Data Strategy

Data estates grow by accident. A warehouse here, a second one after an acquisition, a lakehouse started by one team, a Power BI workspace for every department, and a bill nobody can break down by use. Each choice was reasonable when it was made. Together they cost more than they should, give people different answers to the same question, and leave leaders unsure what to fix first. A strategy review lays out what exists, what it costs, and what a better arrangement would be, in an order that your budget can follow.

A clear picture of today

Sources, pipelines, platforms, owners, and costs on one set of diagrams that your whole leadership team can read.

A target that fits your business

A data architecture sized to your data, your team, and your plans, not to the latest conference talk.

A plan in stages

Each step has a cost, an owner, and a point where you can decide to continue or stop.

Current-state audit

Sources, pipelines, platforms, and reports mapped and checked against how the business actually uses them.

Target data architecture

A reference design covering platform choice, layers, ownership, and access, with the reasoning behind each major choice written down.

Platform choice and cost review

A fair comparison of Microsoft Fabric, Databricks, Snowflake, and what you run today, with licence and capacity spend broken down by use.

Data quality and readiness assessment

A check on quality, ownership, and access across your main data sources, with the weakest points ranked.

Governance and security review

Gaps in access control, sensitive data handling, lineage, and cataloguing, rated by how much they would hurt if they failed.

Phased roadmap and budget

Sequenced work over twelve to twenty-four months, with costs, dependencies, and decision points.

• Should we consolidate platforms?

  What we bring

  An honest comparison of what each platform costs, who uses it, and what it would take to merge them.

  You leave with

  A decision on what to keep, merge, or retire, with the saving shown.

• Fabric, Databricks, or Snowflake?

  What we bring

  A view of your data, your existing Microsoft use, your team’s skills, and your contracts, with a realistic account of the cost of running more than one.

  You leave with

  A platform choice you can justify to your board and procurement.

• Why does our data platform bill keep rising?

  What we bring

  A line-by-line review of capacity and compute against real usage, with each cause traced to an owner.

  You leave with

  Quick wins you can act on this month and a structure that keeps costs down.

• Is our data ready for AI and analytics?

  What we bring

  Hands-on checks of the data your priority use cases depend on, using real samples.

  You leave with

  A ranked list of data gaps, with the work sized and ordered.

• What do we do first, and what can wait?

  What we bring

  A phased plan that releases spend in stages and shows what each stage delivers.

  You leave with

  A twelve-month plan with costs and a decision point at each step.

1. Phase 1

   1 to 2 weeks

   Discover

   • Interview data engineers, analysts, finance, and the business owners of key data
   • Collect diagrams, usage and cost reports, and read-only access to your platforms
   • Agree the questions the review must answer

   You receive

   Findings summary and an agreed scope for the review.

2. Phase 2

   2 weeks

   Audit and design

   • Map sources, pipelines, reports, and spend, and test the main assumptions
   • Rate the gaps by risk and cost
   • Draft the target architecture and test it with your team

   You receive

   Current-state audit and a target data architecture.

3. Phase 3

   1 to 2 weeks

   Plan

   • Cost each stage of the change and the saving it brings
   • Order the work around your dependencies and budget cycle
   • Present to your leadership and record the decisions made

   You receive

   Roadmap, budget, and a leadership readout.

How we work with you

Independent advice

We do not resell platforms or software, and we take no commission, so recommendations rest on fit and cost.

Read-only access

For the audit we use read-only access you grant and can withdraw at any time.

Confidential by default

Architecture diagrams and cost data stay in a restricted workspace, under an NDA signed before we start.

You own the output

Every diagram, model, and report is yours, in formats your team can edit.

No lock-in

The roadmap can be delivered by your team or any partner. Working with us on delivery is optional.

Methods and tools

Architecture

Reference architectures, decision records, and diagrams in your own tools

Platform usage and cost

Fabric capacity metrics, Databricks system tables, Snowflake account usage, licence and invoice data

Estate discovery

Query logs, lineage and catalogue exports, pipeline run history, report usage statistics

Data assessment

SQL profiling, dbt and lineage tools, data catalogue exports

Governance review

Microsoft Purview, Unity Catalog, access reviews, and NIST-aligned checks

Planning

Spreadsheet models, roadmaps in your planning tool of choice

We use the tools and usage data you already have for the audit. There is nothing to buy or install.