Current-state view
One agreed picture of technology, data, processes, cost and accountability across business and IT.
ASSESSMENT · ARCHITECTURE · ACTION PLAN
Choose the right direction before the next data investment.
We connect business priorities, architecture and the way the team operates. An assessment of Power BI, Microsoft Fabric or a data warehouse results in explicit decisions, a target architecture and a realistic implementation plan.
Situations and scope of work
WHEN IT HELPS
Does this sound familiar?
- The Power BI estate has grown without a shared plan, ownership model or standards.
- The organisation is considering Fabric or a warehouse modernisation but lacks an agreed architecture and sequence of work.
- Reports exist, yet the business still questions metrics, sources or KPI definitions.
- Cost, refresh times or manual effort are increasing faster than the value delivered.
- AI ideas are moving ahead of data quality, semantics, security and accountability.
SCOPE OF WORK
What the engagement may include.
- 01
Business and IT workshops covering goals, decisions, users and critical use cases.
- 02
A map of sources, flows, models, reports, integrations and responsibilities.
- 03
Assessment of quality, security, performance, cost, maintainability and team maturity.
- 04
Identification of duplication, bottlenecks, technical debt and operational risk.
- 05
Target architecture options with selection criteria and consequences.
- 06
A future governance, ownership and delivery operating model.
- 07
Priorities, dependencies, owners, risks and a phased action plan.
SOLUTION COMPONENTS
Decisions based on a complete view of the environment.
The assessment does not end with a list of issues. Every finding is connected to a decision, an owner and a next action.
Target architecture
A development scenario matched to business needs, skills, security requirements and practical constraints.
Implementation plan
Phases, quick improvements, dependencies, risks and measures that move the work from recommendation to delivery.
AI DATA READINESS
AI data readiness
AI cannot repair inconsistent definitions, unknown data provenance or missing accountability. We assess quality, semantics, context, flows, access, security and ownership against specific AI use cases. The result is a gap map and a sequence for building the required foundations, rather than a generic claim of readiness.
- 01quality and timeliness
- 02semantics and context
- 03security and access
- 04ownership and governance
OUTCOME
What remains after the engagement.
- A shared view and decision language for business, IT and data owners.
- Explicit criteria for architecture choices and further investment.
- A prioritised plan that reduces risk, cost and disconnected initiatives.
- Materials that can be used to launch the work and track its progress.
NEXT STEP
Define the right scope.
Describe the current state and expected outcome. The first step may be an assessment, an architecture review or a focused implementation.