How to Ensure Data Governance Across Teams

The Challenge of Data Governance

We all know the importance of robust data governance, especially when building reliable, compliant, and high-performing AI applications. 

However, in many large organizations we speak to, data governance has become increasingly complex due to multiple data-producing and data-consuming teams, fragmented datasets, and evolving compliance requirements. 

This issue is further compounded by companies having a lack of clear data ownership, well-defined semantics, and automated workflows. As a result, organizations are struggling to maintain data quality and consistency.

This is where Snowplow’s Data Product Studio comes in. 

Data Product Studio is a tool within Snowplow CDI that helps businesses collaborate on and govern their data tracking requirements while ensuring data quality.  It’s designed to address the aforementioned challenges by providing a governance model that ensures clear ownership, compliance, and consistency across teams, enabling organizations to manage data as a product.

Snowplow’s Data Product Studio: Building Trust Through Governance

• Clear Ownership and Control:
  • With Snowplow’s Data Product Studio, you can  establish clear ownership of datasets by enabling your teams to define, create, and manage data products. Each dataset is associated with a specific team, making it easy for you to understand who owns the data, how it was created, and what it represents.
  • This level of ownership is essential if your organization has numerous data-producing teams, as it prevents data silos and ensures transparency.
  • For example, if your marketing team creates a dataset related to campaign performance, Data Product Studio clearly attributes it to that team, allowing other departments (e.g., analytics or compliance) to understand the context and origin of the data.
• Enforcing Schemas and Triggering Rules:
  • Snowplow’s approach to data governance includes enforcing schemas and triggering rules, which are critical for maintaining data quality.
  • Schemas define the structure of the data, ensuring that data conforms to expected formats and fields. This prevents unexpected changes that could disrupt downstream applications, such as AI models or analytics tools.
  • Triggering Rules specify what events in the real world should generate specific data points, making it easier for you to understand the conditions under which the data was created. This is particularly important for AI models that rely on consistent and well-understood inputs to deliver accurate results.
  • Together, schemas and triggering rules ensure that data creation is intentional, structured, and aligned with organizational standards.
• Automated Workflows with GitOps:
  • GitOps workflows in Snowplow’s Data Product Studio play a crucial role in managing data changes and ensuring compliance.
  • With GitOps, data definitions, changes, and updates are treated as code, meaning that they follow the same processes as software development. Changes to data products are version-controlled, reviewed, and approved before being merged, ensuring that no unauthorized or unreviewed data changes impact the system.
  • This workflow is particularly valuable if you work within a regulated industry, where compliance teams must review data creation and changes to meet legal requirements. Say for example you have a compliance process that involves monthly review of tracking requests, which Snowplow’s GitOps workflow can automate, ensuring that no changes are made without compliance approval.
  • Additionally, GitOps enables centralized data teams to enforce conventions and standards across all datasets, even when hundreds of different teams are producing data. For instance, you could enforce the inclusion of specific properties (e.g., “object action”) in every event schema to maintain consistency across the data estate.

Snowplow’s Governance Model

Snowplow’s governance model emphasizes clear ownership, robust semantics, and automated compliance workflows to ensure data consistency, quality, and reliability.

• Ownership and Semantics:
  • Ownership is at the core of our data governance. When using Snowplow, each dataset is attributed to a specific team, making it clear who is responsible for its creation, management, and updates. This is essential for resolving issues quickly and efficiently.
  • Semantics are captured alongside schemas, detailing the meaning of each data point, what it represents, and why it was collected. This level of semantic clarity reduces ambiguity and makes it easier for data consumers (e.g., data scientists or analysts) to use the data correctly.
• Automated Compliance with GitOps:
  • Snowplow uses GitOps to automate compliance workflows, ensuring that all data creation, changes, and updates go through a structured approval process. This prevents unauthorized changes and ensures that your compliance requirements are met at every step.
  • The GitOps approach also facilitates collaboration between data-producing and data-consuming teams, enabling teams to propose changes, request additional data fields, and update existing datasets in a controlled and traceable manner.
• Scalability and Consistency:
  • Finally, our governance model scales across organizations with hundreds of teams, enabling consistent data management even in complex environments. The ability to enforce schemas, semantics, and data standards at scale ensures that all teams adhere to the same data conventions, making data more reliable and easier to integrate.

Why Does Data Governance Matters for AI?

Data governance is not just a compliance requirement; it is foundational for building successful AI applications. 

By now, most of us are familiar with phrases like “Good AI comes from good data” (Harvard Business Review) and “When AI eats junk food, it’s not going to perform very well” (Matthew Emerick). 

There’s a reason we’re seeing quotes like this crop up more and more. That reason being that AI models simply need consistent, high-quality data to deliver accurate results. 

Our governance model ensures that data is not only compliant but also reliable and well-understood by AI models, making it a critical component for organizations aiming to build advanced AI use cases such as data flywheels. 

Organizations like Klarna and NVIDIA are using data flywheels to create a sustainable competitive advantage. In Yali Sassoon’s recent talk at the Data Science Connect COLLIDE conference, he delves into what data flywheels are and explains how building data quality and governance into your data flywheel from the start is critical for gaining sustained competitive advantage in AI applications.

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