Business-Ready Data: Why AI only works with clean data
- René Haag
- 1 day ago
- 4 min read
Key takeaway in one sentence: Gartner predicts that by 2026, around 60 percent of all AI projects will be abandoned – not because of poor models, but because of a lack of AI-ready data. A Forrester TEI study commissioned by Syniti and SAP shows what companies can specifically achieve with "business-ready data."
In short (TL;DR)
The problem: Fragmented systems, duplicate data sets, and inconsistent formats prevent reliable AI results.
The number that counts: According to Gartner, 60% of all AI projects will be stopped by 2026 due to a lack of AI-compatible data.
The proof: Forrester TEI study on SAP ADMM users shows 218% ROI, $4.1 million in realized benefits, amortization in under 6 months.
The key: Automation reduces data management effort by ~30%, audit preparation by 80%.
The context: In one case, data compliance increased from ~45% to almost 99% after the introduction of structured data processes.
What does "business-ready data" actually mean?
Business-ready data refers to data that is reliable, consistent, and prepared in such a way that it can be used directly for business decisions and AI applications. The term deliberately distinguishes itself from mere "data quality": it's not just about data being correct, but about it being in a state that an AI system can use productively without manual intervention.
For years, companies have been investing in data transformation to optimize processes and reduce costs. With the AI boom, this investment takes on a new strategic dimension: it is a prerequisite for AI projects to become productive in the first place.
Gartner's warning: 60 percent of AI projects are on the verge of collapse.
Gartner is issuing a stark warning to companies that are rushing their AI initiatives: By 2026, 60 percent of all AI projects are expected to be abandoned because they are not based on AI-compatible data.
This is the key figure in this article – and it shows that the bottleneck for successful AI implementation is rarely the model itself, but almost always the data that feeds it.
As companies move from initial AI experiments to enterprise-wide deployment, data quality, consistency, and governance become crucial success factors. The added value of even the most powerful models is ultimately limited by the quality of the underlying data.
What the Forrester TEI study specifically shows
The Total Economic Impact™ (TEI) study by Forrester Consulting, commissioned by Syniti and SAP, examines companies that use SAP solutions for their data transformation. Key findings:
Key figure | Result |
|---|---|
ROI | 218% |
Realized benefits | USD 4.1 million |
Payback period | under 6 months |
Reduction of data management effort | ~30% |
Audit preparation reduction | 80% |
Data compliance (case study) | from ~45% to ~99% |
Beyond the pure cost advantages, the surveyed companies report higher productivity, greater employee responsibility, better organizational agility and optimized cross-departmental collaboration.
Without data quality, there can be no successful use of AI.
Many companies continue to struggle with fragmented systems, duplicate data sets, and inconsistent formats. The result: data sets that are only partially usable or not usable at all.
Previously, this primarily led to business risks. Today, it directly jeopardizes the success of AI initiatives: Poor data quality means inaccurate results, unreliable recommendations, and declining trust in AI-generated insights.
Automation as an accelerator
The greatest effort before productive use of AI often lies in data preparation: cleaning, validation, reconciliation, documentation – done manually, this ties up enormous resources.
According to a TEI study, increased automation and standardization reduces this time expenditure by approximately 30 percent. Replacing fragmented processes with integrated workflows significantly shortens the path from initial pilot projects to productive AI deployment.
Governance and accountability: not a nice-to-have
With increasing AI integration into business processes, consistent data governance becomes a basic requirement – transparency about data origin and processing, as well as compliance with guidelines throughout the entire lifecycle.
The TEI study shows how automated workflows, audit trails, and greater transparency in data origin, approvals, mappings, and transformation processes improve governance. One highlight: 80 percent less effort for audit preparation.
Scalable innovation instead of individual projects
The success of AI rarely hinges on a single project. Long-term added value arises when innovations can be scaled across business areas, functions, and use cases.
Companies that have centralized and standardized their data report improvements in reporting, forecasting, and decision-making—and can implement future transformation projects without rebuilding data structures each time. The same logic applies to AI: professionally managed data facilitates the scaling of AI initiatives.
Conclusion: Data transformation is the real competitive advantage.
Sustainable competitive advantages in the AI age arise not only from powerful algorithms, but also from the quality and trustworthiness of the underlying data. The Forrester TEI study confirms that data transformation creates the foundation for governance, consistency, and data quality—and thus for scalable AI applications. Companies that consistently modernize and professionally manage their data will have a distinct advantage.
Frequently Asked Questions (FAQ)
What does "Business-Ready Data" mean?
Data that is so reliable, consistent, and processed that it can be used directly for business decisions and AI applications without manual rework.
According to Gartner, how many AI projects fail due to a lack of data?
Gartner predicts that by 2026 around 60 percent of all AI projects will be discontinued because they lack AI-compatible data.
What ROI does the Forrester TEI study show for Business-Ready Data?
According to the study, companies using SAP ADMM achieved an ROI of 218 percent, realized benefits of 4.1 million US dollars and a payback period of less than six months.
How much does automation reduce the effort required for data management?
According to the companies surveyed in the study, the savings amount to around 30 percent for ongoing data management activities and 80 percent for audit preparation.
Why is data governance so important for AI projects?
Because AI systems are only as trustworthy as the data they are based on. Governance ensures transparency regarding data origin and processing and helps to meet regulatory requirements.
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