The Transformation Sequence: Why Data Initiatives Stall without Business Alignment

Stefan Tropper is Director of Digital Solutions and Data Platform at Magna with nearly 20 years of experience across various IT leadership roles. Over the past decade, his work has focused on enterprise data, analytics and BI platforms as platform owner. He is part of Magna’s Data Transformation Core Team, working at the intersection of business transformation, data strategy and technology enablement.

Through this article, Tropper explains that lasting impact comes when business transformation leads and data follows. And true value appears when business and IT move together, with shared ownership, clear accountability, and a focus on turning insight into action rather than just producing more information.

Many data initiatives begin with strong momentum. Platforms scale, dashboards multiply and access to information reaches an all-time high. Yet in many organisations, the expected improvement in cross-functional alignment and datadriven decision-making does not fully materialise. This is rarely a question of data quality or technology maturity. More often, it is a problem of transformation sequencing.

When data initiatives run ahead of business transformation, they tend to optimise existing structures rather than challenge them. Sustainable impact emerges when business process transformation sets the direction and data initiatives follow with clear intent. In this model, business leaders own the outcomes and definitions, while IT evolves from solution provider to acceleration engine.

Why Technical Progress Alone Does Not Create Business Value

Over the past decade, the promise of the modern data stack was clear. 

Broader accessibility would lead to better insight and faster decisions. Success was measured through adoption. 

More users onboarded. More dashboards created. More data sources connected.From a technology perspective, this progress is real. Platforms mature. Capabilities expand. However, technologyled data initiatives often result in incremental improvement rather than a fundamental shift in how the business operates. While data availability increases, decision-making patterns frequently remain unchanged. Organisations continue to spend time reconciling numbers instead of discussing actions.

The constraint is not the capability of technology. It is the absence of shared accountability across business functions for the outcomes that data is meant to drive. Without clear ownership of semantics, priorities, and decisions, data initiatives reach a natural plateau.

Recognising the Plateau and Resetting the Course

At Magna, we recognised that our data initiatives had reached a plateau. Technical progress was visible, but it wasn't translating into the expected end-to-end alignment and business value.

We shifted the conversation by introducing a Business Transformation Team and establishing a Process Center of Excellence. Data moved from being an "IT topic" to a shared business priority. This realignment followed a simple framework:

• Executive Governance: Elevating data transformation & strategy to a steering committee at the executive management level.

• Joint Ownership & Clear Accountability: Building a unified Data Transformation team where Business defines the "what" (semantics and rules) and IT defines the "how" (technology, scale and integration).

• High-Value Prioritisation: Moving away from broad "selfservice" toward specific functional boundaries where shared semantics create the most immediate value.

The first deliverables focus deliberately on direction rather than scale. A redefined data strategy establishes a common frame across the organisation.

 Aligned data policies clarified ownership and decision rights. Initial priorities target high-value areas where shared semantics and governance across functional boundaries promise the greatest impact. These steps create the conditions for value creation before further acceleration.

AI as an Accelerator and Abstraction Layer

This reset coincides with a broader shift driven by AI. The real impact of AI is not limited to advanced analytics or faster automation of data tasks. Its value increasingly lies in how it can abstract complexity rather than eliminate it. Instead of fully fixing legacy data landscapes upfront, AI enables organizations to work across them. Ontologies, semantic layers, and agentbased capabilities can map, translate, and reason across fragmented systems and definitions. This reduces manual effort and shortens cycle time, allowing teams to spend less time preparing data and more time acting on it.

The effectiveness of these capabilities still depends on ownership. Business leaders define meaning, intent, and decision thresholds. They remain accountable for priorities and exceptions. IT provides the platform and operationalizes at scale. In this model, AI decouples semantic alignment from physical data consolidation. For large and complex organizations such as Magna, this is a meaningful shift. Mature platforms combined with AI make it possible to accelerate value creation even in imperfect data environments, without adding pressure to already constrained teams.

Moving Together for Compound Value

My experience shows a simple but powerful pattern: Data initiatives create momentum, but business transformation creates direction. Value only emerges when both move together.

Treating data transformation as a technical program delivers a solid foundation. Treating it as a shared leadership responsibility is what unlocks real impact. The goal is not to do more with data. It is to ensure that every step forward in data capability translates into measurable business outcome.