#WhatCountsPodcast

Most counterparty data reconciliation projects fail at the same assumption: that one identifier — usually a tax ID — can resolve who you’re actually doing business with. In Episode 133 of What Counts, Maura Dunn walks through a real two-year project to reconcile 350,000 counterparty records across eight systems at a company built through acquisition: four contract management platforms, one ERP carrying both customer and supplier masters, and three trading systems, each with its own naming conventions, character limits, and overflow fields. She unpacks the 18 months of unproductive matching that came first, the rule-precedence approach that finally worked once Snowflake and Elasticsearch replaced the spreadsheet attempts, and the 10-to-1 collapse from 350K records down to 35K true entities. She also makes the case for where AI fits this kind of work today — and the one thing it still can’t do unless you put deep institutional knowledge into the prompt. If you want to see what’s hiding in your own shared drives right now, search TrailBlazer Insight in the Microsoft Store — it scans locally for PII, HIPAA, PCI, and other compliance risks with no cloud upload and no IT ticket required.

This episode picks up where Episode 132: Data Reconciliation Before AI left off — Maura delivers the full case study we teased last time.

Topics covered in this episode include post-acquisition contract data cleanup, duplicate counterparty detection across multiple CLM platforms, rule-based data matching at scale using Snowflake and Elasticsearch, and the role of AI in contract data reconciliation when source systems lack consistent identifiers.

Episode length: 00:21:01

0:00 – Pre-roll: TrailBlazer Insight — local compliance scanning for PII, HIPAA, and PCI

0:20 – Show intro

0:47 – Setting up the case study: 350,000 records across 8 systems

1:52 – How growth through acquisition created 7 (then 8) active counterparty sources

3:55 – Why the same legal entity can appear differently in every system

6:22 – The small business analogy: 4 addresses in 13 years

8:09 – The first 18 months: why tax ID matching failed at scale

10:48 – Name matching, character limits, overflow fields, and legacy system formatting

12:41 – Spreadsheet-by-spreadsheet spinning wheels

13:25 – The breakthrough: contract type bucketing + multi-variable matching

15:08 – Moving to Snowflake and Elasticsearch for rule-precedence matching

16:14 – Where AI could accelerate this today — and what it still needs from you

17:41 – The result: 350K records collapsed to 35K true entities

18:13 – What came out of all that work

19:49 – Teaser: next episode covers how to prevent this from happening again

What Counts is produced by TrailBlazer Consulting, LLC and hosted by Lee Karas and Maura Dunn. Learn more at trailblazer.us.com or email us at info@trailblazer.us.com. Explore compliance-ready training at the TrailBlazer Learning Academy. Read more from Maura at mauradunn.substack.com. Music by Jason Blake. Full disclaimer.

Episode 132 – Most organizations are being pushed to adopt AI-powered workflows before their data is anywhere close to ready. In this episode of What Counts, Maura and Lee pick up where they left off — diving deep into the concept of data reconciliation and why it must happen before AI ever touches your records. Using a real-world example built across multiple systems — a customer database, a work order system, and a contract management platform — they break down what it means to have duplicate, overlapping, and contradictory counterparty data, and why humans, not algorithms, are the ones who can resolve it. Maura introduces the concept of the golden record, explains the role of metadata mapping and fuzzy matching, and walks through the governance framework — policy, process, and system-of-record designation — that prevents the mess from coming back. If your organization is facing an AI initiative, a system migration, or growing pressure around data privacy and right-to-be-forgotten compliance, this episode gives you the foundational framework you need to start doing it right.

Episode length: 00:26:36

Learn more by visiting our website, or by sending TrailBlazer an email at info@TrailBlazer.us.com.

Episode 130 – When a company acquires another business, it inherits a tangled web of contracts — each with its own payment terms, clauses, and conditions. Contract one says 30 days, contract two says 45, and contract three says you never have to pay. So what happens when you point AI at this mess? In this episode of What Counts, we explore why contract data governance must come before AI deployment. AI is a powerful governance accelerator, but only when organizations harmonize their terms, configure their systems, and maintain human oversight every step of the way.

Episode length: 00:17:12

Learn more by visiting our website, or by sending TrailBlazer an email at info@TrailBlazer.us.com.

In this episode of What Counts, Lee and Maura move beyond diagnosing the tension between growth and governance and dive into what real solutions look like. From the “records police” stereotype to the engineering metaphor of an ungoverned engine ready to blow, they unpack why organizations struggle to balance speed with safety—and how cross‑functional coalitions can change everything. Through stories about generators, pencils, rogue sales promises, and the realities of legal, IT, compliance, and business teams working in silos, they reveal how governance becomes a true accelerator when everyone solves the problem together instead of alone.

Episode length: 00:13:12

To find out more about TrailBlazer Consulting, LLC, please visit our website at www.TrailBlazer.us.com.