Why Good Data is the Foundation for Success in the AI Era

Generative AI’s success hinges on high-quality data: Organizations face challenges in preparing and processing data effectively for AI initiatives.

The data dilemma: Many businesses struggle with data preparation for AI projects, leading to potential setbacks in their artificial intelligence initiatives.

• Gartner analysts predict that at least 30% of generative AI projects will be abandoned after proof of concept through 2025, with poor data quality cited as a primary reason.
• Having data ready for AI can drive greater business outcomes by 20%, according to Gartner Senior Director Analyst Roxane Edijlala.
• Organizations often lack clarity on how to prepare their data, especially those without in-house data or AI expertise.

The foundation of AI success: Good data is crucial for generative AI applications, requiring relevant, accurate, and highly accessible information to function effectively.

• AI results are only as powerful as the data fed into the system, emphasizing the need for well-prepared and easily manageable data.
• Organizations need access to all data across their infrastructure, which adds complexity due to the varied formats and locations of information.
• Both physical and digital data, spread across different enterprise systems and data silos, must be considered in the preparation process.

Key steps for data preparation: Organizations should follow a structured approach to prepare their data for AI applications.

• Create a comprehensive inventory cataloging all data, its location, and format.
• Assess and address data quality by establishing key standards for accuracy, completeness, and reliability.
• Implement governance and security measures to protect data and ensure compliance with regulations.
• Ensure data is sourced legally and ethically, respecting privacy, confidentiality, and intellectual property rights.
• Maintain the ability to trace data back to its source, crucial for accountability and transparency.

Common pitfalls to avoid: Organizations should be aware of potential errors that can hinder AI success.

• Failing to maintain a full, comprehensive dataset that includes both structured and unstructured data.
• Overlooking the importance of unstructured data, which typically comprises about 60% of an organization’s information.
• Attempting to implement AI initiatives on too large a scale initially, rather than starting with focused use cases.

Leveraging technology for data readiness: Tools like Iron Mountain InSight® Digital Experience Platform (DXP) can assist organizations in preparing their data for AI applications.

• InSight DXP helps create workflows to prepare data and consolidate information from various sources into a single, accessible location.
• The platform uses AI to evaluate and consolidate data, enabling intuitive interaction with documents through generative AI-powered chat.
• Iron Mountain reported a 65% reduction in manual activity for their contracts lifecycle management systems after implementing InSight DXP.

The importance of ongoing data governance: Maintaining data quality and relevance is an ongoing process, not a one-time effort.

• Regular assessment and maintenance of data quality are crucial for long-term AI success.
• Integrated information governance suites, like those in InSight DXP, can help overcome challenges in maintaining data quality.
• Low-code and automation capabilities enable organizations to create workflows that ensure data remains clean, relevant, and trustworthy over time.

Looking ahead: The future of AI-ready data: As organizations continue to invest in AI initiatives, the focus on data preparation and quality will likely intensify.

• The success of generative AI projects will increasingly depend on an organization’s ability to effectively manage and leverage its data assets.
• Continued development of tools and platforms that streamline data preparation and governance processes may help more businesses overcome initial hurdles in AI adoption.
• Organizations that prioritize data readiness and ongoing data management are poised to gain a competitive edge in the rapidly evolving AI landscape.