#WhatCountsPodcast

Records findability starts at the point of creation

Trustworthy information has four attributes. It must be accurate, reliable, timely, and findable. Last episode covered the first two. This week, Lee Karas and Maura Dunn take on the other two.

The rule does not change. Capture the record where the work happens. Capture it when it happens. Capture it from the people doing the work. As a result, timeliness and findability follow along.

Why your file dates cannot be trusted

Open a shared drive and sort by date. The dates look authoritative. However, they are not.

Sometimes opening a document changes the date. The created date gives way to the modified date. Meanwhile, that modified date may only mean someone glanced at the file.

Auto-save makes it worse. Word saves before you rename your copy. Your manual version scheme then breaks quietly. It never runs at all in Microsoft Project or Visio. Even in Excel, it can miss when OneDrive has not finished logging in.

Timeliness is proof, not a timestamp

An approval date proves timeliness. So capture it deliberately. A workflow engine already knows who drafted, who commented, who approved, and when. Record that context anyway, because property values alone will not carry it.

Maura describes a client who did this on paper. Every requirements document carried five or six signature blocks. Each reviewer initialed and dated in ink. Then the team scanned it. An e-signature process gets you the same result today, with less effort.

Traceability at every step

Business requirement one becomes functional requirements 1.3, 1.4, and 1.5. That trace increases accuracy. The sign-offs at each step add timeliness.

Universe search versus precision search

Library science calls keyword search a universe search. You give it three words. In return, it gives you everything near those words. For example, ask for “training” and you also get “learning.”

The result set grows. The usefulness does not. Instead of the whole company, you now face a couple thousand documents.

A precision search works differently. First you set the file type. Next you name the business process. Then you name the contract or the facility. Now the found set is small enough to use.

Metadata is what makes records findable

Four elements matter most: function, process, record category, and trigger date or event. Those are real data, not keywords. Because of that, you can filter on them and build a collection. In other words, you get a virtual folder without a physical file plan.

Maura and Lee revisit an old FileNet argument on exactly this point. An IT lead wanted no folders and no file plan. He trusted full-text search to sort out billions of documents. He was partly right, but for the wrong reason.

Search, access, and retrieval

Findability has three parts. Search identifies potentially responsive material. Retrieval pulls it back so you can use it. Access decides whether you may open it at all.

Sometimes users can see that a record exists but cannot read it. Sometimes they cannot see it at all. That choice is yours, so make it on purpose.

Episode chapters

00:00 Timeliness and findability: the two attributes we still owe you
00:58 Why the date on the file lies: created versus modified
02:12 Auto-save versus manual versioning, and when OneDrive has not logged in yet
04:35 A tangent worth an episode: password management as a mapping problem
05:34 Capture at the point of creation, then make capture into storage
06:39 What the workflow engine already knows, and what you must record by hand
08:09 Ink initials and signature blocks: traceability from business to functional requirements
09:44 The universe search: three keywords, a million results, ranked by confidence
11:51 The precision search: file type, process, and facility narrow it down
12:58 Process metadata tags every file behind the scenes
14:27 The FileNet argument: “we will just use search”
15:24 Function, process, record category, and trigger as real metadata, not keywords
17:19 Contract records split: construction and non-construction retention
18:47 Collections as virtual folders, and knowing your repository before you commit
19:46 Search, access, and retrieval: three parts of one problem
20:39 Should users know a record exists if they cannot open it?

Closing

What Counts is produced by TrailBlazer Consulting, LLC and hosted by Lee Karas and Maura Dunn. Learn more or reach out directly at info@trailblazer.us.com. Explore compliance-ready corporate training programs at the TrailBlazer Learning Academy. Read more from Maura at Maura’s Substack. Music by Jason Blake. Full disclaimer.

Corporate data capture risk is a critical vulnerability that can completely disrupt an enterprise during sudden regulatory compliance audits or high-stakes litigation holds. Welcome back to What Counts, the podcast that digs into the messy information governance problems organizations inherit, ignore, and discover too late. In this episode, hosts Lee Karas and Maura Dunn move past theoretical concepts to break down exactly how unstructured file systems multiply your operational liabilities. Manual filing structures inevitably fail under heavy daily workloads, placing the burden entirely on your technology infrastructure to secure critical corporate history.

Clean dashboards and reliable data don’t happen by accident. In this episode of What Counts, Lee Karas and Maura Dunn pick up from the shift from records management to information governance and move into the three action principles that actually get you there: stop making copies, focus on data creation points, and touch once, use many times. They trace how society lost its mindfulness about creating data from Sumerian clay tablets to the printing press to the Federal Records Act and walk through a painfully familiar example of how a single shared link quietly multiplies into four copies. Along the way, they unpack the reliability paradox at the heart of information governance: the more copies you make, the less you can trust your data. It’s a candid, practical conversation about why these simple-sounding principles are so hard to follow, and why the tools we use every day keep reinforcing our worst habits.

New here? Start with the previous episode: Records Management vs. Information Governance.

Key Takeaways

The more copies you make, the less reliable your data becomes. People hoard copies because they don’t trust they’ll find the original later, which only makes the underlying data less trustworthy. It’s a paradox, but it holds.

We’ve lost our mindfulness about creating data. When information was expensive and hard to produce, more thought went into it. Now anyone can create and disseminate data in seconds, so almost none goes into it.

Three principles work together: stop making copies, focus on data creation points, and touch once / use many times. They’re simple to say, clear to understand, and genuinely hard to do.

Data creation deserves rules. Who’s allowed to create a new contract, location, or counterparty record? Where does it live? Who can change it, and how? Answering these up front prevents downstream chaos.

Acquisitions are data creation events. When data comes in through an acquisition, you need a crosswalk from old identifiers to new ones or you end up asking, “Where’s all the data for Armadillo Ranch?”

Bad data has real costs: failed audits, unanswerable litigation and e-discovery requests, duplicate survey spend, and revenue you can’t collect because you can’t prove your rights.

Your tools may be the problem. Email and collaboration platforms often reinforce copy-making habits or force workarounds, because the value of doing it right isn’t obvious in the moment.

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.