From Warehouses to Products: SAMR for Your Cloud Data Platform

Financial Services, Insurance, Wealth Management, and Professional Services have a gift—and a curse—when it comes to data.

The gift is that these industries know how to run critical systems with discipline. The curse is that we’re so good at controlling risk that we often rebuild the same constraints in every new platform we adopt.

That’s why so many “modern cloud data platforms” in these sectors end up feeling like the old data warehouse with a new hosting model: better infrastructure, familiar bottlenecks. Slow change. Long planning cycles. A platform measured by what it ingests, not what it enables.

In the previous post on AI and SAMR, the point wasn’t that AI is inherently transformative. The point was that without a disciplined framework, we use new tools to reproduce old workflows. The same is true for data.

Here’s what I’ll cover in this follow-up:

• Why cloud platforms in regulated, risk-sensitive industries so often replicate warehouse-era behaviors.
• How SAMR can be used as a simple truth-teller for your data platform strategy.
• Why data products are the most practical “unit of redefinition,” and why you don’t need Data Mesh to benefit from them—though mesh and product thinking dramatically expands your toolset.
• How ground-up design thinking keeps your platform anchored to real business goals, not elegant architectures

If your cloud platform still takes six months to change a definition, you didn’t modernize. You relocated.

When “Modernization” Becomes a Cloud Warehouse With Better Plumbing

In these industries, the gravitational pull toward warehouse replication is strong—and understandable.

You’re operating under real constraints:

Regulatory reporting, auditability, retention, lineage, and controls aren’t optional. Risk teams need consistency and explainability. Data quality failures don’t just break dashboards; they can create compliance exposure, financial loss, and reputational damage.

So the default pattern is predictable:

A central data team stands up a lakehouse or cloud warehouse, migrates key sources, builds curated layers, and declares victory when “most enterprise data is onboarded.”

Technically impressive. Strategically incomplete.

The symptoms look familiar across the sectors:

In Financial Services, platforms become factories for reconciled reporting and downstream extracts, but struggle to support rapid product launches, real-time fraud decisions, or faster model iteration. The platform is “safe,” but slow.

In Insurance, the cloud becomes an intake pipe for policy admin and claims systems, but underwriting and claims transformation still happens in spreadsheets and local marts because change in the curated layer is too slow and too centralized.

In Wealth Management, the platform collects account and holdings data, yet advisor workflows still rely on exported reports because the semantics required for suitability, tax-aware rebalancing, and household-level relationships never become stable, contract-driven assets.

In Professional Services, leaders want margin visibility, staffing optimization, and revenue forecasting—yet the platform becomes a collection of timekeeping, CRM, and ERP data stitched together for monthly reporting, not a system that improves weekly decisions.

The common thread isn’t tooling. It’s orientation.

Warehouse-era thinking optimizes for inventory: land data, harmonize data, publish reports. It’s well-intentioned—but it produces the same organizational outcomes as the old warehouse:

Low speed of change, inattention to business goals, and long planning times that sap credibility and adoption.

If we’re serious about modernizing, we need the same kind of discipline we’ve been applying to AI: we need to ask what’s actually changing.

SAMR as a Reality Check for Regulated Data Platforms

SAMR is useful because it names a pattern many teams feel but struggle to articulate.

When applied to a cloud data platform, SAMR becomes a simple lens:

Substitution

You moved the warehouse to the cloud. Same model. Same backlog dynamics. Same “central team builds, everyone else waits.”

In financial and risk-heavy contexts, this can masquerade as maturity: centralized control feels like governance. But if change is slow and consumer trust is built on personal relationships rather than product contracts, you’re still substituting infrastructure.

Augmentation

You layered in quality checks, catalogs, observability, and security automation. Things are easier to run and slightly easier to consume.

This is valuable—and in regulated environments, it’s often necessary. But augmentation still doesn’t change the platform’s core job. It’s still a warehouse that’s better managed.

Modification

The platform’s work changes: it’s no longer primarily a storehouse and transformation engine. It becomes a system for delivering stable, owned, outcome-oriented data products.

This is where platform “speed” begins to mean something more than query performance. Speed becomes the ability to introduce new business semantics safely, with versioning and predictable contracts.

Redefinition

New kinds of work become normal: teams outside the core data group can reliably build and evolve products; AI and analytics workflows compose trusted data assets; the platform supports decision loops, not just reporting cycles.

In other words, the platform becomes a place where the business can move—not just a place where data lives.

Most organizations in Financial Services, Insurance, Wealth, and Professional Services claim to be aiming for Modification or Redefinition. But a lot of the actual work stays in Substitution and Augmentation, because governance pressure nudges us back toward centralization and “perfect models.”

SAMR gives leaders permission to say the quiet part out loud: a cloud migration is not a platform transformation unless the purpose shifts.

Data Products: A Practical Bridge From Risk Control to Business Outcomes

This is where data products—specifically as discussed on edudatasci.net—become essential.

