A governed, trusted, AI-ready Dynamics 365 data platform, with a roadmap to get there

Data warehouse strategy and advisory for Dynamics 365 is the target data architecture, the build-vs-buy call on extraction, the data-governance framework and the roadmap for your data estate. It sets five things: the store your data lands on, how that data leaves Dynamics, the governance framework over it, a funded roadmap, and an honest Microsoft Fabric and AI-readiness verdict, so that whatever you build and run is governed, reconciled and AI-ready by design.

Most data estates do not break on a single report. They drift. The extraction pipeline was set up years ago and now sits on a retirement clock. The data model has grown table by table, and no two people reconcile the same number the same way. The roadmap, if there is one, was written before Microsoft Fabric existed. And the board has started asking about AI while the foundations underneath it are quietly unready.

This is the work that comes before the build, or alongside it. It is not a health-check that diagnoses a broken report, and it is not the build itself. It is the strategy: which data store your estate should land on, how your Dynamics data should leave the system now that the old export routes are retiring, the governance framework that keeps the data trustworthy, and the honest read on whether any of it is ready for Fabric and AI.

The fix is direction and a build-once governance framework, not a rebuild. Set the architecture once, set the extraction pattern once, design the governance once, and you have a data platform that build teams can deliver against and a managed service can run, with the numbers reconciling all the way through.

Choose a Fabric Data Warehouse for structured, star-schema, SQL-first finance reporting, and a lakehouse for data engineering, unstructured data and medallion transformation. Both sit on OneLake in open Delta format, share the same SQL engine, and you can add either later, so the choice is about your team and your workload, not lock-in.

The heaviest call on this page is which store your data platform lands on. The reassurance to lead with is that it is a less final decision than it sounds. In Microsoft Fabric, every data store lands in OneLake in open Delta format by default, and the Warehouse and the Lakehouse share the same SQL engine. You are choosing a development model and a workload shape, not locking yourself into a store you cannot add to later.

A Fabric Data Warehouse is the T-SQL store: full data-query and data-modification support, ACID multi-table transactions, strong schema enforcement. It suits structured, star-schema, enterprise and departmental reporting and SQL-first teams, which is the typical Dynamics finance-reporting warehouse.

A lakehouse is the Spark store: structured and unstructured data, a medallion bronze-silver-gold pattern, and a read-only SQL analytics endpoint on top. It suits data engineering, data science and varied formats.

The other options are Azure SQL and the retiring Synapse dedicated SQL pool, which is legacy and migrates to a Fabric Data Warehouse. The simple decision logic: Spark points to a lakehouse; T-SQL and multi-table transactions point to a warehouse; structured-only points to a warehouse; unstructured or "we are not sure yet" points to a lakehouse. Many Dynamics estates land on both, a lakehouse to ingest and transform, a warehouse for the refined reporting layer. Our job in the advisory is to set that shape for your estate.

It depends on your estate, and the point of independent advice is to give you the honest verdict: you can build a bespoke Dynamics data warehouse, buy the pre-built reconciled Reveal foundation, or blend the two. We advise on which fits, we do not just sell a build.

Before the store, there is the question of how your Dynamics data reaches it, and that question now has a deadline. Export to Data Lake is deprecated, with decommissioning scheduled from 25 March 2025. That is the single sharpest reason to set the strategy now rather than re-point an ageing pipeline.

There are two go-forward extraction patterns, and the right one depends on how much control and how much simplicity you want.

• Link to Fabric. The low-friction, fully-managed pattern. No copy, no ETL, your data stays inside the Dataverse governance boundary on Dataverse-managed storage, and all tables come across by default. It is the SaaS answer.
• Azure Synapse Link for Dataverse. The control pattern. It exports continuously to your own storage account, you build and manage the pipelines, and your admins choose which tables move. It is the bring-your-own-storage answer.

