Services

Enterprise architecture for a coherent enterprise data layer

When every team builds its own data, the same question gets several answers. Veratas establishes the enterprise data architecture, ERP-aligned staging, conformed dimensional models, and a data-quality framework, that gives finance, operations, and analytics one trusted foundation to build on.

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The problem

Why numbers stop agreeing

Inconsistency is rarely a data problem. It is an architecture problem.

Most organisations do not set out to build conflicting data. It accumulates. Finance builds a revenue model against the general ledger, operations builds one against the order system, and a third team copies a spreadsheet that was right in 2022. Each is defensible on its own. Together they produce a board pack where three slides disagree, and a week is spent reconciling rather than deciding. The cause is not bad analysts. It is the absence of a shared definition of customer, product, period, and revenue.

Enterprise architecture fixes this at the structural level. Instead of policing reports after the fact, it defines the staging layer, the conformed dimensions, the fact models, and the quality rules once, so that every downstream warehouse, mart, and Power BI dataset inherits the same meaning. The work is not glamorous and it does not produce a demo. What it produces is the quiet result of a finance number and an operations number matching without anyone having to check.

It is also a defence against drift. Source systems change, a new ERP module goes live, an acquisition arrives with its own chart of accounts, and a team spins up a quick mart under deadline pressure. Without a reference architecture and a governance cadence, each of these events reopens the inconsistency problem. With them, new work is measured against a known target and the estate stays coherent as it grows.

Fixed-fee assessment

An architecture review, from $7,500

A fixed-scope review of your current architecture and a target-state roadmap. You see exactly what the price covers, and a fair quote for anything more.

Fixed fee from $7,500   |   2 to 3 weeks   |   Price shown up front

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What $7,500 covers

A target-state architecture and roadmap

We review where you are and map a pragmatic path to where you want to be.

  • Current-state architecture assessment
  • Target-state architecture design
  • Prioritised modernisation roadmap
  • Risk, dependency, and cost review
  • Technology and platform recommendations
  • Findings readout with recommendations

Any platform or licence costs are billed separately. The $7,500 covers the assessment services.

No surprises

What is in the $7,500, and what we quote separately

The implementation is scoped from the roadmap and quoted before you commit.

In your $7,500Beyond the review, quoted at a fair rate
Current and target-state architectureThe implementation itself
Prioritised roadmapBuilds, migrations, and integrations
Risk and cost reviewPlatform deployment
Technology recommendationsOngoing operations
Findings readoutA centre of excellence

You pay $7,500 for the review. The implementation is scoped from the roadmap and quoted transparently, always shown before you decide.

What we deliver

A coherent enterprise data layer

We design the structures and standards that keep data consistent as the organisation, and the systems beneath it, keep changing.

ERP-aligned staging

A staging layer modelled on the real structures of Dynamics 365 and your enterprise ERP, ledger, dimensions, order and inventory tables. Source data lands in a stable, well-understood shape before any transformation, so downstream models are insulated from upstream change and version upgrades.

Conformed dimensional models

Conformed dimensions for customer, product, organisation, and time, plus shared fact models built on a Kimball-style approach. Finance and operations join to the same dimension keys, so their reporting reconciles by construction rather than by negotiation.

Data-quality framework

Rules, thresholds, and monitoring for completeness, validity, uniqueness, and referential integrity. Issues are detected at load time, assigned to a named owner, and tracked, so a quality problem surfaces in a dashboard, not in a board report.

Reference architectures and standards

Documented reference architectures, naming and modelling conventions, and patterns for staging, transformation, and semantic layers. New development has a blueprint to follow instead of inventing structure each time.

Architecture governance model

A lightweight governance cadence, design review, a decision log, and clear ownership, that keeps standards live as teams and systems change, without becoming a bottleneck that slows delivery.

Adoption roadmap

A subject-area-by-subject-area plan for aligning the existing estate to the target architecture, sequenced by business value and risk, so the organisation gains coherence incrementally rather than through a disruptive re-platform.

How we deliver

Establishing the architecture

Enterprise architecture work runs through five phases, balancing a sound target design against practical, incremental adoption.

01

Assess

We review current data structures, models, pipelines, and standards across systems, and document where inconsistency is causing measurable pain. The output is a clear picture of the estate and the highest-value gaps to close.

02

Design

We design the target enterprise data architecture: the staging layer, conformed dimensions, fact models, and the data-quality framework. This is a buildable design, validated against your real source systems, not a reference diagram.

