forbes.com
External Data Integration Challenges for AI: A 2025 SAP Survey Highlights Data Quality Concerns
A 2025 SAP survey of 1,200 business and technology leaders highlights poor data quality and consistency as the biggest challenge (55%) to AI innovation, emphasizing the need for credible, consumable, and contextual external data integration.
- How do legal and regulatory frameworks, such as GDPR, impact the use of external data for AI, and what strategies can mitigate potential risks?
- Using external data for AI models necessitates addressing legal and logistical challenges. Data must be credible, consumable, and contextual, requiring verification of usage rights and alignment with internal data definitions. Data architecture decisions should consider regulatory compliance, data shelf life, and storage costs.
- What are the key challenges businesses face when integrating external data into their AI systems, and what percentage of leaders cite data quality as a primary obstacle?
- Businesses increasingly rely on external data for AI, but integrating it requires careful consideration of credibility, access rights, and data architecture. A 2025 SAP survey revealed that 55% of business leaders cite poor data quality as a major hurdle to AI innovation.
- What are the future implications of relying on external data for AI, and how can companies ensure the long-term sustainability and effectiveness of their data integration strategies?
- Future success in AI depends on effective external data integration. This involves developing robust data governance, including metadata tagging and taxonomy, to ensure data quality, consistency, and usability across diverse sources. Human intervention will remain crucial for data validation and interpretation, especially for unstructured data.
Cognitive Concepts
Framing Bias
The framing is slightly negative, emphasizing the difficulties and risks associated with using external data. While the challenges are valid, the article could benefit from a more balanced presentation that highlights the potential rewards as well. The headline, if there was one, would heavily influence this.
Language Bias
The language used is largely neutral and objective. However, phrases like "persistent gap in trust" and "innovation stalling" carry slightly negative connotations. More neutral alternatives could include "challenges in establishing trust" and "obstacles to innovation.
Bias by Omission
The article focuses on the challenges of using external data for AI and analytics, but it omits discussion of potential benefits beyond improved insights and wider perspectives. It also doesn't address the ethical considerations of using external data, such as privacy concerns or potential biases in the data itself. This omission limits the reader's ability to form a complete understanding of the topic.
False Dichotomy
The article doesn't present a false dichotomy, but it could benefit from acknowledging that the challenges of using external data don't necessarily outweigh the benefits. A more balanced approach would explore both sides more fully.
Sustainable Development Goals
The article discusses the crucial role of external data in enhancing AI models and business decision-making. Accessing and integrating diverse data sources improves the quality and scope of insights, driving innovation and efficiency across industries. This directly contributes to SDG 9 (Industry, Innovation, and Infrastructure) by fostering technological advancement and improving industrial processes.