Consultancy evaluation · Step 4 of 5
Review the technical and commercial proof
Confirm where AnalyticsCreator fits technically, understand its delivery boundaries, and test whether the commercial case is credible for your consultancy.
The proof required
A credible decision needs more than a successful demonstration
A product demonstration can show that AnalyticsCreator works. It does not, on its own, prove that it is suitable for your consultancy, your delivery standards or your customer architectures.
The evaluation should establish two things. First, that AnalyticsCreator can support the technical work you repeatedly deliver. Second, that the value created is greater than the licence, enablement and adoption effort required.
The right question is not simply whether AnalyticsCreator can generate a warehouse. It is whether it can support your architecture, improve your delivery economics and remain compatible with the way your customers need to operate.
Technical fit
What AnalyticsCreator is designed to support
AnalyticsCreator is designed for consultancies delivering data warehouse, business intelligence and analytics solutions within the Microsoft data stack.
Multiple warehouse approaches
Support for dimensional, normalised, Data Vault and hybrid modelling approaches, allowing the design to reflect the customer’s architecture rather than imposing one fixed methodology.
Native database assets
Generate warehouse structures, tables, views, transformations, relationships, historisation logic and related database objects.
Data movement and orchestration
Generate delivery assets for supported Microsoft integration and orchestration environments, including established and cloud-based delivery patterns.
Semantic model alignment
Maintain alignment between the warehouse design and supported Power BI and Analysis Services semantic model structures.
Delivery and DevOps assets
Produce deployment artefacts that can be integrated with existing source-control, release and customer delivery processes.
Lineage and documentation
Generate lineage and documentation from the same metadata used to create the implementation, reducing the gap between the designed and documented solution.
Operating architecture
The generated solution remains in the customer environment
AnalyticsCreator is used during design and engineering. The generated workloads are deployed into the customer’s existing environment and do not require an AnalyticsCreator production runtime to execute.
AnalyticsCreator is used for
- Importing and organising source metadata
- Designing the target data architecture
- Defining models and transformation rules
- Generating dependent technical assets
- Maintaining lineage and documentation
- Regenerating assets when the design changes
The customer environment operates
- The deployed databases and warehouse structures
- The integration and orchestration workloads
- The semantic and reporting layers
- The production security and access controls
- The deployment and operational processes
- The generated native code and assets
The customer should be able to inspect, deploy and operate the generated assets within its own Microsoft environment. The production solution should not depend on a separate proprietary AnalyticsCreator runtime.
Technical boundaries
Confirm the exclusions as carefully as the capabilities
A reliable evaluation should identify where AnalyticsCreator is not the primary implementation tool. This prevents a successful demonstration from being interpreted as support for every possible customer architecture.
Strongest technical fit
- Microsoft-centred data warehouse delivery
- SQL Server and supported Azure architectures
- Microsoft Fabric warehouse and analytics delivery
- SSIS, Azure Data Factory and supported pipelines
- Power BI and Analysis Services semantic models
- On-premises, cloud and hybrid Microsoft environments
Requires separate tooling or validation
- Databricks-first implementation architectures
- Native SAP HANA warehouse development
- Generation of Spark notebooks
- Non-Microsoft platforms as the core delivery target
- Highly specialised code outside supported generators
- Customer-specific operational requirements not covered by standard patterns
Customer-specific transformations, integrations, performance requirements and operational exceptions may still require manual engineering. The purpose is to automate the repeatable foundation, not to pretend that every project is identical.
Technical evidence
What your technical team should verify
The evaluation should use a representative architecture and inspect the generated result, not only the design interface.
- Can AnalyticsCreator represent your preferred modelling approach and architectural layers?
- Are the generated database objects understandable and acceptable to your engineers?
- Can your team implement the required historisation and change patterns?
- Do the integration outputs fit the customer’s selected Microsoft services?
- Can semantic models remain aligned with changes to the warehouse design?
- Can the generated assets enter your normal Git, DevOps and deployment process?
- Does the generated documentation reflect the implemented solution accurately?
- Can the solution continue to operate without an AnalyticsCreator production runtime?
Commercial fit
Test the economics against your real project model
The commercial case should be based on the work your consultancy actually performs. Generic claims about faster delivery are not enough to establish whether the investment will improve margin, capacity or revenue.
Licence and enablement investment
Understand the relevant commercial model, expected licence commitment, training effort and the internal time required to establish your delivery standards.
Engineering time released
Estimate how much repeatable implementation work can be reduced and whether that capacity can be converted into more projects, shorter lead times or less reliance on senior staff.
Rework and delivery risk
Assess whether model-driven regeneration can reduce unplanned effort when requirements or architecture change during fixed-price and tightly budgeted projects.
Additional delivery opportunities
Determine whether increased capacity could support more customer projects, larger engagements or services that were previously difficult to deliver economically.
Ongoing customer value
Consider whether the maintained design model can support enhancements, documentation, managed services, migrations and future architecture changes.
Organisational change
Account for the effort required to train consultants, agree modelling standards and introduce a model-first delivery process across the team.
Commercial calculation
Compare the investment with measurable delivery effects
Investment to include
- Licence and partner programme costs
- Training and consultant enablement
- Time spent defining internal standards
- Initial project support and adoption effort
- Internal ownership of templates and best practices
- Change to existing delivery processes
Return to measure
- Engineering hours saved on repeatable work
- Reduction in rework after design changes
- Faster onboarding of additional consultants
- Greater project throughput and shorter lead times
- Improved margin protection on fixed-price delivery
- Additional follow-on and managed-service revenue
Time savings create value only when they reduce cost, prevent rework, improve project margin, shorten delivery or allow the team to perform additional billable work.
Evidence package
What to review before making the decision
Product evidence
Review an end-to-end demonstration and inspect how the design becomes generated technical assets.
Architecture evidence
Map your typical customer architecture to the supported modelling, integration, semantic and deployment outputs.
Commercial evidence
Review the relevant partner model and calculate the economics using representative project effort and revenue.
Practical evidence
Select one bounded project through which technical fit, team adoption and commercial value can be measured.
Decision checkpoint
Are you ready to test the fit in practice?
- The typical customer architecture falls within the supported Microsoft delivery scope.
- The technical team understands which assets are generated and which work remains custom.
- The absence of an AnalyticsCreator production runtime meets customer ownership requirements.
- Your team has identified enough repeatable engineering work to justify structured automation.
- The commercial calculation includes licence, enablement and adoption costs.
- Expected value is connected to capacity, margin, risk reduction or additional revenue.
- A suitable first project can be isolated without placing a critical customer delivery at unnecessary risk.
- Technical and commercial success criteria can be measured before the project begins.
The question is whether there is enough evidence to justify a controlled first project. That project should prove the fit with your architecture, your consultants and your commercial delivery model.
