What is Shift-Left Data Governance and How Does Snowplow Enforce It?

In this post, we’re going to explain what we mean by shift-left data governance and how Snowplow enables this approach. 

We speak to hundreds of different data teams every week, and one topic that seems to be resonating is shift-left data governance. Why? Well, for many organizations, the issue of poor data quality persists. Too often we see companies trying to fix problems with their data after it has spread throughout their systems. 

What these companies need isn’t perfect data - it’s intentional data. They need to take a more deliberate approach to collecting and governing what matters most to their business, right from the start. 

The impact of failing to shift governance left is profound. It’s not just expensive - consuming 20-40% of IT budgets according to data management expert Dr. Peter Aiken - but it’s also unsustainable. This is especially true for organizations looking to build more sophisticated data applications.  

This is where shift-left data governance comes in. We’re going to use this post to show you exactly what this means, why it’s important for your data team to understand, and how Snowplow can enable your company to implement this approach. At the end of the article, you’ll have gained practical advice for shifting data governance left and ensuring high-quality data from the start. Let’s dive in. 

What is Shift-Left Data Governance?

Shift-left data governance is about performing data governance closer to the point of data collection, rather than just after the data has been collected and stored. So, to give you a practical example of how this works, let’s take a fictional ecommerce company. 

Like most ecommerce companies, they need reliable customer data–names, emails, orders–right at collection. Using Snowplow, they kick things off with a schema-first design. So, before tracking a single click, they define a “purchase” event (e.g., “email” as a valid email address format, “order_id” as an integer, "product_id" as a required string, “item_count” as non-zero). 

These schemas are combined into data product definitions, which are then assigned clear ownership. Snowplow makes this ownership explicit, so the ecommerce company's data team can control schemas through pull requests, ensuring only vetted changes go live. This means any updates to tracking must go through proper reviews and approvals before implementation. 

Snowplow’s pipeline then enforces early validation: a customer enters an erroneous email like “john@gmail” or they submit a zero or negative item count, and it’s rejected instantly. Bad data never hits the warehouse or lake. 

In parallel, real-time quality monitoring tracks metrics like “100 purchases today, 0 failed validations.” 

The company's developers are supported by strongly typed classes and functions within code generated by Snowplow tooling. This means when developers implement tracking, they work with code that understands what data types are expected and required for each event. If they code “order_id” as “ABC” as an example, the IDE flags it before deployment. 

This proactive setup slashes downstream cleanup costs for the ecommerce company (think back to Dr. Aiken’s 20-40% IT budget drain) and delivers trusted data fast–perfect for the company’s next AI-driven marketing campaign. In a nutshell, Snowplow makes trust systematic, not a retrofit. 

Why is Shift-Left Data Governance Important?

Quite simply, it’s important because poor data quality is costing your business huge amounts of money! Plus, modern data applications, particularly AI, depend on high-quality data. Too many data teams are wasting their time fixing poor data when their time could be better spent on innovation and new data initiatives.

Let’s explore some of the key reasons why shift-left governance has become critical: 

Cost Efficiency and Resource Optimization 

As we’ve touched on already, without shift-left data governance you’re likely to be wasting huge amounts of money and time. We know that a “capture everything” approach where data is cleaned after collection leads to significant downstream costs. 

Snowplow’s Director of Engineering, Costas Kotsokalis explained in a recent webinar:

“When new clients come to us, we often find they're dealing with a common problem. Looking at their current data model, we discover they've accumulated 100s of different versions of what should be the same event. This happens because every time they make even a tiny change to an event definition being sent to their CDP, the system creates a completely new event type. So instead of having one clean 'page_view' event to build models from, they end up with 'page_view', 'page_view_1', 'PageView', and dozens of other variants – all representing the same user action but treated as completely different events. This makes it impossible to build a correct, unified data model without extensive cleanup."

All this leads to increased storage costs, processing overhead, and maintenance complexity.

Data Quality and Trust 

By shifting governance left, you address your data quality at the source. That way, you prevent the proliferation of bad data and the need to fix it later. Thomas int’Veld from Tasman Analytics notes on a webinar, "trustworthiness is much more important... much harder to build up from scratch and very easy to lose."

Using tools like Snowplow’s Data Product Studio, you can validate data against predefined schemas and implement quality checks at collection. As a result, you can ensure data trustworthiness from the start. 

Accelerated Time to Value 

Early governance enables faster deployment of data applications and analytics. Rather than spending time cleaning and reconciling your data, you can instead focus on generating insights and building new capabilities. As one Fortune 100 Financial Institution head of data noted, "We have 40,000+ ETL jobs today. It's chaos...shifting data governance left would be transformational for our organization."

These benefits will become particularly apparent if you’re moving towards sophisticated data applications like AI. In this scenario, data quality is pivotal to model performance and reliability 

How Snowplow Enables Shift-Left Data Governance

By this point in the article, we hope it’s become clear that shift-left data governance is becoming a necessity. But how does Snowplow enable this approach? Let’s explore in more detail. 

Schema-First Design

With Snowplow’s Data Product Studio, you can define your data structures and event specifications before implementing any tracking:

• Create data structures with the graphical editor or JSON editor, specifying properties, data types, and validation rules
• Define event specifications that specialize these structures for specific tracking needs
• Set required vs. optional fields and acceptable values for each property

In a recent webinar, Costas shows how to implement tracking for a fictional meeting scheduler application:  

• A "new_poll" event specification using the user workflow data structure
• A "poll_option_added" event with validation that only weekdays (not weekends) are acceptable values
• A "vote_submitted" event with optional user entity context for name and email

As Costas explained, "This is the narrowing of the data structure. We've defined the particular event specification such that weekdays are included, but not Saturday or Sunday. So users can only select Monday through Friday as valid options." 

