{"id":512,"date":"2026-10-21T19:57:19","date_gmt":"2026-10-21T19:57:19","guid":{"rendered":"https:\/\/www.dataradar.io\/blog\/?p=512"},"modified":"2026-10-21T20:02:27","modified_gmt":"2026-10-21T20:02:27","slug":"dataradar-puts-every-snowflake-credit-behind-data-you-can-trust","status":"publish","type":"post","link":"https:\/\/www.dataradar.io\/blog\/dataradar-puts-every-snowflake-credit-behind-data-you-can-trust\/","title":{"rendered":"DataRadar Puts Every Snowflake Credit Behind Data You Can Trust"},"content":{"rendered":"
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Most AI projects never reach production. The model is rarely the reason. Neither is the team. The problem is the data underneath: stale pipelines, silent schema changes, duplicate records, and inputs nobody can vouch for. AI does not fix those flaws. It makes every one of them worse, and it does so at scale.<\/p>\n

That is the problem DataRadar\u2122 and Snowflake\u00ae set out to solve together. DataRadar, a Confie\u00ae product and a Snowflake Native App and partner since 2023, has expanded its work with Snowflake to help joint customers make their Snowflake data AI-ready. The two companies share a simple goal: AI projects reach production because the underlying data is ready. Available on Snowflake Marketplace, DataRadar is the only Snowflake Native App that unifies data quality monitoring and cost optimization. It measures every workload against the five pillars of The DataRadar Observability Framework and shows which Snowflake credits fund AI-ready data and which do not.<\/p>\n

This post covers three things. Why the two halves of that problem, data trust and Snowflake spend, must be solved together. What the research says about the cost of keeping them apart. And how DataRadar and Snowflake close the gap.<\/p>\n

The numbers behind the problem<\/strong><\/p>\n

Every study of AI failure lands in the same place. The data.<\/p>\n

Gartner put the data problem in plain terms. In a survey of data management leaders, 63% said their company either lacks the data practices AI requires or is unsure whether it has them. Gartner predicts that through 2026, companies will abandon 60% of AI projects that are not supported by AI-ready data (Gartner, 2025).<\/p>\n

The trust problem runs deeper than most leaders expect. Precisely and Drexel University’s LeBow College of Business conducted a global study of data and analytics professionals. In the study, 67% said they do not fully trust the data they use for decisions, up from 55% the year before. Only 12% said their data is sufficiently high quality and accessible for effective AI. Data quality was the top data integrity challenge for 64% of respondents, and 49% cited a lack of tools to automate data quality work (Precisely, 2024).<\/p>\n

Even the companies already winning with AI say the same thing. In research by Enterprise Strategy Group for Snowflake, 92% of early adopters said their AI investments are paying for themselves. Yet 59% cited measuring and monitoring data quality as a top hurdle, and 58% cited making data AI-ready (Snowflake, 2025). The companies that found ROI are still asking for better data.<\/p>\n

Then there is the bill. Forrester reports that more than a quarter of data and analytics staff who say poor data quality holds their company back estimate the loss at more than $5 million a year. Seven percent estimate it at $25 million or more (Forrester, 2024).<\/p>\n

Put those findings together, and a pattern emerges. AI projects fail because the data is not ready. The data is not ready because quality problems go unseen. These quality problems cost real money, both in lost AI value and in compute spent on data that will never reach a model.<\/p>\n

Why the tools built to fix this keep missing<\/strong><\/p>\n

The tools built to catch data problems grew up in two camps that do not communicate with one another.<\/p>\n

Data quality tools monitor pipelines, flag anomalies, and track freshness and schema changes. They can see that a pipeline broke. They cannot see what the broken pipeline costs because they cannot see or control Snowflake spend.<\/p>\n

Cost tools right-size warehouses, tune queries, and forecast spend. They can see the bill went up, but they cannot see why because they are blind to the quality issues driving it.<\/p>\n

Companies that need the full picture have been forced to buy two tools and manage two vendors. They sit through two security reviews. They line up two dashboards that never connect quality to cost. So enterprises keep paying for data that never makes it into a model, and neither tool can tell them.<\/p>\n

