Srinivasa KalyanVangibhurathachhi: Data Mesh- A New Paradigm For Decentralized Data Ownership And Governance

The contemporary times have necessitated organizations to collect more and more data, and many are struggling to manage it in a way that’s fast, flexible, and trustworthy. The traditional approach, where a central team handles all data, can slow things down and create roadblocks. That’s where data mesh comes in.

Data mesh is a growing trend that shifts responsibility for data to the teams that work with it every day. Instead of one central group controlling everything, each department or business unit owns and manages its own data. This decentralized model is assisting firms move faster and make better use of the information they already have.

A senior data leader working in this field, Srinivasa KalyanVangibhurathachhi, has supported several organizations in the U.S. adopt this new model. With a background in building large-scale data systems, he has played a key role in guiding teams through major transitions—from traditional data warehouses to a mesh structure that supports independent data ownership, and he shared how the results have been striking. In one organization, the time it took to access new datasets dropped from three weeks to just three days. In another case, the workload for the central data engineering team was cut by 60% after the shift to a more self-service system. Data users reported being more satisfied, and the overall use of the platform tripled.

Discussing his projects, he mentioned work in healthcare sector, where data must be handled with extra care. He shared that he designed a framework which allowed each department to manage its own data, while still meeting strict privacy rules like HIPAA. In retail and finance, similar changes were made, helping those organizations better manage and trust their data while staying compliant.

However, as the professional noted, making this shift came with significant challenges. One of the key hurdle was getting past the old habits of central control. When everything flows through single team, delays and miscommunication are common. Moving to a model where domain experts are responsible for their own data required a fundamental rethinking of roles and responsibilities. It also required new tools, like Snowflake’s data platform and automated tracking systems, to keep things running smoothly. Another major issue was establishing trust. When multiple teams manage their own data, how can one be sure that the quality is good and nothing is missing or duplicated? To address this, the expert implemented data contracts—clear agreements between data producers and users that outline expectations for reliability and updates.

The key takeaway from this work is that decentralizing data ownership doesn’t mean losing control. In fact, it can lead to better results—faster delivery, less confusion, and stronger accountability. But for it to work, organizations need the right culture and systems in place. Teams must be trained and supported, and central teams should act more like service providers—offering tools, policies, and guidance instead of controlling everything. As Kalyan rightly suggested, “Decentralization does not mean chaos. It means empowering experts, enforcing trust and automating the boring parts.”

Looking ahead, it is expected that data mesh will continue to grow, particularly as tools become smarter and more automated. Features powered by AI could soon enable teams to spot issues in their data before they become problems. And more companies are likely to treat data platforms as internal products—designed to serve the people who use them. In a world where data is only becoming more important, finding better ways to manage it is critical. Data mesh offers a practical approach—one that’s already delivering results in the real world. As more organizations adopt this model, the idea of shared responsibility for data may become the new standard.

Lastly, sharing his insights, Kalyan added, “A successful data mesh is not just architectural but cultural. Without accountability and ownership at the domain level, it is just decentralization without discipline.”

About Srinivasa KalyanVangibhurathachhi

Srinivasa KalyanVangibhurathachhi is an experienced technology professional who has spent nearly two decades working in data engineering and IT leadership roles. With a background in Electronics and Communication Engineering, he holds both a Bachelor’s and Master’s degree. Over the years, he has worked across industries like transportation, insurance, and manufacturing, contributing to large-scale projects. One of the highlights of his career was leading a major application rollout for Indian Railways, which helped enhance operations across stations and junctions throughout the country.

Today, Srinivasa works as a Solution Architect at Adept Consulting Group Inc., where he helps businesses make better use of their data using cloud platforms like Snowflake, Databricks, and Microsoft Azure. He has expertise in building data pipelines, managing data quality, and creating tools that turn raw data into useful insights. Known for his collaborative approach, Srinivasa has led several successful projects, from improving billing systems to developing enhanced performance metrics. He is now focused on learning and applying new technologies like Generative AI to further enhance his work.