Key Takeaway: You don’t get better results by turning up the heat; you get them by preparing the ingredients. In IP operations, AI and analytics rely on clean, well-governed data. Without that foundation, advanced tools create noise, not insight.

Before we go any further, a quick clarification: this article is not really about bacon.

You don’t have to like bacon or eat it at all to understand the point. The metaphor works because it highlights a universal truth: good outcomes don’t happen by accident. Whether you’re cooking a meal, building a house, or modernizing an IP legal function, what happens before the finished product dictates the outcome itself. Skip the preparation, rush the process, or use poor ingredients, and the result will disappoint, no matter how advanced your tools are.

The phrase ‘data is the new bacon’ is a reminder that in IP legal operations, clean, well-prepared data is the foundational ingredient behind every shiny promise of AI, analytics, and automation. And right now, too many organizations want results without investing in the fundamentals.

The Sizzle Problem in Legal and IP Operations

Scan the legal technology landscape and the message is loud and clear: AI is everywhere. Whether it be predictive analytics, generative tools, real-time dashboards, or portfolio optimization engines, each new solution promises faster insights, better decisions, and competitive advantage. For IP departments in particular, the pressure is intense.

The implicit assumption is simple: we already have the data, now let’s do something impressive with it. And that’s where things start to break down.

Conversations about data in legal ops tend to focus almost exclusively on what data can power, not on whether that data is actually fit for purpose. Data quality gets a brief nod, often reduced to a familiar ‘garbage in, garbage out’ disclaimer, before attention swings back to algorithms, dashboards, and capabilities.

As a piece in Bloomberg Law put it, explicitly calling out this disconnect, data quality is ‘the overlooked foundation of successful legal AI,’ and even sophisticated algorithms consistently underperform when trained on poor-quality data. Yet, most organizations still focus on tools rather than the data beneath them.

Clean Data Is Not a Technical Footnote    

Most IP leaders would agree that clean data matters. Far fewer have a shared understanding of what “clean” actually entails, or the unavoidable trade-offs involved. Clean data isn’t created by installing a tool or handing the problem to IT. It results from deliberate decisions about ownership, process discipline, prioritization, and sequencing.

As recently as this month (as well as last September), contributors to the Forbes Technology Council have warned that AI and analytics initiatives fail not because the models are immature, but because organizations lack clear data ownership, governance, and accountability. The point of failure is often upstream in leadership decisions about what to prioritize, not in the technology itself.

The Illusion of “Ready” Data

Many IP departments believe their data is usable simply because it exists somewhere. Patent records live in a docketing system, financial data lives in its own system, and reports can be pulled across both. This creates the impression of cohesion, even though the underlying data may be fragmented across systems that aren’t fully connected or aligned.

Availability, however, is not the same as reliability. A widely cited Forrester study found that knowledge workers spend roughly 30% of their time searching for or reconciling information across disconnected systems. This is a hidden productivity tax driven by fragmented data, not a lack of tools.

Why AI Raises the Stakes

AI does not solve data problems; it exposes and often amplifies them. That is the misconception many organizations still carry into AI initiatives: if the data is messy, incomplete, inconsistent, or poorly governed, the technology will somehow smooth it out by itself. It won’t. Advanced tools depend on consistent definitions, structured inputs, and reliable historical records. When those conditions are missing, outputs range from underwhelming to misleading. In some cases, AI does not create a new problem so much as make an existing one impossible to ignore.

That is why AI raises the stakes. Weak data practices that may have been tolerated in manual reporting or fragmented workflows become far more visible once organizations try to automate insight or scale decision-making. What used to be a hidden operational issue becomes a credibility issue. 

Research summarized by Gartner and Forbes consistently shows that the majority of AI initiatives fail to reach production, with poor data quality and data readiness cited as primary drivers, not algorithmic sophistication.

The Lesson With or Without Bacon

You can embrace the metaphor or ignore it entirely; the lesson stands either way. You don’t get dependable results by skipping preparation or starting with poor-quality ingredients. Simply put, in IP legal operations, clean, well-understood data is what makes analytics, automation, and AI credible.

If leadership demands advanced outcomes without investing in the work that makes those outcomes achievable, the problem isn’t the tools or the teams. It’s the expectations and the priorities behind them.

Learn more about Epiq Legal Department and Law Firm Operations.


 Jennifer Karr, Senior Manager, IP Operations, Epiq Advisory
Jennifer Karr is recognized as a leader in legal operations and IP management with over 20 years of experience integrating law, technology, and business strategy. She has shaped industry standards through thought leadership roles, including serving as co-chair of CLOC’s IP Proficiencies Committee and LegalOps.com Vendor Management Committee. 

Jennifer has led transformative initiatives that accelerate adoption, improve data integrity, and enhance operational performance. With experience managing the entire IP lifecycle, she has a comprehensive understanding of how to strengthen alignment between people, processes, and technology.