Azure Data Lake Storage Gen2 gives you one secure, scalable repository for structured and unstructured data. We design the structure, ownership and governance that decide whether anyone still trusts it in three years.
If a warehouse or Microsoft Fabric alone would serve you better, we will tell you that before we design a lake.
Legacy data to trusted foundation
01Legacy dataWhatever the last decade actually left behind.
02ProfileMeasure it before promising anything.
03ClassifyMigrate, archive, reference or retire.
04CleanThe business decides what is correct.
05MapOld structure meets the future design.
06MigrateIn a sequence that respects relationships.
07ValidateUsers check real scenarios, not row counts.
08ReconcileProve the position, do not assume it.
09UsePeople trust the system enough to stop exporting.
Applies at every stage
Ownership
Quality
Security
Business sign-off
Sound familiar?
The data is not missing. It is unusable.
Most organisations have more data than they can account for, held in places that were never designed to be read together.
01
The data exists in multiple places
ERP, CRM, spreadsheets, machine output and a warehouse nobody has documented since 2019.
02
Reporting takes a week and still gets challenged
Because every number is assembled by hand from a different extract.
03
The lake became a landing zone
Files arrive, nothing is catalogued, and after two years nobody trusts what is in it.
04
Storage cost is rising with no clear owner
Nobody can say which datasets earn their keep, so everything is kept.
05
AI is on the agenda but the data is not ready
Models and Copilot both inherit whatever the underlying data actually says.
What it actually does
Four capabilities, and one design decision behind each.
Azure provides the platform. What determines the outcome is how it is structured, secured and fed.
Storage1 of 4
One repository, structured and unstructured.
Azure Data Lake Storage Gen2 combines Blob Storage economics with a hierarchical namespace, so files are organised into a real folder structure rather than a flat bucket.
What this gives you
Logs, documents, exports, images and telemetry can live in one place without inventing a new platform for each type.
We design the zone and folder structure, naming and retention up front, because retrofitting structure onto a live lake is the expensive version.
The lake is queried directly through Synapse SQL or Spark, modelled in Microsoft Fabric and presented in Power BI, without copying it somewhere else first.
What this gives you
One version of the data behind dashboards, models and ad hoc analysis.
We agree which layer is the source of truth for reporting so two teams cannot publish two different answers.
Not as somewhere to put the things nobody wanted to decide about.
Structure before volume
We design the zones, naming and ownership first. Loading everything and organising later is how lakes lose trust.
Business meaning, not just plumbing
A dataset without an owner and a definition is not an asset. We insist on both.
Connected to the systems you run
We deliver Dynamics 365 and Power Platform daily, so the ERP and CRM feeds are built by people who know those schemas.
Cost you can predict
Tiering, lifecycle rules and compute sizing are part of the design, not a surprise on the third invoice.
Independent on scope
If a warehouse or Fabric alone would serve you better than a lake, we will say so.
Accountable afterwards
Named support ownership, monitored pipelines and scheduled reviews rather than a handover document.
Before you call
The questions data and finance leaders ask first.
A conversation, not a demo
Start with the reporting you cannot trust.
Tell us which numbers get challenged and where they come from. We will tell you what a lake would fix, what it would not, and whether a simpler option gets you there.
