Data Reliability Checks — 10 LinkedIn Posts

Data Reliability Check #01

🔄 Refresh Monitoring

Data Reliability Check #01: Refresh Monitoring

A successful refresh does not always mean reliable data.

Reliability check:
Track:
refresh status
refresh time
duration
row counts
source availability
errors and warnings

Common mistake:
Only checking whether the dashboard opened.

BI impact:
Users may trust reports that are stale or partially loaded.

Key idea:
Reliability starts with knowing whether the pipeline actually ran.

More practical resources:
https://jobaqui.com
https://hialeahoficios.com
https://miamioficios.com

#DataReliability
#DataQuality
#BusinessIntelligence
#ETL
#DataEngineering
#DataAnalytics
#LearningByDoing

Data Reliability Check #02

🏗️ Source Availability

Data Reliability Check #02: Source Availability

Reports depend on source systems.

Reliability check:
Ask:
Was the source online?
Did credentials work?
Did the API respond?
Did the file arrive?
Did the schema change?

Common mistake:
Troubleshooting the dashboard while the source is the real issue.

BI impact:
A broken source can create missing or outdated KPIs.

Key idea:
A dashboard is only as reliable as its source.

More practical resources:
https://jobaqui.com
https://hialeahoficios.com
https://miamioficios.com

#DataReliability
#DataQuality
#BusinessIntelligence
#ETL
#DataEngineering
#DataAnalytics
#LearningByDoing

Data Reliability Check #03

🔢 Row Count Validation

Data Reliability Check #03: Row Count Validation

Volume is a reliability signal.

Reliability check:
Compare current row counts against normal patterns.

Look for:
sudden drops
unexpected spikes
zero rows
duplicate loads
partial files

Common mistake:
Accepting a load because it completed without error.

BI impact:
Partial data can look clean but mislead decisions.

Key idea:
Count the rows before trusting the report.

More practical resources:
https://jobaqui.com
https://hialeahoficios.com
https://miamioficios.com

#DataReliability
#DataQuality
#BusinessIntelligence
#ETL
#DataEngineering
#DataAnalytics
#LearningByDoing

Data Reliability Check #04

🧱 Schema Change Detection

Data Reliability Check #04: Schema Change Detection

Small schema changes can break big reports.

Reliability check:
Monitor:
new columns
removed columns
renamed fields
data type changes
new status values
changed relationships

Common mistake:
Treating schema changes as purely technical.

BI impact:
Measures, joins, and visuals may silently change meaning.

Key idea:
Data structure changes need impact review.

More practical resources:
https://jobaqui.com
https://hialeahoficios.com
https://miamioficios.com

#DataReliability
#DataQuality
#BusinessIntelligence
#ETL
#DataEngineering
#DataAnalytics
#LearningByDoing

Data Reliability Check #05

🕒 Freshness SLA

Data Reliability Check #05: Freshness SLA

Reliable data needs a freshness expectation.

Reliability check:
Define:
expected refresh time
allowed delay
late data threshold
owner to contact
escalation rule

Common mistake:
Saying “daily” without defining by what time.

BI impact:
Users cannot tell whether a report is current or late.

Key idea:
Freshness should be measured against an expectation.

More practical resources:
https://jobaqui.com
https://hialeahoficios.com
https://miamioficios.com

#DataReliability
#DataQuality
#BusinessIntelligence
#ETL
#DataEngineering
#DataAnalytics
#LearningByDoing

Data Reliability Check #06

🧬 Duplicate Load Detection

Data Reliability Check #06: Duplicate Load Detection

A pipeline can load the same data twice.

Reliability check:
Look for duplicate keys, duplicate files, repeated batches, and repeated timestamps.

Common mistake:
Only checking totals after aggregation.

BI impact:
Revenue, cases, transactions, and incidents can be inflated.

Key idea:
Reliable pipelines prevent double counting.

More practical resources:
https://jobaqui.com
https://hialeahoficios.com
https://miamioficios.com

#DataReliability
#DataQuality
#BusinessIntelligence
#ETL
#DataEngineering
#DataAnalytics
#LearningByDoing

Data Reliability Check #07

📁 Missing File Check

Data Reliability Check #07: Missing File Check

Many failures start with a file that never arrived.

Reliability check:
Verify:
expected file name
expected folder
expected date
file size
record count
delivery time

Common mistake:
Waiting for users to report missing numbers.

BI impact:
A missing file can create incomplete dashboards.

Key idea:
Check the arrival before checking the analysis.

More practical resources:
https://jobaqui.com
https://hialeahoficios.com
https://miamioficios.com

#DataReliability
#DataQuality
#BusinessIntelligence
#ETL
#DataEngineering
#DataAnalytics
#LearningByDoing

Data Reliability Check #08

🚨 Alerting and Escalation

Data Reliability Check #08: Alerting and Escalation

Reliability needs alerts, not surprises.

Reliability check:
Create alerts for:
failed refresh
late data
row count anomaly
source unavailable
schema change
high error rate

Common mistake:
Discovering problems during executive meetings.

BI impact:
Trust is damaged when users find failures first.

Key idea:
The system should tell you before users do.

More practical resources:
https://jobaqui.com
https://hialeahoficios.com
https://miamioficios.com

#DataReliability
#DataQuality
#BusinessIntelligence
#ETL
#DataEngineering
#DataAnalytics
#LearningByDoing

Data Reliability Check #09

🛠️ Recovery Plan

Data Reliability Check #09: Recovery Plan

Failures are normal. Recovery should be planned.

Reliability check:
Define:
who responds
what to check first
how to rerun
how to rollback
who to notify
how to document the incident

Common mistake:
Improvising every time data breaks.

BI impact:
Recovery delays create business confusion.

Key idea:
Reliable teams plan for failure.

More practical resources:
https://jobaqui.com
https://hialeahoficios.com
https://miamioficios.com

#DataReliability
#DataQuality
#BusinessIntelligence
#ETL
#DataEngineering
#DataAnalytics
#LearningByDoing

Data Reliability Check #10

🚦 Reliability Before Publishing

Data Reliability Check #10: Reliability Before Publishing

A dashboard is not production-ready until reliability is reviewed.

Checklist:
source stability
refresh schedule
row count checks
freshness checks
alerts
ownership
known limitations
recovery plan

Common mistake:
Publishing a dashboard before knowing how it will be monitored.

BI impact:
A report can be beautiful on day one and unreliable on day two.

Key idea:
Publishing is the beginning of reliability, not the end.

More practical resources:
https://jobaqui.com
https://hialeahoficios.com
https://miamioficios.com

#DataReliability
#DataQuality
#BusinessIntelligence
#ETL
#DataEngineering
#DataAnalytics
#LearningByDoing