The executive meeting where finance, operations and sales bring three different figures for the same measure is not a reporting problem. It is an architecture problem: three extracts, three definitions, three refresh schedules. Fabric addresses it by putting one copy of the data in one place with one governed set of definitions on top.
Fabric is a software-as-a-service analytics platform covering the full workflow — ingestion, transformation, real-time processing, analytics and reporting — with OneLake as the shared storage underneath. These are the four problems that make organizations look at it.
Finance pulls from the ERP, sales from the CRM, operations from the production system. Each applies its own filters and definitions, so three reports about the same month disagree and nobody can prove which is right.
The same data exists in a warehouse, a lake, three cubes and a large number of spreadsheets. Each copy has a different age, and the reconciliation work is nobody's actual job.
Dashboards are beautiful and the data behind them is eighteen hours old, so operational decisions are still made by phoning someone who was there.
A figure on an executive dashboard cannot be traced back to source. In a regulated environment that is a finding; everywhere else it is a slow erosion of trust in the reporting.
Where the return actually comes from: The measurable return is engineering effort: pipelines that no longer move the same data four times, and analysts who stop spending most of their week reconciling instead of analysing. The strategic return is decision speed — when the definition is agreed once and applied everywhere, meetings move from arguing about the number to deciding what to do about it. And there is a real AI dependency now: agents reasoning over your data are only as good as the governance underneath, so this work sits on the critical path for anything you plan to build with Copilot or Copilot Studio.
One platform with role-specific workloads over shared storage.
A single logical data lake for the entire tenant, built on Azure Data Lake Storage and storing tables in Delta Parquet or Iceberg — open formats, so your data is not locked into a proprietary shape.
Ingestion from hundreds of sources, plus shortcuts that let you reference data where it already lives — including in other clouds — without copying it first.
Spark notebooks for engineering and data science, and a full T-SQL warehouse experience, both operating over the same OneLake storage rather than separate copies.
Event streams and eventhouses for operational data that matters in the moment, alongside the batch analytics for data that does not.
Reports querying lake data directly, without an import step or a refresh window — which is what closes the gap between a dashboard and reality.
Discovery, lineage, governance posture and security management in one place, with the catalog surfaced in Teams and Excel so people find data where they already work.
Two things are worth planning around. First, open table formats mean Fabric interoperates with Snowflake, Databricks and other engines rather than requiring an exclusive commitment — a genuinely important architectural point. Second, Fabric is becoming the data foundation for Microsoft's AI stack, with Copilot in Dynamics 365 already researching across Fabric data. Governance work you do here shows up later as AI you can trust.
We baseline from your current pipeline count, refresh latency and the analyst time currently spent on reconciliation.
Removed as copies consolidate onto OneLake
For the datasets that genuinely need it
Recovered for actual analysis
Applied everywhere rather than per report
Every figure traceable back to its source
A governed foundation agents can reason over
How to read these: How to read these: the figures above are typical ranges we plan and measure against, not guarantees. In your first engagement we agree the baseline, the target and the measurement method in writing, then report against them.
Microsoft's documentation frames the platform's value in the following terms.
Where this comes from: Where this comes from: these themes follow Microsoft's Fabric and OneLake documentation on learn.microsoft.com, including the platform overview, OneLake storage on Delta Parquet and Iceberg, and the OneLake catalog. The numeric ranges above are ours and are planning figures rather than Microsoft benchmarks.
The architecture is general; the first workload is always specific.
Machine and MES data joined to ERP cost data, so plant performance and product margin finally appear in the same picture.
Clinical volume joined to financial and payer data for service-line performance, with governed access to anything patient-identifiable.
Sales, inventory and fulfilment across every channel with cost-to-serve modelling by customer and product.
Portfolio, exposure and profitability reporting with documented lineage for the examiner who will ask.
Utilisation, realization and project margin from delivery systems, on definitions agreed once across practices.
Program outcomes and expenditure joined to funding sources, with the traceability grant reporting depends on.
Definitions first. A technically excellent platform serving disputed numbers has solved nothing.
The least technical and most important part of any data programme.
The architecture: ingestion, storage, transformation and serving.
The part the business actually sees, on the foundation underneath it.
Implementation, customization, support and integration — delivered as workloads that each produce something usable rather than as a platform programme.
Environment design, tenant and licensing setup, configuration, data migration, testing and go-live — scoped to a fixed price and a fixed date, against outcomes agreed in writing before we start. For Fabric that means delivering one complete workload end to end — ingestion through to a report someone uses — before building the second, so value arrives before the platform is finished.
Where the product stops short of your process, we extend it inside the platform rather than beside it, and we build it as configuration you can maintain wherever that is possible. Semantic models, medallion layers, transformation logic and row-level security built as maintainable artefacts with source control rather than as notebooks nobody can read.
Managed support after go-live: a named team, agreed response times, release management for Microsoft's update cadence, and a backlog we work through with you. Capacity monitoring and cost management, pipeline operations, and the tuning work that keeps consumption from surprising you at renewal.
Connecting this platform to the systems you are keeping, with monitored, re-runnable interfaces and a documented contract for every field that moves. Ingestion from Dynamics 365, line-of-business systems, files and other clouds — using shortcuts to reference data in place wherever copying it is unnecessary.
Capacity cost is the thing that surprises organizations on Fabric. Compute is consumption-based and an inefficient Spark job or an over-refreshed model can be expensive in a way a fixed-licence product never was. We build cost monitoring in from the first workload rather than after the first invoice.
Data platform projects fail on definitions and governance far more often than on technology.
We map where data lives, who owns it and which numbers are currently disputed.
We get the definitions agreed across functions and written down before building anything.
We deliver one workload end to end, to a report someone actually uses, then iterate.
Lineage, access, sensitivity and catalog structure applied as the estate grows, not afterwards.
Capacity, cost and performance monitored and tuned against agreed thresholds.
The definitions workshop is the least popular meeting in any data programme and the one that determines whether it succeeds. If finance and operations cannot agree what a customer is, no architecture will reconcile their reports for them.
Data platforms are easy to start and hard to make trustworthy.
The hard part is agreeing what a number means across functions. We do that first because a platform built before that conversation just serves the disagreement faster.
Each phase ends with something someone uses. A twelve-month platform build that produces its first report in month nine loses its sponsor by month six.
Consumption-based compute is genuinely different from licensed software. We build monitoring and cost discipline in from the start rather than explaining the invoice afterwards.
The systems generating the data, the platform holding it and the access model around it designed together rather than by three separate parties.
Two illustrative engagements showing the shape of the work.
Finance, operations and sales each produced monthly performance figures from separate extracts. The executive meeting routinely spent its first half reconciling before it could discuss anything.
Service-line performance was assembled from several systems by an analyst each month. Nobody could demonstrate how a published figure had been derived, which had become an audit concern.
Practical pieces on unification, governance and cost.
Why three teams produce three numbers, and how to run the workshop that fixes it.
Read moreReferencing data in place versus moving it, and the cost and governance consequences of both.
Read moreWhere Fabric costs actually accumulate, and the monitoring worth setting up in week one.
Read morePick a measure two teams currently disagree about — revenue, active customers, units shipped. We will build the pipeline and semantic model for that one measure in a demo environment and show you the number with full lineage back to source.
Describe the situation in your own words.
