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Navigating Data Governance: Insights from JP Morgan Chase’s Sarita Baksta

In the complex world of data management, understanding and mastering data governance is essential. Sarita Baksta, a key figure in firm-wide data management at JP Morgan Chase, offers an insightful perspective on the evolving role of data governance. Her career path has encompassed various roles in the financial services industry, ranging from developing mortgage trading applications to overseeing risk and finance technology. In her current role, she focuses on leveraging data responsibly and safely, highlighting the significance of effective data management. 

Drawing on her extensive experience in the financial services industry, this article delves into Sarita’s valuable insights into data mesh data governance.

Rethinking Data Governance

Traditionally, data governance has been seen as a barrier to innovation, often equated with restriction and control. Sarita challenges this perception, advocating for a transformative approach within the data mesh framework. By adopting a decentralized approach to data governance, she shifts the role of governance from gatekeeper to facilitative force.

Decentralizing data governance involves distributing decision-making authority to domain teams, granting them more autonomy. This method strikes a balance between central oversight and localized decision making, effectively transforming data governance into a supportive tool rather than an obstacle. In a decentralized framework, domain teams, who are deeply knowledgeable about their specific data sets, play a crucial role in decision making. With their domain-specific knowledge, they provide expertise that central governance teams often lack.

This shift in governance decentralizes power, moving it away from a single, centralized team. This approach not only places trust in those who are most familiar with the data but also  encourages a more responsible and informed approach to data management. By equipping these domain experts with the right tools and clear guidelines, organizations enable a more innovative, agile, and effective use of data.

6 Insights into Implementing Effective Data Governance

To better understand the practical application of these concepts, here are six critical insights from Sarita on implementing effective data governance.

1. Develop Purpose-Built Data Products

Focus on creating data products designed for distinct use cases. This ensures each product serves a clear purpose and aligns with specific business needs. By making these products extensible and reusable, you can improve their scalability and encourage the discovery of additional use cases over time.

2. Adopt Flexible Governance Principles

Embrace a flexible and adaptive approach to data governance. This involves establishing a governance framework that is capable of evolving with your organization’s needs. Encourage an environment where learning from new insights and adapting to challenges is the norm. You won’t know everything up front, so leave room for your approach to evolve as you learn.

3. Balance Risk Controls with Innovation

Implement risk controls that effectively manage and mitigate potential data risks without stifling innovation. The key is to find a balance: Controls should be robust enough to ensure data safety and compliance, but flexible enough to encourage innovative data use can be challenging. Think of your risk controls as tollgates – they shouldn’t become bottlenecks.

4. Establish Standards for Interoperability and Naming

Create and uphold standards that facilitate interoperability and effective data discovery. This includes implementing consistent naming conventions and ensuring that data products are compatible across different systems and domains. By setting these standards, you help facilitate efficient data navigation and utilization, making data discovery and integration more effective.

5. Ensure Clear Communication Channels

Maintain clear and direct communication channels between domain teams and governance experts. This ensures that domain teams have access to necessary guidance and support and promotes a culture of openness and shared knowledge across your organization.

6. Approach Data Redistribution Strategically

Approach data redistribution with a clear strategy and purpose. You should carefully consider data redistribution efforts to avoid creating unintended data hubs. Effectively managing data redistribution requires you to understand and define the specific roles and purposes of data products within your larger ecosystem. Ensure every act of data redistribution is aligned with a well-defined and intentional goal, reinforcing the strategic use of data within the organization.


Sarita Baksta’s insights invite a reevaluation of data mesh data governance. This new approach, focusing on enabling and empowering, offers the potential for enhanced data management. By adopting these principles, organizations can navigate the complexities of data governance with greater agility and effectiveness.


Learn More about Data Mesh

This article covers key insights from Sarita’s extensive experience managing data in the financial industry. To learn more about her approaches for managing data governance, check out this episode of Data Mesh Radio. For more information about how organizations are leveraging data mesh, check out this list of user journey stories.

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