Thank you to everyone who joined Secoda’s Data Leaders Forum! In our second panel, Rethinking Governance for the Future, we explored how AI, data contracts, and emerging tools like quality scores are shaping the next generation of data governance. Hosted by Secoda’s CEO, Etai Mizrahi, the panel featured:

• Chad Sanderson, CEO at Gable.ai
• Oriol Mirosa, Director of Data Solutions at Brooklyn Data Co.

Relive the rest of the panels:

• ‍Governance Reimagined: How Enterprise Leaders are Innovating
• Governance Reimagined: Faster Access, Stronger Controls

The discussion focused on aligning governance with today’s tech-driven business needs, fostering a governance-focused culture, and how these strategies are transforming the way organizations approach data.

Redefining data governance for the modern era

Data governance has transformed from a compliance-centric function to one that must also address agility, AI, and data-as-a-product principles. Today’s approach allows organizations to balance innovation with data reliability and quality.

From compliance to agility: 

Modern data governance must be flexible enough to adapt to technological changes, allowing organizations to innovate without sacrificing data control. Traditional governance focused on strict compliance, but today’s approach requires both agility and reliability. 

In discussing the shift in data governance, Chad Sanderson emphasized that “Governance in the old days was about security and compliance, almost like a library, where data was finite and centrally managed. Today, it’s more like an Amazon bookstore, with data generated by everyone—from software engineers to third parties—creating a dynamic ecosystem that requires broader governance.” This shift from finite, controlled data to a “marketplace” of data producers and consumers underscores the need for governance frameworks that can handle a vast and varied data landscape.

Core elements for adaptability: 

As organizations grow, data governance must evolve to support scalability, democratization, and AI-driven practices. Foundational governance principles—such as data ownership, quality control, and lifecycle management—remain essential, but they must also be flexible to accommodate evolving business needs.

Oriol emphasized that effective governance goes beyond robust infrastructure; it’s about ensuring data delivers real value by making it accessible, understandable, and actionable for decision-making. This means not only defining what data flows through systems but also clarifying who uses it, how it’s accessed, and how it aligns with the company’s strategic goals. This adaptable, user-focused approach is key to supporting both current and future data governance needs.

Building a culture of data governance

Creating a data governance culture is critical to ensuring that governance practices are embraced across the organization. The panel explored ways to make governance part of the organizational DNA, where accountability and data responsibility become standard.

Core challenges

One of the biggest hurdles in establishing a culture of governance is balancing meticulous governance practices with the fast-paced demands of business. Chad discussed this inherent tension: “The incentive of one group is to slow down and be careful and do the right thing, and the incentive of the other group is to ship as quickly as possible.” These competing motivations often create friction, complicating the integration of governance into the daily workflows of revenue-focused teams.

Chad suggested that this challenge can be met by embedding governance best practices directly into the workflows of business-driving teams. When governance becomes a natural part of their processes, data quality and compliance are upheld without slowing productivity.

To address these challenges and foster a strong governance culture, the panelists recommended several strategies, including integrating “shift-left” practices, establishing clear data ownership, and fostering cross-team alignment through tools like data contracts.

Proactive “shift-left” practices: 

Embedding governance early in the data lifecycle helps foster a proactive culture, making it a natural part of everyone’s workflow rather than an added burden. By adopting shift-left strategies, organizations can transition from reactive governance to continuous, integrated oversight within data operations.

This shift mirrors how QA and security teams moved from directly managing tasks to setting policies that embed quality and security throughout an organization. In data governance, “shifting left” means focusing on clear, proactive policies for key areas, such as PII handling and data encryption, that guide data practices across the board. This approach ensures consistent and secure data management and enables governance teams to update policies as business needs evolve. Ultimately, early governance integration increases efficiency and fosters a shared responsibility for data quality and accountability, from data producers to end users.

Making governance visible and overcoming resistance: 

Successful governance is most impactful when it produces clear, measurable outcomes, such as improved data quality scores and operational efficiencies. These tangible results help shift governance from an abstract concept or compliance formality into a concrete, valued asset that stakeholders recognize and trust.

Chad emphasized that “visibility is the first step of the journey” in making data governance accessible, allowing teams to see the broader impact of their work: “As soon as people start to realize their data is actually doing incredible things all across the business, it totally changes the calculus on their behavior.” By helping data producers understand where and how their data is used, governance becomes an enabler of business success rather than a set of restrictive policies.

