The foundation of AI-ready data in healthcare
If you run data or analytics in healthcare, you’re likely dealing with legacy EHRs, old databases and too many spreadsheets. PHI ends up in downloads and shared folders, and your privacy team never is in control. We help you change that. We bring your data from legacy systems into Snowflake, cutting reporting lead times by 80%, adding healthcare-grade governance and turning it into trusted, AI-ready datasets.
Compliance you can trust
You need governance that satisfies strict regulations without slowing down innovation. Our expert Snowflake consultants design and implement frameworks aligned with GDPR, SOC2, and HIPAA to protect your organization while driving AI-ready growth and trusted decision making.
Reliable data, every time
Your teams should not waste time questioning datasets or second-guessing reports. As your Snowflake consulting partner, we implement a reliable healthcare-ready Snowflake architecture that brings clinical, operational and financial data together from legacy systems.
AI-ready data for healthcare
AI in healthcare only works if the data is right. We set up Snowflake so your key healthcare data is governed and safe to use, with the datasets and pipelines your teams need. That way, AI projects have a solid base and a chance of making it into day-to-day work in your health organisation.
Why healthcare leaders work with us
Trusted AI outcomes
Run every AI initiative and machine learning project on trusted data foundations.
Compliance under control
Privacy, legal and security see what’s happening with the data, which makes approvals and audits much easier.
One view of your data
Instead of five versions of the truth, you get one. Clinical, operational and financial data is pulled together on Snowflake.
Competitive AI advantage
Deliver new healthcare AI use cases faster than competitors stuck with data governance challenges.
Our signature Snowflake consulting methodology
No big bang, no black boxes
You never wait months with nothing to show. Instead of one risky launch work is delivered in steady steps you can test and trust.
Progress you can follow
You don’t get vague updates or empty promises. Regular checkpoints, demos, and updates keep you in the loop.
Flexibility when priorities change
Your priorities can change. Our approach adapts without slowing down so new requirements or scope changes never stop delivery.
Commitment you can rely on
You gain a partner, not just a vendor. With solid planning and experienced teams, we keep projects aligned and accountable.
Speed with quality built in
You reach production with no shortcut. We deliver your solution to production quickly with quality and security built in.
Value that drives your business forward
Every step delivers measurable impact. We focus on what reduces cost, speeds up insight, and drives real business growth.
How our Snowflake consulting transforms data operations
How a $45B FMCG leader regained control of their Snowflake platform with Snowstack
Companies that fail to master their data platforms in 2025 will not just fall behind. They will become irrelevant as AI-native competitors rewrite the rules of the market.
Companies that fail to master their data platforms in 2025 will not just fall behind. They will become irrelevant as AI-native competitors rewrite the rules of the market. One global FMCG manufacturer recognized this early on. By partnering with us, they turned their underperforming Snowflake environment into an innovation engine.
Key outcomes:
- 30% reduction in Snowflake costs through intelligent optimization
- 60% faster incident resolution with 24/7 monitoring
- 5+ new AI/BI use cases unlocked from reliable, curated datasets
- 100% audit readiness for SOC 2 and GDPR frameworks
Having a dedicated Snowflake team that truly understands our platform made all the difference. We no longer chase incidents or firefight pipeline issues - we’re focused on enabling the business. Their ownership, responsiveness, and expertise elevated our data platform from a bottleneck to a strategic asset. - Senior Director, Data Platforms
Client overview
The client is a multinational Fast-Moving Consumer Goods (FMCG) manufacturer operating in over 180 countries through both corporate offices and an extensive franchise network. With global revenues exceeding $45 billion and more than 6,300 employees worldwide, they manage a diverse product portfolio distributed through complex regional supply chains.
The challenge
Despite investing in modern cloud infrastructure, the client was stuck. Their internal teams lacked the specialized expertise needed to run the platform. When key engineers left, so did the expertise. This resulted in growing technical debt. Critical pipelines regularly failed or ran late. Compliance and audit demands became difficult to satisfy due to inconsistent governance. Without proper optimization, Snowflake costs increased. As a result, the platform’s reputation fell from being seen as an innovation enabler to becoming a business blocker.
What made things even harder was the seasonal nature of FMCG operations. Demand for data engineering resources fluctuated throughout the year. Resource needs spiked during busy times and dropped during slow periods. This led to ongoing hiring and retention challenges. Meanwhile, competitors kept moving forward with steady expertise and data strategies.
