The Data Leadership Crisis: Why Generals Without Battle Experience Fail

Over my 30+ years career working in data and analytics across hundreds of organizations worldwide, I’ve noticed a troubling pattern—many senior data leaders, including Chief Data Officers (CDOs), Heads of Data, and even Data Transformation executives, often have little to no real-world experience in executing data projects.

They are in these roles because they are great at self-promotion, networking, or have strong academic credentials. Yet, they have never led a large-scale data implementation, managed a failing data project, or navigated the messy realities of integrating data across an enterprise.

I liken this to having generals in the armed forces who have never served in combat. They may look the part, know the theory, and sound impressive in the boardroom, but when it comes to actual results, they fall short. The worst part? The opportunity cost for organizations is enormous.

The Strategy-Execution Gap in Data Leadership

One of the biggest challenges organizations face is bridging the gap between data strategy and execution. Many senior data leaders are skilled in high-level vision-setting but lack the practical experience to implement that vision effectively. In addition, their lack of practice technical understanding means they easily believe the marketing hype. For example, I recently met with an SVP of Data & Analytics for a global retailer who was promoting the great advantages of a certain, well-known technology. It became very clear with 5 minutes of that conversation, that the person clearly had no real knowledge of the technology, believe the pure marketing spin, and was more focused on speaking at as many even as possible to promote themselves as a “thought leader”.

The result? Strategies that sound great in PowerPoint presentations, and presentations can be made to look amazing, but fail in real-world execution.

According to McKinsey & Company, 70% of large-scale transformation efforts fail, often due to a lack of clear vision and understanding of data & advanced analytics among executive teams.

Symptoms of the Problem

1. Overemphasis on Buzzwords and Trends.   Many leaders focus on the latest hype—Data Mesh, AI, Lake Houses, or a new cloud migration—without understanding the foundational challenges of making data accessible, high-quality, and trustworthy. We experienced this during the big data era when companies blindly poured capital into big data projects without known any of the basic fundamentals

   
   

A Forbes article on "The Failure of Big Data" highlights how leaders often chase trends without addressing core data issues, leading to initiatives that fail to deliver real business value.

2. Lack of Technical Credibility.   Data teams struggle to respect or align with leaders who don't understand the realities of data engineering, governance, or analytics. This creates friction and disengagement within the team. McKinsey emphasizes that top-performing organizations ensure that data leaders possess both business acumen and technical expertise, fostering a data-driven culture where data is accessible and practical.

   
   

3. Repeat Failures of Data Initiatives.   Many organizations cycle through new data strategies every 2-3 years, often because leaders make the same mistakes—underestimating complexity, ignoring governance, or prioritizing tools over business outcomes. As I mentioned earlier, we experienced this during the big data error, but over the last 10+ years I have experienced this in cloud migrations, and we are seeing the same now in AI.

Deloitte highlights that many analytics projects falter due to technical, behavioral, and organizational barriers, including challenges in integrating complex systems and managing data quality.

Accenture emphasizes that digital transformation efforts often fail when organizations apply "digital lipstick" on legacy IT systems instead of addressing fundamental skill and governance gaps.

Deloitte also warns that some organizations fall into a "buy it and the benefits will come" mentality, investing in new technologies without aligning them with clear business strategies, leading to wasted resources.

McKinsey & Company reports that over half of respondents say their companies' key data-management processes—such as ingesting, cleaning, and tracking data quality—are only somewhat automated, contributing to repeated project failures. Without full automation, inefficiencies persist, making it difficult for organizations to scale data initiatives effectively.

Final Thoughts

The crisis in data leadership is real, and the costs are high. Too many organizations are led by "generals" who have never been in the trenches, resulting in data strategies that sound great but fail in execution.

If companies want real results from their data investments, they need to prioritize leaders with practical experience—those who have built, broken, and fixed data systems. The future of the quality of the industry depends on it.

Join a Data Conversation,

Cameron Price

References:

• HR Dive: "Employers Want Communication Skills" (HR Dive)

• The Atlantic: "Meritocracy and the Shift in Practical Execution Skills" (The Atlantic)

• Business Insider: "C-Suite Expansion and Leadership Misalignment" (Business Insider)

• TIME Magazine: "The Reality of Ineffective Leadership" (TIME)

• McKinsey & Company: "Challenges in Data Automation" (McKinsey)

• Forbes: "The Failure of Big Data" (Forbes)

• Deloitte: "Common Barriers in Data Projects" (Deloitte)

• Accenture: "Digital Lipstick on Legacy IT" (Ardoq)

• Gartner: "High Turnover in Data Leadership" (Gartner)

• Candra McRae, Tableau Software: "Building Organizational Trust as a Data Leader" (Data Leadership Collaborative)