Data quality scoring is one tool that makes governance more tangible, replacing vague terms like "trusted data" with precise metrics. As teams work toward these quality benchmarks, governance naturally integrates into daily workflows, fostering a culture of ownership, accountability, and shared responsibility across the organization.

Cross-team alignment and data contracts: 

Aligning governance across functions—particularly between data and engineering—creates a unified approach that fosters cross-team buy-in. Consistent, repeatable processes make governance more approachable and scalable as the organization grows.

Data contracts were highlighted as a practical tool to formalize governance and encourage team alignment. As Etai Mizrahi explained, “Data contracts can offer low-hanging fruit for early-stage governance, enabling organizations to start small by verifying core metrics and key dashboards, which drives buy-in across teams.” This shift to data contracts helps establish shared data quality standards, fostering accountability and creating a foundation for cohesive, organization-wide governance from the outset.

Process-driven success in data governance

While tools play an important role in governance, the surrounding processes are what ultimately drive long-term success. Establishing adaptable, well-defined processes is essential to building a governance framework that can scale with rapid organizational growth.

Data quality and accountability: 

Governance processes that prioritize data quality and accountability lead to trusted data use across the organization. When governance is directly tied to business objectives—like data democratization—it becomes essential rather than optional.

Oriol highlighted that building strong governance isn’t about complex structures but rather “understanding which core data sets are critical and who the users are.” This foundational mapping enables teams to make informed decisions and use data effectively. Importantly, starting data governance doesn’t require a large, time-consuming initiative. By simply identifying critical data assets, understanding their origins, and knowing who produces and consumes them, organizations can begin governance on a manageable scale. Tools like data contracts can formalize this process, but even straightforward conversations can bridge gaps and establish foundational governance practices that grow with the organization.

Data contracts as a process shift: 

Data contracts offer a proactive approach to governance by formalizing data quality and availability expectations across teams. This model establishes clear accountability and consistency, particularly valuable in high-compliance industries such as finance and healthcare, where governance practices must be precise and enforceable. In these sectors, requiring data contracts has become a best practice, ensuring that each data set has a designated owner and that key attributes—such as the semantic meaning of the data—are well-defined from the start.

As Chad explained, "Data contracts represent a proactive shift in data governance, helping formalize data quality and availability expectations that reinforce accountability and reliability across the organization." This approach transforms governance from a one-time setup into a continuous, organization-wide process that strengthens data reliability and trust.

Looking ahead: Emerging trends in data governance

The panel wrapped up with a discussion of emerging trends and technologies that are shaping the future of data governance. AI, machine learning, and the shift toward treating data as a product were highlighted as transformative forces that will drive governance practices forward.

AI and automation for efficiency: 

AI and machine learning are increasingly essential for automating governance processes and ensuring data reliability. Automation is particularly valuable for monitoring data quality and tracking lineage, allowing governance practices to scale efficiently across the organization. By implementing automated audits and policy enforcement, organizations can maintain continuous oversight without the need for manual checks, making governance both scalable and sustainable.

Enabling business agility: 

Data governance frameworks that support agile, data-driven decision-making empower organizations to respond quickly to evolving business needs. By providing a consistent platform for applying governance rules across the organization, these frameworks make trusted data more accessible, enabling teams to make faster, more informed decisions. A flexible approach to governance not only enhances data reliability but also fosters business agility, allowing governance to adapt seamlessly as the organization grows and changes.

Data as a product: 

Treating data as a product involves creating governance frameworks that ensure data is as accessible, transparent, and quality-driven as any other product your team might deliver. This approach ensures that data is readily available, well-documented, and reliable, establishing a solid foundation for future growth and innovation. Etai emphasized, “Governance isn’t just an organizational layer; it’s about defining frameworks that will scale with the company.” This shift supports long-term scalability, making governance an enabler of both immediate and sustained business value.

Conclusion

Data governance is evolving from a back-office function into a strategic, business-enabling initiative. As Chad concluded, “When people realize the value of data governance and the value of data in general, and there’s clear visibility in line of sight, that’s when a lot of the change will start to manifest.” This panel was a great opportunity to explore how companies are pushing the boundaries of governance, and we look forward to continuing the conversation in future Data Leaders Forum sessions.

Thank you to everyone who joined us at the Data Leaders Forum. If you missed the live stream, we hope this recap provides valuable insights into balancing agility with robust governance. Visit our Data Leaders Forum website for additional resources and connections with our panellists.