Data Analytics

Why State CIOs Should Treat Data as Their Most Strategic Asset

Strong artificial intelligence plans depend on clean, governed data.

Francisco Ramirez is Chief Architect of State and Local Government for Red Hat.

When I was recently asked, on a panel about the future of artificial intelligence, to name the most exciting technology I’m working on, my answer — “data” — drew a sidelong glance from the moderator. They were expecting something flashier. But as the other panelists introduced themselves and provided their answers, data became the recurring theme. By the end of our session, data was designated as “the new sexy.”

I don’t know if I would categorize data in that way, but I do know it is critical for successful AI deployments. Artificial intelligence runs on information, and without a solid data framework built on accuracy, governance and trust, agencies risk undermining their efforts to improve efficiencies and serve citizens effectively.

It’s time to stop viewing data as a by-product of operations and treat it as a strategic asset, an essential resource underpinning state and local AI initiatives. Agencies must break down data silos through open collaboration, improve data literacy across the workforce and commit to building a long-lasting legacy of good data management.

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A Strong Data Foundation Supports Open Collaboration

Stakeholder collaboration is one of the reasons open-source software has been successful. Developers freely collaborate not just with each other but also with other members of their organization to produce technology that meets shared needs and benefits the entire open-source community.

Agencies should take a similar collaborative approach when developing their data governance frameworks. Good data governance requires identifying who owns the data within an agency. Those data owners, along with the people who manage, analyze and secure the data, all need a seat at the table.

Building a strong data foundation isn’t something one department can do on its own; it takes a community of practice. This could take the form of a center of excellence, where data owners, stewards and technical experts from across agencies come together to share best practices, establish common standards and solve problems collectively.

In this model, participants retain ownership of their domain data while working within a shared framework. When agencies align on shared data standards, they not only improve data quality but also accelerate their ability to deploy new technologies safely and at scale.

Build a Data-Literate Culture for Good Data Management

Many of the leaders I speak with say they spend as much time educating their teams about AI as they do implementing it. Some of that work involves easing fears that AI will replace jobs. But much of it is about helping people understand what makes AI successful in the first place: good data practices, data sharing and standardization.

The truth is, you can’t build effective AI without first building data literacy. People need to understand why data matters and how it’s gathered and applied before they can trust it or use it effectively. That takes communication, training and patience. Agencies need to create environments where data isn’t viewed as something to be hoarded or feared, but as a shared organizational asset that empowers better decision-making.

Developing that mindset won’t happen overnight. But every conversation that demystifies AI and reinforces the value of clean, well-governed data moves agencies closer to being truly AI-ready.

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Powerful Data Fundamentals Leave a Great Data Legacy

Building data literacy prepares today’s workforce to manage tomorrow’s challenges. But sustaining that progress will depend on those who’ve spent their careers navigating government data and grappling with data management challenges for years.

Seasoned professionals understand where the data lives, how it has evolved and what needs to be done to make the most significant positive impact. These are the people who are well suited to build a solid data foundation today and leave a legacy that their agencies will continue to benefit from.

Because ultimately, treating data as a strategic asset is about stewardship. Agencies that commit to building strong data foundations now are setting themselves up for long-term success with AI and beyond.

Sexy? Maybe not. Essential? Definitely.

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