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.
Good data is the bedrock for genAI success: How can organizations process and prepare their data?