Strategic documents often suffer from a paradox: the more information they contain, the harder it becomes to see the actual strategic priorities. A 58-page strategy paper with 152 measures can be comprehensive and still make it difficult to answer the most important questions: What matters most? Which measures are connected? Where are the bottlenecks? Which actions produce measurable results?

This is precisely where Data2Space can add a different analytical dimension. Data2Space Instead of presenting the analysis as a sequence of pages, paragraphs and tables, the entire strategy can become an interactive spatial model in which measures, objectives, funding instruments, stakeholders and expected outcomes are connected.

The analysis of the German Startup and Scaleup Strategy (Startup- und Scaleup-Strategie der Bundesregierung) provides an excellent example. The underlying assessment identifies a central problem: the government presents 152 measures, but the existence of many measures does not automatically constitute a strategy. The analysis argues that a strategy must prioritize, reject alternatives and make itself measurable. Data2Space can make exactly this distinction visible.

Instead of showing “152 measures” as a number, a three-dimensional strategy map could display all 152 measures as nodes. The eight strategic fields could form major spatial clusters. Individual measures could be positioned according to their policy objective, implementation status, funding volume, responsible ministry and expected economic impact. Immediately, the user could see whether the strategy is genuinely concentrated around a few priorities or dispersed across a large number of relatively small initiatives.

A second layer could distinguish between measures that are implemented, measures that are planned and measures that merely involve examination or review. This would transform a linguistic observation into a visual analytical result. The repeated formulations “we examine”, “we aim to” or “we advocate” would no longer appear merely as words in a document. They could become a separate category in the spatial model.

The distinction between a delivered measure and an announced intention is particularly important. The analysis identifies the roll-over provision under the Standortfördergesetz, increasing the threshold from €500,000 to €2 million, as an example of a substantive improvement that has actually been delivered. In Data2Space, this could be represented as a completed node with a direct connection to the affected companies, financing process and expected economic effect. A measure that is still only being examined would occupy a different status.

The same model could make the capital-market argument considerably more concise. The analysis lists the Zukunftsfonds and Deutschlandfonds, Wachstumsfonds II, HTGF V, Scale-up Direct, the FOAK programme, ETCI 2.0 and EIF German Equity. Rather than explaining each programme sequentially, Data2Space could place them around a common “public venture capital” node and visualize the respective capital commitments and target groups. The resulting structure would immediately reveal the concentration of public capital instruments.

The next spatial layer could show the missing private-capital side: pension funds, insurers, foundations and other institutional investors. The analysis explicitly identifies this as the actual lever on the supply side of private capital and observes that relatively little is happening there. Data2Space could therefore turn an abstract policy criticism into a visible imbalance: a large cluster of public financing instruments on one side, comparatively weak private-capital connections on the other.

The same approach works for regulation. The analysis notes that 88% of startups identify less bureaucracy as the most important factor that could influence a decision to found a company abroad, while twelve of the 152 measures address bureaucracy and none actually removes a regulation.

A Data2Space visualization could reduce this entire argument to one highly informative spatial relationship: “startup demand for deregulation” versus “actual regulatory reduction”. The user could click from the 88% figure to the twelve relevant measures and then to the individual provisions. What currently requires several paragraphs of interpretation becomes an immediately visible discrepancy.

The Mittelstand provides an even stronger example. The analysis points out that cooperation between startups and SMEs or industry has fallen from more than 70% around six years ago to approximately 56%, while roughly 75% of startups generate their revenues from B2B business.

In a conventional report, these figures appear one after another. In Data2Space, they could be connected directly: “75% B2B revenue” → “56% cooperation rate” → “decline from >70%” → “five government measures” → “policy gap”. The analytical conclusion becomes visible through the geometry of the information rather than through several pages of explanation.

This is particularly powerful because Data2Space can display both quantities and relationships. A number tells the reader how large something is. A spatial relationship can show what it is connected to.

