As you might imagine, much of the work I’m doing nowadays is with companies trying to leverage AI. Each company and use case is different, but a common issue is data quality.

So let’s take a look at why data quality issues arise, and how to prevent them…

Every data team has faced the frustration of discovering unreliable data after it has already shaped decisions. Poor data quality doesn’t just waste time; it damages trust in analytics and costs businesses money. The good news is that most issues are preventable with the right processes in place. Here are the most common causes of data quality nightmares and how to address them before they spread.

1. Inconsistent Data Entry/Creation

When data arrives from multiple sources and in various formats, there are often issues in the raw data (e.g. dates written differently, names misspelled, missing values, etc.). Such issues make analysis unreliable.

If you have some control over how the raw data is created, implement validation rules at the point of entry. For example, enforce drop-down menus instead of free-text fields in forms. This small step saves hours of cleanup later.

Suppose you don’t have any control over how the data is collected or created. In that case, you need to profile all of your raw data to generate complete metadata and understand what exactly is in your data.

2. Lack of Source System Alignment

Different systems often define fields in conflicting ways. A “customer” in CRM may include prospects, while finance may only count paying accounts. Without alignment, merged datasets become misleading. Solve this with a data dictionary and standardized definitions agreed upon across departments. Review these regularly to adapt as business terms evolve.

3. Manual Processes and Hidden Spreadsheets

Critical business data often lives in untracked spreadsheets, maintained manually by teams. These files bypass governance and quickly go stale. The fix is to automate ingestion from trusted systems and phase out uncontrolled spreadsheets. A lightweight data catalog helps track where information originates, ensuring visibility.

4. Inadequate Monitoring

Too many teams wait until reports look wrong to investigate quality issues. By then, damage is done. Instead, treat data like a production system: set up continuous monitoring. Track freshness, completeness, and accuracy with automated checks, and trigger alerts when thresholds are breached. This keeps small issues from snowballing.

5. Missing Data Ownership

If no one is accountable for specific datasets, errors linger. Establish clear ownership by assigning data stewards for key domains. Their role isn’t to “fix everything” but to define quality standards, oversee checks, and respond when issues arise.

The Takeaway.

Data quality problems are rarely the result of one bad query. They stem from weak processes, unclear responsibilities, and unchecked manual work. By standardizing inputs, aligning definitions, automating ingestion, monitoring continuously, and assigning ownership, data teams can eliminate recurring nightmares and build trust in the numbers that drive decisions.

What about you? How do you ingest and process your data to prevent data quality issues? Please comment – I’d love to hear your thoughts.

Also, please connect with DIH on LinkedIn.

Thanks,
Tom Myers