Nextdata OS and the Promise of Autonomous Data Products

Data Mesh Vision Meets Reality

At the end of the 2010s, Zhamak Dehghani made the data world take notice with the data mesh concept – an architectural and organizational model to address what she called the shortcomings of decades of data challenges and failures, and a new way to share data at scale across the enterprise​.

In prior Breaking Analysis episodes, we discussed data mesh principles with Dehghani, noting that it was not a single tool or rigid architecture, but a paradigm shift toward decentralized data management​. Early adopters embraced the vision, but many struggled with DIY implementations and gaps in technology. Fast forward to today: Dehghani’s startup Nextdata has launched Nextdata OS, a platform that aspires to make the data mesh vision a reality by delivering autonomous, decentralized data products as first-class assets. In this research note, we’ll analyze how Nextdata OS represents a shift toward autonomous data product development and why this approach tackles major inefficiencies in traditional data architectures. We’ll also explore the implications for operations and business in an era of AI agents and real-time analytics, compare Nextdata’s model to legacy data lakes and pipelines, and forecast how incumbents like Snowflake, Databricks, and the cloud giants could respond if Dehghani’s latest endeavor moves the market. Our goal is to provide senior IT and data leaders with context, insight and actionable advice as we enter the age of agents and autonomous data products.

An OS Purpose Built for Data Products

Nextdata describes its OS as a unified data product development and operating platform for building, governing and discovering autonomous data products​. In essence, what an operating system does for software, Nextdata OS aims to do for data. Dehghani asks: “What if your data platform worked like an operating system – one that abstracts complex processes and empowers you with simple, composable functions?” Nextdata OS “converts cumbersome data workflows into agile, self-governing data products that autonomously manage the entire data lifecycle—from creation to deployment and governance.”​

The core concept behind this vision is the data product container – a new unit of data value that encapsulates code, data, metadata and policy into a portable, self-contained package. Such containers are lightweight and claimed to be consistent across environments, continually sensing their surroundings and adjusting processing and orchestration in response to changes​. In other words, a data product container is an active, intelligent data construct that knows how to self manage. The problem Nextdata is aiming to solve can be found in today’s brittle pipelines and static catalogs. As Dehghani puts it, Nextdata OS “does for data what containers and web APIs do for software.” By providing standard APIs and a containerized approach, Nextdata OS hopes organizations will lean in to create, share, and run analytical and machine-learning workloads in a distributed fashion, without having to copy and move data.

Each autonomous data product in Nextdata OS is claimed to be easily understood and consumed by users, persistent and reusable across multiple business contexts, and self-governing from the start​. These data products are designed to span the full data lifecycle; and include the necessary code to transform or serve data, the metadata describing the data, and policies embedded as code that enforce governance and security at every point of use​. In practical terms, an autonomous data product might thought of as a recommendation engine that is customized with a real-time analytics dashboard, or a predictive model – delivered not as hard-coded scripts and tables, but as a data product that is discoverable and can be used by others​. By treating data products as software, Nextdata OS aims to bring a product mindset (think versioning, quality, user experience) to data sharing. Fundamental to this approach, and drawing upon original data mesh principles, is a mindset shift from centralized control to domain-driven ownership where domain leads can build and manage their own data products, while the platform ensures they all interoperate and adhere to global governance rules.

In speaking with organizations implementing data mesh principles, this is perhaps one of the most challenging organizational, implementation and change management headwinds. 

The Goal: Transform Pipeline “Hairballs” to Autonomous Data Products

One of the strongest arguments for Nextdata OS is the state of many enterprise data pipelines. Traditional data architectures – with monolithic warehouses, lakes, and endless ETL pipelines – have become an incomprehensible nest of complex, brittle processes​. In large organizations, it’s common for data engineers to be playing wac-a-mole, stomping out issues introduced upstream by changes in source systems. As described in an early data mesh case study published on siliconangle.com, written by Paul Gillin, ​centralized data teams struggle to keep up with growing demand, diverse data types, and ever-changing business needs. The result is often a backlog of requests, slow delivery of data to those who need it, and data that’s stale or stale by the time it’s usable. Moreover, attempts to unify governance in a central catalog or warehouse often fail – the more data sources and use cases, the harder it is to maintain control and context. As Dehghani notes, traditional centralized operations are “poorly suited to increasingly complex environments characterized by multiple data types and repositories.” This complexity “frustrates efforts to unify data governance and creates bottlenecks that cause delays.” The situation is worsened by “constant data migration, unmanageable data pipelines and operating budgets consumed by ongoing maintenance and support burdens.”​

In short, today’s prevailing data architectures are inefficient, fragile, and unable to scale to modern requirements.