A data product isn’t “a dataset.” It’s an accountable asset, built for a defined audience and decision, delivered through a stable interface with explicit expectations.

In these industries, that framing is not a cultural leap. It’s familiar.

You already think in contracts, SLAs, owners, controls, and operational readiness. Data products simply apply those instincts to analytical and decision-grade data.

A few examples that fit the sectors cleanly:

• A KYC & Customer Risk Profile product that supports onboarding, periodic reviews, and AML investigation workflows (not just “customer tables”).
• A Fraud Signals & Device Graph product with defined latency, quality expectations, and versioned features that models and investigators can depend on.
• A Claims Lifecycle & Leakage product that links claims events, payments, adjuster actions, and outcomes to support operational improvement—not just month-end reporting.
• A Household & Holdings product in wealth management that resolves relationships and hierarchies (household, accounts, beneficiaries, tax lots) with explicit semantics that both advisors and systems can trust.
• A Project Margin & Utilization product in professional services that aligns time, staffing, rates, write-offs, and revenue recognition into an asset leaders can use weekly—not a reconciliation exercise once a month.

These products do something that “cloud warehouse modernization” often fails to do: they force clarity.

A product has to answer:

Who is it for? What decision does it improve? What does “good” look like? Who owns it? How does it change safely?

That’s the beginning of true platform redefinition.

Mesh Isn’t Required—But It Expands the Toolset and the Team

There’s a misconception that you can’t talk about data products without committing to Data Mesh.

You can.

You can redefine your platform around products inside a centralized team model, with shared infrastructure and common governance. That alone can move you into Modification.

But mesh principles and product thinking make transformation easier for a very specific reason: they expand who can participate, and what the platform can do.

In Financial Services, Insurance, Wealth Management, and Professional Services, the “domain” is often the business unit that already owns risk, operations, and customer outcomes. Those teams have subject matter expertise that central data teams rarely possess in full.

When you introduce data products as the common language, you create a way for:

compliance, risk, operations, and front-office leaders to shape semantics without turning every conversation into a data model debate domain SMEs to co-own definitions and quality expectations the platform team to shift from “builders of everything” to “enablers of safe publishing and consumption”

That’s the core promise of mesh: not decentralization for its own sake, but a larger, more capable system of delivery.

The important nuance is this: Mesh and products aren’t required to redefine a data platform.

But they make it far easier, because they provide a vastly enlarged toolset and engage people outside data teams in a structured, governable way.

In regulated environments, that “structured” part matters. Product contracts give you the control plane that makes broader participation possible without turning governance into chaos.

Ground-Up Design Thinking: How You Keep the Platform Tied to Real Goals

If you want to avoid rebuilding old warehouses in the cloud, you need a counterforce—something that constantly pulls you toward business outcomes.

That counterforce is ground-up design thinking.

Not the buzzword version. The practical version: structured work to understand decisions, journeys, and pain points before you commit to months of ingestion and modeling.

In these industries, the highest-value data products typically sit right next to a decision loop:

An AML investigator deciding whether to escalate a case An underwriter deciding price and terms A claims leader deciding how to reduce cycle time and leakage A wealth advisor deciding what to recommend, rebalance, or flag A professional services partner deciding staffing changes or margin interventions

Ground-up design thinking helps you identify what actually needs to exist in the platform to make those decisions better.

A strong exercise usually produces three outcomes:

It clarifies the decision and success metric (reduce false positives, shrink cycle time, increase retention, improve margin predictability).

It reveals the friction points (semantic disagreement, missing linkages, lack of timeliness, manual reconciliation, “shadow” spreadsheets).

It defines the product surface (dashboard, API, alert stream, feature set) and the expectations that make it trustworthy.

This is how you prevent platform roadmaps from becoming inventory projects.

Instead of “integrate all claims sources,” you get “ship a Claims Leakage product that improves settlement accuracy and reduces rework, with contracts the business agrees to.”

And that, more than any specific tooling choice, is what breaks the warehouse replication cycle.

The Discipline Isn’t in the Cloud. It’s in the Choices.

Cloud data platforms don’t automatically create agility. In regulated and risk-sensitive industries, they often do the opposite: they let us rebuild the same slow processes with more compute.

The discipline you need is the same discipline SAMR revealed in AI work:

Be honest about what is Substitution vs. real change.

Measure progress in outcomes, not ingestion coverage.

Use data products as the unit of delivery, because products force ownership, contracts, and purpose.

And keep yourself anchored to real business goals through ground-up design thinking—because architecture without decisions is just organization.

If you’re leading data transformation in Financial Services, Insurance, Wealth Management, or Professional Services, the question isn’t “Are we on the cloud?”

The question is: Are we building a warehouse… or are we building a platform the business can move with?

That’s where SAMR becomes more than a framework. It becomes a guardrail.