Reliable data foundation
- Single point of truth
- Break down data silos
- Sovereign cloud or on-premise
Data Maturity Assessment
Data-readiness check: how reliable is your data foundation for AI?
A structured assessment of your data readiness across five dimensions. You get a quantified score and a sound classification of your most important levers.
Assessment framework
From data to
From data to
productive AI
Reliable AI doesn't happen overnight, but along a clear path. These five dimensions form the framework against which your data readiness can be measured, and show what follows after the check.
Governance & security
- Roles, rights & responsibilities
- Data quality & documentation
- Compliance (ISO 27001, TISAX)
Business use case
- Focus on the business unit
- Clear goals & KPI definition
- Prioritization by measurable ROI
AI agents
- Guardrails & bias control
- Model integration without vendor lock-in
- Connection to existing systems
Scaling & operations
- Company-wide rollout
- BI dashboards for decisions
- Monitoring & continuous learning
Methodology
Three steps to your
Three steps to your
current-state assessment
The assessment is based on the dimensions that, in our consulting projects, regularly make the difference between working and stalling AI initiatives.
Structured survey
Eight questions on data sources, reporting maturity, use-case clarity, governance and team setup. Each question addresses a specific dimension of data readiness.
Quantified score
Aggregated data-readiness score on a scale of 0 to 100, classified into five maturity levels from "Early stage" to "Scale-ready".
Recommended action
Prioritized levers based on your answers. On request we deepen the analysis in a conversation with your team.
Assessment
Current-state assessment
The informative value of the result depends on the accuracy of your self-assessment. There are no right or wrong answers, only a reliable starting point.
Example result
What your result
What your result
looks like
Example illustration of a result. Your result contains your individual score, its maturity-level classification and the levers most relevant to you.
Score
Proven practice
Typical starting points
Typical starting points
from industry
Recurring patterns from consulting projects in the data-intensive mid-market, anonymized and reduced to the underlying problem structure. You might recognize yourself.
Data silos in manufacturing
Problem: Fragmented ERP and machine data prevented reliable analysis and forecasting.
Approach: Consolidation into a single point of truth as the basis for meaningful BI reporting.
Shadow IT in quality management
Problem: Spreadsheet-based processes jeopardized traceability and audit requirements (TISAX).
Approach: Role-based data governance with clear responsibilities and quality assurance.
Lack of use-case clarity
Problem: IT investments with no measurable benefit, because the business unit wasn't involved.
Approach: Structured use-case definition focused on the highest-value pilot.
From score to roadmap
Follow-up
Follow-up
formats
The assessment provides an initial classification. Turning it into a committed roadmap typically happens in one of the following formats.
Take action
Strategy Sprint
Two-week format for structured work on the typical levers identified in the assessment.
See the format
Frequently asked questions
About the assessment
Questions about the process and the informative value of the assessment. General questions about working with LYN can be found on the homepage.
How are the answers processed?
The answers are evaluated locally in your browser. Data is transmitted only when you actively request a results call.
How reliable is a result based on eight questions?
The assessment provides an initial structured classification, not a complete audit. It covers the five dimensions where data readiness is most commonly limited in practice. A complete assessment happens as part of the Strategy Sprint.
Which dimensions are assessed?
Data sources and consolidation level, reporting and analytics maturity, use-case clarity, governance and responsibilities, as well as organizational setup in the interplay between business units and IT.
What if a question can't be clearly answered?
Every question includes a "Can't determine clearly" option. This answer is factored into the score and is often itself already a relevant indicator: where your own data readiness can't be determined, that's regularly a priority lever.