Nextdata claims its OS directly attacks these inefficiencies. By packaging data products with their own code and governance, it eliminates the need for many fragile inter-system pipelines. Data doesn’t have to be continuously copied, moved and transformed through a chain of tools; instead, each data product brings compute (and AI) to the data, performing transformations in place and sharing access to data via APIs​. This significantly reduces data movement and duplication – cutting down on unproductive tasks sand what Dehghani describes​ as “data bloat.” 

The Nextdata platform uses a publish-and-subscribe model for data sharing: when one data product publishes new data or updates, subscribers automatically receive those updates in real-time, with policies that carry through the lifecycle. This is fundamentally different from a traditional data catalog that passively crawls data after the fact. According to Dehghani, today’s “catalogs crawl around and find out what data there is and try to make meaning out of it.” According to Nextdata, in its model, active data products continuously publish live information about their state, the data characteristics, who has access and where the data is being used. 

This type of shift to active data sharing means changes are pushed immediately and data products are always aware of their usage and state. In effect, the goal of the Nextdata OS is to turn a static data environment into a dynamic data mesh of interconnected products. Each product is domain-centric and decentralized. As an example, you could have separate data products for marketing, sales, operations, R&D, logistics etc., all controlled by the Nextdata OS, even if under the hood they sit on any data stack. The concept is to enable local autonomy without sacrificing a facile and unified experience.

Importantly, governance is built-in, not bolted on. Nextdata OS is designed where governance policies are inherent to the platform and into the data product container itself, as federated computational governance, one of the other core principles of the original data mesh concept. That means access controls, privacy rules, quality checks and compliance requirements travel with the data product and are automatically enforced whenever and wherever the product is used​. For example, if a new dataset is added to a product, the platform “senses” it and immediately applies the required controls​. This approach addresses one of the biggest pain points of legacy systems: in a traditional pipeline, a change in source data might break downstream processes or violate compliance rules without anyone knowing until too late. In contrast, autonomous data products are designed to be self-orchestrating and resilient, meaning they adjust to changes (such as a schema update) by automatically modifying the processing logic or flagging issues, preventing downstream breakages​. The combination of embedded governance and self-orchestration builds trust in the data so that data consumers can be more confident that the data product they’re using is up-to-date, reliable, and compliant by design, rather than hoping that a central team, with a lack of domain knowledge, caught every issue. Nextdata’s vision is to enable peer-to-peer data sharing at scale while ensuring governance, discoverability and data quality. 

This addresses a long standing paradox in the tech industry – in other words, the forced tradeoff between time to value and security/compliance/governance. 

Key Efficiency Gains

Let’s summarize how an autonomous data product model compares to existing approaches:

• Eliminating Pipeline Bloat: Instead of chaining together fragile ETL pipelines (which often turn into a tangled “hairball”), each data product container handles its own ingestion, transformation, and serving logic. This reduces interdependencies and points of failure. Updates are handled in place, reducing unnecessary data copies and movements​.
  
• Embedded Governance: Policies and quality rules live with the product (think “policy as code”), ensuring compliance is continuously enforced rather than an afterthought. Legacy data governance often relies on separate catalog or manual processes that lag behind and need to be inserted into the data pipeline at various points in time. – Nextdata’s approach aims to make governance proactive and automated​.
  
• Domain Autonomy, Parallel Development: Domain teams can create and evolve data products independently, in parallel, without having to wait on a central data team’s backlog and serial processes. This decentralization is designed to alleviate bottlenecks and scale out data value across the organization​. At the same time, the goal is to ensure consistency and interoperability, reducing data friction.
  
