In business today, we often hear that data is one of the most valuable assets a company can possess. But is it really? This mindset creates a trap—one where data is seen as something static, a box checked off just because it’s collected and stored. But in truth, data’s value isn’t in having it—it’s in working with it, leveraging it, growing it, and making sense of it.
I’d argue that data is not an asset in the traditional sense. It’s not like real estate or inventory that just accrues value over time. Instead, data behaves more like a skill—it must be actively maintained, refined, and used to retain its relevance. Just as a skill becomes rusty without practice, data can deteriorate if left untouched, poorly managed, or disconnected from evolving business realities.
Allow me to challenge you to think differently about data—not as a static noun, but as an active verb. I’m going to invite you to rethink what it takes for your team to be truly “Data Ready” for your pricing transformation project.
Let’s start with common signs that your data is becoming rusty…
Telltale Signs of Data Rust
- The “You Had to Be There” Column
Without exception, every client has at least one data column that requires a history lesson. Ask a simple question like, “What does this column represent?” and you’ll be taken on a long, winding journey through the past. You’ll hear stories of what the column was originally meant for, how it evolved over time, and where it stands today. It’s rarely a satisfying story—more often than not, it ends with why the data is now unusable. Even the best-intentioned data points lose meaning over time, leaving behind confusing artifacts that only those who “lived through it” can understand. - The Exceptions That Disprove the Rule
Another telltale sign of data rust is when business rules unravel over time. What began as a clear and sensible rule—usually championed by someone who rallied the team—slowly gets buried under layers of exceptions. No one takes ownership of refining or maintaining the logic, and the result is a chaotic mess of “sometimes this, sometimes that.” The consistency and trustworthiness of the data erode, and any semblance of a reliable rule vanishes. - Tolerated Duplicates
Duplicates are sometimes tolerated rather than addressed, and a telltale sign is a customer name marked with “DO NOT USE THIS ONE.” It’s a workaround to avoid tackling the root cause of duplication. Instead of resolving the underlying issue, the business ends up with multiple versions of what should be a single entity—each going by different names and carrying different fragments of information. This fragmented approach makes it nearly impossible to reconstruct a coherent view, leading to confusion, inconsistent data, and missed opportunities for clarity. - Siloed Data
Data rust thrives in silos. When different departments—finance, sales, operations—maintain their own isolated data, critical connections get lost. This fragmentation results in incomplete and inconsistent narratives, leaving teams with outdated or mismatched information. If silos aren’t addressed, the entire business ends up operating with a fractured view of reality. - The “_Archive Folder” Syndrome
Another symptom of data rust is the “_Archive Folder”—where data goes to sit, untouched, indefinitely. Originally saved for “just in case” or with the hope it might be useful someday, these archive folders become digital dumping grounds. Instead of adding value, this hoarded data clutters the landscape, creating noise that makes it difficult to find what’s truly needed. It becomes a black hole of forgotten files—no longer serving a purpose, but too intimidating to delete because of the lingering fear that it might be important.
How to Avoid Rusty Data
- Adapting Data to Changing Business Needs
- Business strategies evolve, environmental factors shift. A sudden disruption—say, the supply chain crisis during the pandemic—requires new data sets, new types of analysis, and often a new lens altogether. Data that’s useful today could become irrelevant tomorrow if not properly aligned with the ever-changing demands of the business landscape.
- You can’t treat data as a static asset. It needs to flow, shift, adapt—to become part of how your business sees the world and responds to it. Organizations that actively engage with their data, that update, question, and evolve it, are the ones that stay competitive. When data stays connected to changing strategies, it’s a tool for navigation, not a relic rusting in the hold.
- Upskilling: An Ongoing Investment in People
- The bar for working with data keeps shifting. It’s not enough to just know your way around Excel anymore. R, Python, and the potential to leverage AI are minimum requirements for handling today’s data needs. But more than tools, what matters is fostering an environment where your team—people across functions—can develop their skills, have a platform to learn, and an opportunity to apply that learning to real-world business problems.
- This isn’t just about sending folks to a workshop. It’s about creating avenues for them to practice. Learning Python on its own isn’t enough—having the chance to write scripts to solve real business challenges makes the learning stick. And that’s what keeps your data fresh and relevant. As employees grow, your organization’s ability to harness its data grows too. Investing in training isn’t a cost—it’s the price of staying adaptable and innovative.
- Collaboration Across Teams
- Now, no one solves data problems alone. Business outcomes demand more than solo heroics; they require collaboration across teams and functions. Cross-functional alignment is where data’s real value comes to life. When Sales, Finance, and Operations all work off the same data management plan, they’re not duplicating work—they’re building on each other’s progress.
- That kind of collaboration also keeps everyone moving forward together. It’s what lets teams stand on each other’s shoulders, building toward a cohesive strategy rather than fumbling through duplicated efforts and misaligned plans. Data evolves best when it’s informed by multiple perspectives—when Finance shares insights with Sales, when Ops can see where the next major product release will impact forecasting. Collaboration prevents data from going rusty in isolated silos, ensuring it’s used in context, updated, and respected.
- Respecting the Process: The Foundation of Quality Data
- Data doesn’t take care of itself. It needs structure—solid processes that respect the importance of good data from the beginning. Consider something as foundational as new item setup. This is the first moment your data’s integrity is put to the test. Without processes that ensure the right information is gathered, recorded, and validated, you’ve set the stage for a future where your data is compromised, right from the outset.
- Processes like these aren’t exciting. They’re not front-page news. But they are the foundation on which data quality is built. Well-defined processes—and ongoing diligence in following them—mean your data is created, maintained, and kept accurate. It’s what keeps ‘data rust’ from spreading and lets your organization continue to adapt and grow.
- Data as a Culture: Seeking Objective Truths
- Beyond technology, beyond skills, and beyond even processes—maintaining data requires a culture. A culture where everyone is committed to seeking objective facts and using those facts to support decision-making, rather than relying on personalities, personal anecdotes, or outdated knowledge. It means fostering an environment where data-backed insights take precedence over individual opinions, ensuring that decisions are grounded in reality and can stand up to scrutiny. This cultural commitment is what keeps data accurate, relevant, and useful.
Data in Pricing Software Implementations
This perspective on data is especially important when it comes to pricing software implementations. Most companies feel a wave of anxiety about their data at the start of a pricing project. It’s understandable—data quality is crucial to success. Often, organizations think they need to ‘fix’ their data before beginning, as if it’s a one-time task that can be completed, checked off, and forgotten. But that’s a misunderstanding.
Rather than asking if your data is ready, ask if your people, your teams, your overall organization is ready for the transformation.
Data must be treated as a dynamic entity—something that grows, changes, and needs ongoing attention. It’s a skillset that evolves, a process that demands consistent nurturing, and a culture that requires the right attitude.