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Data strategy & platforms
Data strategy and Microsoft Fabric platform consultancy
Decide what your data platform should look like before you build it. Konsolute assesses your data estate and reporting priorities, then designs Microsoft Fabric and Azure platforms around your source systems, workloads, team skills and capacity needs.
WHAT THIS HELPS YOU ACHIEVE
A data direction connected to business priorities and delivery
Bring platform decisions, information ownership and the implementation roadmap together before technology choices become long-term constraints.
Prioritise useful outcomes
Connect data investment to the reports, decisions and capabilities your organisation actually needs.
Design a coherent foundation
Define storage, processing, serving and access patterns around workload requirements and source constraints.
Make ownership explicit
Assign platform, data and reporting responsibilities so the architecture has an operating model behind it.
Sequence investment and change
Identify dependencies, capacity assumptions and delivery stages, with evidence needed before commitments.
OUR DATA STRATEGY & PLATFORMS SERVICES
From estate assessment to a usable platform foundation
Define a data strategy, review a proposed architecture or establish the platform foundations for a prioritised delivery programme.
Data estate & readiness assessment
Review source systems, interfaces, reports and existing platforms. Identify ownership, quality issues, duplicated datasets and constraints, giving you evidence for what to keep, improve or replace.
Business priorities & data roadmap
Map reporting and analytics needs to business decisions and reusable data products. Prioritise use cases by value, feasibility and readiness, with a staged route from foundations to outcomes.
Target data architecture
Design ingestion, storage, transformation and serving across Microsoft Fabric, Azure and existing services. Compare lakehouse and warehouse options and record decisions on boundaries and scale.
Fabric & Azure platform foundations
Set up the agreed tenant, workspace and Azure resource structures. Establish environment separation, connections and deployment practices, with permissions and readiness validated before go-live.
Data governance & operating model
Assign data owners, stewards and platform responsibilities. Define classification, access, lineage and quality expectations, using Microsoft Fabric and Purview capabilities where they fit.
Capacity, cost & platform planning
Assess workload demand, processing windows, concurrency and storage. Model capacity and licence needs, plan for scaling and set up monitoring before committing to running-cost assumptions.
CAPABILITIES & DELIVERY SCOPE
The decisions behind a governed data platform
Business requirements, workload behaviour and ownership shape the architecture. These workstreams make platform choices, delivery assumptions and operating boundaries explicit.
Business decisions & data product priorities
Identify the decisions and processes that need better information. Define the consumers, required measures and freshness for priority data products, with acceptance criteria and owners. Separate the business outcome from a preference for a particular tool.
- Business questions and consumers
- Prioritised data product backlog
- Measures, freshness and acceptance
Estate inventory & readiness
Map applications, databases, files, interfaces, reports and existing platform components. Assess source access, quality and ownership with the available evidence, recording gaps and dependencies that need discovery or validation before implementation.
- Source and reporting inventory
- Quality, access and ownership findings
- Dependencies and evidence gaps
Storage, processing & serving architecture
Compare Fabric lakehouses and warehouses with appropriate Azure patterns. Define where data lands, how it is transformed and how applications or analysts consume it. Record how shared data, copies, lifecycle and recovery requirements affect the design.
Workspace & environment foundations
Map workspaces and Azure resources to delivery teams, data boundaries and operational responsibilities. Separate development, test and production, including data access and connections, and validate deployment for the items in scope.
Ownership, security & governance
Define ownership and stewardship across domains, datasets and reporting models. Assess classification, lineage and access across every layer, so a report restriction is never relied on to protect direct storage access.
Data development & lifecycle management
Define how pipeline, model and platform changes are versioned, tested and released. Assess Git integration and deployment pipelines, with environment-specific configuration, acceptance evidence and item limitations recorded.
Capacity & operating economics
Profile workloads and consumption patterns rather than sizing capacity from data volume alone. Assess processing windows, concurrency, storage and licences, with assumptions tested and reviewed after go-live.
Transition roadmap & delivery model
Sequence platform foundations, source onboarding and data products around dependencies. Plan migration or coexistence for existing assets, with validation gates, clear ownership and evidence before a service is retired.
TECHNICAL DESIGN DECISIONS
Resolve the platform choices before they become dependencies
A useful strategy explains why the architecture fits, how it will be delivered and which assumptions need validation.
Business need and platform scope
Start with the data products and service requirements that matter. Define the foundations they need rather than planning every platform capability at once.
Shared data and access boundaries
Decide where information should be reused, copied or kept separate. Test access through every layer and plan how sensitive or restricted data is handled.
