Examples of ETD (Part 2)
Examining Engineering Data Creation and Management
One aspect of engineering data that frequently sparks debate is file naming and part number schemes. A quick web search yields countless discussions on significant vs. insignificant part numbering schemes, with opinions ranging widely. This contentious topic is likely to persist for years to come. However, the crux of the matter lies not so much in the specific numbering scheme a company adopts, but rather in its consistent application and avoidance of confusion or ambiguity.
It's crucial for organizations to establish clear and standardized conventions for file naming and part numbering, ensuring that all team members understand and adhere to them rigorously. Consistency in naming and numbering fosters efficiency, facilitates collaboration, and minimizes errors. Regardless of the chosen scheme—whether it's significant, intelligent, or otherwise—its effectiveness ultimately hinges on its consistent implementation and the clarity it provides to all stakeholders.
In the example on the right, we observe four columns of data: the file name, followed by three columns of metadata. It's evident that this system is burdened with technical debt. The file name, description, and part number exhibit significant overlap in their data, lacking specificity and failing to add value for downstream use or searchability for reuse. Additionally, the revision scheme appears inconsistent, and there are empty data fields further complicating the situation.
In this example, we also encounter four columns of data. The first column serves as the part number, also doubling as the file name. The second column contains clear descriptions, devoid of any part number ambiguity. A third column denotes the item type, indicating that these files represent fabricated items with a single reference file. Notably, the revision scheme maintains consistency, distinguishing unreleased files with a dash (-). This dataset is highly searchable and lacks ambiguity, facilitating efficient data utilization across the board.
Technical Debt
Technical Equity
The examples above provide a stark contrast between burdensome engineering technical debt and engineering equity. The second dataset stands out as significantly more valuable over time compared to the first. While the first dataset suffers from overlapping and ambiguous data, inconsistent revision schemes, and empty fields, the second dataset demonstrates clarity, specificity, consistency, and high searchability. In essence, the latter dataset represents a more efficient and effective utilization of engineering data, highlighting the importance of managing technical debt in engineering systems.
Next installment: Examples of ETD - Part 3, coming soon.