How to Make Data AI-Ready: Guide for Federal Agencies

How to Make Data AI-Ready

Making data AI-ready starts with having strong data policies that enable consistent classification, access control and governance. AI-ready data is like a well-prepared mission brief: accurate, accessible, structured and governed.

“In government, that means your data isn’t just sitting in silos; it’s connected across systems, cleaned up, labeled properly and available in real time,” says Mia Jordan, public sector industry adviser at Salesforce.

Flawlessness doesn’t have to be the goal, but agencies do need alignment on definitions, access rules and context.

“Otherwise, your AI will be guessing, and that’s a gamble we can’t afford when trust and outcomes are on the line,” Jordan says.

How Poor Data Quality Derails AI Efforts

Poor data quality yields poor insights and inconsistent AI outcomes.

“Failing to prioritize data quality when beginning AI initiatives will likely lead to a struggle to bring AI projects into production, and you will eventually pay the price,” Whippen says.

The true value of AI becomes apparent when there is a strategic foundation that eliminates data silos and ensures consistency and reliability across departments.

“This becomes nearly impossible without quality data and often leads to duplicative efforts, inefficiencies and higher long-term costs,” Whippen says.

Poor data quality has a compounding effect that turns AI from an asset into a liability.

“If the data is biased, outdated or incomplete, the model learns the wrong patterns and then scales that wrongness or inaccuracy at speed,” Jordan says. “In the public sector, this isn’t just inefficient, it’s dangerous.”

Decisions about federal benefits, inspections or emergency response can’t afford hallucinations or half-truths.

“Bad data leads to bad predictions; bad predictions lead to broken trust,” Jordan says.

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Automating AI Data Preparation for Accuracy and Scale

Centralized data management is the key to automating data cleanup for accuracy and scale.

“Many governmental agencies are operating on a hybrid cloud model, which introduces complexity by fragmenting data across a number of locations, each with its own standards, access controls and formats,” Whippen says.

Teams reduce tool sprawl, vendor duplication and, most important, manual and repetitive tasks by prioritizing centralized data management.

Once data cleanup has been automated successfully, organizations can begin to scale this process to eliminate duplicate records and ensure accuracy, boosting consistency and performance in the AI models that leverage that data.

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There are several practical, tested methods to fill in missing values and standardize data formats at scale for large data sets. Automated scripts can sweep through messy date formats or addresses and standardize them for consistency.

“Teams can schedule routine cleanups and handle these problems at scale, rather than having employees go through cell by cell,” Whippen says. “AI brings an entirely new level of automation to this process.”

Users can run programs to find outliers and problems instantly, instead of using keyword-based scripts.

“Smart alerts can be sent to key figures when something appears to go wrong, and you can intervene at the point of error, rather than combing through months of old work to try and find it,” Whippen says.

Building a Strong Data Foundation for Government AI

Agencies should start with strategy — not spreadsheets — to build a strong data foundation. For too long, governance boards have debated data quality in 90-minute meetings, only to adjourn with another action item while the system is still rejecting claims because of mismatched field formats, Jordan says.

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“We have to flip the script,” she says. “The best disinfectant is sunshine. That means leadership must prioritize data visibility over data perfection.”

First, make the data accessible and usable so that flaws can be seen, understood and resolved in real time.

For example, instead of waiting years for perfect integration across legacy systems, the Department of Veterans Affairs exposed structured data through its Benefits Intake application programming interface that lets them standardize claims data intake across the board.

“It wasn’t perfect at first, but by making the data available, they could spot mismatches, fix formats and train AI models with cleaner inputs over time,” Jordan says. “That’s a data foundation in action, not theory.”

Agencies can also use APIs and integration layers to connect systems that were never designed to talk to each other, putting data in motion through integration platforms and layering in real-time visibility.

“It means data governance is operationalized, not just documented,” Jordan says. “Strategy without action is just a fancy spreadsheet.”

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