No data strategy, no data- and AI-driven company: how to get started

There are plenty of dashboards but few insights.

In many companies, data is collected, analysed and visualised, yet the hoped-for impact on actual decision-making fails to materialise. The reason: it is not a lack of data or tools, but a lack of a clear data strategy.

If you really want to run your business in a data-driven way, you need more than just modern technology. What is crucial is a strategic framework that defines what the data is used for, what added value it is intended to deliver, and how it contributes to better decisions. Only then can a truly data-driven company emerge.

Why a data strategy is essential

Many companies embark on their data journey with great enthusiasm: new BI platforms, data lakes, AI pilot projects. Yet without a strategic framework, these initiatives often fizzle out.

Typical signs of this include:

• isolated analytics solutions in individual departments
• contradictory metrics and no ‘single point of truth’
• high investment with little business impact
• unclear responsibilities

A data strategy provides direction here. It prioritises initiatives, pools resources and ensures that data makes a measurable contribution to value creation rather than becoming an end in itself.

The right starting point: your corporate strategy

A data strategy should never be developed in isolation. It is always derived from the overarching corporate strategy. So, start by asking yourself: Which decisions do we want to make better, faster or more informed in future?

Strategic questions might include:

• How can we sustainably increase customer loyalty?
• Where are we currently losing margin or efficiency?
• Which decisions are still based on experience rather than facts?

For example: if the strategic goal is to strengthen customer relationships in the long term, a data-driven CRM system with consistent customer profiles and predictive analytics could be a key use case. In this context, the technology follows the strategy, not the other way round.

From vision to implementation: The Data Journey Roadmap

A concrete roadmap is derived from the strategic vision. It defines which use cases are to be prioritised and how implementation will take place step by step.

Bear the following aspects in mind:

Value creation: Which problem should we tackle first? Focus on measurable results that demonstrate the benefits of the data strategy internally.

Implementation effort: How quickly can we achieve our goal? Assess the technical complexity and the quality of the existing data infrastructure before you begin.

Involvement: Involve all relevant departments, not just IT.

Technology: What tools and platforms do you need?

Organisation: Who takes on which role in the data process?

Resources: What skills do you need, either internally or externally?

Timetable: When should each milestone be reached?

Important: A data roadmap is not a rigid framework. It is reviewed regularly and refined as business objectives or circumstances change.

The strategy must be understandable to everyone

Your data strategy isn’t just a document to be filed away. It should be clear, inspiring and practical, so that even staff without an IT background can understand and get behind it. After all, it’s not just a basis for planning, but also part of the data culture you want to embed within your organisation.

For management, this means:

• Making decisions visibly data-driven
• Creating transparency around key performance indicators
• Fostering trust in data

In this way, the data strategy becomes a central element of a data-driven corporate culture.

Conclusion

A data-driven company is not created by the use of tools, but by creating clarity. A well-founded data strategy links business objectives with technology, organisation and culture. It ensures that data is used where it creates real added value, thereby becoming a decisive competitive factor.