Issues related to data lifecycle management continue to trip up organizations that have plenty of data but struggle to use it effectively with their AI initiatives.

As organizations accelerate their AI investments, many still struggle to get data into a reliable, usable state. Leaders cite data quality, accessibility and consistency issues as key factors that slow deployment timelines, inflate costs and extend the gap between expectations and operational reality.

To get real value from AI, data and analytics leaders must take on the less glamorous but essential work of preparing and governing the data to make it trustworthy for use in AI systems.

Go back to basics to make data AI ready

The promise of AI continues to fuel spending, but many organizations still haven't built the foundation required to turn those investments into results.

"They need to transform that data in such a way that AI systems can understand it and utilize it to predict outcomes or to perform [tasks]," said Shrinath Thube, a senior member in the professional organization IEEE. "Generating enough data is not the challenge. Everything now generates data. Categorizing it, cataloging it, labeling it and using it. Those are the real challenges now."

The challenge is widespread: 43% of organizational leaders cited data readiness as the biggest barrier to aligning AI with business objectives, according to the 2026 State of Data Integrity and AI Readiness from Drexel University's LeBow College of Business.

The issue isn't new.

"It sounds almost cliché now, but the cardinal rule of computing is still garbage in, garbage out," said Deepak Seth, a director analyst with Gartner. "And so lots of data doesn't necessarily lead to better AI. Good data leads to good AI. Bad data leads to bad AI."

He said getting "good data" requires nonstop work, which many organizations have failed to do. Even those with established systems still have improvements to make, he said.

Start with foundational data management steps

Experts say there are several key moves that organizations must make to achieve data readiness for AI use cases, and many of them center on improving how they manage and govern their data.

Organizations must first identify what data they need for AI based on their strategic objectives, said Matt McGivern, managing director for enterprise data and AI governance at consulting firm Protiviti. From there, teams must collect and centralize that data, often in data lakes or lakehouses, to make a consistent source of truth.

McGivern said organizations also need to create a data inventory to account for what data exists, where it resides and whether it is structured, unstructured or semistructured. They also need to classify their data based on privacy, security and regulatory requirements.

Align the data to the organization's AI needs

Once those steps have been taken and the data infrastructure is in place, enterprise leaders can focus on readying the data for their AI use cases.

Seth said Gartner research shows that producing high-quality data for AI involves three requirements:

• Align the data to the use case to make sure it suits the right sources and business context for the model or application.
• Qualify the data, which involves continuously monitoring its quality to match workload requirements.
• Demonstrate strong governance, including lineage and compliance with internal and external standards.

This alignment applies to a range of scenarios, whether it's predictive maintenance where the data required is specific and well-defined; a GenAI use case, such as a customer service chatbot that accesses structured and unstructured data from multiple sources; or workflows that bring in data using retrieval-augmented generation (RAG).

Establish and maintain strong data governance

An AI workload needs the right amount of quality data at the right time, every time it performs a task to work accurately and reliably. Consistently delivering the needed quantity and quality of data requires a strong data governance program, McGivern said.

A mature governance program defines and enforces the organization's data standards, spanning management practices, quality rules, security requirements, privacy controls and compliance expectations. These structures ensure data stays accessible, accurate, consistent and of high quality. Moreover, governance ensures sensitive data is not used in ways that violate privacy and security requirements.

Thube stressed the need to include data lifecycle management as part of this governance workv to prevent the use of stale data in AI models.

"We have all this data, but we forget to make it retire," he said.

Metadata matters for more reliable AI results

Metadata, often described as the data about the data, captures essential information about a data set's structure, meaning and lineage. This context is critical for AI systems, especially nondeterministic AI models such as GenAI, which must correctly interpret data that can have multiple meanings or represent different things.

Many organizations struggle with metadata management, which in turn harms their ability to successfully use AI, according to Seth.

"Companies have a lot of data, but the context part is not very clear. And that lack of context can lead to ambiguity and confusion," Seth said.

He explained this importance of metadata by citing the multiple meanings of the word "pig." Depending on the context, pig could refer to:

• the farm animal,
• a slang term for an obnoxious individual,
• a pipeline inspection gauge,
• a programming language, or
• a type of metal, such as pig iron.

Metadata provides context clues to let AI systems distinguish between these meanings and interpret information reliably.

Put a plan in place to maintain data quality

Data management is not a one-time effort but an ongoing process to ensure AI systems receive accurate, reliable information.

"It's not just monitoring for quality, but it's monitoring continuously," Seth said.

This level of oversight requires consistent assessment and adjustment. He said that maintaining data quality requires ongoing validation and verification, regression testing, auditing and gathering observability metrics to confirm the data remains accurate, relevant and aligned to keep pace with changing conditions. For business leaders, establishing a long‑term plan for constant data quality monitoring will help AI models perform as intended.

Mary K. Pratt is an award-winning freelance journalist with a focus on covering enterprise IT and cybersecurity management.