Data Strategy & Architecture

Design, optimise, and operationalise SQL platforms that perform - covering schema design, performance tuning, integration patterns, and governance for reporting and line-of-business workloads.

SQL remains at the heart of many business systems - powering reporting, integration, and operational workloads. However, SQL estates often grow organically: schemas evolve without standards, stored procedures accumulate, and performance issues are addressed reactively. Over time, this creates instability: slow queries affect user experience, reporting extracts become unreliable, and technical teams spend increasing time firefighting instead of improving the platform. In parallel, organisations increasingly need SQL systems to integrate cleanly with analytics platforms such as Power BI and Microsoft Fabric, which adds pressure for consistent modelling and predictable refresh patterns.

LW IT Solutions delivers SQL Database Engineering & Optimisation as a pragmatic service that improves reliability and performance without unnecessary disruption. We can assess your current SQL environment, identify bottlenecks and risks, design improvements (indexing, query patterns, schema refactoring, and operational routines), and implement changes safely with validation and rollback planning. We also support integration patterns for reporting and data pipelines, ensuring SQL becomes a dependable foundation for analytics and automation. Scope varies significantly by estate size and constraints, so we define a clear, measurable scope during discovery.

Talk through your requirements and leave with a clear next-step plan.

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Service Overview

Highlights

Clear link between business outcomes and data platform decisions
Target architecture covering ingestion, storage, analytics, and consumption
Defined operating model for ownership, governance, and change control
Cost-aware design aligned to workload patterns and scale expectations
Actionable roadmap rather than theoretical architecture

Business Benefits

Create a clear target data architecture aligned to business priorities and constraints
Reduce duplication and rework by defining standard patterns for ingestion, storage, and analytics
Improve decision-making through consistent, trusted data models and KPIs
Control cost and complexity by aligning platform choices to actual workload needs
Establish ownership and governance so data platforms remain supportable as they scale

Typical use cases

Rationalising fragmented data platforms and reporting tools
Planning adoption of Microsoft Fabric or modern analytics services
Improving trust and consistency in organisational reporting and KPIs
Preparing for increased data volumes, new products, or acquisitions
Aligning data strategy to AI, automation, and advanced analytics initiatives

Objectives & deliverables

What Success Looks Like

Improve query performance and reduce user-impacting latency
Increase reliability of reporting extracts and integrations
Reduce operational risk through standardised maintenance and governance routines
Implement consistent schema and modelling practices that scale
Create a remediation backlog with prioritised changes and measurable outcomes

What You Get

SQL health check report with key findings and prioritised recommendations
Performance baseline and target outcomes (documented)
Implemented improvements within the agreed scope with validation evidence
Operational guidance: monitoring, maintenance routines, and governance suggestions
Backlog for further refactoring, data quality work, or platform evolution

How It Works

Discovery and current-state review - understand business goals, data use cases, existing platforms, and pain points
Architecture definition - design the target data platform, integration patterns, and analytics approach
Operating model design - define ownership, responsibilities, governance, and decision-making forums
Roadmap and cost model - produce a phased delivery plan with sequencing, dependencies, and cost considerations
Readout and alignment - review recommendations with stakeholders and agree next steps for delivery

Engagement Options

Strategy Assessment - current-state review and high-level target architecture
Strategy + Roadmap - target architecture with phased delivery and investment plan
Architecture Deep Dive - detailed platform, security, and governance design
Follow-On Delivery - implementation support aligned to the agreed roadmap

Common Bundles

Customers who use this service often bundle with these services

ETL/ELT Pipeline Design & Delivery
Design and deliver reliable ETL and ELT pipelines with batch and event-driven ingestion, monitoring, and cost-aware performance tuning.

Power BI Dashboard Design & Integration
Power BI dashboard design and integration delivering trusted executive and operational reporting through strong data modelling, security and reliable refresh.

Microsoft Fabric Enablement (Capacity + Per-user Model)
Enable Microsoft Fabric using capacity and per-user licensing, aligning readiness, governance and operating model for analytics workloads enterprise.

Fabric Lakehouse Design & Build
Design and build a governed Fabric lakehouse with structured OneLake storage, medallion layers, engineering standards, and controls for reliable analytics.

Fabric Data Warehouse Implementation
Design and implement Microsoft Fabric data warehouses with clear models, controlled access, and predictable performance for trusted enterprise reporting.

Fabric Data Factory (ETL/ELT) Pipelines
Design and build Microsoft Fabric Data Factory pipelines with repeatable patterns, reliable scheduling, monitoring, and error handling across data sources.

Azure Cost Optimisation Assessment
FinOps-aligned Azure cost assessment identifies waste, rightsizing opportunities, reservations, and guardrails so teams regain control of cloud spend.