Fact-based transformation
Data, Experiments, Learning: Shaping change based on facts – not gut feeling.
Many change initiatives fail to deliver real impact: decisions are based on assumptions, progress is not measurable, and adjustments come too late.
A fact-based transformation creates transparency, validates hypotheses, reduces risk, and enables an organization that learns faster, prioritizes better, and remains sustainably adaptable.
How to recognize that action is needed:
- A vision and target picture exist but barely influence the actual change work.
- Decisions about change are driven by opinions, not by data – impact remains unclear.
- Experiments rarely take place or are not evaluated in a structured way.
- Teams and leaders adapt too slowly to changing market conditions.
- Investments in change are not systematically prioritized or managed.
- Progress, risks, and learning outcomes are not transparent – change efforts fade away.
Why is now the right time to act?
Market shifts, technological developments (e.g., AI), rising complexity, and increasing efficiency pressure require organizations to learn and decide faster.
Traditional change programs rarely provide this level of learning and adaptability.
What is critical for success today:
- Decisions based on real data instead of assumptions
- Courage to experiment and learn quickly
- Ability to steer initiatives based on impact
- Adaptability in short cycles
- Transparency that makes change measurable
A fact-based transformation integrates all of this: people, organization, and technology—ensuring that change becomes measurably effective.
Our Approach
We design transformation as a continuous process of learning and improvement — data-driven, experimental, and adaptive rather than linear and politically driven.
We combine principles from business agility, lean, product development, organizational design, and data-driven decision-making.
Our services include:
- Clarifying purpose, vision, and change goals
- Building a data-based steering model (OKRs, KPIs, leading indicators)
- Introducing experimentation logic (hypotheses, tests, validation)
- Operationalizing change initiatives in short learning cycles
- Review and feedback loops to assess progress and impact
- Leading based on fact-based decision models
- Integrating technology, data, and organization (AI-supported process analyses, metrics, dashboards)
- Coaching and enabling teams and leaders to build a sustainable change culture
What you gain:
- Clarity about the impact, progress, and risks of your change initiatives
- Faster decisions through reliable data instead of opinions
- More agility and adaptability through short learning cycles
- Reduced misinvestments through validated assumptions
- Higher effectiveness of transformation efforts
- A culture that promotes learning, experimentation, and transparency
Typical results include a reduction of ineffective initiatives by 20–40%, shorter decision-making times, higher success rates in change investments, and significantly improved alignment between strategy, portfolio, and execution.
Ready to start – formats for the beginning:
- Transformation Assessment (incl. diagnostic for data, experiments, learning capability)
- Experiment Design Workshop
- Format: Steering & Metrics in Transformations
- Workshop: “Leading with Fact-Based Decision Models”
In‑Depth Support
- Development of a Transformation Operating Model
- Introduction of structured learning & review cycles
- Coaching for evaluating and prioritizing initiatives
- Implementation of an experiment framework (hypotheses, metrics, validation)
- Development of a data-driven transformation dashboard
Our Recommendation for the First Step:
Start with a Transformation Assessment:
It provides transparency about maturity level, challenges, and key levers—and forms the starting point for a fact-based, impactful transformation.
Let’s design a transformation that truly works—measurable, adaptive, and sustainable.