Enterprise data governance strategy: The essential elements

Enterprise data governance isn’t optional - it’s essential. A solid enterprise data governance strategy leads to crafting high-quality, reliable, and secure data with minimal errors. This builds confidence in data and leads to better decision-making.

The problem is crafting a data governance strategy that can keep pace with the speed of your business. The emergence of Generative AI (GenAI) use cases, in particular, has created a skyrocketing need for high-quality data drawn from across the enterprise.

Keeping up with the pace of change requires more than “data governance as usual.” It requires adapting new strategies, paired with good tools, that enable all teams in an enterprise to improve and monitor data quality, collaborate on data across teams, and automate data pipelines to reduce downtime and minimize human error.

In this article, we’ll look at how enterprise data governance is changing in the age of AI and the strategies you need to implement to meet the moment. We’ll also see how dbt supports turning these strategies into reality by enabling teams to collaborate on data governance easily and effectively.

How enterprise data governance strategy is changing

Traditionally, enterprise data governance has been a largely static, top-down process. A central authority laid out standards and policies for all teams. Data stewards worked with teams to implement and enforce policies on a local level.

This was largely a slow, established, and manual approach to data governance. And it worked…for a while.

These traditional approaches to enterprise data governance, which were already strained, don’t scale in the age of AI. There’s too much data, from too many different teams, for a manual approach to governance. In addition, AI technology - and the regulatory environment that governs it - is changing daily.

This means AI requires an approach to enterprise data governance that is:

• Dynamic, continuous, and multidisciplinary
• Automated and dynamic
• Proactive and responsive to a fast-changing regulatory environment

The additional governance concerns raised by AI

Additionally, AI raises enterprise data governance concerns that either didn’t previously exist or existed on a smaller scale.

Large language models (LLMs), which serve as the heart of most AI applications, are trained on vast amounts of data to generate a probabilistic model. The responses are stochastic - i.e., not directly correlated to the inputs.

This makes LLM output hard to predict. It also raises concerns around the LLMs themselves, which operate as black boxes. This leads to issues such as:

• Bias in the underlying data, leading to biased responses that harm certain individuals or groups;
• A lack of transparency or auditability in how a given LLM arrived at a certain output; and
• An inability to explain or control why the model made the decisions it did.

In addition, AI is susceptible to a number of unique threats. These include data poisoning, prompt injection, model inversion, and private leakage attacks, among others.

The essential elements of a modern data governance strategy

The good news is that these challenges aren’t insurmountable. Tackling them, however, requires that your enterprise approach managing data differently.

In most enterprises, data is siloed and fragmented. Data engineering teams all use different data storage systems and different tools to transform, move, and manage data. The result is inconsistency, little cross-team collaboration, and a lack of overall trust in data.

dbt acts as a data control plane for data across your enterprise. Using dbt, your enterprise can manage data in a flexible and cross-platform way that avoids vendor lock-in, encourages collaboration, and produces trustworthy outputs.

With dbt, you can implement a flexible and proactive data governance strategy centered around three core principles:

• Enable all teams to create and publish high-quality datasets
• Emphasize data collaboration
• Define a continuous release process centered on quality

Let’s look at each one of these in detail and at how dbt helps you turn each of these strategies into business reality.

Enable all teams to create and publish high-quality datasets

In one way, AI has changed the game. But in a fundamental sense, nothing’s changed. Producing

high-quality data consistently is still the name of the game.

The key is “consistently.” In most companies, data quality varies considerably from team to team. Even enterprises with data quality standards in place may struggle to determine which teams are adhering to them.

Adopting a single data control plane like dbt means that every team across the enterprise uses a single toolset to model and transform data. dbt supports a built-in testing framework, enabling data engineers to build out test suites that verify all changes before release. This ensures that data transformations generate the correct outputs before data is made available to downstream consumers.

In addition, the new dbt Fusion engine accelerates the development of high-quality data transformation code. dbt Fusion’s deep SQL comprehension emulates the SQL syntax of all popular data warehouses. That means that data engineers can see errors in their code as they type, and run tests locally on their development machines - no code check-in or remote data warehouse required.

Emphasize data collaboration

Do-it-yourself approaches to data quality make it hard, if not impossible, for teams to work together or share their results. There are two key reasons for this.

First, everyone is using different tools for data transformation. One team might use dbt for their data pipelines, while another is running Python scripts, and a third is editing stored procedures directly in a data warehouse. This makes it impossible for teams to work together on common problems or share data transformation code.

Second, there isn’t a single place to find data once it’s ready. Without an easily accessible single source of truth, data consumers may spend hours, days, or weeks hunting across various Snowflake instances for the data they need.

Using dbt as your data control plane, anyone who knows SQL can understand and contribute to data models. Data engineering teams can share common transformation code across projects, reducing the need to start from scratch with each new dataset.

Engineering teams can further increase data governance for AI by packaging and deploying their datasets as data products. Data products are structured and curated assets designed to solve a specific business problem. They’re a great fit for AI workloads, as they make it easier to verify the origin and quality of data that comprises a given AI solution.

Once ready for production, data models are published and discoverable via dbt Catalog. Anyone with the appropriate permissions can find the data, read its accompanying documentation, see its lineage, and leverage it for their own work - whether that’s a quarterly report, a machine learning model, or an AI application.

You can even use dbt to centralize the definition of critical business metrics. With dbt Semantic Layer, you can define metrics - such as quarterly revenue - using a single formula with standardized naming. This eliminates data quality issues caused by teams implementing their own definitions based on potentially unreliable sources.

Define a continuous release process centered on quality

Writing data transformation code is only part of the battle. The hard part is ensuring that only high-quality code makes its way to production. Error-riddled code results in data errors and privacy leaks that can undermine both decision-making and customer trust.

Using dbt, you can easily create a continuous integration (CI) release process to deploy data transformation code changes. After an engineer checks in a change, it goes to another team member for peer review. This ensures no change goes live without at least two sets of eyes on it.

Once deployed, the CI process can automatically run all data tests against a non-production database, verifying that the new code produces correct outputs before it touches production data. Once in production, these tests can be continuously run against incoming data and monitored via data health signals in dbt.

When data is distributed across multiple systems, it can be difficult to control who has access to production systems. By defining a CI process in dbt, you can use role-based access control to define who’s authorized to make changes to specific models.

dbt as your data control plane for better enterprise data governance

Data sprawl, data siloes, and inconsistent tooling make a comprehensive approach to enterprise data governance impossible. Using dbt as your data control plane, you can provide a consistent, governed approach to managing your enterprise data, no matter where it lives.

To learn more about how dbt can improve your enterprise data governance, schedule a demo today.