Is Your Business Flying Blind? The Five Warning Signs of Unreliable Data
- Bailey Proulx
- Jul 21
- 7 min read
When Your Dashboards Lie: The Hidden Problem with Business Intelligence
Let me tell you about a pattern I see repeatedly in growing businesses. You invest in better reporting. You set up beautiful dashboards. Everything looks great on paper.
Then something goes wrong.
A client deliverable misses its deadline despite the project being "on track" in your system. Sales forecasts show a strong quarter, but actual closings fall short. Your marketing spend produces all the right metrics but leads aren't converting.
The strangest part? The reports aren't technically wrong. The dashboards work perfectly. But somehow, the decisions you make using this information keep missing the mark.
This isn't a technology problem. It's a data foundation problem. Your business is flying blind because you don't know if your data is reliable or where it's coming from.
The Two Questions That Determine If Your Data Can Be Trusted
Before you make any critical business decision based on your data, you need to answer two fundamental questions:
1. Do You Know Where Your Data Comes From?
Every number in your business has a journey. It starts somewhere (a form, an entry, a calculation), moves through various systems, gets transformed along the way, and eventually lands in your reports or feeds your automation.
This journey (lineage) – is rarely visible to business leaders. But without it, you have no way to verify if a number is trustworthy or track down problems when they occur.
For your data to be trustworthy, you need to see:
Where each critical number originated
How it transformed as it moved between systems
Who or what modified it along the way
Where else it's being used across your business
2. Do You Know If Your Data Is Healthy Right Now?
Even if you understand where your data comes from, you need to know if it's healthy and reliable at the moment you're using it to make decisions.
This health check (observability) goes beyond a one-time audit. It requires ongoing monitoring across five dimensions that tell you if your data is actually fit for use.
Without these health checks, you're making decisions on potentially compromised information. It's like driving with a dashboard that might or might not be showing your actual speed.
The Five Warning Signs Your Data Can't Be Trusted
There are five specific warning signs that your data might be misleading you. These warning signs serve as practical business indicators that something is wrong:
Warning Sign #1: Your Team Doesn't Update Information
When your team doesn't consistently update project statuses, client information, or sales data, your automation and reports work with partial reality.
The core issue centers on whether the data is actually being entered in the first place. Your systems might process information perfectly, but they can only work with what they receive.
For data to be trustworthy, you need:
Clear expectations for what information needs updating and when
Simple processes that make updates easy and consistent
Regular checks to confirm critical information is current
Automated data collection wherever possible to remove manual burden
Warning Sign #2: Volume Suddenly Changes
When you suddenly see more or less data than usual (many more leads, far fewer support tickets, a spike in project tasks), without there being direct correlation to a new rollout, it often signals that something broke upstream. These volume shifts are rarely visible without deliberate monitoring, but they're critical indicators that your data pipeline might be leaking information.
These volume shifts are rarely visible without deliberate monitoring, but they're critical indicators that your data pipeline might be leaking information.
For reliable data, you need to:
Know what normal volume looks like for key data points
Get alerted when volumes suddenly spike or drop
Understand what caused the change before making decisions
Warning Sign #3: Data Looks "Off" But You Can't Explain Why
Sometimes data passes all technical checks but still doesn't look right. Values drift outside normal ranges, null values appear where they shouldn't, or category distributions suddenly change.
These subtle shifts (distribution anomalies) – often indicate quality problems that formal validation won't catch.
For trustworthy decisions, you need to:
Track what normal patterns look like for critical metrics
Monitor unusual value distributions or unexpected nulls
Investigate significant pattern changes before trusting the data
Warning Sign #4: Structure Changes Without Warning
One of the most disruptive problems occurs when the structure of your data changes unexpectedly: new fields appear, formats change, or validation rules shift.
These structural changes, often caused by system updates or new configurations, can break your automations and reports while appearing technically correct.
For stable data, you need to:
Require sign-off before changing data structures used in critical processes
Get alerted when schemas change unexpectedly
Test how changes might impact downstream reports and automation
Warning Sign #5: You Can't Trace Problems to Their Source
When something goes wrong with your data, the most telling sign of trouble is how long it takes to find the source. If tracking down the root cause requires days of investigation, your data foundation has a critical weakness.
