Ronald Konijnenburg, Principal Consultant Data Analytics at Interdobs, part of SUPERP, sees it every day: “Organizations have data, but they don’t trust it.”
Do you recognize this?
Your team resolves incidents, keeps systems running, and tries to maintain an overview. Meanwhile, everyone is talking about AI, but your reports are not even accurate. Data is scattered across SAP, Microsoft, cloud platforms, and legacy systems.
“The biggest challenge for customers is not technology, but trust in data,” says Ronald. “Organizations struggle with data quality, governance, and fragmentation.”
AI requires a reliable foundation
With AI in more and more data platforms, there is no room for error. SAP takes this seriously with Business Data Cloud and standardized data products.
AI that works on enterprise data makes fewer mistakes. But then your foundation has to be right.
“If your data is flawed, even the smartest AI won’t help you,” says Ronald.
One model, less chaos
Many organizations have different data models, limited source visibility, and insufficient knowledge of underlying structures. SAP introduces a single semantic model: a single source of truth, flexible for self-service analytics and AI integration.
Ronald sees opportunities in generative AI for data analysis. Think of queries such as “show the margin per region for Q3.” Decision-makers gain insights without having to be data scientists.
“That saves time and reduces pressure on your team.”
Hybrid knowledge is a necessity
The data world is hybrid. You need knowledge of SAP, Azure, and Databricks. “We provide that expertise through SUPERP, Interdobs, and Powerdobs,” explains Ronald.
By building a modern data foundation with clear data definitions and governance principles, you gain control over information. That’s what our SynTouch colleagues are good at.
Technology solves nothing without organization.
People think that data analytics is a technical issue. With the right tools, insights will come naturally. That’s not true.
Data analytics is organizational change. Making departments responsible for data products requires skills, training, and change. “The latter is often overlooked.”
Microsoft Fabric and SAP Datasphere are not one-click solutions. They require well-thought-out architecture, governance, and knowledge of source systems.
“Without a foundation, AI will not be better than the data you put into it.”
Start with the question: why?
Want to get started with Data Analytics? Don’t start with technology. Start with: why? What do you want to achieve? Which decisions do you want to substantiate better?
Many projects fail because they start with tooling instead of strategy.
Ensure a solid data foundation. Data quality, governance, and security are fundamental. Without reliable data, every dashboard is just a pretty visualization of incorrect information.
SAP BW 7.5 is going out of maintenance. Now is the time to review your data strategy. Migrate, modernize, or renew? Make that choice consciously.
Start small, think big
Start with one use case. Real-time insight into margins or inventory. Build a future-proof data model around it. Use SAP Business Data Cloud or Microsoft Fabric to make data-driven working scalable.
“Involve the business from day one,” Ronald emphasizes. “Data analytics is not an IT party. It is a joint journey involving technology, processes, and people. Organizations that understand this are taking steps toward a data-driven future.”
Calmness in your operation
A modern data strategy gives your team peace of mind. Less ad hoc work, more control over priorities. Time for improvement instead of firefighting.
“We understand how it feels to be in the middle of an operation,” says Ronald. “We combine knowledge of SAP source systems with modern cloud platforms. Pragmatic, focused on value. We don’t start with technology, but with the question: which decisions need to be better substantiated?”
Want peace of mind and control over your data? Contact us and our data experts will be happy to help you find the right solution.
Data analytics is not about technology
“Organizations think that data analytics is a technical issue. But the biggest challenge is trust in data. Projects that start with tooling rather than organizational change are doomed to fail.”
