Rethinking Productivity: Better Metrics for a Successful Data Strategy

In technical organizations, productivity is often seen as the holy grail of metrics. Leaders want to know: are we doing enough, fast enough? Unfortunately, measuring productivity in a meaningful way is nearly impossible. Metrics like lines of code written, number of commits, or tickets closed are superficial and often misleading, failing to account for the nuances of technical work. This is particularly true in the context of a data strategy, where the focus should be on long-term value, quality, and organizational efficiency rather than short-term outputs.

As we continue to strive for greater productivity, it’s crucial to measure the right things. Instead of attempting to quantify productivity directly, organizations should focus on three key indicators:

1. Decreases in Defects
2. Amount of Reuse Across the Organization
3. Time to Resolve Technical Debt

These measures collectively point to a more productive, efficient, and sustainable technical organization. Let’s explore each of them in detail.

Decreases in Defects

One of the clearest signals of improved productivity is a reduction in defects. Defects—whether in code, data pipelines, or dashboards—represent inefficiencies. They create rework, delays, and frustration, all of which diminish an organization’s ability to deliver value.

When defects decrease, it suggests that systems and processes are improving. This often correlates with better testing, more thorough code reviews, and a deeper commitment to data quality. Fewer defects mean that the team can focus on new initiatives instead of firefighting, enabling faster delivery and greater innovation. To measure this effectively, track:

• The number of defects detected per release or sprint.
• The severity of those defects (e.g., minor issues vs. critical failures).
• Trends over time, ensuring a steady decline in defect counts.

Amount of Reuse Across the Organization

Efficiency in a technical organization is closely tied to the ability to reuse existing work. Whether it’s reusing components, data models, or automation scripts, a culture of reuse eliminates redundancies, accelerates development, and standardizes processes.

For a successful data strategy, reuse can manifest in many ways:

• Standardized data pipelines or ETL workflows used across multiple projects.
• Reusable analytics models or pre-trained machine learning algorithms.
• Shared documentation, templates, and frameworks for consistent practices.

By measuring reuse, organizations gain insight into their ability to leverage past work and reduce unnecessary duplication. This metric also fosters collaboration and the creation of shared resources, breaking down silos and encouraging a more cohesive technical culture. To assess reuse, track:

• The number of projects utilizing shared assets or components.
• The frequency with which reusable frameworks or pipelines are adopted.
• Feedback from teams on the effectiveness and accessibility of reusable tools

Time to Resolve Technical Debt

Technical debt is an unavoidable reality in any organization, but how quickly it is resolved can speak volumes about productivity and efficiency. If resolving technical debt consistently takes years or is perpetually delayed, it indicates a systemic problem. Long-term delays in addressing debt often result in compounded inefficiencies, greater risks, and slower development cycles.

A key aspect of a successful data strategy—and any technical strategy—is maintaining a balance between new initiatives and the regular resolution of technical debt. Shorter resolution times demonstrate that an organization values long-term sustainability over short-term gains. To measure this effectively:

• Track the time it takes to resolve planned technical debt from the moment it’s identified.
• Monitor the backlog of unresolved technical debt, ensuring it doesn’t grow unmanageable.
• Ensure there is a clear plan and cadence for addressing debt in every sprint or project cycle.

A Holistic Approach to Measuring Success

By focusing on these three areas—defects, reuse, and technical debt resolution—organizations can move beyond the flawed notion of measuring productivity directly. Instead, they can measure outcomes that correlate with a healthier, more effective technical ecosystem. Fewer defects signal that systems and processes are improving, allowing teams to focus on creating value rather than fixing problems. Increased reuse demonstrates that the organization is working smarter, not harder, by eliminating redundancies and promoting collaboration.

Consistent resolution of technical debt ensures that the organization remains agile and avoids being bogged down by legacy issues. These metrics don’t just apply to data strategy—they are equally relevant for any technical organization striving for long-term success. 

By targeting these areas, leaders can create an environment where teams are empowered to work efficiently, deliver quality results, and sustain progress over time. When you stop chasing the impossible goal of measuring productivity and instead focus on these meaningful indicators, you’ll find that true productivity emerges naturally—through fewer bottlenecks, greater innovation, and a stronger foundation for growth.