• Real-Time Discovery & Delivery: With a publish/subscribe mechanism, data consumers get timely updates. New data is discoverable via search or natural language query as soon as it’s published, and relationships between data products are visible in a graph​. Legacy catalogs often operate on outdated metadata and require users to manually find data and validate its integrity. An active mesh can push relevant data to consumers and allow natural language discovery across the data landscape​.
  
• Reduced Maintenance Cost: Autonomous data products, to the extent they work as advertised, require less ongoing babysitting. Because they self-adjust to changes and issues, the operational burden on data engineers is lessened. Organizations can reduce time spent on pipeline fixes and migrations, and more effort on developing new analytics and insights. In essence, the business case from a cost standpoint is the operating expense of data infrastructure should drop as more routine work is automated​.
  
• Faster Time to Value: Perhaps most compelling from an economics viewpoint, Nextdata claims that its generative AI-assisted toolkit can cut the time to develop a new data product from months to hours. By auto-suggesting pipelines or code based on existing assets, it accelerates the data product development cycle. Even without AI assistance, the ability for domain experts to directly create data products (with governance) means less waiting in the queue. The sooner a data product is available to users, the sooner it can drive business value – whether that’s better decisions, automated processes, or new customer insights.
  

Autonomous Data Products in a World of Intelligent Data Apps 

The timing of Nextdata OS’s arrival aligns with an inflection point: enterprises are pushing hard to infuse AI and real-time intelligence into their operations and across their application portfolios. However data readiness has become the single biggest blocker. While the excitement of AI builds, practitioners realize if their data house isn’t in order, their AI will be subpar and largely ineffective.  

LLMs and agentic systems are heavy data consumers. Not just any data but fresh, high-quality, context-rich data. Traditional architectures struggle to deliver this and do so in real time. We learned in the days of Hadoop that you can’t just dump data into a lake or, as the saying goes, it becomes a swamp. LLMs and generative AI help but they are unreliable for mission and business critical use cases. Enterprise knowledge is scattered across multiple systems, applications, databases and data lakes. The SaaS explosion only added to the complexity. As such, AI needs a way to tap into data sources without months of integration work​. Moreover, the volume and ever-changing nature of real time data required to justify AI investments is exploding. Simply training AI models or even just doing RAG-based chatbots with LLMs might require data flows that are orders of magnitude larger than traditional use cases. The shape of data is more complex too (think fine-tuning datasets, embeddings, feedback loops, model outputs), and LLMs themselves generate an ocean of new data that needs careful management.

All this puts enormous strain on any traditional data pipeline approaches. If proven, autonomous data products offer a compelling solution in this AI-driven context. Because data products are self-contained and can run on distributed tech stacks, they allow data to be served directly from where it lives (with local processing) to the AI or application that needs it​. Nextdata claims its OS supports data products running on different underlying technologies and storage types – whether that’s a relational database, a distributed file system, or a vector database for embeddings – and exposes them via LLM-friendly APIs natively​. This means an AI agent can query a data product in real-time to get the latest information, without a human having to prep that data centrally. In effect, autonomous data products become real-time data services for AI and analytics. They can publish events or notifications when new data is available, enabling event-driven architectures where decisions are made by software (or AI agents) on the fly as soon as data changes. The low-latency, on-demand access to trusted data is critical for scenarios like customer personalization, automated supply chains, fraud detection, or AI copilots assisting employees with up-to-the-minute knowledge.

Nextdata’s model promises a future where AI agents might themselves assist in data management. We already see early signs, for example Nextdata OS includes generative AI assistants to help developers build data products by analyzing existing assets​. It’s not unreasonable to imagine AI-powered agents monitoring data product health, optimizing queries, or even autonomously creating new data products to meet emerging needs. An autonomous data product platform could be well-suited to such AI augmentation, because it has clear interfaces and encapsulated functions that an AI agent can understand and manipulate. This reinforces a virtuous cycle: AI needs accessible, well-governed data (which data products provide), and in turn AI can further optimize the creation and management of those data products. Dehghani has often claimed that enterprise data leaders are increasingly realizing that a decentralized approach to data/AI is the only path forward if they hope to deliver on AI use cases​. 

