KPI definitions and semantic layers
Align calculations, dimensions, time frames and business ownership across departments.
Data & AI Engineering
Data Analytics: Make decisions from metrics you can trust
More dashboards do not fix inconsistent definitions. We develop business metrics, analytical models and operational reporting that teams can verify and use in their work.
The challenge is not just the tool
Different teams can report different values for the same metric because filters, periods and source logic are inconsistent. The result is slower decisions and more manual reconciliation.
We begin with the decision, not the BI product: who uses the result, what action follows and which definition can be verified? This lets the data model and interface serve a specific operating need.
What we engineer
Dashboards and business intelligence
Build reporting with meaningful drill-downs, clear filters and documented freshness.
Embedded operational analytics
Integrate metrics directly into business applications and decision workflows.
Exploratory analysis
Investigate segments, trends and anomalies with transparent assumptions.
Forecasting and modelling
Evaluate time-series and statistical forecasts against relevant baselines and uncertainty.
Architecture and trade-offs
A visually polished dashboard can still be wrong. Definitions, historical changes and data granularity need to be resolved before the interface is designed. A shared semantic layer can reduce repeated and conflicting calculations.
Descriptive analytics, diagnostics, forecasting and automated decisions require different methods. Correlation is not causation, and predictive performance must be evaluated out of sample. If the data supply is unreliable, Data Engineering is usually the first step.
From the requirement to production
01 · Clarify the decision
Identify stakeholders, metrics and the action informed by each result.
02 · Agree on definitions
Document the calculation logic, exceptions, ownership and reporting periods.
03 · Develop the data model
Connect sources, validate measures and test transformations.
04 · Deliver analytics in context
Implement reporting, dashboards or embedded analytical experiences.
05 · Evaluate ongoing use
Review quality, adoption, update cadence and decision relevance.
Related capabilities and use cases
Create reliable sources and pipelines for analytics.
Control access, data definitions and accountable ownership.
Apply analytics to concrete operations in mid-sized organisations.
Frequently asked questions
Is Data Analytics the same as BI?
Business intelligence often focuses on standardized reporting. Analytics also includes exploratory analysis, statistics and forecasting.
Should we replace our BI tool?
Not before inspecting the metric definitions and data model. Existing tools are often sufficient when the underlying data is dependable.
How do you avoid inconsistent KPIs?
By versioning calculations, maintaining shared definitions, testing data changes and assigning business ownership.
When does machine learning become relevant?
When the decision requires predictions or classifications and the available data supports meaningful evaluation.
Explore a related capability
Forecast demand, energy and load with proper backtesting.
Technical discovery
Which decisions still depend on inconsistent reports?
We review current systems, technical boundaries and a practical first implementation step.
Aleksandar Basara
Managing Director
aleks@valanor.co
+49 (0) 711 16 44 6 83
Lindenspürstrasse 22, 70176 Stuttgart
