Achieving AI-grade data fitness requires more than just deploying the latest GenAI technology; organizations must also address underlying data management issues that often go unnoticed. Many businesses are still working with outdated data management practices, which prevent them from fully realizing the potential of AI for product development and innovation.
The risks tied to poor data management have become painfully evident through well-publicized AI project failures. These setbacks are frequently linked to systemic issues in how data is managed and provided to machine learning models. Despite having advanced technologies at their disposal, many organizations struggle to provide the right data at the right time, and according to RAND researchers, the infrastructure to manage data remains an elusive goal for many companies.
At the heart of AI’s potential lies data—large, reliable, and high-quality data that can be governed effectively. Too many models are trained on substandard data, reinforcing the “garbage-in, garbage-out” concept that still holds true today. The question then arises: how can data professionals meet the demands of AI systems?
One of the main challenges is the manual management of data. Engineers are often required to build data pipelines, classify data, and troubleshoot issues manually, which consumes vast amounts of time and resources. This inefficiency leads to unreliable outcomes, and adding more human resources won’t solve the fundamental problem.
Another significant issue is data visibility. In many organizations, data is hidden in a “dark” state, meaning there’s no clear insight into its ownership, source, or history. This lack of transparency creates risks when feeding incomplete or incorrect data into models, which can not only lead to AI failures but may also breach intellectual property or data protection laws. Additionally, this lack of visibility makes regulatory compliance challenging to enforce.
The inability to operationalize data effectively is a key indicator that an organization’s data management is not AI-ready. This manifests in several ways: difficulty accessing consistent data, poor control over its use, and challenges in enforcing protection policies. Without operationalized data, projects face rising costs, delays, and increased risk of compliance breaches.
To address these issues, organizations should take a three-step approach to preparing their data for AI:
Automate and streamline data preparation: The first step is to eliminate the overhead involved in manually preparing data. This can be done by adopting automated systems that classify, test, and organize data at the source, regardless of its format. Automation tools, like pipeline templates, can speed up data delivery, even in large and complex environments.
Establish insight and control over data: It’s essential to label and classify data as it’s created using terms that are relevant to the organization. A comprehensive catalog can then track data’s journey, monitor its quality, and implement access rules and protection at the metadata level. This ensures that data is both secure and useful for AI models, while also making it easier for teams to manage and access.
Ensure efficient data delivery: Data should be delivered consistently and without manual errors. Automating this process removes the burden from engineers and ensures that the data used for AI models is of high quality. Automation also helps mitigate the technical debt often caused by manual processes, allowing IT teams to focus on more strategic tasks.
Organizations that want to leverage GenAI effectively must not only address these core data management issues but also lay a solid foundation for the entire lifecycle of data—from access and classification to governance and delivery. By doing so, they can transform their AI pilots into large-scale successes that deliver true value to the business.
