Every computing revolution is, at its core, architectural: a fundamental change in the relationship between software or hardware, communications, memory, or data storage.
From mainframes to minicomputers and client-server to the cloud, each era reshaped the relationship between these components. Yet through 50 years of these shifts, one relationship has held uncontested: Data existed to serve the application design, and indeed, the computer operating system. Over the last 30 years, applications have been the center of gravity.
App sprawl and AI are reversing this hierarchy. For years, organizations have declared that data is their most important asset; yet IT infrastructure has continued to be architected in an application-centric design. Each application is built on its own data set and holds the context and meaning required to interpret it. App sprawl has created data sprawl, which has fragmented data semantics. When stripped of that embedded context, data becomes effectively opaque: It cannot be used by other applications without first being cleaned and transformed in the costly and time-consuming process to extract, transform, and load it into a different form (i.e., ETL).
This has created multiple challenges for enterprises because they now own the integration problem between applications. The typical enterprise today has dozens, hundreds, or even thousands of applications each with its own data set, and generally many copies thereof. Most common company activities, such as creating a quote for a customer, require accessing multiple applications and data sets that often do not share consistent information.
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.
The architecture that built the modern enterprise
To understand the present and future, we need to understand how deeply app-centricity became embedded in the enterprise DNA.
The original vision behind large-scale ERP systems was a single enterprise application capable of modeling every business process—finance, procurement, supply chain, HR, logistics—within one unified semantic framework. One schema with a shared understanding of what a “customer,” an “order,” or an “asset” meant in a specific business setting.
But business IT followed a different path. A best-of-breed era arrived, as enterprises carved out specialized domains and redefined CRM, HR, and IT operations. Where organizations gained velocity and feature innovation, they lost semantic coherence across functions.
This is the origin of data sprawl and fragmentation, which SaaS has accelerated. Each new application brought its own definitions, required its own data copies, ETL pipelines, and integration layers to communicate with the next.
That complexity outpaced any company’s ability to unify data across domains. The internet, mobile, and cloud turbocharged data volumes, even as the architectural assumption underneath remained.
The application remained primary. Data remained secondary. The semantic layer—the meaning of data and its relationships—stayed locked inside the application.
The results are large, ungoverned estates of data lakes and data silos, with copies of data across dozens of environments. In the race to AI, this complexity isn’t just more challenging and costly. It’s unsustainable.
AI and business efficiency demand an inversion of the hierarchy
AI and business efficiency invalidate the app-centric model. Nonetheless, SaaS and the multi-application world is here to stay.
Enterprises have many needs and constant change requires flexibility in developing workflows that model and optimize the business. But if there is not a single all-encompassing application or workflow that can serve as the system of record, then what can organizations rely on for consistency and accuracy in their workflows? The answer must be with their data architecture. Data, or specific data sets, must serve as systems of record that multiple applications can use for company workflows.
For IT leaders, AI transforms the architecture from “what can we automate and optimize?” to “what can my agents do to optimize the business?”—a shift from task optimization where the app was the focus to outcome-driven workflows and intelligence where data is the focus. This is not an incremental adjustment. It’s a paradigm inversion.
Every AI action depends on understanding data from multiple data sets in relation to workflows—their semantics, history, and connections across the business. A company’s data is now a growing corpus of intelligence whose strategic value far outweighs the applications that created it.
For AI and agentic systems to function in an app-centric architecture, data has to be transformed before it can be consumed. It has to merge sources, normalize schemas, and build context. Which version is current? Which copy is authoritative? Data lakes are, by definition, not current or real time, and consistently transformed to fit specific analytic needs and other analytic tasks requiring a different set of transformations. Very inefficient and not real time.
AI applied in existing app-centric environments has to consistently deal with the inconsistency problem. This is not a hypothetical problem. It’s what is meant by “garbage in, garbage out.” Inconsistent data results in incorrect actions.
Data primacy
To scale enterprise efficiency and agentic AI, enterprises must move to a data-centric architecture. Data must become primary, and apps secondary. Data must be self-describing, and the relationships between different systems of record must be recorded in self-describing metadata.
In this architecture, data systems of operation and systems of record become the core, foundational asset. Real-time data is more valuable than old data. All application environments—operational, informational, and agentic—access that same real-time data. Apps read from, or write to systems of record; they do not store authoritative information. And rather than copying and transforming data to fit another application’s requirements, context is created, stored, and maintained alongside the data itself, as metadata.
This is a consequential change: For the first time, organizations must design a true enterprise-wide data architecture where the application architecture will follow.
We call this architecture data primacy.
Data primacy rests on three foundational principles:
First, semantics must travel with the data (not be embedded in the application).
Operational data must carry the meaning needed to be understood and used outside the system that created it. This means building semantic descriptions alongside data and relationships that define how a piece of data connects to everything else in the enterprise. These semantics turn raw data into self-describing, contextually rich assets that AI can reason over accurately.
Second, data must be conserved, not copied.
Enterprises have been conditioned to duplicate data for every new application, analytics environment, and AI use case. Data primacy inverts that imperative. A single authoritative source of real-time data that is governed, secured, and semantically described serves all applications and all agents. This is both an efficiency and an accuracy imperative. A system of record cannot be controlled by a single group; it must be centrally governed as an enterprise resource.
Third, governance must be embedded at the data layer.
In an app-centric world, access is managed through role-based access controls tied to applications. In a data primacy world, AI agents operate across multiple systems simultaneously. Access must be contextual and dynamic, driven by the attributes of the data itself, the identity of the requesting agent, and the purpose of the action.
The locus of value is data that is governed, managed, and understood by the enterprise, ready to be utilized for multiple purposes by multiple applications and agents.
Our path forward
At Everpure, the journey to data primacy started many years ago.
Our first decade was spent solving the complexity of third-party storage systems and scaled storage. In our second decade, we’ve introduced the Enterprise Data Cloud, an architecture built by organizations, enabled by the Everpure Platform. Its Unified Data Plane spans all protocols, tiers, and workloads, so organizations can manage storage as one system. An Intelligent Control Plane on top of that allows data policies to travel with data regardless of where it lives or how workloads evolve.
With Everpure™ Data Intelligence (formerly 1touch), we bring the ability to discover, classify, and contextualize data across the entire enterprise estate—on-premises, cloud, SaaS, and legacy environments—and to build a catalog and semantic knowledge graph around these data sets of information.
The Enterprise Data Cloud optimizes how data is stored and managed across workloads and networks. Everpure Data Intelligence provides the semantic intelligence to make all enterprise data governable, understandable in a given context, and ready for AI. Together, they allow organizations to build a data-primary future, preserving what works today while transitioning toward a more efficient and effective architecture.
To a new organizational model
AI is rapidly changing how work gets done, and leaders are beginning to grasp the organizational implications of agents that will inform every level of work.
AI will empower employees to harness their company’s distinctive advantages and customer relationships. It will compel infrastructure teams to rethink their architecture and will transform the roles of storage admins, infrastructure leaders, CDOs, and CTOs.
The transition starts with the data itself: enhanced with its own context and semantics creating meaning that is accessible across the business serving not just one application but all applications, every agent, and every inquiry, with security, governance, reliability, and trust built in.
Enterprises that make the shift to data primacy will run more efficiently and deploy AI better. They’ll build a new data architecture that is more resilient to workflow changes and modifications. Where decisions are made faster, and with greater confidence and accuracy.
Everyone talks about the importance of data. Now it’s time to put data first. Everything else will follow.
