprietary networks that don’t speak the same language as IT’s cloud-based analytics platforms. To bridge this, a robust interoperability architecture is required. This architecture must support: Industrial Data Lake: A single storage platform that can handle block, file, and object data is essential for bridging the gap between IT and OT. This unified approach prevents data silos by allowing proprietary OT sensor data to coexist on the same high-performance storage as IT applications (such as ERP and CRM). The benefit is the creation of a high-performance Industrial Data Lake, where OT and IT data from various sources can be streamed directly, minimizing the need for data movement, a critical efficiency gain. Real Time Analytics: OT sensors continuously monitor machine conditions including: vibration, temperature, and other critical parameters, generating real-time telemetry data. An interoperable architecture built on high performance flash storage enables instant processing of this data stream. By integrating IT analytics platforms with predictive algorithms, the system identifies anomalies before they escalate, accelerating maintenance response, optimizing operations, and streamlining exception handling. This approach reduces downtime, lowers maintenance costs, and extends overall asset life. Standards Based Design: As outlined in recent cybersecurity research, modern OT environments require datasets that correlate physical process data with network traffic logs to detect anomalies effectively. An interoperable architecture facilitates this by centralizing data for analysis without compromising the security posture. Also, IT/OT convergence requires a platform capable of securely managing OT data, often through IT standards. An API-First Design allows the entire platform to be built on robust APIs, enabling IT to easily integrate storage provisioning, monitoring, and data protection into standard, policy-driven IT automation tools (e.g., Kubernetes, orchestration software). Pure Storage addresses these interoperability requirements with the Purity operating environment, which abstracts the complexity of underlying hardware and provides a seamless, multiprotocol experience (NFS, SMB, S3, FC, iSCSI). This ensures that whether data originates from a robotic arm or a CRM application, it is stored, protected, and accessible through a single, unified data plane. Real-World Application: A Large Regional Water District Consider a large regional water district, a major provider serving millions of residents. In an environment like this, maintaining water quality and service reliability is a 24/7 mission-critical OT function. Its infrastructure relies on complex SCADA systems to monitor variables like flow rates, tank levels, and chemical compositions across hundreds of miles of pipelines and treatment facilities. By adopting an interoperable architecture, an organization like this can break down the silos between its operational data and its IT capabilities. Instead of SCADA data remaining locked in a control room, it can be securely replicated to IT environments for long-term trending and capacity planning. For instance, historical flow data combined with predictive analytics can help forecast demand spikes or identify aging infrastructure before a leak occurs. This convergence transforms raw operational data into actionable business intelligence, ensuring reliability for the communities they serve. Why We Champion Compliance and Governance Opening up OT systems to IT networks can introduce new risks. In the world of OT, "move fast and break things" is not an option; reliability and safety are paramount. This is why Pure Storage wraps interoperability in a framework of compliance and governance, not limited to: FIPS 140-2 Certification & Common Criteria: We utilize FIPS 140-2 certified encryption modules and have achieved Common Criteria certification. Data Sovereignty: Our architecture includes built-in governance features like Always-On Encryption and rapid data locking to ensure compliance with domestic and international regulations, protecting sensitive data regardless of where it resides. Compliance: Pure Fusion delivers policy defined storage provisioning, automating the deployment with specified requirements for tags, protection, and replication. By embedding these standards directly into the storage array, Pure Storage allows organizations to innovate with interoperability while maintaining the security posture that critical OT infrastructure demands. Next in the series: We will explore further into IT/OT interoperability and processing of data at the edge. Stay tuned!Healthcare AI: Why the "Build Reflex" is Killing Your ROI
In this article, For Healthcare Leaders, Build vs. Buy Determines ROI on Enterprise AI, featuring Matthew Crowson, MD, of Wolters Kluwer, Matthew argues that healthcare organizations must abandon their traditional "build reflex" for AI solutions, citing a high 95% failure rate. This traditional habit in the healthcare system is in stark contrast with tight margins and the competitive AI talent market. Crowson advocates for a shift to a hybrid partnership model where the organization "buys" a vendor's customizable platform. This model is crucial because it addresses trust issues by ensuring that sensitive patient data (PHI) remains secure behind the facility's firewall. He stresses to first focus on problem diagnosis, be realistic about their in-house talent, and ensure their data foundation is clean before engaging any vendors. This pragmatic approach is essential for achieving a positive ROI on enterprise AI. Community Question: What do you think? Is your organization currently struggling with the build vs. buy decision? Let's discuss! Click through to read the entire article above and let us know your thoughts around it in the comments below!
