Episode 6: Beyond The Policy: Fixing the Workflow : Iron Carrot

Season 5 Episode 6

Transcript

Welcome to Season 5 of the Law Firm Data Governance Podcast. I’m CJ Anderson from Iron Carrot, helping law firms do more with their data by improving their data governance. This season, we’re levelling up law firm data from intake to insight, with clarity, confidence and practical steps to move your firm’s data forward.

In this episode, I’m sharing why data governance only really works when it shows up in everyday workflows, right where your data is created. And what changes when firms stop writing policies and start shaping how work actually gets done?

A lot of law firms are investing seriously in analytics, pricing and AI-assisted tools across knowledge and client services right now. And when those initiatives stall, it’s rarely because the technology isn’t capable. What tends to get in the way is much simpler. missing fields, inconsistent reference data, or definitions that quietly drift between systems.

And that gap isn’t theoretical. It’s showing up as returned invoices, as pricing models that need manual overrides, as AI outputs that sound confident but pull from classifications that don’t quite line up. And what’s interesting is that none of that failure typically shows up as a data problem. It’s not on anyone’s job description. It’s showing up as rework. as awkward client conversations, as analysts rebuilding datasets that really should have been usable the first time around.

And this is why data governance only becomes effective when it meets people in the moment they’re already working, not through more documentation, but by shaping their workflow itself.

When you look across the law firms where data quality is genuinely improving, not just on paper but in day-to-day work, there are a few patterns that tend to show up. Not because those firms adopted a particular framework, but because the system itself nudged people towards better decisions.

The first of these is that they protect what’s born upstream. Most firms feel a strong pull to standardise everything at once, and that instinct makes sense because inconsistency hurts. But what we see over and over is that when everything is treated as critical, nothing really is.

What tends to work better is identifying the few data points that always travel downstream, the ones that show up again and again in reporting and pricing and billing and analytics and other systems workflows, and protecting those at the point they’re created. That usually starts with mapping a single journey that’s causing friction. Something like client intake or matter opening or time capture.

From there, firms define a small set of minimum fields and allowed values. Not everything, just the data that actually carries value forward. And that kind of focus creates consistency without creating form fatigue. It’s less about control and more about making downstream work possible.

Secondly, they start putting support into the flow of work. So another pattern that we see is a shift away from expecting people to read the policy first. Instead, help is showing up where the decision is being made.

That might look like reference data being surfaced as a lookup list instead of providing a free text field, or a short line of context next to a tricky field explaining why it matters so that people don’t skip over it. There’s a big difference between an error message that simply blocks progress field required, and one that explains the purpose. We use this to route work to the right pricing model, choose the closest fit. One of these stops work, the other supports work.

When data governance shows up in this way, it starts to feel less like compliance and more like the system doing its job, and people generally respond to that.

Thirdly, they close the loop with visible outcomes. So in firms where data governance sticks, rules don’t exist in isolation. Each one is tied to a payoff that people can feel. That might be fewer invoice returns because of missing data, faster matter opening times, or pricing models that run without human intervention.

Importantly, these improvements are made visible, not through long decks, but through small, clear signals, a single screen that someone can glance at and see that things are better than last month. And that visibility matters. It creates momentum. And momentum, in this kind of work, tends to matter more than perfection.

When firms take this approach, the rollout usually follows a fairly natural progression. Early on, the focus is on understanding where friction actually shows up, selecting one of these journeys, identifying the data that matters most, and being honest about where things break down.

From there, attention shifts to configuration, defaults, lookups, definitions, and light touch validation embedded into the systems that people already use. Often this is piloted in a single practice, where feedback is fast and adjustments are easier to make.

Once that’s in place, measurement starts to come into view. Small dashboards, tracking completeness or error rates, short remediation lists that can be cleared quickly and locally without escalation. And over time, the conversation changes. It moves away from theory and towards evidence. Fewer returns, smoother pricing, better downstream use of data. And by that point, governance isn’t being sold as an idea. It’s being shown in the outcomes.

When embedding data governance starts to feel slow or resistant, it’s usually because one of a few things has crept in. Sometimes it’s over-collection, making fields mandatory that don’t actually influence decisions or operations downstream. Sometimes it’s leading with policy instead of configuration and expecting behaviour to change before the system does. And sometimes it’s what you might call data council theatre, escalating issues that could be resolved locally, which slows everything down and drains energy.

The important thing is that none of these are personal failings, there signals that the system isn’t quite aligned yet. And the encouraging part is that most of these issues fade once data governance stops living in documents and starts living in data workflows.

Just like we talked about last episode, this isn’t about making people care more. It’s about changing the system they’re working in. When governance helps people do real work faster and with less friction, adoption stops being a change programme and starts becoming part of the job. That’s the quiet power of embedding data governance at the point where data is born.

Thank you for joining me for this Law Firm Data Governance podcast episode. If you want to see where your firm stands today and what to prioritise next, download the Law Firm Data Governance maturity benchmark at ironcarrot.com. or drop me a note and I’ll send you the report and a one-page action checklist.

If we haven’t connected yet, follow me on LinkedIn for weekly law firm data governance tips, insights and episode updates. You’ll find the link in the show notes. And don’t forget to subscribe so you don’t miss any of this season’s insights, or head over to ironcarrot.com to get in touch with your questions and ideas for future episodes.