Snowflake has become the cloud data and AI platform of choice for modern enterprises, and for good reason. Its cloud-native architecture, separation of storage and compute, and ability to handle structured and semi-structured data at scale make it a powerful foundation for analytics, AI, and data engineering. For organizations serious about getting more from their data, Snowflake is often where that ambition begins.
But investing is the first step. Getting the most out of that investment means addressing one of the most persistent challenges in data management: making sure the data inside Snowflake is trustworthy, consistent, and governed well enough to support confident decision-making.
That is where many organizations find themselves stuck, not because Snowflake is lacking, but because the data flowing into it still carries the problems it always has.
Duplicate records, inconsistent formats, ungoverned access, and fragmented pipelines don’t disappear when you move to the cloud. Without a strategy to actively manage and master that data, even the most powerful platform will struggle.
The data quality and governance challenges faced by Snowflake users
Snowflake is exceptionally good at storing, processing, and scaling data for business intelligence and analytics. What it does not do on its own is guarantee the accuracy, consistency, or governance of that data. That responsibility falls to the teams and tools working within the ecosystem.
Several challenges commonly surface for Snowflake users as their data operations grow in scale and complexity.
Data quality issues
Duplicate and inconsistent records are amongst the most widespread issues facing Snowflake customers. When data arrives from multiple ingestion pipelines, the same entity can appear under different names, identifiers, or formats.
Without a process to identify and resolve these data quality issues, downstream analytics are built on a flawed foundation.
Schema drift
Schema drift is another common problem. This occurs because source systems change over time, and when the structure of incoming data shifts unexpectedly, it can introduce errors that propagate through reporting, AI models, and operational systems before anyone catches them.
Data governance gaps
As Snowflake environments grow, managing who can access what becomes increasingly complex – and data governance gaps can have a major impact. Overly permissive access increases security and compliance risk, while overly restrictive controls slow down the teams that need data to do their jobs. Snowflake’s Horizon Catalog can help with managing data products and assets at the aggregate level as well as for security purposes.
Pipeline complexity
ETL and pipeline complexity adds further strain. Organizations often rely on multiple pipelines to bring data into Snowflake, each introducing potential points of failure, latency, and inconsistency. The more systems involved, the harder it becomes to maintain a coherent, up-to-date view of core business data.
To be clear: these are not challenges inherently caused by Snowflake. They are the natural result of data growing faster than the quality and governance practices designed to manage it.
The case for managing master data natively
An increasingly popular response to these challenges is to introduce a separate master data management (MDM) platform alongside Snowflake.
The idea makes sense: MDM provides the core capabilities that make this work, including:
- Consolidation, which brings together data from multiple sources into a unified master record, resolving the fragmentation that leads to conflicting versions of key business entities like customers, products, and suppliers
- Standardization, which enforces consistent formats, definitions, and structures across all incoming data, eliminating the inconsistencies that undermine reporting and analytics
- Deduplication, which identifies and resolves duplicate records so that the organization is always working from clean, authoritative data
- Certification, which validates and approves master data so that every downstream team, system, and decision is based on a trusted, governed source or commonly known as a “golden record”
- Stewardship, which provides a business-friendly user experience so non-technical users in your team can easily access, fix, and use data effectively without creating bottlenecks
However, depending on the architecture, moving data out of Snowflake and into an external MDM environment introduces its own set of complications:
- Additional pipelines create more failure points.
- Synchronization delays mean that the master data feeding decisions is never quite current.
- Sensitive data crossing platform boundaries increases security and compliance risk.
- The architectural complexity of maintaining two separate environments adds cost and overhead that work against the very efficiency Snowflake was chosen to deliver.
A more sustainable approach is to manage mastered data in Snowflake, where it can be made available to analytics, AI, and data-engineering teams without unnecessary movement between platforms.
This can be achieved with Semarchy Data Platform Self-Hosted, as well as with Semarchy MDM deployed as a Snowflake Native App. The Native App takes this a step further by running the MDM application natively within Snowflake, using Snowflake’s own engine for storage and processing.
Native MDM inside Snowflake, not alongside it
Semarchy MDM is the first and only MDM solution available as a Snowflake Native App. Rather than treating MDM as an external process, it operates entirely within Snowflake – managing and certifying master data hubs using Snowflake’s own engine for storage and processing.
In practice, this means:
- No risk, cost, or overhead from data movements
- No data movement or synchronization delays
- Certified master data is immediately available to analytics, AI, and data engineering in the same environment
Native MDM supports a more mature, efficient Snowflake deployment across several dimensions:
- Reduced complexity and cost: Eliminating data movement cuts latency, failure points, and unnecessary compute overhead.
- Security that stays within the platform: Master data inherits the full benefit of Snowflake’s encryption and access controls, with Semarchy’s SOC 2 and ISO 27001 certifications adding a further layer of assurance.
- AI-ready data: For teams exploring Snowflake Cortex AI, native MDM ensures the data foundation is solid before those workloads are introduced.
- Simplified procurement: Semarchy MDM is available directly in the Snowflake Marketplace, and in select regions, existing Snowflake consumption credits can be applied through the Marketplace Capacity Drawdown program.
Self-hosted deployment with the Semarchy Data Platform
Other organizations may need a self-hosted deployment to align with internal hosting standards, security requirements, operating models, or broader enterprise architecture.
In those cases, the Semarchy Data Platform (SDP) Self-Hosted provides a flexible option for mastering and governing data while integrating with Snowflake and the rest of the data ecosystem.
The right choice depends on where you need MDM to run, where governed data must be consumed, and the degree of operational control your organization requires.
Trusted data for everyone, not just engineering teams
Semarchy MDM extends data stewardship beyond technical teams through AI-augmented, no-code and low-code applications. Business users can interact with master data through intuitive browser-based interfaces – enabling closer collaboration between business and IT, and broader organizational confidence in the data driving decisions.
Already using Snowflake? See how you can build a trusted single source of truth directly within Snowflake – without adding another platform or moving your data.
Explore Semarchy MDM on the Snowflake Marketplace.
Considering Snowflake? Discover how native MDM with Semarchy can help you simplify governance, improve data quality, and accelerate time-to-value from day one.
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