Critical tools for data governance

No organization that relies heavily on data can survive without a detailed data governance strategy. However, implementing that strategy - particularly at scale - requires having the right tools. In this article, we’ll take a look at the tools that are indispensable for maintaining the quality, security, and transparency of data.

Why data governance requires data governance tools

“Data governance” is a broad term that encompasses several aspects of data:

• Data quality. Poor data quality costs companies up to USD $12.9 million yearly. Low-quality data undermines data trust, which prevents many new data projects from ever getting off the ground. Maintaining standards and tools to format, clean, verify, and track the movement of data increases trust. It also saves millions in expensive and time-consuming troubleshooting.
• Data security. The average cost of a data breach is USD $4.88 million. Security breaches - both external and internal - can undermine customer trust and incur lasting reputational damage. A data governance security strategy uses role-based permission controls to manage and audit access to data.
• Data compliance. Companies need to manage sensitive data - company financials, medical records, personally identifiable information (PII) - in accordance with both industry best practices and local regulations. This requires classifying all system data according to its sensitivity levels, tracking its movement across the organization, and maintaining thorough logging and audit trails.
• Data interoperability. The same critical data often ends up replicated in multiple formats across an organization. Interoperability efforts standardize core data structures and calculations, reducing broken data pipelines and confusion over diverging data.

The trick is implementing these practices at scale. Data continues to grow exponentially year over year. The rise of Generative AI - which requires large data volumes for accuracy - is only increasing the demand for accurate, high-quality data sets.

Data governance tools enable governance at scale by combining automation with human oversight. They enable data producers and consumers to manage large volumes of data effectively - e.g., by finding data easily wherever it lives in the organization, or automatically allowing or denying access to data based on user role and data classification.

Automating governance using data governance tools enables teams to work independently while ensuring and demonstrating compliance with all applicable data standards and regulations. This federated computational approach makes data governance a shared, community effort in which data producers, consumers, and governance experts collaborate to create high-quality data sets.

Critical data governance tools

There are a number of data governance tools, with more appearing on the market by the day. However, the following have proven indispensable to any organization looking to implement governance at scale.

Data catalog

The first step in data governance is getting a handle on everything you own. That means somehow keeping track of data across hundreds or thousands of different data stores.

This is where a data catalog comes in. A data catalog is the single source of truth for data in an organization. It provides a repository that describes, not just the data itself, but all of its associated metadata - e.g., who owns it, when it was last updated, etc.

Data catalogs work by connecting directly to data sources or to an intermediary description of your data (e.g., a dbt data model). They provide an interface where other users across the company (depending on their permissions) can then discover and use this data in their own data projects.

Capturing all data in a data catalog lays the foundation for all other data governance practices. It provides a central location where the organization can monitor data security, classification, and quality, no matter where the data itself lives.

Data lineage

Seeing how data flows throughout your company is critical to increasing trust in data. A data lineage tool shows, in visual form, a sequential workflow of how data travels through your system at a data set or columnar level. You can track data back to its source and see who’s consuming it - via reports, applications, etc. - throughout your organization.

You can use data lineage to improve data governance and quality in a number of ways:

• Root cause analysis: When a data problem occurs (e.g., a data pipeline breaks due to a malformatted value), data engineers can use lineage to find and fix the problem at its source.
• Impact analysis. Leverage data lineage to see when a change to a data set’s values might result in downstream breakages, so you can work with the impacted stakeholders before releasing it.
• Verify data provenance. Data consumers and business stakeholders can use data lineage to assess

Data security management

A data catalog enables anyone to discover data across the company. However, that doesn’t mean everyone should have permission to access any and all data. Every teams needs controls they can leverage to control which data they expose, and to whom.

With data security management, teams can establish fine-grained access controls around data. They can expose certain data sets to company users based on the user’s roles, while keeping other data sets private for their own internal use.

Data classification

Regulations such as the General Data Protection Act (GDPR) require strict handling of sensitive customer information. Penalties for violating these regulations are often stiff and costly.

Data classification provides tools to tag data according to its classification. Using data classification, you can identify sensitive data and enforce appropriate data governance policies, such as restricting access or expunging data after a certain time period.

For example, say a data set stores a customer’s email address along with their credit card information. You can classify the email address as Moderate sensitivity and the credit card info as High sensitivity. In turn, data governance tools can automatically restrict access to this data, as well as audit any access to it.

Data quality management

Data quality often suffers because data engineers lack the tools required to control, track, approve, test, and track changes to data.

Data quality management tools enable data producers and consumers to use a DataOps methodology to manage data changes. In a DataOps framework, data changes are modeled as code, so that data teams can commit, review, test, and approve every change prior to release.

With DataOps, every change goes through a Continuous Integration and Continuous Deployment (CI/CD) process that tests it in multiple environments before pushing it to production. Once live, data teams can continuously run their tests against incoming data, raising alerts and sending notifications if their tests detect any anomalies.

How dbt Cloud supports data governance

For years, data teams have relied on dbt to model and transform their data at scale. With dbt Cloud, your company can build a data control plane that provides a firm foundation for data governance.

dbt Cloud provides numerous features to enable data governance at scale, including:

• dbt Explorer: See a bird’s-eye view of all of your end-to-end data pipelines, along with all of their dependencies. See the flow of your data down to the column level via an intuitive data lineage graph to diagnose data quality issues and perform impact analysis.
• dbt Mesh: Enable each data domain team to create, maintain, and ship its own dbt projects. Find and import models from other data teams. Give teams the tools to define and manage access permissions to their dbt models. Track global standards for data governance across all teams to measure and improve the overall quality and compliance of your data estate.
• Continuous integration. Automatically test your changes against a test schema before merging new code to production. dbt Cloud builds and tests only the code that changed.
• Tests and alerts. Develop assertions against your data using a few lines of code and test them with every job run. Run tests continuously in production to validate incoming data, and raise alerts and notifications via Slack, email, or webhook if your tests catch an anomaly.
• dbt Semantic Layer. Standardize on key metrics by defining them in a single, centralized location alongside your dbt models. Enable anyone across the organization to access metrics via Tableau, Google Sheets, Hex, and a host of other analytics and BI tools.

To learn more about how dbt Cloud can support your data governance initiatives, ask us for a demo today.