Our solution
The client wanted a better way to manage their data and prepare for future growth. They asked us to provide a full Snowflake delivery team that could handle the project from start to finish. Instead of hiring separate contractors, they gained a team of Snowflake-certified experts who worked together to deliver the solution quickly.
Our execution
With our support, the client regained platform stability, resolved recurring system issues, and accelerated the delivery of new data solutions. The Snowflake environment became easier to manage, more predictable, and better aligned with business priorities.
Structured collaboration
The client led a phased rollout, supported by bi-weekly service reviews and backlog planning sessions. We worked directly within their workflows (Slack, Teams, Jira) and joined daily stand-ups and steering meetings. To help address long-standing challenges with knowledge retention, we introduced clear RACI ownership and thorough documentation practices.
SLA-Driven Support Model
The engagement featured a service model tailored to the client’s operational needs. Platform support was aligned to business hours, extended hours, or 24/7 coverage depending on requirements. SLAs were defined by incident severity, with guaranteed response and resolution times in place. To give the client real-time visibility and control, we implemented automated monitoring and alerting.
Platform optimisation and future-proofing
The client was committed to building a Snowflake environment that could scale with the business. With our support, they focused on optimising performance, controlling costs, and staying ahead of future demands.
Faster delivery, greater impact
We supported ongoing initiatives by onboarding new data sources, integrating BI tools and APIs, and maintaining platform standards across internal and third-party teams. Automation and reusable pipelines cut source-to-Snowflake integration time from weeks to days.
Continuous improvement and strategic reporting
Monthly platform reports provided clear visibility into KPIs, usage trends, incidents, and optimisation opportunities. This helped the client move from reactive support to a proactive and data-driven platform management.
Governance and security practices
To support regulatory and internal compliance requirements, we implemented platform-wide governance controls. These included RBAC, data masking policies, access audits, and full alignment with SOC 2 and GDPR frameworks.
The results
Strategic value
Owning a data platform is not the goal. Making it work for the business is.
This partnership showed how Team as a Service can turn a complex platform into a strategic asset. By working directly inside the client’s operations, our certified Snowflake experts turned a complex, high-maintenance platform into a scalable foundation for growth.
Now, they are ready to take on AI and advanced analytics, backed by an architecture built to grow with the business.
At Snowstack, we don’t just help companies manage Snowflake. Our model helps enterprises stay ahead in a data environment that keeps changing
Ready to turn your Snowflake platform into a competitive advantage?
Let’s talk about how our team can help you get there
How a top global logistics leader boosted BI performance by 65% with Snowflake
One wrong move during their Snowflake migration could have brought down hundreds of BI applications and reports. With legacy systems built over 15 years and rising maintenance costs putting operations at risk, this top-5 global logistics company faced its most critical data challenge yet.
One wrong move during their Snowflake migration could have brought down hundreds of BI applications and reports. With legacy systems built over 15 years and rising maintenance costs putting operations at risk, this top-5 global logistics company faced its most critical data challenge yet.
Our experts at Snowstack stepped in to navigate this complex transformation. The outcome? A smooth migration that turned the company’s greatest risk into a long-term competitive advantage.
Key outcomes:
- Report performance improved by 65%, with dashboards running in minutes instead of hours.
- Infrastructure costs fell by 40% while system performance increased.
- The migration achieved zero disruption, maintaining 100% uptime.
- Over 65% of legacy SQL was converted automatically, saving months of effort.
- More than 40 developers were trained and upskilled on Snowflake.
Over the years, our BI teams developed an effective approach to data modeling, which had long been a strength. However, with the ongoing migration of the central data warehouse to Snowflake, we knew that adopting new tools could take months, if not years. We urgently needed support from Snowflake professionals to guide the adoption process and help our BI teams incorporate the new technology into their workflows. - Lead Data Architect
Client overview
The client operates as one of the top 5 key players in the industry, managing supply chains that span multiple continents and serve millions of customers worldwide. Their data ecosystem had evolved organically, supporting hundreds of BI applications that power everything from real-time shipment tracking to route optimization algorithms.
The client’s BI reports weren't just internal dashboard. They powered customer-facing systems that enterprise clients used to track shipments worth millions of dollars. Any disruption to these systems could trigger contract penalties and damage relationships with major accounts.
