Every team has its own Excel reports; the numbers never quite match.
Data has to be compiled manually from several systems.
Changes to KPIs (e.g. margin, contribution) take weeks.
There is no clear single point of truth for management KPIs.
Raw data
Cleansed & integrated data
Standard reports
What happened?
Ad-hoc analyses
Why did it happen?
Self-service BI
Scaled descriptive & diagnostic analytics
Predictive analytics
What will happen?
Prescriptive analytics
What should we do?
Human-machine interfaces & autonomous systems
Execute / assist
Many companies sit somewhere between manual analysis, early BI structures and scalable data platforms. This short assessment helps identify your current position and the most useful next step.
Reduce manual effort and create a first reliable set of figures.
Consistent KPIs across teams and a scalable data structure.
A reliable data foundation at scale to move from what happened to what happens next.
Choose the option that best matches your reality today.
1 / 10
Question 1 / 10:
Where does reporting usually start for you?
Scoring: (each answer maps to a level)
A = Level 1 (foundations / mostly manual)
B = Level 2 (structured, but still developing)
C = Level 3 (scalable, automated & future-ready)
Tie-breaker rules: (in the event of a tie)
If question 6 = A → Level 1
If question 5 = C and question 8 = C → Level 3
In all other cases → Level 2
Deep dive on master data
Master Data Management (MDM)Depending on your starting point, governance requirements and target vision, we don't rely on one standard stack. We work with clear architecture variants that fit maturity, team, budget and long-term strategy.
For companies that want maximum openness and low dependency on a single vendor, we build an open data platform with lakehouse/warehouse logic, open-source components for ingestion and transformation, and a deliberately tool-independent consumption layer at the reporting level.
The data foundation stays stable even if dashboard or frontend tools change later.
Our BI projects are led by experienced specialists who know reporting, data modelling and platform architecture from practice. The team holds Databricks and Microsoft certifications and deep experience in building modern lakehouse and data platform architectures
Focus on KPI logic, reporting architecture, self-service BI and the clean connection between business, data model and governance.
Focus on medallion logic, data pipelines, data platform design and the technical foundation for scalable analytics and AI use cases.
We don't work tool-driven, but along your target vision, data maturity, governance requirements and long-term scalability.
Platforms & lakehouse
Transformation & data engineering
Storage & open formats
Reporting / BI / consumption layer
Depending on the starting point we build on Microsoft-centric platforms, lakehouse architectures or open data stacks. What always matters to us is that the data foundation stays cleanly structured, resilient and as tool-independent as possible at the reporting level.
We combine proven technologies for ingestion, transformation, storage, governance and consumption — from Microsoft Fabric and Power BI to Databricks and open lakehouse/warehouse components through to flexible dashboard and consumption layers.
This avoids unnecessary dependency on a single frontend or tool, and changes in the system landscape can be implemented with far less friction.
Margin analyses, cashflow, scenarios & forecasts.
Inventory, lead times, delivery reliability, bottlenecks.
A consolidated view across entities, countries and channels.
Pipeline transparency, campaign performance, customer journey.
Which questions should be answered? Which KPIs are critical?
Selecting the right platform (e.g. classic DWH vs. lakehouse), defining the data model.
Building pipelines, data processing, first reports & dashboards.
Training users, self-service rules, governance & continuous optimisation.