Comparison: Autonomous Data Products vs. Traditional Data Architectures

The table below compares Nextdata OS’s autonomous data product model with traditional centralized approaches (data warehouses/lakes with catalogues and pipelines) on key attributes. 

Note: The table below is based on Nextdata claims and past statements made by Dehghani. Practitioners should validate these claims with technical and business teams and be aware of the cultural mindset shifts and technological skills required to not only move to a data mesh concept but implement an automated data pipeline approach. 

Sources: Nextdata and Dehghani’s public statements.​

Impact on Data Platform and Cloud Vendors

Let’s go through a thought exercise. Imagine Nextdata is wildly successful and becomes a de facto standard for building automated data products. In that longshot instance, the emergence of Nextdata OS and the autonomous data product paradigm poses important questions for incumbent data platform vendors like Snowflake and Databricks, as well as hyperscale cloud providers (AWS, Microsoft Azure, Google Cloud). While Nextdata OS is new, the movement it represents – data mesh and decentralized data management – has been building for a few years. Vendors have not stood still; many have added data sharing features or touted their own data mesh concepts; and have addressed governance in a variety of ways (think Polaris, Unity, Horizon and a spate of specialized approaches to data governance). A true data-product-centric, multi-platform approach as championed by Dehghani, to the extent it takes hold, may force strategic shifts.

• Snowflake: Snowflake has thrived with its cloud data warehouse and the concept of a “Data Cloud”, where organizations bring data into Snowflake and share it confidently within the platform. It also offers a data marketplace and data sharing between accounts. However, Snowflake’s model is still fundamentally a centralized repository (albeit a very scalable one) – essentially the antithesis of decentralized ownership. If Nextdata OS gains traction, Snowflake may need to ensure it can participate in a data mesh as an underlying node rather than the sole hub. This could mean making Snowflake’s storage and compute more easily accessible via open APIs or connectors managed by open frameworks. We might also expect Snowflake to emphasize its governance and catalog features (like Snowflake’s Information Schema, data masking, row access policies) to show it can support embedded governance per domain. Snowflake has already been adding support for external tables and cross-cloud data access (e.g., Snowflake External Tables for on-prem S3, etc.), which nods toward more distributed architectures. Even extending Horizon to open governance approaches. In a world of autonomous data products, Snowflake could become one processing engine among many within a mesh – a role it likely wants to play, as long as the data gravity (and spending) still often lands in Snowflake; which it may just based on the strength of its engine. Strategically, Snowflake might also consider partnerships or integrations with Nextdata if customers demand it, ensuring that Snowflake-stored data can be packaged as Nextdata OS products easily. The challenge for Snowflake will be to maintain its ease-of-use and performance advantages while accommodating a more federated model of data ownership. If it resists and pushes only a centralized vision, it could appear increasingly outdated in the face of decentralized trends.
  
• Databricks: Databricks is the champion of “Lakehouse” and has been the most vocal about openness – with open source Delta Lake, MLflow, and a focus on both AI and BI workloads. Databricks has even referenced data mesh in its messaging (for example, enabling data governance across “domains” using Unity Catalog). However, much like Snowflake, Databricks ultimately wants your data in its Delta format (or under its platform control for Iceberg) and your pipelines running on its platform. Nextdata OS approach could pressure Databricks to further open up its interfaces. One can imagine Databricks integrating with Nextdata OS by allowing Delta Lake tables or ML models to be wrapped as data product containers that Nextdata’s catalog can discover. In fact, Databricks could leverage its strength in machine learning to position itself as the ideal execution engine for certain types of data products (like heavy ML model training or large-scale feature engineering), running under the governance of a Nextdata-like layer. Another impact is on Unity Catalog and Databricks’ own governance tooling – these may need to evolve from a single-cluster or single-platform scope to a more federated scope to truly support data mesh implementations. Given Databricks’ emphasis on AI (they’ve been rolling out features for LLMs and acquired MosaicML for model training), they will likely highlight that Lakehouse architecture plus a tool like Delta Sharing can align with data mesh principles (Delta Sharing allows secure data sharing across tenants). Still, Nextdata’s vendor-agnostic stance could diminish the proprietary lock-in of Databricks if customers adopt it to orchestrate across multiple backends. In response, Databricks might double down on performance and native ML integration – essentially saying “you can implement your mesh on us with fewer moving parts.” But as with Snowflake, the company will need to show it can plug into a larger ecosystem of decentralized data products if that becomes the expected norm.
  
