Data is the foundation of AI. It acts as the experiential element, providing AI systems with the necessary information to understand patterns, make decisions, and predict outcomes. From simple algorithms to complex neural networks, the quality, quantity, and variety of data directly influence the effectiveness of AI solutions.
However, outdated information, dirty data, and poor data quality are hampering AI initiatives, reports PwC. According to “2026 Digital Trends in Operations Survey,” the foundation for leveraging data in operations and supply chains is improving but remains a work in progress for many. Only about half (51%) of respondents say their companies establish a clean, structured data foundation before scaling digital initiatives, and 60% stated that poor data quality has had some impact on achieving value for those initiatives.
“The quality of data fed into AI systems has a profound impact on their reliability and efficiency. High-quality data leads to more accurate and reliable models, reducing errors and improving decision-making,” Silver Tree said. “Once collected, data must be preprocessed before it can be effectively used in AI models. This involves cleaning, normalizing, and transforming data into a format suitable for analysis. Preprocessing is crucial as it directly affects the accuracy and efficiency of AI systems. Inaccurate or incomplete data can lead to flawed conclusions, making preprocessing a critical step in the AI development process,” Silver Tree noted.
PwC says companies can “move forward without perfect data, but [they] should have a structured approach to improving it.” Teams should “[l]aunch priority AI use cases using available data while deploying AI-enabled data governance, cleansing, and enrichment in parallel. Treat data improvement as an ongoing capability embedded in transformation, not a prerequisite that delays it.”
“For AI systems to remain effective, continuous learning and adaptation are essential,” Silver Tree recommends. “As new data becomes available, AI models must be updated to reflect current trends and changes. This ongoing process ensures that AI solutions stay relevant and effective over time, especially in rapidly evolving fields including IT and cybersecurity.”
To support organizations in navigating through new challenges and a rapidly evolving Big Data ecosystem, Big Data Quarterly presents 2026’s “Data and AI 75,” a list of companies driving innovation and expanding what is possible in terms of collecting, storing, and extracting value from data. The list is broad, including some companies that are longtime industry leaders that continue to innovate, as well as others that are newer arrivals on the data management and analytics scene.