Capacity and demand
Consider refresh, transformation and interactive demand together. Validate isolation, capacity and licence assumptions with representative workloads.
Operating responsibility
Assign platform administration, data quality, reporting definitions and incident ownership. Configuration alone does not create accountable data governance.
OUR APPROACH
How we deliver data strategy and platforms
Six stages take you from agreed business priorities to a validated platform foundation, an owned governance model and a delivery roadmap.
Agree the business priorities
Identify decisions, users and reporting or analytics outcomes that the strategy must support.
Assess the data estate
Map sources, interfaces, existing assets, quality concerns and current operating constraints.
Compare architecture options
Evaluate Fabric and Azure patterns against workload requirements, capability and commercial assumptions.
Define and validate foundations
Document the platform design and implement or test the agreed foundation components.
Sequence data delivery
Plan source onboarding and data products, with dependencies, migration and acceptance gates.
Confirm governance and ownership
Agree stewardship, platform operations, delivery practices and the next-stage workstreams.
Assess.Design.Deliver.
WHERE DATA STRATEGY CAN HELP
Establish the foundation before expanding the data estate
Common starting points include organisations planning a new platform or deciding how an existing estate should evolve.
See it in practice: Konsolute designed and built a governed Azure data platform on a medallion architecture for Nuclear Waste Services, giving the organisation trusted data for reporting.
WHAT WE DELIVER
A strategy and platform design your teams can deliver
The agreed scope defines the level of assessment, design and foundation implementation, with acceptance criteria and open decisions recorded.
Estate and priority baseline
Source and reporting inventories, readiness findings, business priorities and a data product backlog.
Target architecture and foundation
Design diagrams, workspace and environment definitions, platform decisions and agreed configuration assets.
Governance and operating model
Ownership, access principles, stewardship, quality responsibilities and the approach to change and support.
Roadmap and planning assumptions
Delivery waves, migration dependencies, capacity and licence assumptions, validation tasks and decision gates.
FAQ
Data strategy and platform FAQs
Scope, Fabric or Azure, lakehouses, governance, capacity and handover.
The scope can include estate assessment, business priorities, target architecture, governance, capacity planning and agreed platform foundations. We define which outputs are advisory and which involve configuration or implementation, with acceptance criteria for the engagement.
Not necessarily. We assess the current data services, reporting estate and team capability before recommending what to retain, improve or replace. A focused change to ownership, data structure or delivery practices may be more appropriate than a wholesale migration.
We compare source and destination requirements, engineering patterns, existing investments, connectivity, skills and operating constraints. The architecture may combine services where that is justified, with decisions and dependencies documented rather than assume one platform meets every need.
We assess data types, transformations, query patterns, team skills and downstream use. The design identifies the responsibilities of each storage or serving component, including any copies and access paths. A combined approach needs a clear reason and operating model.
Where included, we can implement agreed workspace structures, environment boundaries, connections and supporting configuration. Deployment and access behaviour are validated for the relevant items, with administrative ownership and remaining actions handed over.
We define practical responsibilities for data ownership, access, definitions and changes, with review points proportionate to the data and workload. Supported platform controls and clear decision ownership help teams deliver within agreed boundaries.
The strategy assesses reporting priorities, existing models and how data will be served to consumers. Detailed model or report development can be scoped as a delivery workstream. The roadmap connects that work to source readiness, definitions and platform dependencies.
Readiness depends on the specific use case, data suitability, permissions, context and evaluation requirements. The strategy can identify the foundations and preparation needed, but platform deployment alone does not establish that every dataset is appropriate for an AI application.
We can assess assets, dependencies and operating requirements, then define retention, coexistence or migration waves. Detailed conversion and data movement require a scoped implementation plan, with reconciliation and retirement criteria established before cutover.
We consider processing patterns, concurrency, storage, reporting consumption and current entitlements. Assumptions are recorded and tested where appropriate, with usage monitoring and review after deployment. Fixed running costs should not be promised without suitable evidence.
Where required and supported for the items in scope, we can define and configure versioning and promotion between environments. The design records configuration, permissions, testing and any platform limitations or manual steps that remain.
The agreed outputs can include estate findings, a target design, foundation assets, a governance model and a prioritised roadmap. Open assumptions, capacity and licence dependencies, operating responsibilities and next-stage engineering tasks are documented.
TAKE THE NEXT STEP
What should your data platform make possible?
Tell us which reporting priorities, source systems or platform decisions are driving change. Konsolute will help define the architecture, ownership and delivery plan needed to turn those requirements into a usable data foundation.