Data debt: the hidden saboteur in RevOps

In today’s fast-paced RevOps environment, data is often seen as the fuel driving growth. But what happens when that fuel is contaminated? Enter data debt—the sludge clogging up your RevOps engine and slowing down your organization’s momentum.

While many leaders are familiar with tech debt, which can be addressed with a system overhaul or reset, data debt is a different beast. While tech debt involves the compromises made in software development that can eventually hinder progress, data debt refers to the costly and complex problems that accumulate from poor RevOps data quality and data management practices. And unlike tech debt, there’s rarely an option to start over with a clean slate. Data debt accumulates silently over time and slowly erodes your organization’s ability to make informed decisions.

Why data debt persists while tech can be reset

One of the unique challenges of data debt is its permanence. You can re-engineer code or declare bankruptcy on a failed tech stack, but data debt doesn’t offer a simple escape route. Whether through mergers, acquisitions, or the steady addition of SaaS tools, organizations end up with layers of data that need to be moved, transformed, and often painfully merged.

RevOps leaders need to understand that while tech debt can be resolved with a strategic overhaul, data debt lingers. The more outdated, incomplete, or incorrect the data you have, the more it costs your organization in the long run. Ignoring data debt isn’t an option—it’s a liability that can cripple your RevOps strategy if left unchecked.

The roots of data debt: SaaS and data hoarding

Data debt began its rapid accumulation with the rise of SaaS platforms. These tools promised to revolutionize go-to-market strategies by capturing vast amounts of engagement, behavioral, and operational data. While this data can offer valuable insights, the ease of acquisition often leads to unchecked data hoarding. As storage became cheap, organizations kept adding to their repositories without much thought about long-term management.

In the rush to collect as much data as possible, RevOps teams often didn’t stop to consider the consequences of storing everything. Now, like the thousands of photos we keep on our phones, our data is spread across numerous tools, with no easy way to sift through it all. This excess data becomes a burden rather than an asset, clogging systems and making it harder to extract actionable insights.

Symptoms of data debt: how to spot it early

If you’re wondering whether your organization is suffering from data debt, look for the following symptoms:

• Bad database quality: Your database has a growing percentage of outdated, irrelevant, or incorrect data.
• Data silos: Different teams or tools have isolated data sets that don’t communicate with each other.
• Duplicate or conflicting records: Your systems contain multiple versions of the same data, leading to confusion and errors.
• Slow report generation: The time it takes to generate meaningful reports is increasing due to the volume and poor quality of data.
• Questionable data origins: You’re not sure where all your data came from or whether you can trust its accuracy.

If any of these issues sound familiar, your data ecosystem is bloated with debt, making it difficult for your team to work efficiently and effectively—and putting your RevOps strategy at risk,

Consequences of ignoring data debt: efficiency, cost, and risk

Unchecked data debt has real-world implications for RevOps teams. First, it drags down efficiency. The more your team has to sift through irrelevant data, the more time they waste. This inefficiency isn’t just inconvenient; it’s also expensive. Data debt also poses a significant risk to your organization. With data spread across different silos and systems, the likelihood of security breaches increases, along with compliance risks.

And then there’s the impact on decision-making. RevOps data quality is the backbone of analytics and reporting. If your data is riddled with inconsistencies and inaccuracies, every decision based on that data is suspect. Poor data quality means your team is flying blind, which can result in missed opportunities, lost revenue, and a weakened competitive position.

Breaking the cycle: how to start tackling data debt

Fortunately, there are ways to contain and eventually reduce data debt. Here are three strategies to get you started:

1. Take control of data onboarding: One of the most effective ways to reduce data debt is to prevent low-quality data from entering your systems in the first place. Implement strict data onboarding protocols. Before importing new data, screen it for completeness, accuracy, and relevance. For example, if you’re loading a list of leads, ensure each record meets a minimum quality standard. Automate these processes wherever possible to scale with your data needs.
2. Create standards and normalize new data: Without data standardization, every data record is a unique snowflake that requires manual work to integrate. Define data standards for key fields like company size, industry, and engagement type, and enforce these standards consistently. By normalizing data, you reduce the workload on your team and streamline your reporting processes.
3. Establish and empower data ownership: Clear data ownership is critical to maintaining RevOps data quality over time. Establish a central data governance committee that can assign data ownership across departments. This committee should be as lean as possible to avoid bureaucracy while ensuring that data management responsibilities are clear. Empower data owners with the tools and autonomy they need to manage data in their respective areas effectively.

Data debt is here to stay – but you don’t have to let it grow

In the world of RevOps, data debt is an unavoidable reality. However, by recognizing its causes and symptoms and taking action to prevent its growth, you can minimize its impact on your organization. The key is to act now before data debt spirals out of control and undermines your entire RevOps strategy. Remember, while you can declare bankruptcy on tech debt, data debt is here to stay—and it will only get worse if you don’t tackle it head-on.

The future of RevOps strategy depends on the foundation you build today

Our new framework for RevOps data quality redefines data quality across three tiers—technical, operational, and strategic—to help you ensure that data isn’t just reliable, but also actionable and valuable. Ready to tackle your data debt and position your organization for a stronger future? Download our ebook now and start building a RevOps strategy that’s built to last.

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