Time and again, it became clear that failure was rarely due to the technology or an unsuitable strategy. In most cases, inadequate data was the decisive factor. I am convinced that our entire business transformation approach needs to be rethought. For too long, we have settled for „good enough“: good enough data, good enough implementation and good enough definitions of success.
But when the roll-out disappoints, the dashboards do not reflect reality and the business remains unchanged despite high investments, the question arises: how did we end up here? Historically, we have measured success by technical milestones. Was the migration completed on time? Did the system go live? Was the schedule met? However, this does not guarantee that the company is actually in a better position.
Managers don't invest in transformation to tick boxes, but to achieve results: more efficient processes, smarter decisions, real ROI. As a CIO once told me: „You don't get fired because the project wasn't technically implemented correctly. You get fired because the goals weren't met.“ For better results, we therefore need to raise our standards.
Validated ≠ valuable
Many companies still assume that their data is „good“ as soon as all fields are filled in and the formats are correct. However, formal correctness does not mean that data is also useful. It is often proudly proclaimed that the data quality is 85 or 95 percent. What would correspond to a good grade at school is an alarming result in business life.
I have seen data that looked perfect at first glance but caused huge problems: Inventory data showed products as available, but they were not. Supplier data complied with all formal checking rules but contained incorrect bank details. Customer data appeared flawless at first glance, but led to billing errors and compliance violations.
Even an accuracy of 95 percent is not sufficient here. The remaining 5 percent can quickly add up to losses worth millions: through wasted time, lost sales and loss of trust. Business-relevant data is fundamentally different from merely „clean“ data. It is complete, contextual and closely linked to actual business processes. It is not only checked by systems, but also validated by decision-makers who understand its importance in the operational context.
The underlying systems are no longer just back-office tools - they form the backbone of modern companies. They control inventories, orders, payroll, finances, procurement and supply chains. So why should we expect less than 99.9 percent? Is your corporate partner carrying the load - or just shifting it? Too many partners are only measured on delivery, not impact.
This is often due to the fact that responsibility for data work is delegated to the specialist departments instead of being located where it belongs. Teams know their business. However, it is the partner's job to actually understand the data: in-depth, precise and impact-oriented. If they do not take on this responsibility, gaps remain that slow companies down in the long term. Hence my appeal to everyone: partners should be held accountable.
Ask the decisive questions: Does the partner have real data expertise and a qualified team? Does he have in-depth industry knowledge and reliable references? Does he work with a proven methodology? Do they present a clear plan with clear responsibilities? Every company deserves a partner who helps to make quick and smart decisions. If you don't ask these questions, you simply don't expect enough.
Completed ≠ delivered
Everyone talks about AI, but hardly anyone talks about what drives it: Data. If data is duplicated, incomplete or inconsistent, AI will only accelerate the chaos. If it is accurate and fit for purpose, AI becomes a real accelerator. It helps to reconcile data sets, automate validations and identify problems before they escalate. AI cannot repair a crumbling foundation. But it can help build a more solid one. What happens when expectations are low: Mediocrity becomes the norm.
As soon as we start to expect more, everything changes. Transformation no longer means pure implementation, but impact. This is why I have been advocating a data-first mentality for years, because data is not just the foundation, it sets the direction. Expecting more does not end with the go-live. Our industry has the tools and the know-how. It's time to hold ourselves accountable so that everyone achieves more because they expect more. Or, to quote Maya Angelou: „If we know better, we should do better.“
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