• AWS, Azure, and Google Cloud: The cloud providers have a slightly different angle. They provide a multitude of data services (storage, lakes, warehouses, ML tools) and have started to offer reference architectures for data mesh. For example, AWS has published blog posts on how to implement data mesh using a combination of AWS Glue, Lake Formation, and other services (something we covered with JP Morgan Chase on Breaking Analysis in 2021). Azure promotes Microsoft Purview as a governance layer across data estates, and Google Cloud has Dataplex to manage distributed data lakes. However, these are still toolkits rather than turnkey solutions. Nextdata OS, by contrast, aspires to be a holistic platform on top of the cloud – potentially abstracting underlying cloud services. If Nextdata OS is successful, it could reduce the differentiation of cloud-specific data services, making them more commoditized backends. That said, the cloud vendors could benefit from Nextdata’s needs: autonomous data products will still consume storage and compute on AWS/Azure/GCP. We may see cloud providers ensure their services integrate smoothly with Nextdata OS (e.g., AWS could provide an official connector or Quick Start to deploy Nextdata OS on AWS, similar to how they embraced Kubernetes early on despite it abstracting their VMs). [In reality – it will be up to Nextdata to do this work – but we’re imagining a future state here where Nextdata has achieved escape velocity and has major market traction]. In the long run, though, if data product containers allow easy portability across clouds, it strengthens customers’ ability to be multi-cloud – a trend the cloud providers have a love-hate relationship with. We anticipate cloud vendors will respond by enhancing their own data sharing and governance capabilities: for instance, AWS might deepen Lake Formation’s cross-account sharing features or introduce an “autonomous data product” concept in their ecosystem; Azure might integrate data products with its AI services so that an Azure data product seamlessly feeds Azure OpenAI models; Google could expand Dataplex to manage not just Google Cloud storage but also data on other clouds or on-prem, to counter a third-party OS. Each of the big cloud players will likely stress that their platform can be the best host for data products. They might also emphasize managed services for what Nextdata is doing – for example, suggesting that what Nextdata calls a data product container could be implemented via their serverless functions plus metadata catalogs. However, unless they truly adopt openness, those efforts may be seen as cloud-specific silos. The pressure will be on them to support open standards for data products if Nextdata OS or similar initiatives gain a foothold. It wouldn’t be surprising to see an open source or open standard emerge (perhaps led by Nextdata or a consortium) for defining a data product container – much like Kubernetes became an open standard for container orchestration – and cloud vendors would need to align with it.
  

In summary, Nextdata OS’s ascendency to market heights would be a wake-up call for incumbent data platforms and cloud providers. If you believe the future of data management is heading toward autonomy, decentralization, and interoperability then this thought exercise has meaning. Leading vendors in such a scenario, will need to adapt by embracing these principles – either by integrating with platforms like Nextdata OS or by evolving their own offerings to provide similar capabilities. Those that stick to purely centralized, closed models risk being viewed as obsolete. 

The question remains: could Nextadata do to data management what Docker and Kubernetes did for application deployment?  

Buyer Beware: Potential Pitfalls and Gotchas

As with any new category of product—especially one aiming to disrupt entrenched practices—Nextdata OS comes with risks and unknowns. Senior technology leaders should carefully consider the following points before moving forward:

1. Complex Organizational Change
   
   • Requires Decentralized Mindset: Shifting to autonomous data products can expose cultural and organizational resistance. Teams long accustomed to centralized control may find it difficult to embrace domain ownership.
     
   • Skills Gap: Even with user-friendly tooling, building and managing data products requires new roles (Data Product Owners, Policy-as-Code experts) and interdisciplinary collaboration that may be in short supply.
     
   • Governance Challenges: Federated and computational governance can become a messy middle ground if not carefully structured. Without a well-defined framework and executive sponsorship, local autonomy can devolve into chaos or compliance oversights. As well, automation often has unintended consequences.
     