That gap is why the cost side of the problem matters as much as the quality side. FinOps Foundation’s 2025 survey of 861 practitioners, representing about $69 billion in public cloud spend, found that cutting waste from workloads is the top FinOps priority by a clear margin. The same survey found that 63% of FinOps teams now manage AI spend, up from 31% a year earlier (FinOps Foundation, 2025). Flexera’s 2025 State of the Cloud report found that 84% of companies cite managing cloud spend as their top cloud challenge. Cloud budgets are running 17% over their limits. And 72% of companies now use public cloud generative AI services (Flexera, 2025).<\/p>\n

In other words, AI spending is growing quickly, cost control is the top concern, and nobody can tell which part of that spend is funding data ready for AI.<\/p>\n

What DataRadar and Snowflake announced<\/strong><\/p>\n

DataRadar has expanded its partnership with Snowflake to help joint customers make their Snowflake data AI-ready. Available now on Snowflake Marketplace, DataRadar is the only Snowflake Native App that unifies data quality monitoring and cost optimization in a single application. Customers can buy it with the Snowflake credits they already have. That is how DataRadar closes the gap: quality and cost come together on the same platform, inside Snowflake.<\/p>\n

Built with the Snowflake Native App Framework, DataRadar runs entirely within the customer’s Snowflake account. Data and metadata never leave the customer’s security and governance perimeter. The app inherits the Snowflake controls the customer already has in place: role-based access control, network policies, encryption, and audit logging.<\/p>\n

Because quality and cost go hand in hand, every data quality issue is tied to the Snowflake compute it uses. Failed pipelines trigger retries that burn credits. Duplicate data inflates storage costs. Slow queries drain warehouse resources. DataRadar connects those dots on its own. Teams fix the data problems standing between them and production AI, and they recover the spend those problems waste.<\/p>\n

\u201cEvery CEO of an enterprise on Snowflake is asking the same question. Are our AI projects built on data we can trust?\u201d said Chris Alfaras, Chief Information Officer of Confie and EVP of DataRadar. “DataRadar answers it. When a workload clears the first four pillars of The DataRadar Observability Framework, the data is reliable, the pipelines are healthy, the queries are efficient, and usage is understood. That is AI-ready data. The savings come from cutting spend on data that was never going to make it to production, and Snowflake customers put those credits back into the AI workloads that reach production.”<\/p>\n

Five pillars, one definition of AI-ready<\/strong><\/p>\n

The DataRadar Observability Framework provides a working definition of AI-ready data. A workload is AI-ready when it meets the first four pillars. The fifth pillar shows where the money is going.<\/p>\n

Data reliability. <\/strong>Is the data accurate, complete, and consistent? DataRadar checks data quality across all nine attributes identified by leading analyst research: accuracy, completeness, consistency, timeliness, validity, uniqueness, accessibility, precision, and relevance. It detects anomalies early, so the data delivered to AI is always ready. Most competing tools check only a few.<\/p>\n

Pipeline health.<\/strong> Do the pipelines run on time, and when one breaks, do you know why? DataRadar tracks lineage, volume, freshness, latency, and health. Machine learning models learn the normal patterns of a customer’s data and alert on volume anomalies, freshness delays, latency, and schema changes, while lineage shows where the data came from and why it broke. The models account for weekends, holidays, and seasons, so you don’t have to set manual thresholds. Manual thresholds do not scale, because yesterday’s normal is tomorrow’s anomaly.<\/p>\n

Performance optimization.<\/strong> Are the warehouses and queries efficient? DataRadar monitors and optimizes warehouse performance, query efficiency, and resource use. It reads Snowflake’s own query telemetry to find the queries and warehouses that use more than they should and to prevent bottlenecks before they hit.<\/p>\n

Usage intelligence.<\/strong> Who is using the data, and how much? DataRadar gives visibility into utilization at the user, role, and department level, identifies the top consumers, and shows which datasets matter and who depends on them, so teams can optimize what they run.<\/p>\n

Cost visibility.<\/strong> Which Snowflake credits are funding data that has cleared the first four pillars, and which are not? DataRadar tracks spend, finds inefficiencies, and sends real-time alerts on cost spikes before they become problems. It attributes cost at the query level by department, user, and warehouse, and budget forecasts show where credits go and whether the data they fund is ready for AI.<\/p>\n

The order matters. Pillars one through four qualify the data. Pillar five shows where Snowflake spend should go. The savings are real, but they are a byproduct of getting the data right, not the reason to start.<\/p>\n

Why a Native App changes the math<\/strong><\/p>\n

The Native App architecture is not a packaging choice. It is the core of how DataRadar works. It removes four hidden costs that come with outside observability tools.<\/p>\n

