5 Ways to Feed Your AI Quality Data for Better Results
▼ Summary
– AI outputs depend entirely on input data quality, so feeding it poor data produces poor results.
– Organizations should focus on collecting the critical 20% of data that drives business value rather than storing everything.
– Develop flexible data strategies that can adapt to evolving AI models and changing business needs over time.
– Prioritize identifying and extracting valuable insights (“gold dust”) from data to improve specific business processes.
– Effective data cataloging and semantic context are more valuable than raw data for enabling meaningful AI queries.
Securing high-quality data is the single most critical factor for achieving successful outcomes from artificial intelligence initiatives. The principle is straightforward yet profound: if you provide an AI system with poor data, the results it generates will be equally poor. As companies accelerate their adoption of generative and agentic AI through 2026 and beyond, possessing the correct data assets becomes non-negotiable. How can organizations guarantee they are gathering the appropriate information? Five industry leaders share their essential strategies.
Adopt a Deliberate and Considered Approach
Paul Neville, Director of Digital, Data, and Technology at The Pensions Regulator, emphasizes the importance of a meticulous strategy for data collection in new technology projects. He states that the quality of AI output is directly dependent on the quality of the input data. Strong foundational practices in data governance and clear ownership are vital for transforming raw information into actionable intelligence. His organization also keeps a close watch on advancements from technology partners like OpenAI and Microsoft Azure, recognizing that evolving models can alter results. This necessitates continuous monitoring and process adaptation, which is why they have established dedicated AI governance and strategy roles.
Concentrate on the Vital Few
Ian Ruffle, Head of Data and Insight at the RAC, advises against trying to predict every type of data that might be needed for future innovations. He recommends focusing intently on what is currently most important to the business, suggesting it is better to excel in a few key areas than to be spread too thin. While modern data platforms make it inexpensive to store vast amounts of information, sifting through it all for valuable insights can be a major drain on resources. Ruffle suggests a more efficient method is to identify and focus on the critical 20% of data that will deliver the most significant impact, especially when building models for specific business processes like call center operations.
Develop an Adaptable Data Plan
Dominic Redmond, CIO at PageGroup, believes that business leaders must craft a forward-looking and flexible data collection strategy. The central challenge is determining which pieces of organizational data will prove most valuable over time, a question that becomes even more complex with the rapid evolution of AI. Since data needs in year one may differ from those in years two or three, the best approach is to create a plan that pinpoints essential data for upcoming projects while retaining the agility to adapt to new business demands and market shifts. Success hinges on being data-savvy and ensuring you capture information for future, yet-unknown requirements.
Uncover the Hidden Gems
Sacha Vaughan, Chief Supply Chain Officer at Joseph Joseph, stresses the need to balance long-term vision with immediate operational priorities. She ties data storage directly to defined business processes, such as using analytics to enhance supply chain efficiency. While collecting every possible piece of data might seem appealing, the practical challenges of storage and management can be prohibitive. Vaughan is particularly interested in leveraging data and AI to extract deep customer insights from sources like reviews and complaints. She points out that there is often “gold dust” hidden in this feedback that can directly inform future product design, provided there is a mechanism to mine and deliver these granular insights to designers effectively.
Pay Close Attention to Context and Meaning
Steve Lucas, CEO of Boomi, frames the central question for leaders as how to store the right data that matters for AI. He notes that most organizations already possess vast amounts of data, comparing it to sand. The real challenge lies not in the volume of data but in understanding its context. Effective data cataloging and the use of metadata are crucial, as they allow professionals to find meaningful patterns and similarities within the information. In many cases, this contextual metadata can be more valuable than the raw data itself. Lucas also suggests that businesses can learn from the massive investments made by large technology firms, using their advancements to guide and streamline their own data strategies.
(Source: ZDNET)