The challenge
15 years of business growth had created a BI environment that was difficult to manage. Hundreds of reports were built independently by different teams with varying skill levels. Although they all drew from the same data warehouse, each team applied its own transformation logic within separate BI systems. What began as team-specific solutions had grown into a web of technical debt that no one fully understood.
Our solution
Recognizing the critical need for modernization, the client made the strategic decision to unify their data model and move it to Snowflake alongside their ongoing data warehouse migration. We guided the client through five steps.
Step 1: identifying the foundation
Together with the client, we analysed their extensive BI landscape to identify the datasets most frequently used across reports. This joint assessment defined a minimum viable product (MVP) scope that would deliver immediate value and build momentum for the broader transformation.
Step 2: building the Snowflake environment
We worked with the client to establish a dedicated Snowflake environment designed specifically for BI collaboration. Together, we implemented:
- Standardized schemas and roles to ensure consistent data access patterns across teams
- Compute scaling strategies optimized for BI workloads
- Role-based access control (RBAC) to strengthen governance
- BI-specific access patterns tailored to Snowflake datasets
Step 3: automating the migration process
To accelerate the transition and protect prior investments, we partnered with the client to implement automated migration scripts that converted legacy SQL into Snowflake SQL. This achieved a 65% automatic refactor success rate, dramatically reducing manual work while preserving business logic.
Step 4: orchestrating seamless integration
In close collaboration, we designed and deployed new orchestration pipelines that synchronized Snowflake model builds with BI report refreshes. These pipelines were integrated with the client’s existing technology stack, including:
- Airflow Snowflake Operator for workflow management
- AWS SNS for notifications
- AWS S3 for data staging
- Git for version control
Step 5: investing in the team
Recognizing that technology transformation must go hand-in-hand with people transformation, we partnered with the client to deliver training for more than 40 BI developers. This knowledge transfer ensured teams could confidently work with Snowflake as their new backend, embedding long-term value into the organization.
Foundation for Future Innovation
Still running hundreds of disconnected BI reports with inconsistent data models?
Upgrading your BI architecture is no longer a matter of if. The real question is how quickly you can create a one source of truth before competitors pull so far ahead you can’t catch up. The companies winning today are those replacing broken reporting with accurate, unified data that every team can trust. Each month you delay, they improve decision accuracy and grow their market share.
We help you close that gap fast. Our Snowflake-certified experts bring years of experience and a proven approach to modern BI transformation. We can take years of messy, disconnected systems and turn them into a single, reliable analytics platform in months. With one source of truth in place, your teams spend less time fixing reports, more time acting on accurate information, and deliver faster business decisions.
Ready to unify your BI architecture on Snowflake?
Enterprise-grade security
Enterprise security controls and governance frameworks built into every Snowflake implementation. Role-based access, data encryption, and audit trails configured from day one.
SOC 2
Compliance
What our clients say
AI-ready data in healthcare is clinical, operational and financial data that is clean, well-labelled, governed and easy to access for a specific use case. It includes clear metadata, lineage, quality checks and access rules, so it can safely be used to train or run AI models.
Data governance is critical in healthcare AI because it controls how PHI is used, who can see it and how decisions are audited. Good governance reduces privacy risk, supports HIPAA and GDPR compliance, and makes AI outputs more reliable and easier for clinicians and regulators to trust.
Ownership is usually shared. A data leader (CDO or Head of Data), CIO/CTO, security or CISO and the DPO/Privacy Officer set policies, with clinical leaders involved for safety and ethics. A dedicated data platform team or platform team as a service then applies those rules in Snowflake day to day.
Our Snowflake consultants implement data quality controls, create curated datasets, establish data lineage, and configure APIs that ML teams can use reliably. Your data scientists get clean, documented datasets ready for model development.
Most companies who work with us see improvements in data quality and governance within the first 30 days. Using our Sled Framework, a full AI ready foundation with governance and secure data sharing is typically in place within 90 days.
No. Client data stays in your environment under your full control. Our Snowflake consultants designs governed architectures where data is only used for the agreed business use cases. In pharma and finance projects, we implemented controls to prevent non-auditable access and ensure data was never exposed outside the client’s Snowflake and cloud setup.
HIPAA sets rules for how PHI is stored, accessed and shared in the US, while GDPR governs personal data for people in the EU. For healthcare AI, that means you must have a lawful basis to process data, minimise what you use, protect it with strong technical measures and be ready to explain and audit how models use that data.