Consider the public procurement issue. The analysis states that only 7% of startups had public-sector customers in 2025 and criticizes the absence of incentives encouraging public procurement from young companies. A Data2Space model could place “7% public customers” at the centre of a procurement ecosystem connecting startups, public authorities, procurement rules, thresholds, liability, incentives and market access. The user could immediately identify where the bottleneck occurs.

The analysis also proposes five concrete changes: putting the efficiency check at the beginning, deciding rather than merely examining employee participation, opening the research allowance to commissioned research and startup development services, and introducing incentives for procurement from young and innovative companies. These recommendations could become a second, forward-looking layer of the model. The user would then be able to switch from “What does the strategy contain?” to “What should change?”

This creates an important distinction between descriptive and normative analysis. The first spatial layer represents the strategy as it exists. The second represents the analyst’s assessment. The third can represent proposed interventions. Users can therefore compare policy design, observed weaknesses and recommended solutions without losing the connections between them.

Data2Space can also introduce a time dimension. Measures could be arranged according to their implementation dates, allowing users to move from the current situation toward 2027, 2028 and later years. A strategy therefore becomes not merely a static document but a dynamic roadmap. The same principle can be applied to financial resources. Funding volumes can be represented by the size of spatial objects, while policy importance can be represented by position and relationships. A €1.6 billion programme and a small regulatory adjustment do not need to occupy the same visual weight. The resulting environment can distinguish between financial scale, strategic relevance and implementation status.

This also makes contradictions easier to identify. A strategy may allocate substantial financial resources to one area while the principal bottleneck lies elsewhere. A large number of measures may address a problem without changing the underlying constraint. A policy field may contain many initiatives but few measurable outcomes. These relationships are difficult to perceive when information is distributed across pages.

Data2Space is particularly suited to such analysis because it can combine multiple datasets in the same environment. The strategy document can be connected with startup statistics, venture-capital data, regulatory information, funding volumes and economic indicators. Instead of analyzing each dataset separately, users can explore their intersections.

For example, the decline in startup–Mittelstand cooperation can be connected to B2B revenue dependence, funding conditions, procurement policy and innovation incentives. The question then changes from “What does measure 7.x say?” to “Which combination of policies affects the startup–Mittelstand relationship?” This is where spatial analysis becomes more than visualization. It becomes an analytical interface.

A decision-maker does not have to read 152 measures in sequence. The decision-maker can enter the strategy space, identify the largest clusters, isolate unresolved issues, examine the links between measures and outcomes, and then drill down into the underlying documents. The result is not less information. It is more accessible information. That distinction is fundamental. Data2Space does not simplify a complex analysis by eliminating complexity. It makes complexity navigable.

A conventional report asks the reader to construct the relationships mentally. Data2Space externalizes those relationships. A funding programme is connected to its beneficiaries. A regulatory measure is connected to the companies it affects. A policy objective is connected to its instruments. An identified problem is connected to the measures intended to solve it. A recommendation is connected to the weakness it addresses.

This approach is particularly valuable for financial, economic and rating analysis, where relationships often matter more than individual data points. The quality of a strategy cannot be understood solely from the number of measures it contains. It depends on how objectives, resources, implementation and outcomes interact. Data2Space makes precisely these interactions visible.

What currently requires a long analytical text can therefore become a compact, interactive model: 152 measures become a navigable strategy landscape; eight policy fields become spatial clusters; funding programmes become capital structures; gaps become visible distances; dependencies become connections; priorities become focal points; and recommendations become alternative pathways through the same information space. The result is a new way of presenting complex policy analysis: not as a document that has to be read from beginning to end, but as an information environment that can be explored according to the questions of the user.

For strategy analysis, this is a fundamental change. The objective is no longer merely to summarize what a government has written. It is to make visible what the strategy actually does, where its resources flow, where its weaknesses lie and which interventions could change the outcome.