Why Organizational Evolution is Key to Data Success

Why Data Projects Fail: A Call for Change in Organizational and Process Thinking

In the race to solve data challenges, many companies rush to adopt new technologies such as Lakehouse architectures, believing they are the magic bullet. However, the frequent failure of data projects shows that technology alone cannot fix organizational and process issues that are deeply entrenched in outdated, centralized structures. While enterprises often focus on the latest tools and platforms, they overlook the importance of evolving their organizational culture and processes to be agile and distributed—essential traits for thriving in today’s data-driven landscape.

This oversight leads to a fundamental disconnect, where companies invest heavily in technology like “centralized” Data Lakes and Lakehouses, only to claim the technology failed when the project doesn’t deliver expected results. But here’s the truth: the failure is not in the technology itself. The issue lies in trying to apply modern, distributed, and scalable solutions with organizational structures and processes that remain centralized and slow.

The Myth of the Centralized Lakehouse

First, let’s dispel a common myth: there is no such thing as a “centralized Lakehouse.” Modern data systems, especially Data Lakes and Lakehouses, are inherently distributed and scalable. The architecture is designed to handle data at scale, with flexible compute and storage layers. The problem is that many organizations still try to apply these distributed technologies in a centralized, command-and-control manner, creating bottlenecks and inefficiencies.

While the technology is ready to solve today’s data challenges, many companies are not. Their organizational structures and processes are outdated, built around central data teams and hierarchical decision-making that worked in the past but no longer fit in the agile, fast-paced world of modern data management.

Centralized Organizations: A Bottleneck for Innovation

The traditional approach to data projects typically follows a centralized model:

• Central Data Teams: A central data engineering team is responsible for moving data from operational systems (OLTP databases) into a centralized Data Lake or Data Warehouse. Data moves through multiple pipeline stages (Bronze, Silver, Gold) before being made available for analysis.
• Data Ownership Disconnect: By the time data reaches the centralized repository, the original data owners or domain experts are disengaged. They either don’t trust the central data team’s version of the data or have moved on to using their own solutions, such as Excel or Python scripts, within their silo to get the job done.
• Slow Feedback Loops: Centralized organizations tend to have slow feedback loops, making it difficult for teams to innovate quickly. It takes time to get access to data, validate it, and derive insights, and by then, the business needs may have shifted.

Despite the massive investments in centralizing data management, these projects often fail, not because the technology is flawed, but because the organization is structured in a way that stifles agility and prevents domain teams from acting independently.

Enter Data Mesh: A Paradigm Shift in Data Management

One of the most exciting developments in data management is the rise of Data Mesh, a concept that may seem revolutionary to data professionals but is, in fact, a reflection of the microservices architecture that has transformed software development.

Applying the Microservices Approach to Data: Data Mesh

Data Mesh brings this same distributed, domain-oriented ownership to the world of data. Instead of centralizing data governance, processing, and ownership, it decentralizes these responsibilities to domain-specific teams. Each team owns its data as a product, ensuring quality, governance, and availability.

In this model:

• Data Silos Are Not Bad: Contrary to popular belief, data silos are not inherently harmful. They allow data domains to remain independent and agile. With the right governance model, data silos can become the cornerstone of a well-functioning data ecosystem.
• Data Contracts and Governance: To solve the issues of trust and data quality, Data Mesh emphasizes data contracts. These contracts define clear agreements between teams about the data they produce and consume. Data contracts set expectations around data quality, schema, and governance, ensuring that each data product can be trusted and used by other teams in the organization.
• Empowering Teams: Much like microservices, the goal is to empower teams to move fast and deliver value. A domain team can plan, build, and deploy a data product quickly, respond to feedback, and iterate without relying on a central data engineering team. This eliminates the bottlenecks created by centralized processes.

The Centralized Data Paradigm Is Obsolete

In the last decade, organizations have poured resources into eliminating data silos and centralizing data management, believing this was the path to better data governance and insight. But in doing so, they’ve often hindered the agility of individual teams, creating disengaged data owners and slower innovation cycles.

