As organizations scale, a centralized approach to data management often becomes a bottleneck, hindering agility and domain-specific insights. VTS, a commercial real estate platform, recognized these challenges and embarked on a journey to decentralize its data management. Archit Shah, Staff Software Engineer - Data, shares how VTS has made a strategic shift to decentralized data management to address these challenges head-on. This post outlines a practical, step-by-step framework based on VTS' experience, offering guidance for organizations aiming to empower domain teams and enhance data governance.
This post is part of INNER JOIN, a live show hosted by Select Star. INNER JOIN brings together thought leaders and experts to discuss the latest trends in data governance and analytics. For more details, visit Select Star's Inner Join page.
Table of Contents
- Key Considerations for Decentralized Data Management
- Best Practices for Effective Decentralized Data Management
- Applying VTS’s Decentralization Playbook to Your Data Strategy
Key Considerations for Decentralized Data Management
Centralized data management has long been the default for many growing organizations, but it comes with significant drawbacks. Common limitations include:
- Operational bottlenecks: Central data teams can become inundated with requests, causing delays in data access and slowing down decision-making.
- Lack of domain context: Centralized teams often don’t have the deep understanding needed to interpret data in the context of specific departments like sales, marketing, or finance.
- Misaligned metrics and duplication: Without strong alignment, different teams may create redundant reports or use conflicting definitions, eroding trust in data.
- Scalability constraints: As organizations grow, a single centralized team often struggles to support increasing data demands across the business.
At VTS, these pain points were all too familiar. Their centralized data governance model created dependencies that limited the agility of individual teams and overwhelmed the central data function with the volume of requests, causing bottlenecks that slowed down business decision-making. Recognizing these limitations as the organization grew, VTS saw an opportunity to transition toward decentralized data management. This approach offered a path to scale by shifting ownership to domain teams, allowing them to manage, document, and trust their own data assets, while still maintaining governance and alignment across the organization. Understanding these limitations and connecting them to organizational needs is a critical first step in any successful decentralization effort.
Best Practices for Effective Decentralized Data Management
Implementing decentralized data governance model isn’t just a tooling decision—it requires thoughtful coordination across people, processes, and technology. Based on VTS’ experience, success came from aligning stakeholders, empowering domain teams, and continuously refining their approach. The following best practices outline how organizations can operationalize decentralization in a way that balances autonomy with governance and delivers long-term value.
1. Secure Executive Buy-In
Leadership support is crucial to the success of decentralized initiatives. VTS aligned their executives by highlighting the benefits of decentralization, such as improved agility, improved decision-making, and domain accountability. This alignment ensured resource allocation and organizational support throughout the transition.
2. Identify and Empower Domain Teams
A successful decentralized model starts with clear ownership. VTS designated data stewards within each domain who were responsible for managing data assets, maintaining documentation, and ensuring data quality. By giving teams the authority and responsibility over their data, VTS fostered accountability and enabled faster, more relevant insights.
3. Implement the Right Tooling
Decentralized data management requires infrastructure that supports visibility and governance. VTS adopted Select Star to automate data documentation, enable lineage tracing, and facilitate data discovery across teams. With a central platform that provided both flexibility and structure, domain teams could operate independently without sacrificing consistency or compliance.
4. Provide Hands-On Training and Support
Transitioning to a decentralized model requires cultural change in addition to process change. VTS invested in training programs and internal enablement to ensure that teams were comfortable with new tools and responsibilities. This included documentation, workshops, and regular check-ins to reinforce best practices and encourage adoption across technical and non-technical roles.
5. Establish Governance Guardrails
While decentralization emphasizes autonomy, governance remains critical for maintaining trust in data. VTS implemented a lightweight but effective governance model, including standardized definitions, role-based access controls, and periodic audits. These guardrails allowed domain teams to move quickly while ensuring data quality and compliance.
6. Measure Success and Iterate
Measuring the impact of decentralized data management is essential to ensure the model delivers on its promise of agility, ownership, and scalability. At VTS, this meant establishing clear success metrics from the outset and regularly reviewing them to inform iterative improvements.
- Data Access Times: Speed of data retrieval by domain teams. Improvements here signaled greater autonomy and more efficient workflows.
- Data Quality Scores: Accuracy and completeness of datasets. These scores helped reinforce accountability and highlighted areas needing better governance or tooling support.
- User Satisfaction: Feedback from data consumers. This qualitative input uncovered friction points, surfaced training needs, and provided a pulse check on adoption.
Together, these metrics offered a holistic view of the decentralization initiative's effectiveness and allowed the team to fine-tune its approach over time, ensuring long-term success and scalability.
Applying VTS’s Decentralization Playbook to Your Data Strategy
VTS' journey illustrates that decentralized data management, when executed thoughtfully, can enhance agility, empower domain teams, and maintain data governance. By following these steps—securing executive support, empowering teams, implementing the right tools, providing training, establishing governance, and measuring outcomes—organizations can navigate the complexities of decentralization effectively.
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