Issues related to data lifecycle management continue to plague organizations that have large amounts of data but struggle to use it effectively in AI initiatives.
As organizations accelerate their investments in AI, many still struggle to make their data reliable and usable. Leaders cite data quality, accessibility, and consistency issues as key factors slowing deployment timelines, inflating costs, and widening the gap between expectations and operational reality.
To derive real value from AI, data and analytics leaders must tackle the less glamorous but important task of preparing and managing data to make it trustworthy for use by AI systems.
While the promise of AI continues to fuel spending, many organizations have yet to build the foundation needed to turn that investment into results.
“Data needs to be transformed in a way that AI systems can understand it and use it to predict or take action. [tasks]”It’s not difficult to generate enough data,” said Srinath Tube, a senior member of the professional organization IEEE. “Everything generates data these days. Classifying it, cataloging it, labeling it and using it. Those are the real challenges now.”
The challenges are wide-ranging. 43% of organizational leaders cite data preparation as the biggest barrier to aligning AI with business goals. Data integrity and AI readiness in 2026 Graduated from Drexel University’s Lubow College of Business.
This problem is not new.
“It may almost sound like a cliché now, but the golden rule of computing is still garbage in, garbage out,” said Deepak Seth, director analyst at Gartner. “So more data doesn’t necessarily lead to better AI. Good data leads to good AI. Bad data leads to bad AI.”
Obtaining “good data” requires constant work, he said, and many organizations are failing to do so. Even companies with established systems still have room for improvement, he said.
According to experts, there are several important steps organizations can take to prepare their data for AI use cases, many of which focus on improving how data is managed and governed.
Matt McGivern, managing director of enterprise data and AI governance at consulting firm Protiviti, said organizations must first identify the data they need for AI based on their strategic goals. From there, teams must collect and centralize that data, often in a data lake or lakehouse, to create a consistent source of truth.
McGivern said organizations also need to create data inventories that take into account what data exists, where it resides, and whether it is structured, unstructured, or semi-structured. Data must also be classified based on privacy, security, and regulatory requirements.
Once these steps are taken and the data infrastructure is in place, enterprise leaders can focus on preparing data for AI use cases.
Seth said Gartner research shows that generating high-quality data for AI requires three requirements:
- Tailor your data to your use case and ensure it fits into the right source and business context for your model or application.
- Evaluate the data. This includes continuously monitoring the quality of your workloads in line with their requirements.
- Demonstrate strong governance, including lineage and compliance with internal and external standards.
This alignment applies to a variety of scenarios, such as predictive maintenance, where the required data is specific and well-defined. Use cases for GenAI include customer service chatbots that access structured and unstructured data from multiple sources. Or a workflow that uses search extension generation (RAG) to ingest data.
For AI workloads to work accurately and reliably, they require the right amount of high-quality data at the right time to perform each task. A strong data governance program is needed to consistently provide the required quantity and quality of data, McGivern said.
A mature governance program defines and enforces an organization’s data standards, spanning management practices, quality rules, security requirements, privacy controls, and compliance expectations. These structures ensure data access, accuracy, consistency, and high quality. Furthermore, governance ensures that sensitive data is not used in a way that violates privacy and security requirements.
Mr. Thube emphasized the need to incorporate data lifecycle management as part of this governance effort to prevent the use of stale data in AI models.
“We have all the data, but we forget to retire it,” he said.
Metadata is often described as data about data that captures important information about the structure, meaning, and lineage of a data set. This context is important for AI systems, especially non-deterministic AI models such as GenAI, which need to correctly interpret data that has multiple meanings or represents different things.
According to Seth, many organizations struggle with metadata management, which hurts their ability to successfully leverage AI.
“Companies have a lot of data, but the context part is not very clear, and the lack of context can lead to ambiguity and confusion,” Seth said.
He illustrated the importance of metadata by citing the multiple meanings of the word “pig.” Depending on the context, pig can refer to:
- livestock,
- Slang term for someone who is unpleasant
- pipeline inspection gauge,
- programming language, or
- A type of metal such as pig iron.
Metadata provides context clues that allow AI systems to distinguish between these meanings and reliably interpret the information.
Data management is not a one-time task, but an ongoing process that ensures that AI systems receive accurate and reliable information.
“We’re not just monitoring quality, we’re continuously monitoring it,” Seth says.
This level of oversight requires consistent evaluation and adjustment. He said maintaining data quality requires continuous verification and validation, regression testing, auditing, and the collection of observability metrics to ensure data is accurate, relevant, and adjusted to respond to changing circumstances. For business leaders, establishing a long-term plan to continuously monitor data quality helps ensure that AI models work as intended.
Mary K. Pratt is an award-winning freelance journalist focused on covering corporate IT and cybersecurity management.
