What To Do When Your Data Is Erroring Out

One day it loads. The next day, it doesn’t. You’ve double-checked the logic. Yet the numbers are off, the chart won’t render, or your page times out. Before you can troubleshoot, someone’s already pinged you with, “Is this right?” Data errors don’t usually announce themselves. Most of the time, it’s a quiet break like a missing column, an unexpected value, or a null where something should be. 

These aren’t edge cases; they’re part of working with data systems that move fast, connect to many sources, and evolve in the background. It’s not about blaming the tool, the process, or the team. It's about recognizing the patterns and building instincts around what to check first when things feel off.

This blog post discusses the small warning signs, common causes, and fixes that can make data errors less disruptive. It's a working reference for real scenarios that surface every week across modern data teams.

The everyday causes of broken data

Data doesn’t fall apart all at once. It chips and sags. One small change, one overlooked assumption, and something quietly stops working.

A renamed column in a source system, an updated schema from an external API, or an analyst hardcoding a filter to last quarter’s values and forgetting to update it. These things break reports usually due to work moving faster than its documentation. These problems often appear in ways that don’t immediately scream “error.” 

For example, if a table in Snowflake drops or renames a column, your workbook might return a partial dataset. If an upstream calculation was edited without warning, you might not notice until your totals start drifting from another team’s version. In most cases, the underlying issue is a disconnect between what your workbook expects and what the source delivers.

Other culprits are more behavioral. A copy-paste job that overwrites validated cells, versions of the data model someone duplicated without version notes, or logic that worked when a table had ten categories, now struggles with fifty. These are quiet failures. Many of these issues happen because today’s data stacks are stitched together from live systems, cloud warehouses, and external platforms. Over time, the number of places where things can go wrong grows faster than the processes meant to keep them in check. That’s why understanding the common failure points is fundamental to situational awareness.

How to catch problems before your stakeholders do

The worst data error is the one you learn about from someone else. The damage is already done when a stakeholder flags a missing number or a misaligned chart. Confidence starts to wobble. Even if the error is fixed quickly, its memory sticks around longer than the fix. Preventing those moments requires early detection. That starts by noticing when your dashboards don’t behave like they used to. If something loads slower than usual, returns fewer rows than expected, or shows a number that feels out of step with what came before, those are worth pausing. They're not always bugs, but they’re almost always signals.

Some teams rely on regular gut checks. They open the same dashboards every morning and glance at the numbers. Others take it a step further, building "canary" views: small tiles designed to surface issues before they reach an executive report. These aren’t meant to look polished; their job is to break first. If your Canary dashboard is missing data or shows strange behavior, it’s your early cue to investigate. 

Automated checks can also catch what your eyes might miss. When paired with warehouse metadata, freshness indicators can flag when a source hasn’t updated on schedule. Row count tiles, set to compare against a baseline or last week’s total, help spot drops that would otherwise go unnoticed. Tools that monitor for missing values or abnormal spikes, especially in time series data, can provide an extra layer of awareness, without needing to dig manually.

That said, machines won’t catch everything. Make it easy for end users to flag odd results directly in the dashboard. A simple comment box or shared channel can create space for people to say, “This number seems off.” When reporting feels collaborative instead of locked, errors don’t linger in silence.

When data reporting breaks, how do you know where?

Some errors speak loudly, others just give you a vague sense that something’s off. In both cases, the work is the same: trace the problem back to its source without making it worse in the process. Panic is often the first instinct, especially if stakeholders are already waiting. When a report doesn’t match expectations, a table looks thinner than usual, or a metric that’s supposed to stay steady suddenly swings, it’s tempting to make a quick fix and move on. That approach might buy you time in the moment, but also makes it easier for the same error to resurface later in a new form.

Start by narrowing the field. Ask what’s changed in the data source, the logic applied to it, or the report's structure. If the dataset originates from a live connection, verify that the upstream table still exists, check if any fields have been renamed, and confirm that permissions have not been altered. Version history can provide clues about when something shifted, even if the changes look small. 

