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Many companies are currently investing heavily in data & analytics – yet they’re still barely making better decisions. However, this is rarely due to a lack of technology or the volume of available data. Rather, the reason is that the existing data isn’t reliable enough to truly support decision-making.
The real problem runs deeper. In business practice, we see similar patterns time and again: Data is stored in silos, metrics aren’t clearly defined, reports contradict each other, and decisions are based more on experience than on data. These challenges are real – and they must be addressed.
Create a data foundation that your systems, your AI, and your decision-makers can trust
Technology is needed – and more
Modern platforms such as SAP Business Technology Platform (BTP), SAP Business Data Cloud (BDC), or SAP Analytics Cloud (SAC) create the necessary foundation for integrating and harmonizing data and making it usable for planning, reporting, and business AI. Such a technological foundation is necessary – but not sufficient.
SAP has just clearly underscored this development at its customer and partner conference, SAP Sapphire, in Orlando: With initiatives centered on SAP BDC and the consistent further development of SAP BTP, it is clear that the focus is increasingly on an integrated data and platform strategy.
The goal is not merely to make data available, but to ensure it is contextualized, consistent, and usable across system boundaries – as the foundation for planning, management, and the use of business AI. Yet this is precisely where the real challenge lies in many organizations: Technology is scaling faster than governance.
Data Excellence is an organizational issue
Many initiatives fail not because data is missing or the necessary technology is unavailable – but because neither is embedded in a clear organizational structure. The crucial questions are:
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Who is responsible for data?
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Who defines key performance indicators?
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Who decides which figure “counts”?
Without this clarity, Data Excellence remains a concept that fails to take effect. This becomes particularly evident in the context of business AI: Artificial intelligence (AI) doesn’t simply need data. AI needs reliable, contextualized, and accountable data. Without this foundation, AI doesn’t scale the benefits, it scales the inconsistencies.
What Data Excellence really means
Data Excellence is therefore not merely a data project. It is an organization’s ability to bring together data, technology, and responsibility in a way that leads to sound decisions. This includes:
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Clean (master) data management
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Clear data governance
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Integrated data platforms
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Consistent integration with planning, reporting, and business AI
Only when these elements interlock does a data foundation emerge that is not only available but also effective.
Conclusion: It’s not the technology that makes the difference
Ultimately, it is not the volume of data that determines success. It’s about the ability to derive reliable decisions from data. And this is precisely where most organizations fail today - not technically, but structurally. Artificial intelligence exposes what companies have been able to compensate for over the years: poor master data, unclear responsibilities, and a lack of governance.
If you are serious about Data Excellence, you must address both: the quality of the data and the way organizations work with it. It is only through this synergy that sustainable value is created. We have summarized further information on how companies can concretely shape this path in our latest white paper, “Data Readiness Blueprint.”
