Authored by Mr. Manish Godha, Founder & CEO, Advaiya Solutions Inc
A few years ago, I was part of a conversation that will be familiar to many technology leaders. An organization wanted to standardize operations by moving every team onto a set of common applications. The intention was sensible: fewer platforms, greater control, and simpler governance. But the deeper we looked, the clearer the problem became. Sales, finance, operations, project teams, and customer service did not work in the same way; different business units catered to different products, regulations, and customer types. Forcing them into common applications would create consistency on paper but also introduce restrictions and workarounds in practice.
This was before AI became mainstream, when the focus was on reducing IT complexity and cost and improving governance. Yet there is a deeper lesson — one that reverberates now that business agility and innovation have become key drivers of growth.
The organization did not need everyone to use the same applications or platforms. It needed every application to work from the same trusted business data. That distinction has become increasingly important in the AI era.
Application standardization asks everyone to work through a common interface. Data standardization establishes shared definitions, quality rules, ownership, security policies, and governance across the organization. When the data foundation is consistent, teams gain flexibility. Sales can work through a CRM, operations through an ERP, project teams through specialized planning tools, and leaders through dashboards. Customers may engage through portals or AI assistants. The experiences can differ. The underlying enterprise truth should not.
What a data platform is
A data platform, as I mean it here, is not another application and not simply a larger database. It is the layer beneath the applications: where an organization’s data is brought together, defined once, governed once, and made available to everything that needs it — typically a data lake or lakehouse, with pipelines to keep it current, a governance and catalog layer for definitions and access, and analytics and AI running on top. The point is not the storage. It is that meaning, quality, and control are set in one place and reused everywhere. Note the distinction from where we began: consolidating the data is not consolidating the applications. A data platform unifies the foundation while leaving the experiences diverse.
Organizations today have multiple mature platform options. For organizations built on Microsoft,
Microsoft Fabric is the natural centre — data engineering, analytics, and AI on one SaaS foundation, with OneLake as a single logical data lake in open formats, governance through Microsoft Purview, and Power BI built in; Dataverse serves as the governed data layer for Microsoft 365 and Dynamics 365 applications. Beyond that estate, Databricks offers an open lakehouse for data engineering, streaming, and machine learning, governed through Unity Catalog; Snowflake, a managed data cloud for SQL analytics, governed reporting, and data sharing; and Google BigQuery, a serverless warehouse for organizations already on Google Cloud.
For the mid-market, the right platform is the one that fits the workloads you will actually run, and that you can govern and afford — not the most powerful one on paper. Many organizations end up running more than one. When they do, the discipline that matters is a single governance and definition layer across them, not a single vendor.
Why a common foundation drives growth
As business strategy becomes more dynamic, this flexibility becomes central to growth. Businesses explore newer ways of interacting with customers, partners, and employees, try newer ways of working, and navigate a changing regulatory environment. Until recently, each such move carried a quiet tax: every new use case, process, or experience introduced cost, integration, security, and support considerations. And as the experiments multiplied, so did data fragmentation.
A common data foundation changes the economics of experimentation. When meaning, quality, and governance are established once, the marginal cost of the next experience falls. A new workflow, portal, or assistant can be built against data that is already trusted and governed, rather than assembled and re-secured from scratch. Growth in a dynamic market is, in large part, the ability to try more things and reconfigure quickly — and a data platform is what makes that affordable, the mechanism by which technology support keeps pace with a strategy that refuses to sit still.
Agentic development sharpens the point. Building a new application or automation is no longer slow or scarce; rapid experimentation is now widely available and viable. That lowers the cost of trying — and raises the cost of a weak foundation. When anyone can stand up a new experience in days, the constraint is no longer construction but trust: can each new thing rely on consistent, governed data, or does it quietly add another silo? Rapid experimentation on a fragmented foundation only produces fragmentation faster.
Governance follows the data
Fragmented data usually creates fragmented governance. A customer, project, document, or transaction may have different access rules across different applications, and each new platform then requires another layer of permissions, classifications, and controls. A unified data foundation changes that model. Core data policies can be managed closer to the information itself: who can discover it, which fields are restricted, how sensitive data is classified, where it originated, and whether an AI model or agent should be allowed to use it. Application-level permissions will still be needed for specific workflows and actions. But organizations should not have to redefine the meaning, sensitivity, and ownership of the same data every time a new experience is introduced.
AI has two paths into the enterprise
AI is entering the enterprise along two paths, and both depend on the data foundation.
The first is through applications and workflows. Here AI appears as an assistant — supporting reporting, planning, service, analysis, and decision-making — and, increasingly, as an active participant in the work itself, carrying out steps within a process rather than only advising on them.
The second is through the data foundation directly. At the data-science layer, AI turns governed data into foresight: models that forecast, classify, detect, and rank, so the data speaks to decisions rather than resting in reports. And when an AI experience is grounded in the organization’s trusted data foundation, it can reason from consistent definitions, policies, and business context.
The difference matters. An isolated AI assistant may have to reconcile conflicting information across, say, sales, production, finance, and service systems. An AI experience connected to governed enterprise data works from one version of the facts. AI does not automatically “learn” from every enterprise record. Depending on its design, it may retrieve, query, reason over, or be adapted using governed data. What matters is that it works with the right information, meaning, and controls.
Connecting the enterprise through Peripheral Automation
This is where I find the Peripheral Automation approach useful. At the systems-of-record layer, organizations preserve reliable core systems while establishing standardized data, governance, analytics, and data-science capabilities. At the business-process layer, information from finance, sales, procurement, projects, and operations can support connected workflows and shared decision-making. At the engagement layer, employees and customers can interact through applications, portals, dashboards, or AI assistants without directly navigating every underlying system. If a customer asks about a product, an AI assistant should not independently search disconnected production, sales, and service applications. It should work from a governed data foundation that brings the relevant context together.
Where the future lives
The transition ahead is therefore not simply from many platforms to one platform. Nor is it about forcing every function into one application. It is about moving from duplicated and inconsistently governed information toward a consolidated enterprise data foundation that can support multiple applications, workflows, analytics environments, and AI experiences.
When that foundation is in place, data stops being an output of business operations. It becomes the connective intelligence behind better governance, greater application flexibility, more informed decisions, and sustained growth.
The harder question is not whether to build such a foundation, but how much an organization is willing to standardize underneath in order to stay free to change on top.
Covered By: NCN MAGAZINE / Advaiya Solutions
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