2. Integration Overhead
   
   • Existing Tool Sprawl: Larger enterprises often have multiple data platforms—data warehouses, lakes, catalogs, ETL tools, AI frameworks. Adding an extra layer like Nextdata OS might initially increase complexity before it pays off.
     
   • Custom Connectors and Drivers: Although Nextdata OS touts an extensible driver architecture, domain-specific systems or obscure legacy platforms may need custom connectors, adding cost and potential maintenance overhead.
     
   • Vendor Ecosystem Limitations: While Nextdata positions itself as “infrastructure-agnostic,” the maturity of its plugins for popular ecosystems (e.g. Snowflake, Databricks, SAP, IBM mainframes) varies. Early adopters may end up writing or funding the development of these integrations.
     
3. Maturity and Scale-Out
   
   • Early Stage Product: Nextdata OS is new; references to early adopters are still emerging. Enterprises rolling it out at massive scale are essentially forging the path, potentially facing feature gaps or stability issues that come with any 1.0 platform.
     
   • Performance & Latency Concerns: Autonomous data products, with their real-time orchestration, rely heavily on low-latency eventing and robust infrastructure. Misconfiguration or undersized environments could lead to bottlenecks or degraded performance when many data products communicate simultaneously.
     
   • Support and SLAs: As a startup, Nextdata has limited bandwidth for enterprise-grade support compared to established vendors. Complex or mission-critical deployments may outpace the vendor’s support capacity if ramped too quickly.
     
4. Over-Promise vs. Reality
   
   • Claims of “Months to Hours”: While generative AI accelerates certain tasks, domain teams still must define data models, define policies, and handle domain-specific workflows. ROI will depend heavily on internal readiness.
     
   • Autonomous ≠ Zero Maintenance: Despite automation, some level of human oversight and expertise is still essential—especially for conflict resolution, tricky policy enforcement, or custom transformations. True “self-governance” remains an aspiration; Nextdata OS may only automate 80% of the manual work.
     
   • Data Mesh vs. Overlapping Tools: Many organizations already have partial “mesh-like” solutions (e.g., advanced data catalogs, MLOps platforms, etc.). Without careful rationalization, overlapping functionalities can create confusion or duplication of effort.
     
5. Vendor Lock-In and Exit Strategy
   
   • Proprietary Metadata and Containers: Although Nextdata OS promotes open APIs, its container format and orchestration logic could become a lock-in vector if widely adopted and heavily customized. Migrating autonomous data products to another system might be non-trivial.
     
   • Evolving Standards: The broader data mesh community is still coalescing around standards. If a new open standard for “data product containers” emerges that conflicts with Nextdata OS, organizations may find themselves forced to choose between rewriting data products or sticking with proprietary constructs.
     
   • Cost of Rearchitecture: Transitioning from an existing data lake/warehouse to an autonomous product model can incur short-term technical debt—particularly if an organization must maintain legacy pipelines in parallel.
     

Bottom Line

Any organization evaluating Nextdata OS should weigh its transformative potential against these early-stage uncertainties and organizational readiness. Enterprises that underestimate the cultural shift, the integration demands, or the ongoing need for domain expertise risk stumbling early. But with solid planning, executive alignment, and strategic pilots to prove the model, the upside of autonomous data products can outweigh these pitfalls. As always, a diligent approach—starting small, involving the right stakeholders, and defining clear success metrics—can help mitigate risks on the road to achieving a truly decentralized data infrastructure.

Outlook: New Economics of Data Infrastructure and Leadership Actions

The rise of autonomous data products could fundamentally alter the economics of data infrastructure in the coming years. Today, enterprises pour tremendous resources into constructing and maintaining centralized data platforms – data lakes, warehouses, massive ETL operations, duplicate data storage for various analytics, etc. A significant portion of data infrastructure spend (and IT headcount) is devoted to moving, copying, and cleaning data rather than directly generating business value. If Nextdata OS and similar solutions succeed, one could see a shift in spending patterns as follows:

• From Centralized Projects to Domain-Driven Value: Rather than large capital projects to build one giant data repository, investments will shift to smaller, domain-focused data products. This could reduce wasteful spending on unused or over-engineered central infrastructure. Dollars will go toward tools and platforms that empower domain teams and toward cloud resources that directly support business-facing analytics/AI (as opposed to overhead of integration).
  
