With 10 questions about the data strategy

From corporate goals to a concrete roadmap

94% of companies are not yet fully exploiting the potential of their data. It's time to change that.

The pressure to deal with the use of data is increasing. This is because companies are striving for the use of AI, more efficient processes and data-based decisions. There is also a growing desire for transparency, scalability and cross-departmental collaboration.

But it often remains unclear how these goals fit in with the actual data situation: While Excel lists are maintained in marketing, controlling works with SAP systems and sales uses an outdated CRM system. The result is fragmented, sometimes contradictory data worlds that can hardly be used strategically. It is often not the technology that is missing, but the strategic framework for the targeted use of data.

Why many data strategies fail and what makes a good strategy

Most companies start with department-specific data projects, but quickly reach limits in terms of scaling and efficiency. It often becomes clear — after long and complex processes — that only an overarching data strategy can truly scale and sustainably anchor successful approaches across the company.

Many supposed strategies consist of a string of keywords without any concrete direction. Vision, mission, and values are important, but not a strategy. Objectives such as “20 percent more turnover” or meta-goals such as “better data quality” also sound ambitious, but are rarely directly related to concrete use or measurable benefits. No one in the company needs “better data quality” for their own sake. They therefore remain ineffective as long as it is not clear how they are to be achieved through concrete data-driven measures.

A sustainable data strategy combines direction, diagnosis and implementation. It starts with a clear vision: Where does the company want to be in the future and what added value should data create in the process? This is followed by an inventory, focused specifically on the areas that are relevant to this goal. Based on this, measures can be developed that follow each other logically, do not contradict each other and take the entire organization with them.

The strategy thus becomes a roadmap, which prioritizes, estimates the use of resources and time frames, and clarifies responsibilities.

How do you develop a concrete and implementable data strategy?

The following ten questions provide guidelines for developing a concrete data strategy. Whoever answers them as specifically as possible transforms a vague vision into a tangible roadmap that provides orientation and can be anchored in everyday working life. Goals become clear priorities, responsibilities become concrete responsibilities, and ideas become comprehensible steps. This creates a common framework that shows how data can actually be used: not abstractly, but in a practical, comprehensible and effective way.

The 10 most important questions for a successful data strategy

1. Which specific corporate goal should the data strategy support?

The starting point of every data strategy is the question of which overall business goal is addressed by using data. Without a clear direction, data projects run the risk of losing their strategic focus.

2. Which use cases contribute directly to this goal?

Not all use cases make the same contribution to achieving business goals. The relevant use cases should be identified and prioritized, for example, as part of data thinking workshops.

3. What is the current state of your data landscape?

The status quo can be measured based on the areas of data, usage, technology and organization. Data sources, existing reports, technical architecture as well as roles and responsibilities are examined in order to make weaknesses and potential visible.

4. Which technological architecture do you need for your data strategy?

The technical infrastructure must clearly define the basis on which the company will work in the future. Whether with a data warehouse, a data lake, or a hybrid approach, supplemented by suitable tools for integration, analysis and visualization. Data warehouses such as Snowflake are suitable for structured data, and data lakes such as Azure Data Lake are suitable for unstructured data. Modern platforms such as Microsoft Fabric enable a flexible combination of both approaches.

5. Which organizational structure best supports your data strategy?

Clear responsibilities are derived from the goals of the data strategy and ensure that decisions are made quickly, data quality ensured and regulatory requirements are met. Depending on the defined architecture, it is useful to determine which roles, competencies and organizational structures are required. These can be roles such as data owner, data steward, product owner data, BI/analytics/ML teams and legal/infosec, which together interlink business goals, technical implementation and compliance.

6. What resources and capabilities are required for implementation?

In addition to clear roles, personnel resources and capabilities must also be planned, both in terms of number and availability as well as necessary competencies such as engineering, analytics, AI and data governance. Companies should decide which capabilities are built up internally and which are supplemented externally, and at the same time clearly define the budget and priorities for investments.

7. How should the data team in the company be organized?

The data team can be established centrally, federated or in the form of embedded squads, i.e. small, cross-functional teams that work directly in the specialist areas. While the central model ensures standards, governance and efficiency, the federated model brings more proximity to business but requires greater coordination. Embedded squads offer maximum focus and speed in specialist areas, but involve higher costs and the risk of shadow solutions. A strong central platform and governance core is therefore always recommended.

8. How do you build a sustainable data culture in your company?

A data culture is not created by tools, but by an established attitude and clear framework conditions. It requires leadership that exemplifies data use, easy access to reliable information, the targeted development of data literacy, and rules for responsibility and governance. For culture to be effective in everyday life, it also requires active change management, clear communication and incentives that visibly reward data-based work.

9. How do you plan and manage the implementation of your data strategy?

For effective implementation, each step should be provided with specific responsibilities (owners), a binding time frame and clear success criteria (“Done means...”). This shows who is responsible for what, which stages are to be achieved when and how far implementation has already progressed. This transparency provides orientation, makes it easier to track progress and makes it possible to identify and manage dependencies at an early stage.

10. How do you quickly achieve measurable added value with your data strategy?

A good data strategy follows an iterative logic: It aims to generate tangible added value at an early stage, such as through MVPs or clearly defined pilot projects that deliver initial results and learning effects. In this way, acceptance within the company can be established and at the same time check whether assumptions and priorities are effective in practice. In this sense, measuring success means not only tracking goals, but also regularly questioning whether these goals still match current findings. An effective strategy remains adaptive. It is constantly refocusing on the shortest path to actual, beneficial output.

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