{"slug": "public-sector-data-modernization-for-ai-readiness", "title": "Public Sector Data Modernization for AI Readiness", "summary": "Multnomah County, Oregon completed a migration off a decade-old SQL Server environment to Snowflake in five months using Snowflake's SnowConvert AI, combining deterministic code conversion with AI-assisted handling of dynamic SQL, code fixing and automated test case generation, the county reported at Snowflake Summit. Snowflake Director of Migrations and Modernization Center of Excellence Federico Zofaly said \"There is no AI strategy if you don't have a data strategy,\" and Multnomah County Senior Database Administrator Michael Hanna said time spent preparing data \"pays off multifold in the end.\" The county's standardized architecture now spans health and human services, public safety and one of the largest U.S. library systems, and the team recommends starting migrations a year or more before licensing deadlines because code conversion takes weeks while validation and parallel-environment testing take months.", "body_md": "Modernizing government technology is no small task. It requires navigating legacy systems, tight budgets and the high stakes of serving the public.\n\nAt this year's Snowflake Summit, leaders from Multnomah County, Oregon and the City and County of San Francisco shared how they've risen to that challenge and moved their organizations forward.\n\n## The real cost of delaying modernization efforts\n\nLegacy data infrastructure is no longer viewed solely as an IT inconvenience. With federal mandates accelerating, AI adoption creating new competitive pressure, and constituents demanding faster, more responsive services, the cost of standing still has become impossible to ignore. It's seen for what it truly is: a mission delivery problem that impacts entire organizations across operational functions.\n\nPublic sector organizations that haven't modernized their data estate can't support the AI workloads that are becoming central to the ways government organizations serve their communities. The compounding issue is structural: Legacy systems create data silos that present a structural barrier to effective AI adoption.\n\nAs Snowflake’s Director of Migrations and Modernization Center of Excellence, Federico Zofaly framed it during the Summit panel discussion “AI-Powered Modernization: Oregon's Multnomah County Leverages SnowConvert AI”: \"There is no AI strategy if you don't have a data strategy. You need to have your data in order before you can really build on top of that.\"\n\nThe government organizations that are making progress aren't waiting for a perfect budget cycle or a mandate. They're treating their data foundation as a mission-critical asset and moving their organizations forward accordingly.\n\n## How Multnomah County broke down data silos\n\nMultnomah County, the state of Oregon's largest county and home to Portland, was running a decade-old SQL Server environment. It was spread across dozens of business intelligence workloads and was built by engineers who had long since left the organization. Documentation was sparse. Architecture was inconsistent. Costs were climbing.\n\nThe county needed to migrate to a modern data platform. It wanted to migrate quickly, not delaying for years on end as is  often the case for public sector organizations—and indeed the team completed the migration in five months.<sup>1</sup>\n\nWorking with Snowflake's SnowConvert AI, the county used a hybrid migration approach that combined deterministic code conversion with AI-assisted handling of complex tasks. This included dynamic SQL, code fixing and automated test case generation instead of manual rewrites. The county reported results that included a shorter migration timeline, reduced future spend on legacy SQL server licensing costs and a standardized architecture that includes lines of business that were previously siloed, including health and human services, public safety and one of the largest library systems in the U.S.\n\nMichael Hanna, Multnomah County's Senior Database Administrator, was direct about what made the migration work: \"The amount of time that you spend preparing your data and preparing for the migration pays off multifold in the end.\"\n\n### 3 lessons from Multnomah for organizations still using legacy tools\n\n- **Start earlier than you think you need to:** Multnomah began its migration three months before one of its renewal agreements, which ended up not giving the county enough runway. The Multnomah team recommends starting migration efforts ideally a year or more before any licensing deadline.\n- **Don't migrate everything:** Evaluate what to keep, archive or abandon. Right-sizing the workload streamlines the new environment and reduces the overall cost of the project.\n- **Allocate plenty of time for testing:** Code conversion may take weeks. Validation and parallel-environment testing will take months. Plan accordingly.\n\n## How San Francisco is building a citywide data culture\n\nCity and County of San Francisco’s Chief Data Officer, Soumya Kalra, and her team are at an earlier stage in their modernization journey. The most pressing challenge Kalra is navigating is common in government: Departments have historically operated in isolation, each with their own systems, homegrown databases and definition of what \"good data\" looks like.\n\nHer team’s focus isn't only on updating its technology stack. It’s also about implementing a cultural shift to make data insight a genuine part of how services are delivered.\n\n\"I really want us to get to this vision of data embedded within service delivery — how do we capture the right data along the way that gives executives across departments the opportunity to really iterate through their processes?\"\n\n##### Soumya Kalra\n\nChief Data Officer, City and County of San Francisco\n\nThe goal is a citywide unified data platform that breaks down the departmental silos that have made cross-agency insight structurally impossible. But Kalra is clear on what needs to happen first: Before unifying data across the city, the team has to build the trust, shared standards and internal champions that make people want to participate.\n\nThat means involving stakeholders from across operational functions from the start, not only the IT department. For example, it involves designing for the analyst in the public health department who needs to answer a question, not only the engineer who built the pipeline. It also means accepting that change management, not technology, is what determines whether modernization endures.\n\nCity and County of San Francisco’s story is a useful counterweight to the migration timelines and technical benchmarks that dominate modernization conversations. Sometimes the most important work isn't the code conversion. It’s convincing an organization that a different way of working is worth the effort.\n\n## From technical debt to AI runway\n\nThe public sector organizations that succeed at modernizing share a common reframe: They stop treating migration as a one-time project and start viewing it as a shift in how data is managed as a strategic asset.\n\nThat means architecture decisions made today shouldn’t need to be rebuilt in three to five years. It means recognizing that a unified, governed data foundation isn't the end goal. It's the runway for everything that comes next, including cross-agency insights, predictive analytics and AI-powered service delivery that wasn't possible before.\n\nThe path forward follows a clear sequence:\n\n1. Modernize the foundation\n2. Unify the data estate\n3. Activate AI workloads\n\n## Organizations making progress have one trait in common\n\nThe organizations that are moving ahead have stopped treating their data foundation as a back-office concern and started treating it as a mission priority.\n\nThe tools to accelerate that journey, such as AI-assisted migration, automated testing and governed-data platforms, all exist now. The question is whether agencies have the runway and the foresight to use them.\n\n[Learn more about how you can](https://www.snowflake.com/en/solutions/industries/public-sector/) securely collaborate and turn data into actionable insights, maximize resources with cloud efficiency and accelerate innovation with a trusted data foundation for AI.\n\n<sup>1</sup> <sub>Snowflake Summit Session MI201: “AI-Powered Modernization: Oregon's Multnomah County Leverages SnowConvert AI”</sub>", "url": "https://wpnews.pro/news/public-sector-data-modernization-for-ai-readiness", "canonical_source": "https://www.snowflake.com/content/snowflake-site/global/en/blog/public-sector-data-modernization-ai-readiness", "published_at": "2026-10-22 21:59:59+00:00", "updated_at": "2026-10-22 22:42:14.910279+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-tools", "ai-infrastructure"], "entities": ["Snowflake", "SnowConvert AI", "Multnomah County", "City and County of San Francisco", "Federico Zofaly", "Michael Hanna", "Soumya Kalra", "Snowflake Summit"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/public-sector-data-modernization-for-ai-readiness", "markdown": "https://wpnews.pro/news/public-sector-data-modernization-for-ai-readiness.md", "text": "https://wpnews.pro/news/public-sector-data-modernization-for-ai-readiness.txt", "jsonld": "https://wpnews.pro/news/public-sector-data-modernization-for-ai-readiness.jsonld"}}