The Decline of Data Organizations: When Data Becomes Everyone’s Job
Young companies no longer create data teams, while older ones are reorganizing their data teams because the assumptions that once justified a centralized function no longer hold.
After several years living through the same pattern in many organizations, I came to the conclusion that in the most data-mature organizations, two things tend to happen:
First, dedicated data leadership positions eventually become unnecessary. Second, product, engineering, operations, marketing, and other functions increasingly absorb responsibilities that once belonged to specialized data teams.
In younger companies I recently consulted for, these shifts appear even earlier, with core teams assuming data ownership from day one and treating analytical capabilities as a native part of product development rather than an add-on function.
Let’s dissect each statement here to better understand why this is happening.
On the one hand, the main reason for data leadership roles disappearing is that when data fluency truly permeates executive and middle management layers, having someone who exclusively “represents data” at the leadership table becomes redundant. On the other hand, the everyday work of analysts, engineers, and scientists evolves as other roles, such as product managers and software engineers, assume greater ownership of experimentation, insights, and data quality within their domains. Finally, tools in the data space, as well as AI, make data management easier for non-data professionals.
And I believe this isn’t a failure. It’s simply the outcome of an evolutionary trend that has done precisely what it was meant to do.
In this article, I walk you through why companies end up in this pattern, how the data leader role evolves in data-mature organizations, and the implications for data professionals.
Understanding the Evolution
To grasp why data organizations reconfigure, we first need to understand how “old” companies got there, as most progressed through three distinct phases of data maturity.
In the first phase, organizations build their data warehouse. Business Intelligence (BI) teams create reports and dashboards for executives. Data serves primarily for descriptive analytics and financial reporting.
In the second phase, BI teams transform into data platform teams, with ML and AI scientists and engineers joining the crew, and even product analysts and analytics engineers. These professionals work in centralized teams, operating as internal service providers to the rest of the organization. They report to a Head of Data or a Chief Data Officer (CDO).
The third phase marks the transformation in which these centralized structures begin to break down. Organizations stop organizing by function and create cross-functional teams responsible for solving specific user problems or business opportunities. Data professionals embed within business and product teams rather than serving them from a separate unit.
Embedded data people typically adopt a matrix model: they do day-to-day work with product teams while maintaining a dotted line to a functional data manager (i.e., Data Science Manager) for career development and to maintain standards in their function.
However, many newer organizations never build a separate BI or central data team. Instead, product and business leaders who get data work run the core platform from day one (usually under Engineering or the CTO). At the same time, a small set of analysts use easy tools to turn questions into answers. That starting point changes how maturity looks: rather than moving from centralized to distributed, these companies often begin distributed and add central roles, such as functional data managers, only when complexity forces them to.
The New Home for Data Professionals
In mature, data-fluent companies, the “home” for data-specific roles (if they exist as standalone jobs!), such as analysts, analytics engineers, and data scientists, is usually the cross-functional team they support, not the data organization. Their calendar, priorities, and impact live with product and business teams; functional data leadership becomes a chapter lead role focused on craft, hiring, and career paths.
Aside from functional data managers taking on a lighter chapter role, mini data groups emerge inside departments, both in young and mature companies. Some organizations group embedded data people who support related business teams into small mini-data teams. These clusters sometimes have their own data lead, focused on the department’s needs. Their role is narrower and closer to the business, unlike the functional leadership roles of the past.
Meanwhile, the Engineering org often absorbs what remained centralized: the data engineers. Data engineering and the data platform are usually moved under the CTO. Their mission is to offer self-service tools, provide reliable core data, and support governance. They treat the data platform like a product with internal users.
Implications for the Head of Data role
As data literacy spreads, the Head of Data or CDO role changes or sometimes disappears. Where that role remains, it often pivots. In companies that did not create a Head of Data, these accountabilities are distributed across product and engineering leadership from the start.
This evolution creates serious career choices for data leaders. Once the Head of Data role (or CDO) becomes unnecessary, I’ve witnessed several paths open up:
Move into broader operational leadership. Data leaders with strong cross-functional experience transition into roles such as COO or head of product strategy, where they coordinate cross-departmental strategy efforts.
Lead the data platform as a product. Some focus on the centralized capabilities that remain: the data engineering and the data platform.
Become a domain product leader. In areas where data and ML/AI create differentiation, some leaders step into product leadership roles for those domains.
Leave the organization. This happens only when companies mismanage the transition or when leaders resist the shift. It’s not uncommon, and it’s a sign the organization didn’t plan well or moved faster than its people were ready for.
Three responsibilities must still exist
Even without a standalone data leader, three critical functions must have clear ownership. When organizations eliminate the CDO or Head of Data role, they sometimes assume these responsibilities will naturally distribute themselves. They don’t. Without explicit assignment, these functions atrophy, and the organization regresses to pre-transformation behaviors. The three responsibilities are:
Someone in Product Consistently Pushing for Data-Driven Decisions
This person, typically a VP of Product or Chief Product Officer, must actively challenge intuition-based roadmaps. They ensure product discovery and experimentation become standard practice. They ask questions like “What does the data tell us?” and “How will we measure success?”
The product leader must model data-driven behavior personally. They should reference metrics when explaining strategic choices. They should celebrate teams that pivot based on A/B test results.
Someone in Technology Championing Platform Investment
This person, usually the CTO or VP of Engineering, treats data infrastructure as a product with its own users, service level agreements, and continuous improvement cycles. They advocate for sustained investment in the data platform even when business pressure to redirect resources toward customer-facing features.
The technology leader must track platform health metrics and defend maintenance budgets. They also champion the evolution of platform capabilities. As the organization matures, self-service requirements grow more sophisticated. Teams need better tooling, faster data access, and more reliable governance mechanisms.
Someone Ensuring Data Literacy and Governance Continue to Rise
This person, often sitting in Product or Technology leadership, creates formal programs to build this capability. They establish structured data training programs that ensure data skills are systematically spread across the organization.
This responsibility includes governance oversight. As data ownership is distributed across domains, someone must ensure consistency in definitions, quality standards, and compliance practices. They establish cross-domain governance councils led by product teams.
Preparing for change as a Head of Data
If you are a data leader, you need to start being proactive and preparing for the future that I explained in this article, rather than being reactive. That’s why I recommend:
Raise data literacy fast. Give teams the skills and tools to be self-sufficient.
Develop new leadership skills. Grow in operations, product, or technical strategy so you’re ready for the next role.
Document your practices. Create standards and playbooks that survive without central enforcement.
Build cross-functional relationships. Your next role will rely on them.
I genuinely believe the future of data isn’t a data organization. It’s a company where every team uses data as naturally as they write code or talk to customers. Data analytics, data science, AI, etc., are core skills, not separate services or functions.
For data leaders, the challenge is to welcome this shift even when it means your own role will change.
Enjoyed this post? You might like my book, Data as a Product Driver 🚚.