Once the pattern is set, the build-vs-buy call follows. You can build a bespoke Dynamics data warehouse, tailored to your estate. You can buy the pre-built reconciled Reveal connector, our proprietary, pre-reconciled data-warehouse foundation for Dynamics 365 Finance, Supply Chain and Business Central, which shortens time-to-value, lowers delivery risk and supports AX and NAV migration. Or you can blend the two, buying the reconciled foundation and building the estate-specific extensions on top.

The important part is that this is advice, not a sales funnel. Reveal is one option the advice may land on where it genuinely fits. It is never the foregone conclusion, and if a bespoke build is the right answer we say so.

A data-governance framework for Dynamics data is Microsoft Purview governance domains, data products, data lineage, data-quality rules, critical data elements and master-data management, with the security and access design over the data layer. It is a build-once framework, rolled out lightweight and phased, right-sized to an SME, not an enterprise lockdown.

Ask most suppliers what a data-governance framework actually consists of and you get a policy document. Here is the concrete design work, built once, on Microsoft Purview.

• Governance domains and accountability. A Purview governance domain gives structure and ownership over a portion of the estate: who owns what, which roles apply, and a single aggregate data-quality score for the domain. We design the domains and the accountability that goes with them, Data Product Owner, Data Quality Steward, catalogue readers, so responsibility is named, not assumed.
• Data products and critical data elements. A data product is a group of assets with a use case, shared with the people who consume it. A critical data element is a field that matters enough to govern once and enforce everywhere: define the quality rule on the element, and it applies to every data product built on the underlying asset. This is how one definition of a number holds across the estate.
• Data lineage. The mapped relationship between data products and the assets beneath them, so when a number looks wrong you can trace it to source rather than argue about it. Lineage is what turns a data-quality complaint into a fixable root cause.
• Data quality and MDM. No-code and low-code data-quality rules, at column level, scored up to asset, product and domain, plus the master-data and data-quality framework that keeps the reference data clean. This is the layer that makes the reporting trustworthy in the first place.
• Security and data protection. Glossary terms, data-estate mappings and the sensitivity and access design over the data layer. We design this as policy on paper. Where a full implementation is in scope, we lead it and deliver it with the right team.
• The governance roadmap and rollout. The lightest right-sized Purview framework that meets the objective, phased, with quick wins and KPIs. Governance scaled to an SME, not an enterprise regime you will never staff.

We design this framework. Running it day to day, the stewardship, the monitoring, the ongoing quality management, is governance-as-a-service, and that is the managed data service, not this engagement. The boundary is deliberate, and it is covered below.

The right footprint is a Fabric F-SKU capacity sized to your consumption, with F64 as the threshold for free-viewer access, planned around the consolidation of Premium P-SKUs to F-SKUs and the extra Dataverse storage that Link to Fabric consumes, sized and costed before you commit.

Where the platform lands on Fabric, the cost line is a real decision, not a default, and it is the one your Finance Director will fund. F-SKU capacity is the unit you buy, and it should be sized against your actual consumption using the Fabric Capacity Metrics app rather than guessed. F64 is the threshold at which free-licensed users can view Power BI content, which changes the maths on who needs a paid licence. Premium P-SKUs are consolidating onto F-SKUs, so an existing Premium estate has a transition to plan. And if you choose Link to Fabric, remember it consumes additional Dataverse storage, a distinct cost line that is easy to miss.

The full Fabric-licence detail lives on our Power BI advisory and governance page; here the point is simpler. You should walk in with the right capacity and storage footprint for your estate, costed before you commit, rather than discovering it on the first invoice.

A governed, reconciled Dynamics data platform on OneLake, with a single chosen extraction pattern and a Purview-governed data layer, is the AI-ready foundation. So we run a readiness review and set the governed path to get there. What you get from us is an honest verdict, not a promise to implement Fabric or AI.

