Is Your Data Operating Model Broken?

By Aria Wornson

Published on February 19, 2025

In today’s fast-moving business world, the ability to act on data quickly is the difference between capitalizing on an opportunity and missing it altogether. Yet data teams, already overwhelmed by a flood of business requests, find themselves stuck in bottlenecks—too slow to deliver the insights decision-makers need. As demand for data grows, the traditional operating model—where data teams act as gatekeepers, controlling access and enforcing governance—can no longer keep up.

Business leaders need answers now, but centralized data teams are stretched thin, struggling to support an expanding range of business needs. With dozens or even hundreds of domains to cover, it’s impossible for a single team to master them all. Scaling resources to meet demand isn’t just impractical—it’s unsustainable. The result? A data operating model that’s breaking under the weight of its own inefficiencies, leaving enterprises unable to move at the speed of business.

The urgency to fix this broken model has never been greater. The rapid rise of AI is raising the stakes, making it imperative for organizations to harness trusted, high-quality data at scale. Gartner predicts that by 2028, at least 15% of daily work decisions will be made autonomously by AI agents. To remain competitive, businesses must deploy AI solutions that drive efficiency and innovation—but without a modernized data operating model, they risk falling behind.

The pitfalls of the traditional data management model

For decades, enterprises have relied on centralized data teams to build datasets, reports, and dashboards from an enterprise data warehouse (EDW), data lakes, or data lakehouse architectures. The assumption was that consolidating data expertise would streamline delivery. However, decades of experience show otherwise.

Centralized data teams can often be a bottleneck, whereas decentralized data teams deliver faster.

Take a common scenario: major projects structured as 60-day epics, assigned based on resource availability. Each epic begins with a data discovery phase, typically estimated at five days. However, data engineers are often assigned to unfamiliar domains with minimal documentation or context, (delivered in the form of platforms like a data catalog), to guide them. As a result, they must track down data domain experts to gain the necessary business context. This lack of domain familiarity extends the data discovery phase to three or four times its planned duration, jeopardizing project timelines.

Delays cascade, leaving teams with three difficult choices: extend deadlines, allocate more resources, or reduce project scope. None of these options are sustainable. The root cause is clear: domain knowledge is essential for improving productivity.

Learning from agile development

Curiously, agile development teams supporting core business applications operate differently. Each team consists of a product manager, engineers, and quality assurance personnel, making them self-sufficient and able to deliver business needs efficiently. They treat applications as products—and it works. So why not apply the same model to data?

A new product mindset for data

Organizations today are rethinking their data operating models, shifting to a product mindset. Treating data as a product can drive substantial benefits. According to Harvard Business Review, companies that adopt this approach:

Reduce implementation time for new use cases by up to 90%
Lower total ownership costs (technology, development, and maintenance) by as much as 30%
Minimize risk and improve data governance

By aligning data teams around data products, organizations can accelerate delivery, eliminate duplicate data assets, and enable deeper analysis and integration.

Faster data delivery through domain expertise

Domain knowledge is essential for efficiency. A data products team focused on a specific domain or set of domains eliminates the ramp-up time that has historically hindered centralized teams. Additionally, the team dynamic contributes to a more streamlined and effective process. Here’s why.

Before 2010, many organizations followed a waterfall approach, with a centralized team of database administrators and developers supporting multiple applications. No one was dedicated entirely to a single application.

When agile became the new standard, everything changed. A core principle of agile development is self-sufficiency. To meet this standard, centralized team members were embedded in specific application teams. The results were transformative: improved efficiency, faster delivery, happier team members, and consistent adherence to the database development standards the centralized team had created. Being part of a focused team that directly contributed to a product’s success proved far more fulfilling than simply being a resource to complete tasks.

Building a self-sufficient team to create data products has the same results. Team members work together to positively impact the business users (data consumers) and downstream AI systems and business applications they serve. They use their knowledge to deliver understandable, trusted data products that data consumers use to help drive positive business results.

Moreover, these teams benefit from agile practices, such as maintaining a backlog of requests prioritized by business users. This approach allows them to see the evolution of their data products and the creation of new ones, all while working with a value-first mindset. One of the most significant advantages of this model is the ability to establish a single source of truth, reducing the chaos that often comes with uncoordinated data efforts.

Eliminating duplicate data assets

One of the biggest challenges organizations face is managing duplicate data assets—dashboards, reports, tables, and more. These duplicates often lead to confusion and chaos. For instance, the finance team’s dashboard might show a different total number of customers than the customer management team’s dashboard. Over time, this inconsistency erodes trust in the organization’s data, prompting individuals to create even more duplicate assets, perpetuating the cycle.

Consider an example from a real Alation customer: A hospitality company faced conflicting dashboards showing different customer counts across departments, wasting company resources. A lack of trust in the data only worsened the problem, prompting leaders to rethink their approach. The solution? A shift to a product-oriented data operating model focusing on delivering reusable, well-governed, high-value data products.

This model helped the organization guide business users to the correct data while eliminating the noise caused by duplication. By establishing a marketplace of certified and reusable data products, they rebuilt trust, reduced risks, increased speed of delivery, and improved decision-making across the business.

The benefits go beyond efficiency. Trusted data products are becoming a critical foundation for agentic AI applications. For example, the hospitality company leverages AI to enhance customer experiences. Reliable, well-governed data ensures AI can make accurate and impactful decisions, driving better business outcomes.

Enabling deeper analysis and application integration

A data products operating model extends value beyond human users to business applications and AI systems. As time goes on, people will rely more on AI applications to analyze the data and provide them with streamlined insights.

This model relies on deep domain knowledge and fosters a closer relationship between business users and data producer teams. Continuous feedback and collaboration between these groups drive increased value and ensure the data products meet evolving needs quickly.

A key advantage of data products is enriched metadata, which includes:

Data quality scores
Refresh frequency
Data contracts
Security classifications
Sensitive data indicators

This metadata provides essential context, improving AI interpretability and decision-making. The richer the metadata, the more accurate and actionable AI insights become.

Industry trends underscore this shift. According to Grand View Research, the global autonomous AI and autonomous agents market is projected to grow at a Compound Annual Growth Rate (CAGR) of 42.8% from 2023 to 2030. This growth highlights the increasing reliance on AI to automate tasks, provide actionable information, and enhance customer experiences. Metadata will be the cornerstone of this transformation, enabling better, data-driven decisions.

Conclusion

The time has come to move beyond outdated data operating models and embrace a system where data products are delivered swiftly and reliably. By adopting a product mindset and leveraging domain knowledge, teams can create reusable, context-rich data products that empower people and AI to make faster, more accurate decisions.

Alation enables this transformation with a proven data product operating model, helping organizations:

Accelerate delivery – Align teams with domain expertise for faster data product creation focused on specific business objectives.
Eliminate duplicate data assets - Build trust with resuable, single-source-of-truth data products.
Enable deeper analysis and application integration – Provide foundational, metadata-rich data products that benefit human end-users, business applications, and AI.

Alation provides a comprehensive solution for building a data product marketplace, supported by an expert services team with deep expertise in implementing this model for data products. We help transform data chaos and duplication efforts into single-source-of-truth data products that are efficient, reusable, and valuable for the entire organization.

Discover how Alation can transform your data strategy into a powerful engine for efficiency and success: