{"slug": "insights-from-slalom-federal-agencies-should-reconsider-snowflake-as-a-cloud", "title": "Insights from Slalom: Federal agencies should reconsider Snowflake as a cloud data platform", "summary": "Slalom Consulting advises federal agencies to reconsider Snowflake as a cloud data platform, citing the Trump administration's cost-cutting priorities and new acquisition rules that favor commercial solutions over government-unique configurations. The firm argues that Snowflake's proven performance in financial services, where it is used by over 50% of Fortune 500 firms, aligns with the government's need for fast time-to-value, cost efficiency, and high security under austerity conditions.", "body_md": "## The new federal decision calculus\n\nPause for a moment and consider how much federal IT has changed — often quickly and significantly. Take this opportunity to confirm your organization is keeping pace. You likely put thoughtful plans in place — strategy, acquisition approaches, governance, organizational readiness and architecture — but the technology landscape and the current administration’s priorities may have shifted since then. Now is a good time to revisit and realign those plans to today’s opportunities.\n\n**What hasn’t changed? **\n\nMost federal agencies began their data modernization journey motivated by the desire to meet requirements in the Evidence Act and become an evidence-based agency. They started with a commitment to create business intelligence insights, support machine learning for predictive analytics and fraud detection and share data safely and securely across the workforce. They did this all for their mission, including supporting interagency policy objectives, across the military services and to support the DATA Act’s objectives. Many agencies began to consider or adopt cloud data platforms to provide scalable, secure access to data to meet these needs and developed data strategies and data governance approaches to improve data quality, discovery and availability.\n\n**What has changed?**\n\nFirst, AI continues to evolve rapidly. The technology environment required to support it demands high quality data, safe access to cutting-edge generative AI foundation models, and exotic compute hardware, plus rapidly evolving environments and workflows to support the lifecycle, governance and controls for capabilities like retrieval augmented generation (RAG) and agentic AI.\n\nSecond, the Trump administration’s expectations of federal agencies have shifted significantly. The administration has prioritized reducing costs in the federal government, has executed actions to reduce government and contract staff government-wide and expects agencies to maintain operational effectiveness under austerity conditions [by employing AI and automation](https://www.nextgov.com/artificial-intelligence/2025/08/trump-administration-hopes-ai-can-mitigate-staffing-losses-federal-cio-says/407514/). Additionally, the administration has directed new acquisition approaches. The [Revolutionary FAR Overhaul (RFO) section 1.102(a)(3)](https://www.acquisition.gov/far-overhaul/far-part-deviation-guide/far-overhaul-part-1#FAR_1_102) directs a commercial solution preference, maximizing the use of readily available commercial products over “government-unique solutions.”\n\nDecisions on data platforms that were made before these changes grew out of traditional government technology buying patterns. Although the platforms were often intended to be broadly mission-supporting, program justifications were usually built using a narrow focus on enabling specific, priority mission outcomes. This increased preference for “government unique” configurations and integrations narrowly boosted specific mission performance and drove technology evaluations along certain pathways that led the government to prefer certain platforms.\n\nAgencies need data platforms that support general mission success and target bottom-line budget value while maintaining data security with mission assurance and auditability. If you want fast time-to-value with minimal engineering overhead, as well as performance and a user experience that focuses on your business analysts and users, Snowflake is worth a second look. Snowflake is deployed by 40% of the [Forbes Global 2000](https://www.snowflake.com/en/news/press-releases/snowflake-reports-financial-results-for-the-fourth-quarter-and-full-year-of-fiscal-2026/) (as of January 31, 2026). Snowflake is especially strong in financial services, which shares the current government need for cost effective business enablement with high assurance and security. It is used at over 50% of the Fortune 500 financial services firms.\n\nLet’s look at where Snowflake can excel in the current federal context.\n\n## Using an \"and\" strategy to augment existing cloud data platform investments\n\nA common misconception is that agencies must choose a single data platform. This is not the commercial reality where data platforms are frequently leveraged in combination as force multipliers. Use Snowflake as the governed foundation and highly concurrent serving layer for mission analytics and AI.\n\nIn practice, Snowflake becomes the place where agencies establish durable, auditable, shareable “truth” datasets—the curated data products that power dashboards, operational decision support and AI applications. Specialized platforms then connect to that governed foundation to run advanced workflows, and the resulting outputs are published back into Snowflake, so they can be reused across the enterprise rather than trapped in another silo.\n\nSnowflake + Palantir: Palantir Foundry needs a clean, massive-scale data foundation. Snowflake acts as the highly concurrent serving layer, feeding governed data into Foundry more effectively than legacy on-prem storage.\n\nSnowflake + Databricks: Snowflake serves as the perfect \"gold layer\" repository for Databricks machine learning models, making high-value insights immediately accessible to business analysts via standard SQL dashboards, without having to learn Python or Spark.\n\nSnowflake is also positioned to address the concurrency demands for the enterprise as agentic AI use expands. A single agent can trigger many parallel retrieval queries, feature lookups and evaluations, while numerous analysts, dashboards and applications hit the same governed datasets. Snowflake supports this by [isolating workloads with separate virtual warehouses](https://docs.snowflake.com/en/user-guide/warehouses-considerations) and absorbing spikes with multi-cluster warehouses that can add compute clusters when queues form and scale back down as demand subsides.