Here is the thesis, and it is the reassuring part. The work that makes your data platform governed is the same work that makes it AI-ready. A chosen store on OneLake, a single governed extraction pattern, and a Purview-governed, reconciled data layer are the foundation an AI needs to answer correctly. Ungoverned, un-reconciled data does not become trustworthy because you point an AI at it. Governance and AI-readiness are the same work.

What we own is the review. Beat one, what we assess: whether your Dynamics data platform is on the right store, using the right extraction pattern, governed and reconciled enough that an AI built on it would answer correctly rather than confidently wrong. Beat two, the verdict: where you are today, and the governed path to get to ready. If you are not there yet, we tell you, and we set the path rather than sell you an outcome.

A phased engagement that assesses your estate, sets the target architecture and the build-vs-buy call, designs the data-governance framework, delivers a funded roadmap and a capacity and licensing strategy, and ends with an honest Fabric and AI-readiness verdict. It lands an executable plan and an implemented framework, not a shelf-ware strategy deck.

The engagement runs in steps, grounded in your estate rather than a template.

1. Assess and set the target architecture

   Build the estate and maturity view, then set which store on OneLake fits your team and your workload.

2. Set the extraction pattern and the build-vs-buy call

   Link to Fabric or Synapse Link, and whether the answer is to build, to buy the reconciled Reveal foundation, or to blend the two.

3. Design the data-governance framework

   Governance domains, data products, lineage, data quality and MDM, and the security design, as a lightweight-rollout framework built once.

4. Roadmap

   Turn it into a funded, sequenced plan in de-risking order. A roadmap you execute, not a slide you file.

5. Capacity and licensing strategy

   The right F-SKU and storage footprint, sized to consumption and costed before you commit.

6. Fabric and AI-readiness verdict

   The honest readiness position and the governed path. A verdict, not a promise to implement AI.

The engagement lands an executable roadmap and a governance framework designed for a lightweight, phased rollout, not a two-year transformation. The scope is transparent and the cost is shown before you commit.

It is genuinely independent. The advice can recommend, or decline to recommend, any tool or build, including our own Reveal, and the Fabric and AI readiness verdicts are honest, so if you are not ready we say so and set the governed path rather than sell you an AI dream.

The sharpest question on any advisory page is whether the advice is genuinely independent or a build pitch wearing advisory clothes. Ours is independent, in two specific ways.

First, it is not tied to a build. The advice sets the direction and the governance across your whole data estate and can recommend, or decline to recommend, any tool, platform or build, including PrecisionPoint's own Reveal where it genuinely fits. Reveal is one option the advice may land on, never the foregone conclusion.

Second, the readiness verdicts are honest by design. If your data platform is not ready for Fabric or AI, we tell you and set the governed path rather than sell you an outcome you are not positioned to get.

Underneath both is two decades of doing nothing but Dynamics data, so the governance is grounded in your finance numbers reconciling, the multi-entity eliminations, the financial dimensions, not generic data-warehouse hygiene. Independent advice, grounded in the numbers, with an honest read on AI. That is the wedge.

Usually yes. Advisory sets the target architecture and the governance framework, a build then delivers it, and the managed data service runs and governs it day to day. You choose the next rung on the evidence.

1. Advise.

   This page. The target architecture, the build-vs-buy call, the governance framework and the roadmap, set once.

2. Then build.

   See data warehouse design and build for Dynamics 365, or buy the pre-built reconciled Reveal foundation and build the extensions on top.

3. Then run and govern it.

   See how we run your data layer day to day. The boundary is deliberate: here we design the data-governance framework; the managed data service runs it for you as governance-as-a-service.

4. The BI layer above.

   For the strategy and governance of the semantic and report layer above the data platform, see Power BI advisory and governance. We set the data-layer strategy and governance; Power BI advisory sets the BI and semantic-layer governance above it.

For the whole programme, see Data warehouse for Dynamics 365. And if the question is simply whether Microsoft Fabric is the right platform for your Dynamics estate, talk to us.