Without clear lineage, every data problem becomes a complex detective case rather than a straightforward fix.
For efficient problem-solving, you need:
Visual mapping of how data flows through your business
Clear ownership at each stage of transformation
The ability to see which reports and automations use specific data points
When Perfect Monitoring Creates New Problems
This pattern appears consistently across service businesses:
SYMPTOM: A consulting firm struggles with inaccurate project timelines. Projects regularly appeared on track in their system but still missed deadlines, creating resource conflicts and billing issues.
AUTOMATION: They implemented a sophisticated monitoring system that tracked every project metric imaginable – hours logged, tasks completed, milestones hit, budget consumed. It generated beautiful charts and sent timely alerts to project managers. The monitoring system worked perfectly from a technical perspective.
RESULT: While visibility improved dramatically, a new problem emerged: The dashboards showed all the right metrics moving in all the right directions, but projects still missed deadlines and exceeded budgets. Everyone was updating their tasks, logging their hours, and checking their boxes, but client deliverables still fell behind.
ROOT CAUSE: While they perfectly monitored activity metrics (the symptom), they never tracked whether people were entering meaningful status updates that reflected actual progress toward deliverables (the constraint). The system meticulously tracked that updates were happening but couldn't verify the quality or accuracy of those updates.
LESSON: Even perfect monitoring amplifies existing constraints rather than removing them. By first establishing what constitutes meaningful progress indicators and ensuring those were what got tracked, they could have built a system that monitored genuine project health instead of just activity logging.
The monitoring system executed flawlessly - it tracked exactly what it was designed to track. But it was designed to track the wrong things.
The One-Week Plan to Stop Flying Blind
You don't need enterprise-grade tools or data scientists to establish basic data reliability. Here's a straightforward plan any business can implement in a week:
Monday: Map Your Critical Data Path
Identify the 5-10 most important data points that drive your business decisions (client status, project timelines, sales forecasts, etc.). For each one, document:
Where it originates
What systems it passes through
Who modifies it along the way
Where it appears in reports or automation
Tuesday: Establish Health Checks
For each critical data point, define what "healthy" looks like:
How current should it be? (Freshness)
What's the normal volume to expect? (Volume)
What patterns or ranges are normal? (Distribution)
What structural elements must remain stable? (Schema)
Can you trace it end-to-end? (Lineage)
Wednesday: Assign Clear Ownership
For each critical data element:
Name a specific business owner (not a department) responsible for its quality
Establish who to call when something looks wrong
Define what constitutes an "incident" worth investigating
Thursday: Create a Simple Health Dashboard
Build a one-page view showing the status of your critical data points:
Are they up to date?
Is volume normal?
Do values look reasonable?
Has structure remained stable?
Can you trace problems to their source?
Friday: Run a Failure Drill
Simulate a data problem (like missing updates or unusual values) and see:
How long it takes to detect
Who responds to the issue
How quickly you can trace it to its source
What steps resolve it
The Questions That Ensure Data You Can Trust
Next time you're making decisions based on your data, ask these questions:
"How current and accurate is this information, and who last updated it?"
"If this number is wrong, can we immediately see where it came from and what affected it?"
"What checks verify that this data is accurate, not just present?"
"Who's responsible if this data point becomes unreliable?"
These questions shift focus from having data to having data you can actually trust.
Why This Matters to Your Bottom Line
The cost of unreliable data isn't theoretical. It directly impacts your business through:
Wasted time fixing problems that could have been prevented
Missed opportunities from delayed or incorrect decisions
Damaged client relationships from errors or inconsistencies
Lost revenue from flawed forecasts or resource allocation
Building basic data reliability starts with asking the right questions about the information that drives your most important decisions. You can establish strong data practices with simple, focused approaches tailored to your specific business needs.
Your Next Step: Creating a Shared Language Across Systems
Now that you know how to verify if your data is reliable, the next critical challenge is ensuring everyone in your business is talking about the same things. In our next post, we'll tackle how to create shared definitions across your different systems so your CRM, project management, learning platforms, and billing systems all recognize the same clients, projects, and offerings.
You'll discover how to eliminate the constant reconciliation work that happens when marketing's "customer," sales' "account," and billing's "client" are treated as different entities. This shared language is the foundation that makes your data not just reliable, but truly unified across your entire business.
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