Why Business Operations Shouldn’t Fix Bad Data Quality

By J.M. Abrams, Chief Data Culturist – www.dataculturehivemind.com

Cleanup crew for bad data

As someone with a deep background in data and business operations, I've observed firsthand how business operations have long been burdened with inefficient processes to wrangle data—going back to the early days of business information technology.

Before computers were introduced into the workplace, all business operations were governed by manual processes. The slow adoption of information technologies and methodologies meant that most companies blended manual steps with emerging digital tools rather than transforming business processes. Even as IT systems advanced, the mindset around them didn’t. Instead of reimagining workflows from the ground up, many businesses adopted technology intended to augment their existing processes—flaws and all.

While some industries, like manufacturing, have fully embraced and integrated information technology into their operational DNA, others—particularly within segments of the medical field—still lag decades behind. The medical industry is estimated to be 20–30 years behind in adopting fully integrated, user-centered, and interoperable IT systems.

Despite the push from the 2009 HITECH Act for Electronic Health Record (EHR) adoption, complete digital transformation across healthcare remains inconsistent. Many smaller practices and underfunded systems still rely on outdated legacy platforms, paper records, or heavily siloed technology. The result? Persistent inefficiencies and operational workarounds driven by unreliable or poorly managed data—challenges that are amplified when business operations are left to compensate for the gaps.

In organizations where data is the business...

It's easy to overlook how quietly and consistently business operations teams absorb the fallout of poor data quality.

• They create extra steps to compensate for missing fields.

• They reconcile conflicting reports by hand.

• They design entire workflows to catch errors that should have never existed in the first place.

These aren’t signs of operational strength — they’re signs of a broken data culture.

Business Operations Has a Clear Purpose — and It’s Not This

At their core, business operations are about efficiency: optimizing processes, managing resources, and ensuring the smooth delivery of services or outputs. They are not about diagnosing and compensating for upstream data issues. Something has gone wrong when business operations become the clean-up crew for bad data. That burden doesn’t belong on their shoulders, and letting it settle creates a dangerous illusion of control.

Here’s what it leads to:

• Silent inefficiencies baked into everyday work

• Rising costs from repeated workarounds and validations

• Loss of trust in reports, metrics, and decision-making tools

• Culture drift, where people begin to expect and accept bad data as the norm

  
  

In a data-driven business, this isn’t just a workflow problem — it’s a business risk.

Patching Data Problems Isn’t Process Improvement — It’s Process Decay

Well-meaning business operations leaders often create workaround processes to compensate for poor data quality — new Standard Operating Procedures, extra reviews, reconciliation checkpoints, etc. While these seem like solutions at the moment, they quietly erode the very efficiency operations are meant to protect. Fixing the downstream process to work around upstream data issues is not sustainable. It erodes trust, causes a loss of time, and taxes your people. Over time, this leads to normalized dysfunction, where everyone works harder to keep things moving.

Data Is Critical Infrastructure — Treat It That Way

If your company’s value comes from data, then every process that touches that data must be built on trust and clarity — not workarounds.

That requires a data culture where:

• Business operations teams are data literate, not data fixers

• Governance is embedded, not patched

• Quality issues are escalated, not absorbed

• The organization prioritizes root cause over routine compensation

What Data Culture Looks Like

Healthy Vs. Unhealthy Data Culture

A mature data culture doesn’t ask business operations to “just make it work.” Instead, it builds clear ownership across roles:

• Data teams fix the source of truth

• Business operations design efficient processes with reliable inputs

• Everyone speaks a common language when it comes to quality, accuracy, and trust

When the culture is strong, everyone knows how to raise their hand when data isn’t behaving — and knows who’s responsible for fixing it.

The Real Cost of “Making It Work”

For companies whose sole asset is data, every minute spent compensating for poor quality erodes your core value. It’s lost time. It’s lost productivity. It’s lost customer confidence. And worst of all — it’s avoidable.

Let Business Operations Focus on What They Do Best

Business operations excel at driving process efficiency. Let’s not bog it down with the weight of bad inputs.

Instead:

• Fix the data at its source

• Invest in governance

• Empower every team with the literacy to raise the flag when data doesn’t make sense

Because when you protect the quality of your most important asset — data — you protect your ability to operate, innovate, and grow.

✅ Sidebar Callout: Transforming Your Data Culture Starts Here

Are you tired of business operations cleaning up after bad data? It’s time to shift the culture.

Start with these three moves:

• Spot the inefficiencies — Identify where data issues are slowing teams down.

• Define ownership — Clarify who’s accountable for data quality and governance.

• Level up literacy — Give teams the tools and language to recognize and escalate data issues.

These aren’t just technical fixes — they’re cultural upgrades.

👉 Want to go deeper? Discover more insights, stories, and frameworks at www.dataculturehivemind.com

Disclaimer: The opinions expressed on this blog are solely those of the author and do not reflect the views, positions, or opinions of my employer.