\n\nWith Snowflake as the source of “truth” data within the enterprise, the go-to agentic data resource and the foundation for concurrency resilience are aligned.\n\n## Liberating data from ServiceNow and Salesforce \"walled gardens\"\n\nAgencies have invested enormous amounts in low-code platforms like ServiceNow and Salesforce to modernize workflows. There is risk of creating new data silos where mission-critical data is trapped inside these SaaS proprietary formats. Through standard ingestion patterns (connectors, CDC and streaming) plus secure data sharing, Snowflake lets you ingest and keep data from platforms like [ServiceNow](https://docs.snowflake.com/en/connectors/servicenow/ingestion) and Salesforce current on a [scheduled or near-real-time cadence](https://docs.snowflake.com/en/user-guide/snowpipe-streaming/data-load-snowpipe-streaming-overview) without building brittle, bespoke ETL pipelines. This enables cross-system mission awareness. Imagine a dashboard that correlates ServiceNow IT incident data with Salesforce case management data to predict mission outages before they happen. Snowflake makes this integration significantly simpler and more governable.\n\nThe key isn’t just landing data from SaaS platforms, it’s making it usable across missions. Snowflake supports incremental ingestion patterns to keep operational data current, then enables a shared “data product” layer where incident, case, asset and identity data can be standardized and joined once, so downstream programs don’t rebuild the integration every time. The mission payoff is faster correlation across systems for outage prediction, fraud and anomaly detection and operational readiness reporting, all while applying consistent governance controls at the source of truth.\n\n## The \"pay-for-usage\" advantage for financial stewardship\n\nUnlike legacy databases that require you to provision — and pay for — peak capacity 24/7, Snowflake separates compute from storage. [You can turn off your warehouses (compute) immediately](https://docs.snowflake.cn/en/user-guide/warehouses-overview) when a task is finished. This aligns perfectly with administration technology modernization goals. You can demonstrate direct ROI by showing that compute costs scale down with reduced activity, freeing up funds for innovation rather than maintenance. This model is especially powerful for federal demand, allowing for a surge of capability for end-of-month reporting, response operations or model training runs, then scaling back. Instead of paying for peak capacity all year, agencies can right-size baseline warehouses by applying auto-suspend and auto-resume guardrails. They can also allocate dedicated warehouses per workload, including BI, pipelines, data science and AI, to improve cost control and performance predictability.\n\nAdopting Snowflake isn't about replacing your current tools. Rather, it's about making them viable for the future. It provides the governed, secure and scalable foundation that allows your AI pilots to launch, your SaaS platforms to talk to each other and your specialized mission tools to run faster.\n\n## AI-readiness and agentic AI\n\nIn 2024 and 2025, agencies experimented with generative AI. In 2026, the mandate is agentic AI—autonomous systems that can reason and execute tasks. The biggest bottleneck for federal AI isn't the model. It's the data.\n\nSnowflake’s [Snowpark](https://docs.snowflake.com/en/developer-guide/snowpark/index) and Snowflake-managed AI services such as [Cortex](https://www.snowflake.com/en/product/features/cortex/), [where authorized](https://www.snowflake.com/en/blog/cortex-ai-fedramp-moderate-authorization/) allow agencies to bring compute to the data, rather than moving petabytes of sensitive data to the model. This is reinforced by native governance controls, including role-based access, dynamic masking, and auditability that help teams scale access safely. By keeping data governed within [Snowflake Government (SnowGov) environments authorized for FedRAMP High](https://www.businesswire.com/news/home/20231211980298/en/Snowflake-Achieves-FedRAMP-High-Authorization-on-AWS-GovCloud-US-West-and-US-East) (and [DoW IL5 where applicable](https://docs.snowflake.cn/en/user-guide/cert-dodIL5)), agencies can run analytics and AI workflows close to the data within an accredited boundary.\n\nMoving from short-lived AI pilots to repeatable AI operations yields practical benefits: Governed access to curated datasets, repeatable transformation pipelines and controlled execution of feature engineering, retrieval and scoring workflows without exporting sensitive data into unaccredited tool silos.\n\n## Slalom can help\n\nSlalom has delivered over [2700 successful Snowflake projects](https://www.slalom.com/us/en/who-we-are/partners/snowflake) and is the [Snowflake Global Data Cloud Services AI Partner of the Year for 2025](https://www.slalom.com/us/en/who-we-are/newsroom/snowflake-global-data-cloud-services-ai-poy). Slalom is different: We bring the same deep experience applying Snowflake in regulated commercial environments [to our federal customers](https://www.slalom.com/us/en/industries/public-social-impact/federal), combining commercial innovation with the rigor required for federal data, security, and operations.", "url": "https://wpnews.pro/news/insights-from-slalom-federal-agencies-should-reconsider-snowflake-as-a-cloud", "canonical_source": "https://www.snowflake.com/content/snowflake-site/global/en/blog/federal-agencies-reconsider-snowflake", "published_at": "2026-05-29 17:05:09+00:00", "updated_at": "2026-06-04 10:16:16.092265+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "generative-ai", "ai-policy", "ai-infrastructure"], "entities": ["Slalom", "Snowflake", "Evidence Act", "DATA Act"], "alternates": {"html": "https://wpnews.pro/news/insights-from-slalom-federal-agencies-should-reconsider-snowflake-as-a-cloud", "markdown": "https://wpnews.pro/news/insights-from-slalom-federal-agencies-should-reconsider-snowflake-as-a-cloud.md", "text": "https://wpnews.pro/news/insights-from-slalom-federal-agencies-should-reconsider-snowflake-as-a-cloud.txt", "jsonld": "https://wpnews.pro/news/insights-from-slalom-federal-agencies-should-reconsider-snowflake-as-a-cloud.jsonld"}}