From Strategy to Reality: Why Data Adoption Still Falls Short
Wed 8 Apr 2026
Data strategy is rarely undefined. Execution, however, remains uneven.
Across large organisations, strategies are documented, platforms are implemented, and governance frameworks are established. Yet in practice, teams often rely on workarounds, fragmented tooling, and inconsistent decision-making. The gap between strategic intent and operational reality persists.
Ahead of Tech Show Frankfurt on 6-7 May at Messe Frankfurt, Petya Dasheva, Head of Global Digital Identities in Cyber Security for Mercedes-Benz Group AG | Werk 096, shares her perspective on where this gap emerges, how identity and organisational culture influence trust in data and AI systems, and why sustained adoption depends on more than technology alone.
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When organisations take a more human-centred approach to data strategy, what changes in day-to-day decision-making and team behaviour?
When organisations adopt a human-centred data strategy, decision-making shifts from focusing purely on available data and technical metrics to prioritising real human outcomes and user needs. Teams collaborate more closely across functions, combining quantitative data with qualitative insights such as user feedback to guide decisions. As a result, behaviours become more iterative, transparent, and responsible, with greater emphasis on experimentation, trust, and shared data literacy across the organisation.
In large enterprises, where do you most often see misalignment between data strategy on paper and adoption in practice?
In large enterprises, misalignment often appears between ambitious data strategies on paper and everyday operational realities. While strategies promise unified platforms, governance, and “single sources of truth,” frontline teams often face complex tools, unclear ownership, or shifting priorities. The result is a familiar corporate paradox: the organisation has a beautifully documented data strategy, yet many day-to-day decisions are still made with spreadsheets, workarounds, and a bit of educated guesswork.
In your current role focused on digital identity, how does identity architecture influence trust in data and AI systems at scale?
In digital identity and access management, identity architecture plays a key role in establishing trust in data and AI systems at scale by ensuring the right people and systems have access to the right data at the right time. Clear identity governance, authentication, and access controls create traceability and accountability, which are essential for reliable data usage and AI outputs.
In practice, it means organisations can trust not only the models and data, but also who interacted with them and under what permissions. Otherwise, every surprising AI result eventually leads to the same classic enterprise question: “Wait… who actually had access to this?”
Organisations frequently invest in technology yet struggle with meaningful adoption. In your experience, what structural role does culture play in determining whether data initiatives succeed?
In many organisations, the success of data initiatives depends less on technology and more on culture and incentives. Even the most advanced platforms struggle to gain traction if teams are not encouraged, trained, or rewarded to use data in their daily decisions.
In practice, culture determines whether data becomes a shared decision-making tool or just another corporate platform everyone agreed was important… right before going back to their trusted spreadsheet.
You’ve worked across cloud, data centres, software development and AI. How critical is cross-domain coordination in building resilient data strategies?
Cross-domain coordination is essential for building resilient data strategies because data flows across infrastructure, platforms, applications, and AI systems. When cloud, data centre, development, and data teams operate in silos, gaps quickly appear in governance, security, and reliability.
Strong coordination helps align architecture decisions, operational processes, and data ownership, ensuring that platforms, pipelines, and AI models work together rather than competing for control of the same data. In practice, it turns data strategy from a collection of good intentions into an end-to-end operating model.
As AI becomes embedded in decision-making systems, what responsibilities do organisations carry toward employees and customers beyond efficiency gains?
Companies must ensure transparency, fairness, and accountability so that employees and customers understand how decisions are made and can trust the outcomes.
This includes addressing bias, protecting privacy, and providing clear oversight when AI influences important decisions. In practice, responsible AI means organisations optimise not only for performance, but also for trust and explainability. Otherwise, efficiency gains can quickly be overshadowed by the awkward moment when someone asks, “The system decided this… but can anyone explain why?”
As a Global Ambassador for Women in Tech, where have you seen measurable progress in the industry, and where do structural barriers remain most persistent?
I’ve seen real progress in visibility and opportunity. More women are leading AI and data projects, speaking at industry events, and entering tech roles. Many companies are also starting to track diversity with real metrics instead of just talking about it.
However, structural barriers still show up in leadership and decision-making roles, where the number of women drops quickly. In many places, the pipeline is improving, but the promotion path still needs work. Progress is real, but there’s still a gap between good intentions and everyday practice.
What widely held assumption about data or AI strategy do you believe organisations need to reconsider?
An assumption organisations should reconsider is that implementing AI is mainly a technology challenge. In reality, the harder part is often data quality, governance, and organisational culture. Many companies invest heavily in tools and models but underestimate the work needed to make data available, reliable, and teams ready to use it. AI can create real value, but only when the foundations around data, processes, and people are in place.
For senior leaders, what single structural change most improves alignment between data strategy and organisational reality?
One big difference would be giving clear ownership of data at the leadership level. When accountability for data strategy sits across multiple teams without a single point of responsibility, alignment quickly becomes difficult. A senior leader with real authority over data priorities, governance, and investment helps connect strategy to how the organisation actually operates. If you don’t have this solid base, data strategy can easily become a well-designed slide deck that everyone agrees with, but no one truly owns.
What change in thinking would most help organisations build data strategies that are both effective and trusted?
A shift in thinking is to treat trust as a core part of data strategy, not just a compliance exercise. Organisations often focus on the technical side of data, but employees and customers also need confidence in how data is used and protected. Clear governance, transparency, and responsible use should be built in from the start. When trust is missing, even the best data strategy struggles, because if people don’t trust the data, they simply won’t use it.
