I write as an independent founder and builder. Through Living Eden Frameworks, I have developed an automated structural-analysis engine (patent-pending) that reads record-based datasets and identifies structural properties of the kind central to this request. I offer this comment because the three properties the Commissions treat as central to an effective reporting framework, that data be non-duplicative and high-utility, consistent across reporting parties, and complete and accurate, are at root, structural properties of the reported dataset. Decisions about which data elements to harmonize, consolidate, or retire can therefore be grounded in structural evidence drawn from the reported data itself, not from a priori judgment alone. The engine surfaces three things directly relevant to this request: elements that carry no independent information because they are deterministically derivable from other reported elements; instances where the same underlying transaction is reported with divergent values across sources; and expected combinations of values that never appear, which can indicate a reporting gap. Three observations follow. 1. Duplicative and low-utility elements can be identified empirically. The request notes that frameworks generating large quantities of low-utility or duplicative information reduce the ability to draw insights. Which of the (up to) 128 data elements are in fact duplicative is an empirical question answerable from the reported data: an element that is a deterministic function of other reported elements imposes reporting burden without adding information. Such redundancies can be surfaced systematically rather than argued element by element. 2. Cross-party inconsistency is a measurable divergence. The collection of pertinent information from disparate counterparty systems has, as the request observes, produced inconsistent reports. Where the same economic transaction is reported by two parties, or to two repositories, with divergent field values, that divergence is detectable structurally; without field-by-field manual reconciliation. Standardized identifiers such as the UPI and UTI are precisely what make this cross-source consistency analysis tractable, and I would encourage the Commissions to continue and deepen that standardization, as it is the enabler of evidence-based integrity assessment. 3. Completeness gaps are structural voids. Integrity depends on completeness. Combinations of values that are expected to occur yet never appear in the reported data can flag conditions under which required data is not, in practice, being reported — a class of gap that aggregate quality metrics tend to miss. In response to Question 1 (which data elements would benefit from harmonization to minimize inconsistencies): I do not have access to the reported swap and SBS data, and naming specific elements responsibly requires that data. I suggest the most durable answer is methodological; that the Commissions, or the registered SDRs and SBSDRs, apply a structural redundancy-and-completeness analysis to representative reported data to identify, with evidence, which elements are duplicative, which are reported inconsistently, and where completeness gaps exist, and let that evidence guide harmonization. I would welcome the opportunity to assist in, or to demonstrate, such an analysis.