03

Standardise

We document reference architectures, naming and modelling conventions, and a governance model. From this point, new work has a blueprint, so the estate stops accumulating fresh inconsistency while existing work is realigned.

04

Adopt

We align the existing estate to the target architecture subject area by subject area, finance first, then operations, then the rest, sequenced by value and risk. Each increment delivers coherence without a big-bang rebuild.

05

Govern

We run an ongoing architecture-governance cadence: design reviews, a decision log, and clear ownership. The data layer stays coherent as new systems, acquisitions, and teams arrive.

Our approach

Architecture designed for adoption

An architecture only helps if the organisation actually moves to it.

Enterprise architecture has a reputation for producing impressive diagrams that nobody builds. We design the other way round. Every element of the target, the staging structures, the conformed dimensions, the quality rules, comes from teams that build data platforms on Microsoft Fabric and Azure every week, so it is checked for buildability before it is recommended. If a pattern is elegant but expensive to operate, we say so and choose the practical option.

Adoption is incremental by design. A big-bang re-platform is high risk, hard to justify, and tends to stall halfway. Instead we sequence the work by subject area, aligning finance reporting to the conformed model first because that is usually where inconsistency hurts most, then operations, then the longer tail. Each step delivers a visible result, which keeps the programme funded and credible rather than becoming a multi-year project with nothing to show.

We are also honest about scope. A smaller organisation with one ERP and one reporting team rarely needs a full enterprise architecture; a single well-modelled warehouse will serve it better and cost far less. Enterprise architecture earns its keep once there are multiple source systems, multiple teams building data, or a recurring pattern of numbers that do not agree. We will tell you which situation you are in.

FAQ

Frequently asked questions

Quick answers to questions you may have. Can't find what you're looking for? Check out our full documentation.

Data warehousing builds a specific warehouse. Enterprise architecture sets the conformed models, standards, and governance that keep every warehouse, mart, and data product coherent with each other. Architecture is the framework within which warehousing happens. You can build a warehouse without it, but with several teams building data, the warehouses will quietly disagree.
Conformed dimensions are shared definitions of the entities the business measures by, customer, product, organisation, and time, used consistently across every fact model. When finance and operations both join to the same customer dimension and the same period definition, their reports reconcile by construction. Conformed dimensions are the single most effective tool for ending the problem of contradictory numbers.
Often not yet. A smaller organisation with one ERP and one reporting team can usually start with a single well-modelled warehouse, which costs far less. Enterprise architecture becomes worthwhile once you have multiple source systems, multiple teams building data, or a recurring why-do-these-numbers-not-match problem. We will give you an honest read on which applies.
No. A big-bang rebuild is high risk and tends to stall. We align the existing estate to the target architecture incrementally, one subject area at a time, sequenced by business value and risk. Each increment delivers visible coherence, so the organisation improves continuously rather than waiting years for a single cutover.
Microsoft Fabric is a strong platform for implementing the architecture: its lakehouse, warehouse, and semantic model layers map naturally onto staging, conformed models, and certified datasets. But the architecture is platform-aware, not platform-bound. We design the structures and standards first, then implement them on Fabric, Azure, or whatever the estate already runs.
Your organisation does. We design the reference architectures, document the standards, and stand up a governance cadence, then transfer ownership to a named team or architect on your side. The deliverables are written to be used and maintained internally. We can stay engaged for ongoing governance, but the architecture is yours, not a black box.
It defines measurable rules, completeness, validity, uniqueness, referential integrity, with thresholds appropriate to each data set. Checks run at load time, so a failed batch or a broken key surfaces immediately in a monitoring dashboard. Each rule has a named owner accountable for resolution. The aim is that quality issues are caught and assigned, not discovered late in a board report.
The assessment and target-design phases typically run six to ten weeks, depending on the number of source systems and the state of current documentation. That produces a buildable target architecture and a sequenced adoption roadmap. Adoption itself is incremental and ongoing, governed by a regular architecture cadence rather than treated as a fixed-end project.
A current-state architecture assessment, a target-state design, a prioritised modernisation roadmap, a risk, dependency and cost review, technology recommendations, and a findings readout. Typically 2 to 3 weeks.
The implementation itself, builds, migrations and integrations, platform deployment, and ongoing operations are scoped from the roadmap and quoted transparently.
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Establish a coherent data architecture

Stop teams getting different answers to the same question. A coherent, ERP-aligned data layer is the foundation for trusted analytics everywhere. Start with an architecture conversation.

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