Digital Waste Has Real Costs
In model-based enterprises, data defines and drives every stage of the lifecycle. From concept and design through testing, production, and sustainment, digital work shapes how programs perform and evolve. However, many systems are built to collect data, not govern it. This often leads to large volumes of information that do not support outcomes. Over time, unmanaged complexity creates real costs. Teams lose time reconciling conflicting inputs. Engineers rework designs based on outdated data. Traceability gaps delay audits and increase certification risk.
To address this challenge, digital teams such as Digital Engineering, Manufacturing Engineering, and Configuration Management are applying lean principles to data with the same focus seen on the shop floor. Becoming “data waste warriors” means building practices and culture that maintain the value of data while limiting the noise, duplication, and delay that drag on performance.
Operationalizing Lean Data Practices
The most effective place to start is where data waste causes the most disruption. These are the points where confusion, delay, and rework interfere with execution. Common examples include unclear sources of truth for models, repeated migrations to keep systems aligned, and operational data collected without a defined purpose. Supplier documentation also creates friction when the same files live in multiple places with no clear owner or version control.
These are not isolated issues. They reflect patterns and structures that allow waste to persist. Progress comes from focusing on where fragmented data and misaligned systems drive real consequences.
Fighting Waste at the Source
Several areas stand out where A&D companies are making clear progress. Intake is one of the most direct levers. When teams bring in data without a clear plan for how it will be used or maintained, complexity grows fast. Setting purpose, ownership, and expected use up front helps keep the system clean and focused.
Reporting is another. Dashboards and metrics should earn their place. If a report no longer supports decisions or improvements, it may not need to be maintained. Reviewing reporting outputs regularly helps teams focus on what still drives action.
Cleanup is most effective when it’s part of normal work. Tying decommissioning to lifecycle milestones or system transitions helps ensure it happens at the right time—when ownership and relevance are already being reviewed.
System fit is critical. When teams are pushed into tools that don’t match their roles, it slows the work. People create workarounds, duplicate steps, or recheck data. Letting each team use the system built for their function reduces friction and rework. To support that flexibility, many organizations are adopting a best-of-breed approach—choosing purpose-built, industry-specific tools designed to interoperate through modern, standards-based architecture. This supports clean data flow across the lifecycle without relying on one system to do everything.
Shaping a Performance-Oriented Data Culture
Lean data practices require a shift in mindset. Many teams still measure value by volume—how much data is stored or processed. A better question is: What happens if the data is missing, late, or wrong? That answer should guide how teams prioritize, share, and maintain information.
Building trust in shared sources is key. When teams don’t trust what already exists, they rebuild it, adding duplication and delay. One way to improve trust is to observe how data is validated in practice. In Lean, this is called genba—the place where the work happens. Spending time with data validators can reveal which checks take time, where delays occur, and what could be automated or simplified. These insights are often hard to find in reports alone.
The DOWNTIME framework helps teams spot digital waste in a structured way. By naming the types of waste—defects, overproduction, waiting, and others—teams gain a shared vocabulary for identifying issues and improving the flow of information. (For more detail, see the previous blog on Lean Data Management.)
Teams making progress in this area are often simplifying systems, reducing noise, and improving reuse. These outcomes are just as important as launching something new—and deserve the same visibility.
Conclusion: Lean Data as an Enabler of Mission Outcomes
In aerospace and defense, digital teams are central to program success. As model-based engineering expands, the need for structured, reliable data grows with it. Precision, traceability, and speed depend on how well digital work is aligned.
Organizations are improving performance by embedding lean data practices into their operations. They define ownership at intake, use tools that support the work, and focus cleanup and reporting on what delivers value. By connecting purpose-built systems through modern architecture, they support flow without adding friction.
A data waste warrior culture brings lean thinking to digital execution. It supports faster decisions, better collaboration, and more consistent results across the lifecycle. Learn how Solumina enables digital teams through a purpose-built MES designed for interoperability, real-time execution, and data integrity across the enterprise.