Data egress. <\/strong>Outside SaaS observability tools pull data or metadata into a third-party cloud for processing. DataRadar brings the analysis to the data instead. Zero data egress and zero metadata extraction reduce leak risk and simplify compliance with GDPR, CCPA, HIPAA, SOX, and industry standards. The only outbound traffic is alerts sent to the customer’s incident tools, such as Jira, Microsoft Teams, and xMatters. No customer data is copied to or processed on any external DataRadar system.<\/p>\n

Security review.<\/strong> Because DataRadar inherits the customer’s existing Snowflake controls, the security team reviews one architecture it has already approved. It does not have to vet a new outside platform. In practice, that turns a vendor security review that often takes three to six months into a routine approval. For many buyers, that is the difference between a stalled project and a live one.<\/p>\n

Procurement.<\/strong> DataRadar is part of the Marketplace Capacity Drawdown program, so customers buy it with existing Snowflake credits. Purchases count toward Snowflake Committed Use, and finance has already approved the spend. There is no new vendor onboarding, no new budget line, and no new procurement cycle.<\/p>\n

Implementation.<\/strong> Customers find DataRadar on Snowflake Marketplace, install it themselves in under 30 minutes, start a 30-day trial, and pay with existing credits in a single self-service motion. There is no implementation project, no scoping call, and no proof of concept. Joint customers start seeing insights the same day they install.<\/p>\n

Because DataRadar runs on the customer’s own elastic compute and reads Snowflake’s native usage and query telemetry, it sees cost and quality at a depth no bolt-on tool can reach. Every query is attributed. Every warehouse is right-sized. Every quality issue is tied to the compute it wasted.<\/p>\n

Proven at Fortune 500 scale<\/strong><\/p>\n

Caesars Entertainment, a Fortune 500 leader in gaming and hospitality, deployed DataRadar across more than five Snowflake environments and 30+ warehouses.<\/p>\n

The deployment covers both halves of the platform. It includes real-time warehouse monitoring and optimization, as well as cost visibility down to the department, user, and query levels. It also includes schema change detection, anomaly tracking, data quality checks, and automated alerts delivered via Jira, Microsoft Teams, and xMatters.<\/p>\n

The results show both sides working together. After activating the DataRadar Warehouse Optimizer, Caesars Entertainment cut its annual Snowflake costs by over 20%. At the same time, it achieved faster detection and repair of data issues and proactive quality checks on critical datasets. It also gained an audit trail that keeps the security and compliance team audit-ready. Finance now has cost visibility down to the team, user, and query levels, and engineering has the levers to act on it.<\/p>\n

For a Fortune 500 company with strict security and audit requirements, keeping data within its own walls was decisive. “What impressed us most was how effortlessly DataRadar integrated with our existing stack,” Arora said. “No need to extract data, and no compromise in security. It’s more than just optimization; it also enables automated data checks and additional security monitoring.”<\/p>\n

Who DataRadar is built for<\/strong><\/p>\n

DataRadar is built for enterprises that use Snowflake as a core data platform, especially in regulated, data-intensive industries such as financial services, insurance, healthcare, life sciences, travel, hospitality, and gaming. It serves everyone in the chain who depends on trusted data.<\/p>\n

Decision makers. <\/strong>CIOs, CTOs, CDAOs, and CFOs own the Snowflake investment. DataRadar answers two board-level questions at once: are our AI projects built on data we can trust, and is our Snowflake spend under control? The CFO gains cost visibility down to the query level. The CIO and CDAO gain a single platform that covers all five pillars and requires a single security review.<\/p>\n

Technical evaluators. <\/strong>Data engineers rely on pipeline health monitoring, schema change detection, and lineage to catch issues before the business does. AI engineers use performance optimization and quality monitoring to ensure model training and inference run on reliable, fresh data. Data governance teams use usage intelligence and lineage to stay prepared for audits.<\/p>\n

Daily users.<\/strong> Data analysts and business analysts check data quality, trace where a number came from, and work from real-time alerts in the tools they already use.<\/p>\n

DataRadar monitors and optimizes the Snowflake environment. It is not a data catalog, an ETL tool, or a BI platform. It detects, alerts, recommends, and optimizes. The customer’s engineering team remains responsible for fixing the root cause.<\/p>\n

The better-together story<\/strong><\/p>\n

DataRadar was born on Snowflake. Its founding team lived the problem in a Snowflake environment. They pieced together separate quality and cost tools, explained to security teams why yet another external platform needed access to company data, and watched integrations drag on for months. Because the problem lived on Snowflake, the answer was built there.<\/p>\n

