Data & analytics: analysis, BI or database engineering?
Unclear metrics, conflicting reports and slow databases need different solutions. This overview separates review and KPI definition, BI modelling and reporting, and database engineering; RAG is an adjacent AI topic.
A guide to KPI review, BI models, Power BI reporting and database engineering; RAG is positioned as an adjacent AI service.
Services: Data & analytics: analysis, BI or database engineering?
Data analytics & business intelligence
BI, dashboards and analytics for decisions you can trust.
View Data analytics & business intelligence →Database solutions
Database design, migration, performance and scalable models.
View Database solutions →Database & business intelligence
Scope, process and FAQ on the service page – Made in Germany from East Frisia.
View Database & business intelligence →
Estimate your project costs
Use our interactive calculator for a first budget indication—free and non-binding.
Thorsten Frieling – Projektmanagement
Power BI & business intelligence
Scope, process and FAQ on the service page – Made in Germany from East Frisia.
View Power BI & business intelligence →AI knowledge base
Searchable company knowledge – RAG and smart retrieval.
View AI knowledge base →AI implementation in your company
AI coaching, readiness and rollout for your teams.
View AI implementation in your company →Find the right entry point in Data & analytics: analysis, BI or database engineering?
Data quality: context, benefits and typical use cases
When teams report conflicting figures, start with data quality and shared KPI definitions. Choose data analytics & business intelligence for analysis and dashboards, and database & business intelligence for data models and semantic layers. An AI knowledge base using RAG is an adjacent AI service, not classic BI steering. Database design, migration and performance belong to database solutions.
Duplicate master data, missing history or inconsistent terms across ERP and spreadsheets lead to costly wrong decisions—before BI tools even enter the picture.
This hub routes you to the right entry point: from data warehouse & database consulting and KPI dashboard & BI to Power BI.
When companies should prioritise data quality
Prioritise data quality when departments report different revenue or inventory figures, audit finds variances between systems, or a BI rollout would fail without data-quality rules.
We combine analysis, design and delivery: profiling, cleansing, governance and ongoing monitoring—supported by interfaces and data analytics where exploratory work is needed.
Next step: review your data and BI potential.
Which data, analytics or database service fits your starting problem?
| Starting problem | Matching service | Next step |
|---|---|---|
| Analyse questions, define KPIs and evaluate reporting | Data analytics & business intelligence | Book a data strategy call |
| Database design, migration, performance, architecture | Database solutions | Project check |
| BI storage, warehouse, semantic layer | Database & business intelligence | Cost calculator |
| Power BI, DAX, dashboards, Microsoft BI | Power BI consulting | Microsoft 365 |
| Adjacent service: searchable company knowledge with RAG | AI knowledge base | AI knowledge base costs |
| Prepare data sources for a later AI initiative | Adjacent AI & machine learning area | Integrate data sources |
Separate analysis, BI models and database engineering
Choose data analytics for business questions, KPI logic and analysis; choose Power BI for Microsoft dashboards and database & BI for semantic models and BI storage. Database engineering instead addresses schemas, migration, performance and operations.
Sources & silos: ERP/CRM connectivity via API integration in the integration & interfaces cluster. Made in Germany from East Frisia.
Roles in the cluster: data, analytics & databases
So pages do not overlap, each has a clear role. The matrix shows focus and delineation – so you quickly find the right entry point.
| Service | Role | Focus | Delineation |
|---|---|---|---|
| Data analytics & business intelligence | Core service | KPIs, reporting, dashboards | Review and visualisation – not data storage |
| Database & business intelligence | Core service | BI architecture and data storage | Storage and models as the basis for analytics |
| AI knowledge base | Adjacent AI service | RAG search across documents | Semantic knowledge retrieval, not KPI review or BI reporting |
| Database solutions | Specialist topic | Database design and operations | Technical data foundation below analytics |
Related pages for Data & analytics: analysis, BI or database engineering?
Explore costs, solutions, comparisons, and related services for Data & analytics: analysis, BI or database engineering?.
Services & costs
Related clusters
Frequently asked questions: Data & analytics: analysis, BI or database engineering?
Concise answers help you assess service needs, prerequisites and the right next step.
When do we need data analysis and when Power BI?
Data analysis defines questions and KPI logic.
Power BI is the delivery route for Microsoft dashboards; BI models and semantic layers belong to the database and BI service.
Do we need a database solution before BI?
Without a solid data foundation BI rarely pays off.
Data quality, modelling or integration gaps often make database engineering or source-system connectivity the first step.
How does this cluster relate to AI projects?
RAG and AI knowledge bases are adjacent services in the AI cluster.
This hub can prepare sources, models and integrations, but remains focused on analytics, BI and database engineering.
What is the next step after this hub?
Short intro call, matching cost calculator or project check – then the detail page (data analysis, databases, BI layer or Power BI) with concrete scope.
Questions about our services?
Need a tailored quote? Contact us or book a consultation.
Thorsten Frieling – Project management
Or call us:+49 491 960 999 00
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Explore related topics – or return to the full services overview.
Quick context: Overview of this service category – with clear boundaries to related topics.
Service at a glance – for your decision
Data, analytics & databases: compact service description
Definition: This cluster separates analysis and KPI definition, BI modelling and reporting, and database engineering – so metrics are reliable and technical bottlenecks are solved in the right place.
When it applies: When reporting is chaotic, data quality is poor, a DWH/BI layer is needed or Power BI must connect to ERP data.
Audience: Leadership, finance and IT teams that want BI without silos and with ties to operational systems.
Outcome: Usable dashboards, structured models and documented ETL/integration paths – often alongside integration or AI work.
Approach: Clarify sources and KPIs, improve data quality and models, validate dashboards with business teams, document operations and iteration.
Prerequisites and limits: Prerequisites and limits: defined KPIs, data sources with permissions and a business owner. Limits: not a full ERP rollout or app build without a data model—see platform and integration services.
Differentiation: Pure interfaces without BI focus → Integration; AI knowledge bases → AI & ML; bespoke software → Software & platforms.
Trust: Mid-market BI, database and Power BI experience from East Frisia with clear milestones and handover.
Sensible next step: Sensible next step: clarify data sources and KPI questions in an intro call—then model or dashboard roadmap.
Approach in steps
- Approach: Clarify sources and KPIs, improve data quality and models, validate dashboards with business teams, document operations and iteration.
Up to 50% of your investment via BAFA/KfW
Use our funding calculator to see which government grants may apply to your project.
Björn Groenewold – Managing Director
Service cluster
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A guide to KPI analysis, BI models, Power BI reporting and database engineering; RAG is positioned as an adjacent AI service.
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