Trust, But Verify the Data Feeding Your AI Systems

The Backbone of AI: Data

The Challenge of Data Quality and Reliability

Artificial intelligence is only as good as the data behind it — and that’s a big problem. A recent survey shows that only about half of executives believe their data is ready to meet the demands of AI.

Data Concerns

More than half of executives with companies adopting AI, 54%, are worried about the reliability and quality of their data, according to a survey by Dun & Bradstreet. Other concerns include data security (46%), data privacy violations (43%), sensitive or proprietary information disclosure (42%), and data’s amplification of bias (26%).

Data Quality and Timeliness

Data quality, timelines, and consistency have been slowing down technology progress for decades — since business intelligence tools emerged in the 1980s, to the data analytics revolution in the early 2000s, to today’s AI activity.

The Importance of Trustworthy Data

Observers across the industry agree that actionable data is still too few and far between for the AI world. As a result, trust is lacking in today’s AI projects. "Organizations don’t have enough visibility into their data — even with the basics of who owns it, its source, or who has modified it," said Kunju Kashalikar, senior director of product management with Pentaho.

Security Implications

Untrustworthy data "means possibly feeding proprietary or biased data into machine models, likely breaching IP and data protection rules," said Kashalikar. "It also makes it difficult to establish accountability for regulatory compliance. Data must be catalogued at the source with easily understandable terminology so it can flow through various projects like AI with the ability to have streamlined discovery."

The Need for Integrated Data

AI-based applications "cannot be implemented securely without knowledge of proper access controls applied to the data in question," said David Brauchler, technical director at NCC Group. "The quality, quantity, and nature of data are all paramount. For training purposes, data quality and quantity have a direct impact on the resultant model."

The Road to Success

To move forward with AI, it’s critical that data is well-prepared and integrated, said Mary Hamilton, managing director and global lead for Accenture’s Innovation Center Network. "This includes making all relevant data accessible to AI agents in real-time, including unstructured data, through APIs or microservices." She emphasized the need for seamless and integrated data environments to achieve the full potential of AI.

Conclusion

In conclusion, the quality and reliability of data are critical components for the success of AI. As the industry continues to advance, it’s essential to prioritize the development of trustworthy and integrated data systems to ensure the reliability and effectiveness of AI applications.

FAQs

• What is the main challenge in AI development?
  • The main challenge in AI development is the quality and reliability of data.
• What are the concerns of executives regarding AI?
  • The concerns of executives regarding AI include data security, data privacy violations, sensitive or proprietary information disclosure, and data’s amplification of bias.
• How can organizations ensure the success of AI projects?
  • Organizations can ensure the success of AI projects by prioritizing the development of trustworthy and integrated data systems.
• What is the importance of data integration in AI?
  • Data integration is crucial for achieving the full potential of AI, as it enables the seamless and real-time exchange of data between systems and applications.