Once the surface checks are done, go one layer deeper. Use lineage tools to follow where the data flows, from source to dashboard. If you’re working across multiple workbooks or embedded content, open each one and test the queries individually. Look for patterns: queries that now return zero rows, filters that no longer apply, joins that fall apart because one side lost a category or two.

Sometimes, the break is caused by good intentions. A teammate may have cleaned up a data model, not realizing how many reports depended on the original structure. Or someone added a new field but introduced a default value that throws off averages downstream. In these cases, isolation is more valuable than blame. 

Recreate the issue in a test view using a smaller slice of data. That can confirm whether the error is structural or tied to a specific edge case. Collaborative debugging: shared screens, rerunning queries, and narrating assumptions aloud can be faster than troubleshooting in silence. A clear mind, a few diagnostic steps, and the willingness to look beyond the obvious can turn a vague issue into a fixable one.

How team behaviors introduce silent data errors

Sometimes the break begins with a habit like a shortcut that once saved time or a decision that made sense when the stakes were lower. These patterns don’t feel risky at the time. That’s what makes them hard to catch. For example, when multiple people share access to a workbook but treat it like a personal project, it becomes harder to trace changes. A filter gets adjusted for a one-time review, but no one resets it, or a column is renamed for clarity in one report, but the original name still feeds another. These minor edits pile up, and suddenly, a metric returns inconsistent values without anyone realizing why.

Versioning is another quiet culprit. If your team duplicates dashboards to test new logic but skips naming conventions or notes, those variations turn into silos. No one’s sure which version is “live,” so people start maintaining their own. Now you’ve got parallel dashboards showing similar numbers, calculated slightly differently. They’re not broken, but they don’t agree, either. 

Cross-team collaboration introduces another layer of complexity. A change made by someone upstream, like altering a source table or adjusting how a field is populated, might not get communicated downstream. If that change isn’t documented or flagged, you won’t notice until your dashboards stop making sense. Without data contracts or even lightweight expectations between teams, misalignment becomes routine.

None of this is about blame. It’s about recognizing the cultural patterns shaping your team's work with data. A good technical stack won’t protect you from inconsistent habits, unclear ownership, or accidental workarounds that linger longer than they should. Fixing the system means noticing how people behave when the system is under pressure.

Rethinking your response to failure

A data error isn’t the end of the world. It feels that way when it lands in your lap minutes before a meeting or after something goes live. The instinct is often to fix it fast and move on. While that might restore the numbers, it rarely addresses what made them fall apart in the first place. If the same types of errors keep showing up, it’s worth stepping back to study the break. What changed? Who else was impacted? Could this have been caught earlier with a small test or better communication?

Treating these moments like learning opportunities instead of clean-up jobs changes the tone of your data practice. It also makes it easier to spot patterns. Suppose several dashboards depend on a fragile lookup or a volatile source table. In that case, that’s a signal to stabilize that part of your pipeline, document the dependency, or rethink how that field is used. 

Even minor improvements matter. Add version notes when duplicating a workbook, comment in a formula cell explaining an assumption, or schedule a recurring check on a dashboard that tends to break after schema updates. These aren’t huge investments, but they reduce the need for crisis-mode debugging later.

The point isn’t to eliminate all errors; that’s not realistic. The point is to ensure that each one leaves the system stronger than before.

Clean data doesn’t just happen

Most data errors get fixed and forgotten. The numbers return to normal, the report refreshes, and attention shifts to the next request. But every one of those errors carries something worth holding onto. It tells a story about how your systems behave, how your team communicates, and what gets missed when pressure is high.

A quick pause after the fix can reveal more than any alert. What changed? Was the issue spotted early? Did it affect anyone downstream? Was it avoidable, or just bad timing?

That’s why clean data doesn’t happen behind the scenes. It grows from everyday behaviors: leaving a comment in a workbook, noting an assumption in a formula, checking freshness before presenting a chart. Sigma gives you the tools: version history, metadata awareness, and query review, but it’s your team’s attention that makes those tools matter.

Reliable data comes from many small acts done consistently, and when an error slips through, the real measure isn’t how fast it gets patched. It’s what you take from it and whether the system gets stronger afterward. 

Errors will always happen. What matters is what you take from the last one.

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