This schema-first approach in Data Product Studio ensures data quality is built in from the beginning.

Early Validation and Quality Controls

The Snowplow pipeline validates all incoming data against these predefined structures:

• Events that don't match their referenced schemas are separated from the "good stream"
• Invalid data is stored for troubleshooting rather than contaminating your warehouse
• The platform maintains a lossless approach where no data is ever truly lost

Costas expands, "The pipeline does validate according to data structures. So every incoming event and every incoming entity references the data structure that it complies with. And if it doesn't comply with what it references, then it is separated from the good stream."

Developer Experience

Snowplow's Snowtype tool generates strongly-typed code from your data structures and event specifications:

• Developers import the generated functions into their application
• The IDE provides real-time validation against your defined schemas
• Errors are caught before code is even deployed

In the webinar, Costas demonstrates how the IDE flags errors immediately: "If I make a typo, the IDE guides us and tells us this is not what I expected."

Governance in Practice

Snowplow's permissions system ensures proper governance controls:

• Fine-grained permissions for who can create or modify data structures
• Different permission levels for development vs. production environments
• Approval workflows through GitHub/GitLab integration
• Clear ownership and stakeholder definitions for each data product

"Snowplow does support custom fine-grained permissions for a number of different elements of the platform," Costas explained while showing how permissions work in the console.

Quality Monitoring

And finally, Snowplow provides built-in observability tools:

• Near real-time counters for each event type
• Last-seen timestamps for tracking implementation verification
• Metrics for validation success and failure rates

As Costas demonstrates in the quality monitoring portion of the webinar: "When you use your application and trigger events, you'll see near real-time counters incrementing on your dashboard and 'last seen' timestamps updating automatically. These monitoring features serve two critical purposes - they confirm your tracking implementation is working correctly and verify that your events are successfully being captured by the system."

Real Examples of Shift-Left Data Governance in Action

Let’s examine the meeting scheduler application example we mentioned earlier in more detail: 

At the Source:

• Event specifications define exactly what a "new poll" looks like
• Schema validation ensures only weekdays can be added as poll options
• IDE flags incorrect event tracking before code deployment
• Screenshot shows developers exactly where to implement tracking

In the Pipeline: 

Before implementing shift-left governance, the organization had multiple undefined event versions - page_view, page_view_1, page_view_2, pageView, PageView - all representing the same user action.

After implementing schema-first design, they consolidated to a single, well-defined event with consistent properties like URL, title, and timestamp.

Near Real-time Monitoring: 

When someone creates a new poll and adds voting options, the system shows:

• Immediate validation success/failure
• Event counts updating in real time
• Clear tracking implementation status
• Instant feedback on data quality

We've worked with data engineering teams who reduced their event variants from over 100 to just the essential 15 they needed. By implementing shift-left governance principles, they simplified their data pipeline and improved data quality.

This stands in stark contrast to what we see many companies do today. Thomas from Tasman Analytics, summed it up nicely stating that “many organizations end up with 100 different page view events rather than the single one that you need in order to build a correct data model on top of.”

With that, here are some tips and reminders for shift-left data governance: 

1. Start with Business Value: Rather than boiling the ocean, you should begin where it matters most. As Thomas from Tasman advises: "Go and talk to the teams that are actually consuming the output and make sure that you've got a really good read on the priorities of those data-consuming teams." This helps you prioritize which events and data structures to govern first.
2. Make Data Teams Accountable: Move beyond guidelines to actual ownership. As Costas demonstrates, "You can see here who owns the data products... what is the team, what is the organizational area that owns it, which is important because you can reach out if you need something from that data product."
3. Split Your Data Types: Thomas emphasizes: "Having a very good understanding of different types of data" is crucial. Specifically:

• Objective data (like page views, checkouts) - easier to validate
• Subjective data (like lifetime value calculations) - requires more complex governance
• Aggregated metrics - need clear definitions agreed upon by stakeholders

4. Build Trust Through Visibility: Don't just implement controls - show they're working. Implement dashboards showing validation statistics and processing metrics. Let your teams see validation working in real time, building confidence in the data.
5. Don't Perfect Everything: As discussed in our webinar with Thomas, you need to focus governance efforts where data quality matters most. Not every metric needs the same level of rigor - prioritize your core business metrics and expand from there.

Remember, as Thomas notes, "trust is fickle, but it can be built." These practices help establish and maintain that trust systematically.

Closing

So, the key takeaway from this is clear: if you wait to govern data until after it's collected you are going to create problems. And this will become more apparent the more you build sophisticated AI and machine learning applications. Therefore it's essential to shift your governance left especially as: 

• AI applications demand higher quality training data
• Real-time decision making requires immediate data trust
• Data teams need to do more with constrained resources
• Organizations face growing pressure to demonstrate data reliability

The path forward is not about perfect data, but about intentional data - collecting and governing data that matters most to your business, right from the start.

Organizations around the world are already shifting their data governance left with Snowplow. See how we can help you implement proactive data governance. Schedule a demo with us today to learn more about our schema-first approach to data quality.

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