• Lower Data Duplication, Lower Storage Costs: In a mesh paradigm, you avoid making three copies of the same data for different silos (data lake, data warehouse, data marts). Instead, the data product container might reference source data or cache it once with proper governance. Less duplication means potentially significant storage and egress savings over time. It also means fewer pipelines reading and writing huge data volumes over the network, which can reduce cloud data transfer costs.
  
• Automation Reducing Labor Costs: Autonomous data products, with their self-service creation and self-tuning nature, can dramatically improve the productivity of data teams. If one data engineer can maintain 50 data products that largely run themselves, whereas previously a team of engineers struggled with one big pipeline, that’s a major efficiency gain. Enterprises may be able to reallocate engineering talent from maintenance to innovation. Over a horizon of years, we might see the balance of spending shift from heavy data engineering to more data science and AI development.
  
• Faster Time-to-Insight Improves ROI: The quicker a data product can be deployed and start delivering insights or enabling decisions, the faster the business can realize value (revenue, cost savings, risk reduction). This improved time-to-value not only justifies the investment in platforms like Nextdata OS, but it can change how projects are justified – we move to an agile, iterative ROI model for data, rather than betting huge upfront budgets on anticipated benefits down the road. In economic terms, the opportunity cost of slow data projects goes down, freeing the organization to pursue more data-driven initiatives in parallel.
  

Perhaps the biggest economic impact could be seen in the arena of AI applications. AI initiatives often stall due to lack of available data or the high cost of preparing that data. If autonomous data products make high-quality, ready-to-use data available on demand, AI projects can progress faster and with less cost. For example, consider an AI-driven customer service agent that needs access to various customer data (purchase history, support tickets, feedback). In a traditional setup, an integration team might spend months ETL-ing all that data into one place for the AI to use. With a data product approach, each domain (sales, support, marketing) could publish an autonomous data product with the relevant data, and the AI agent can securely query those in real-time. The time and expense saved in integration can be huge, and the AI gets better data (current and rich with context), likely leading to better performance. Multiply this across dozens of AI use cases – from predictive maintenance to fraud detection – and it becomes clear that organizations adopting data products stand to gain a competitive advantage in AI execution. They can experiment more cheaply and implement successful models faster, potentially outpacing competitors who are bogged down in data wrangling.

That said, to realize these benefits, technology alone isn’t enough – leadership and cultural shifts are paramount. Enterprise leaders (CEO, CIO, CDO, etc.) should begin preparing now to take advantage of autonomous data products. Leaders should consider the following seven items:

1. Test the viability of the vision. Can your organization agree on data product ownership and a decentralized data architecture?  
2. Pilot domains. Pick two or three areas to test the concept. 
3. Evaluate the cultural fit with your data architects and asses whether this is a good strategic fit for your organization. 
4. Do you have the technical skills to pull this off and/or the budget to hire outside experts with experience to help? 
5. Can you truly federate governance? What risks are inherent to doing so and how will you manage them? 
6. Is your vendor ecosystem supportive and do they have the mindset required to pull this off? 
7. Can you create the business case, measure the value and sign up for success? 

In conclusion, the launch of Nextdata OS marks an important milestone in systems thinking and the evolution of the data mesh concept. It represents a missing link to simplify data mesh deployments and signals that the concepts of data mesh are transitioning from theory to practice, delivering tangible tools to achieve decentralized, autonomous data management. For enterprise leaders, as with data mesh originally, this is another call to introspection. The way we organize data is on the cusp of a dramatic leap forward, akin to the shift from centralized IT to cloud or from monolithic apps to microservices. Those who seize the opportunity early – by reorienting their teams, processes, and investments around data products – stand to reap rewards in the age of AI and real-time business, if there is a good cultural fit. In the autonomous era, data truly becomes a product and an asset that can drive value continuously, rather than a burden to be piped and shoveled. As Dehghani herself put it, it’s inspiring to finally see “the promise of data mesh, realized.”​

We’ve always been a fan of Zhamak Dehghani’s amazing mind, her vision and contributions to the industry and we look forward to seeing how this latest chapter plays out.