The Cost of Bad Data: How Errors Ripple Through the Business.

I recently provided corporate actions data to an asset manager who had recently missed a voluntary event due to bad data. In this case, the bad data caused a monetary loss. Depending on your industry or the systems relying on the data, the impact of bad data can be much worse.

So let’s explore how to prevent data errors before they escalate…

You and every data professional know that bad data is expensive, but the real cost often lies in how quickly errors multiply once they enter production. A single malformed record can ripple through analytics, models, and dashboards, creating misleading insights that drive poor decisions.

The cost is not just technical. Incomplete corporate actions negatively impact client holdings and trade processing. Inaccurate lead scoring wastes sales effort. Duplicated customer records inflate marketing budgets. Faulty supply chain data delays shipments and increases overhead. For many organizations, the price tag of bad data runs into millions annually, but the root causes often come down to weak processes and inconsistent governance.

Here are some steps you can take to prevent bad data from causing you problems…

Standardize data validation at the point of entry.

Most errors originate at ingestion. Teams should enforce schema validation, range checks, and type enforcement before data lands in storage. Automating these checks in ETL or ELT pipelines prevents downstream rework that consumes analyst and engineer hours.

Implement data profiling as a routine practice.

Regular profiling uncovers anomalies such as unexpected null rates, out-of-range values, or skewed distributions. When integrated into CI/CD workflows, profiling catches issues as soon as new data sources or transformations are introduced, reducing the likelihood of corrupted datasets powering dashboards or models.

Monitor pipelines with quality metrics.

Just as DevOps teams track uptime and latency, your data teams should track freshness, completeness, and accuracy. Establishing SLAs around these metrics creates accountability and ensures that data quality is treated as a measurable deliverable, not an afterthought.

Create feedback loops between data producers and consumers.

Analysts and data scientists often detect quality issues first. Without structured feedback loops, these insights rarely make it back to engineers or source-system owners. Lightweight issue-tracking workflows ensure problems are logged, prioritized, and resolved before they propagate.

Quantify and report the business impact.

The fastest way to gain executive support for data quality initiatives is to tie errors to dollars. For example, tracking the marketing spend wasted on duplicate records or the revenue lost from mispriced products due to bad input data gives leadership a concrete reason to invest in prevention.

The Takeaway.

Bad data will never be eliminated entirely, but disciplined processes can dramatically reduce its impact. For data teams, every hour spent preventing errors saves ten hours fixing them after the fact. The organizations that recognize this shift from reactive cleanup to proactive governance are the ones turning data from a liability into a competitive advantage.

What about you? How do you handle bad data and the errors it can create? Please comment – I’d love to hear your thoughts.

Also, please connect with DIH on LinkedIn.

Thanks,
Tom Myers