Data Driven Gets You Compliant, Data Inspired Gets You Ahead: Why Data-Driven Steering Isn’t Enough

What distinguishes data driven from data inspired?

Data driven: steering through evidence

Data-driven decision-making refers to a process in which courses of action are systematically assessed and selected on the basis of existing, structured data – as distinct from decision-making based primarily on intuition.¹ It is characterised by a closed feedback loop: data is collected, aggregated, converted into key figures, and serves as the basis for decisions whose effects are, in turn, measured and fed back into the system.

Data-driven competence is thus, at its core, an ability to control, consolidate and optimise processes: it ensures that an organisation knows what is going on within it, and that it continually improves existing workflows on the basis of this knowledge. In the management literature, this capability is also described as a competitive factor in its own right, one that allows organisations to distinguish themselves from competitors through the systematic evaluation of their data.²

Data inspired: from interpretation to impulse

Data inspired does not use data primarily to confirm or optimise existing assumptions, but as a starting point for questions, hypotheses and ideas. Here, data is understood less as evidence than as a stimulus for thought – a distinction that is explicitly set apart in the practitioner literature from a purely confirmatory, data-driven approach.³

Data-inspired competence is, at its core, an ability to interpret and generate ideas: it enables an organisation to derive additional meaning from data that goes beyond what is already known.

Summary comparison

This comparison suggests that these are not two levels of maturity on the same scale, but two different models for handling data.

Why data inspired is not merely a further development of data driven

Data inspired is often presented as the “next maturity stage” after data driven – in effect, the crowning achievement of a linear data-maturity staircase. But that is only part of the truth.

Different error logic

An idea that emerges from such “inspiring” data analysis and later proves untenable is a regular, even necessary, part of the process of gaining insight. An organisation’s ability to work with falsifiable, provisional findings cannot be produced by refining existing data-driven processes; it requires a new mindset.

Different data requirements

Data-driven systems depend on structured, complete, quality-assured data. Incomplete or inconsistent data is rightly regarded as a deficiency. Data inspired, on the other hand, derives its value from combining high-quality data with incomplete, heterogeneous or informal data sources — for example, dialogues, market assessments, competitor observations or free-text fields. Data inspired thus builds, in part, on the data infrastructure that data driven creates, but supplements it with additional, often external, data sources that are mostly unstructured. This calls for expanded organisational and architectural concepts.

Different organisational anchoring

Data-driven competencies are typically anchored in reporting, risk and controlling functions, which are optimised for standardisation, traceability and repeatability. Data-inspired competencies, by contrast, tend to develop in innovation, strategy or product functions, whose success criterion is not conformity but novelty. Deepening one system does not automatically produce the other. Both require independent investment in processes, roles and culture.

Interim conclusion

Data inspired builds on the data infrastructure that data driven creates. Without robust, integrated data, there can be no serious exploration. In this sense, a dependency exists. However, this dependency is not a maturity-level relationship but a foundational one: data driven supplies the infrastructure, while data inspired requires an additional, independent capacity for interpretation — one that comes with different tools, different error cultures, and different organisational and architectural structures. “Data driven is the baseline, data inspired is the edge” captures the relationship more accurately than a staircase metaphor: the mandatory element is a prerequisite, but it does not, by itself, produce the discretionary one.

How can the baseline gets you ahead, when it comes to banks

The German banking landscape is particularly well suited to illustrating this distinction, as it appears to be at different stages of development along both dimensions.

Data driven as a regulatory standard

German banks — and credit institutions across Europe more generally — are subject to a regulatory framework that all but forces data-driven capabilities. Requirements such as BCBS 239 (Principles for effective risk data aggregation and risk reporting), published by the Basel Committee on Banking Supervision,⁷ MaRisk (Minimum Requirements for Risk Management) issued by BaFin,⁸ or DORA (the Digital Operational Resilience Act, Regulation (EU) 2022/2554)⁹ demand consistent, complete, timely and auditable data aggregation. In recent years, institutions have made substantial investments in data governance, data quality management and lineage capabilities in order to meet these requirements.

Driving innovation with data inspired

Regulatory-driven excellence in data governance — for instance, as part of BCBS 239 implementation or metadata management – is a necessary but not a sufficient condition for data-based innovative capacity. Institutions that have consistently built up their data-driven infrastructure have a valuable starting point. Whether this translates into data-inspired value, however, depends on further choices – such as deliberately carving out space for exploratory data work, and embedding this organizationally so that innovation functions get genuine data access and the freedom to act on it.

Conclusion

Data driven and data inspired address different questions: one, how an organisation acts in a controlled and compliant manner on the basis of existing knowledge; the other, how it generates new knowledge and new options from data. Both capabilities are complementary but not interchangeable. Data inspired does not arise automatically from deepening data driven; it requires independent investment in error culture, data diversity and organisational anchoring.

The banking landscape illustrates this distinction particularly clearly: regulatory requirements have, in many respects, led to a high degree of data-driven maturity, while data-inspired practices — at least in public presentation — appear underdeveloped.

For institutions seeking to secure their data-based competitiveness in the long term, this implies the need to fulfil data driven as a mandatory requirement, without neglecting the discretionary pursuit of data-based innovation capability.