5 Missteps for CDOs to Avoid on the Road to Success

We’ve all seen the data: Over 50% of chief data officers (CDOs) remain in their role for less than three years. But with the significance of enterprise data growing—and AI being deemed “the most transformational technology in a generation”—many are left wondering why turnover remains high for CDOs.

Despite being around for the better part of 20 years, the CDO role remains a mystery in many organizations. Promising, well-qualified CDOs assume the role, ready to tackle their organization’s data challenges. However, many of these data leaders struggle to show measurable results quickly, raising concerns among the C-suite about the value they deliver. Instead of doubling down on strategy, CDOs often find themselves prioritizing the administrative side of data management. They report on improvements in technical KPIs rather than their impact on business value drivers such as new revenue creation and cross-sell and upsell influence. This can be a costly mistake and further erodes perceptions among executive leadership. 

Instead, CDOs and aspiring CDOs should avoid common missteps so they can master their role and deliver the measurable business value expected of them. 

5 Missteps to Avoid (And What to Do Instead)

By steering clear of these five common pitfalls, CDOs can secure their place in the c-suite and deliver clear business value.

1. Leading with technology 

There’s no question that technology plays an important role when it comes to data management. However, when CDOs prioritize technology above all else, they limit their ability to act as a strategic partner to the business. 

Think about the last time you talked to a business stakeholder. Did they want to hear about the organization’s latest data management tool? Were they impressed by the new data governance platform you implemented? Odds are, they were not. And while tools play a critical role when it comes to delivering the high-quality, trustworthy data stakeholders need to make better decisions, delving into the latest technology innovations is not the way to win them over—especially if you are focusing on tech in the absence of a well-articulated data strategy.

What to do instead

Technology has a place in a CDO’s portfolio of work. But instead of starting the conversation with features or tools, lead with strategy. Talk about the value you add, the role of data in the context of organizational strategy, and how you’ll support the organization’s mission and strategic goals. Tell business leaders how you are going to solve a problem they are facing. Use storytelling, not “tech-speak,” to communicate. And show leaders that you are their partner, helping them to navigate the data-related challenges they face as they work to achieve their goals. 

2. Seeing data quality as a box to check

Data quality is a prerequisite for successful AI. But CDOs who believe that data quality is a one-time project that they need to complete before implementing AI solutions are setting themselves up for failure. 

What to do instead

While clean, trustworthy data is a prerequisite for AI, achieving and maintaining strong data quality for AI use cases requires CDOs to establish ongoing processes that continually assess, improve, and review enterprise data to ensure it is fit for purpose before they operationalize it. That’s the master data management (MDM) journey, and it’s key to ensuring that everyone across the business has access to trustworthy data in real time for decision-making and day-to-day systems use. 

When companies follow the MDM journey, they can confidently answer questions like “How many customers do I have?”, “Which providers deliver the specific set of services I need?”, or “What markets have the highest potential for revenue growth?” 

Steps of the journey

• Assess: Know where you are… and where you want to go
• Improve: Make your data trustworthy by cleaning and enriching it
• Review: Put your data in front of end users to gather feedback and build trust
• Operationalize: Turn your data into a mission-critical asset by connecting it to key business systems

‍CDO tip! Do you know where your organization is on its MDM journey? Take our short quiz to find out. 

3. Chasing “shiny-object” AI projects

AI adoption has become a top priority at many organizations, with “investing in responsible AI” frequently listed among their strategic goals. But all too often, in the rush to stay ahead, organizations chase the flashiest, newest AI innovations without assessing fit or feasibility. 

Instead of evaluating where AI can support business outcomes, CDOs may feel pressure to leap into deploying AI agents or other generative AI (GenAI) tools, driven by FOMO. This can cause them to select use cases that are risky, lack buy-in, or fail to deliver meaningful results.

What to do instead

Instead of chasing the next shiny object, CDOs should start where success is guaranteed. Look at use cases that are low risk; involve high volumes of readily available, trustworthy data; and have strong stakeholder buy-in. These use cases not only reduce financial, operational, and reputational risk, but are also more likely to demonstrate measurable business impact. 

4. Showcasing the wrong KPIs

Many CDOs regularly tout statistics such data accuracy, data freshness, number of dashboards, or incident response times. These are critical indicators of data health, but on their own, they often fail to resonate with business stakeholders or demonstrate impact at the strategic level.

What to do instead

To bridge the gaps between technical success and business value, CDOs should connect data health metrics to outcomes the business cares about. For example, highlight the return on investment (ROI) driven by incremental revenue growth and improvements in productivity. Link data points such as data accuracy and data availability to the broader strategic goals of the company. And show how adoption and use of data and data-related tools—and their benefits—are growing across the organization. 

5. Investing in bolt-on AI enhancements 

Everywhere you turn, companies are touting the AI features they are adding to their products. From chatbots and automation to recommendations and predictive analytics, technology firms are rushing to bolt on AI capabilities so they can check the box on being AI-enhanced. 

But this approach has downsides ranging from disjointed user experiences and narrow feature sets to added complexity, higher costs, and increased friction related to efficiency and effectiveness. 

What to do instead

Pursue AI-native solutions, which, in contrast, are purpose-built with AI at the core. This means all aspects of the solution—from architecture to workflows to user interfaces—tap into the full power of AI. These solutions are future-proof, providing the solid foundation needed to scale as AI capabilities evolve and change. 

Tamr’s AI-native MDM is an example of an AI-native solution that embeds AI at the core, delivering great efficiency and scalability. And CDOs who invest in AI data management and AI-native solutions like Tamr are setting their organizations up for success as we continue to navigate the ever-evolving AI era. 

The Path Forward for Today’s CDOs

Avoiding these common pitfalls isn’t always easy, but it’s necessary to elevate the important role that the CDO and enterprise data play in helping organizations reach—and exceed—their strategic goals.