The partnership works in both directions. Customers get more value from the platform they have already chosen because their data becomes trusted enough to power AI and their spend becomes lean enough to fund it. Snowflake benefits in turn. Optimized environments remove the cost surprises that stall growth. Trusted data accelerates AI workloads that drive consumption. Marketplace purchasing keeps committed spend within the Snowflake ecosystem.<\/p>\n

Snowflake provides the AI Data Cloud. DataRadar makes data AI-ready.<\/p>\n

Where to start<\/strong><\/p>\n

Most customers start with the 30-day trial in a single Snowflake environment. There is nothing to install outside Snowflake, and no connections are required. Many turn on the Warehouse Optimizer first because cost savings appear fastest. From there, they expand across environments and warehouses, then broaden into quality checks on critical datasets, schema change monitoring, and lineage for the governance team.<\/p>\n

The prerequisites are simple. You need an active Snowflake account with Marketplace access and Native App install rights. You need Snowflake credits for Marketplace purchasing. And you need grants for the app to read usage telemetry and the databases it will monitor. No external infrastructure. No agents. No new network connections.<\/p>\n

The CEO’s question is not going away. The answer starts with knowing which of your Snowflake data is ready for AI and which of your Snowflake credits are funding data that never will be<\/strong><\/p>\n<\/div>\n\n

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References<\/strong><\/p>\n

\u00b9 FinOps Foundation. (2025). State of FinOps 2025<\/em>. https:\/\/data.finops.org\/2025-report\/<\/a><\/p>\n

\u00b2 Flexera. (2025, March 19). New Flexera report finds that 84% of organizations struggle to manage cloud spend<\/em> [Press release]. https:\/\/www.flexera.com\/about-us\/press-center\/new-flexera-report-finds-84-percent-of-organizations-struggle-to-manage-cloud-spend<\/a><\/p>\n

\u00b3 Forrester. (2024, July 31). Millions lost in 2023 due to poor data quality, potential for billions to be lost with AI without intervention<\/em>. https:\/\/www.forrester.com\/report\/millions-lost-in-2023-due-to-poor-data-quality-potential-for-billions-to-be-lost-with-ai-without-intervention\/RES181258<\/a><\/p>\n

\u2074 Gartner. (2025, February 26). Lack of AI-ready data puts AI projects at risk<\/em> [Press release]. https:\/\/www.gartner.com\/en\/newsroom\/press-releases\/2025-02-26-lack-of-ai-ready-data-puts-ai-projects-at-risk<\/a><\/p>\n

\u2075 Precisely. (2024, September 18). New global research points to lack of data quality and governance as major obstacles to AI readiness<\/em> [Press release]. https:\/\/www.precisely.com\/press-release\/new-global-research-points-to-lack-of-data-quality-and-governance-as-major-obstacles-to-ai-readiness<\/a><\/p>\n

6 <\/sup>Snowflake. (2025, April 15). Snowflake research reveals that 92% of early adopters see ROI from AI investments<\/em> [Press release]. https:\/\/www.snowflake.com\/en\/news\/press-releases\/snowflake-research-reveals-that-92-percent-of-early-adopters-see-roi-from-ai-investments<\/a><\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"","protected":false},"author":7,"featured_media":514,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7,5],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/www.dataradar.io\/blog\/wp-json\/wp\/v2\/posts\/512"}],"collection":[{"href":"https:\/\/www.dataradar.io\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.dataradar.io\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.dataradar.io\/blog\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dataradar.io\/blog\/wp-json\/wp\/v2\/comments?post=512"}],"version-history":[{"count":7,"href":"https:\/\/www.dataradar.io\/blog\/wp-json\/wp\/v2\/posts\/512\/revisions"}],"predecessor-version":[{"id":522,"href":"https:\/\/www.dataradar.io\/blog\/wp-json\/wp\/v2\/posts\/512\/revisions\/522"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dataradar.io\/blog\/wp-json\/wp\/v2\/media\/514"}],"wp:attachment":[{"href":"https:\/\/www.dataradar.io\/blog\/wp-json\/wp\/v2\/media?parent=512"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dataradar.io\/blog\/wp-json\/wp\/v2\/categories?post=512"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dataradar.io\/blog\/wp-json\/wp\/v2\/tags?post=512"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}