In the microservices approach, software teams can deliver new features to customers in a matter of hours. The same should be true in the world of data. A domain team should be able to create, deploy, and update a data product quickly, without waiting weeks for central data engineering resources or approvals.

The centralized data paradigm is obsolete. It doesn’t work in a world that requires distributed, domain-specific ownership, fast feedback loops, and agility to respond to changing business needs.

Evolving Organizations and Processes Alongside Technology

For companies to succeed with modern data architectures like the Lakehouse, they must evolve their organization and processes in parallel with adopting new technologies. Here’s what needs to change:

• Decentralized Data Ownership: Like Data Mesh, companies need to give domain teams ownership of their data. This means empowering teams to manage, govern, and produce data products that other teams can consume.
• Faster Feedback Loops: Teams need to be agile, able to experiment, get feedback, and iterate quickly. Centralized processes often stifle this agility. Companies must streamline processes to allow for rapid innovation and experimentation in data management.
• Self-Service Data Platforms: The technology (Lakehouse, Iceberg, etc.) must be built to enable self-service. Teams need to have the tools, infrastructure, and governance capabilities to manage data without constant reliance on a central IT or data engineering team.
• Governance with Autonomy: Governance does not need to be centralized. With the right data contracts, governance policies can be enforced across distributed teams without compromising autonomy. This is crucial for scaling data projects in large organizations.

How Lakehouse and Data Mesh Can Be Unified

The three main components of the Lakehouse architecture are Storage, Compute, and Catalog. The Lakehouse Catalog plays a crucial role in aligning technology and processes, making it a key enabler for the unification of Lakehouse and Data Mesh principles.

For instance, consider the open-source catalog Lakekeeper.io, which we’ve built as a flexible interface that communicates with data contract engines. This Contract Engine ensures that data producers won’t violate any Service Level Objectives (SLOs) outlined in the data contracts.

In complex consumption pipelines, where hundreds of producers and consumers interact within a single organization, this alignment builds stable and robust relationships between data domains. These relationships act as a preventive measure, ensuring that changes do not disrupt the wider data ecosystem. Furthermore, with actionable data contracts, we can track business lineage and the impact of any change within the data supply chain, ensuring transparency and trust.

Data practitioners can learn from battle-proven physical concepts that have existed in the manufacturing world for decades. This approach mirrors established processes in manufacturing, where a manufacturer relies on suppliers to deliver parts on time, in the right quantity, and at the agreed-upon quality. Data products are no different. Just like physical supply chains, data supply chains must ensure smooth, well-governed interactions between producers and consumers.

By applying this well-understood paradigm to data, we can seamlessly combine Lakehouse and Data Mesh, enabling organizations to transform their processes and become agile, data-driven enterprises. This convergence ensures both technological efficiency and organizational agility, fostering a robust and scalable data ecosystem.

Conclusion: Transforming Organizations Is Key to Successful Lakehouse and Data Mesh Integration

The failure of data projects over the next 2–3 years won’t stem from the shortcomings of technology. Solutions like the Lakehouse architecture are already designed to be scalable, distributed, and capable of handling modern data challenges. The real obstacle lies in the outdated, centralized organizational structures and rigid processes that many companies still rely on.

To unlock the true potential of Lakehouse and Data Mesh, organizations must undergo organizational transformation, not just technological adoption. This transformation involves decentralizing data ownership, empowering domain teams, and creating agile processes that can support fast feedback loops and continuous innovation. Governance models must also evolve to fit the decentralized world, enabling domain teams to self-manage while adhering to global standards.

The fusion of Lakehouse and Data Mesh principles provides a clear path forward, but without aligning processes and organizational structures to support distributed, domain-oriented ownership, companies risk repeating the failures of the past. Those that fail to evolve will once again blame technology for project failures, when the real challenge lies in adapting their organizational culture to fully leverage the benefits of the new data architecture.

The future of data success lies in this holistic approach, where technology, organization, and processes work together, enabling companies to truly harness the power of their data ecosystems.