Data Governance Checks — 10 LinkedIn Posts

Data Governance Check #01

👤 Data Ownership

Data Governance Check #01: Data Ownership

Data governance starts with ownership.

Business problem:
Many datasets exist inside an organization, but nobody is clearly responsible for them.

When nobody owns the data, problems remain unresolved.

Example:

Dataset              | Current Owner
Customer Master      | Unknown
Employee Records     | HR
Sales Transactions   | Finance
Service Requests     | Operations
Vendor List          | Unknown

Governance check:
Ask:

Who owns this dataset?
Who approves changes?
Who defines the business rules?
Who answers quality questions?
Who decides who can access it?

Why it matters:
Without ownership, data quality problems become everyone’s problem and nobody’s responsibility.

Common mistake:
Assuming IT owns all data because IT stores or moves it.

BI impact:
A dashboard may show incorrect numbers, but no one knows who has authority to validate or correct the source.

Key idea:
Every important dataset needs a business owner.

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

#DataGovernance
#DataManagement
#BusinessIntelligence
#DataQuality
#DataAnalytics
#DataOwnership
#LearningByDoing

Data Governance Check #02

📘 Data Definitions

Data Governance Check #02: Data Definitions

A metric without a definition is a future argument.

Business problem:
Different teams may use the same word but mean different things.

Example:
What is an “active customer”?

Sales may define it as:
A customer with at least one purchase in the last 12 months.

Marketing may define it as:
A customer subscribed to email campaigns.

Finance may define it as:
A customer with paid invoices.

Governance check:
Create a business glossary.

Term              | Definition
Active Customer   | Customer with at least one completed purchase in the last 12 months
Revenue           | Net sales after discounts and returns
Open Case         | Service case not marked closed
Employee          | Active worker in HR system

Why it matters:
Dashboards become dangerous when the same metric means different things to different people.

Common mistake:
Building reports before agreeing on definitions.

BI impact:
Executives may see two reports with different “revenue” numbers and lose trust in both.

Key idea:
Before measuring, define.

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

#DataGovernance
#BusinessGlossary
#BusinessIntelligence
#DataAnalytics
#DataQuality
#Metrics
#LearningByDoing

Data Governance Check #03

🧭 Data Stewardship

Data Governance Check #03: Data Stewardship

A data owner sets direction.

A data steward protects daily quality.

Business problem:
Even when ownership exists, someone must monitor the practical details.

Example responsibilities:

Role              | Responsibility
Data Owner        | Approves business rules and access
Data Steward      | Monitors quality and definitions
Data Engineer     | Moves and transforms data
BI Analyst        | Builds reports and explains insights
End User          | Uses data for decisions

Governance check:
Assign data stewards for critical domains.

Examples:
Customer data
Employee data
Finance data
Operations data
Public service data
Vendor data

Why it matters:
Governance is not only policy.
It is daily care.

Common mistake:
Creating governance documents but assigning no one to maintain them.

BI impact:
Reports become outdated when no one maintains definitions, rules, field meanings, and known issues.

Key idea:
Good governance needs people, not just systems.

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

#DataGovernance
#DataStewardship
#BusinessIntelligence
#DataQuality
#DataManagement
#DataAnalytics
#LearningByDoing

Data Governance Check #04

🧬 Data Lineage

Data Governance Check #04: Data Lineage

If you cannot explain where a number came from, you cannot fully trust it.

Business problem:
A dashboard shows a KPI, but nobody knows the path from source to report.

Example lineage:

Source System
→ Staging Table
→ Cleaned Table
→ Data Model
→ Power BI Measure
→ Executive Dashboard

Governance check:
Document the path of important data.

Ask:

Where does this field originate?
What transformations happen?
What filters are applied?
What business rules are used?
Which report uses this field?
Who depends on this output?

Why it matters:
When numbers look wrong, lineage helps you trace the issue instead of guessing.

Common mistake:
Only documenting the final dashboard and ignoring the data journey.

BI impact:
A small transformation error upstream can affect many reports downstream.

Key idea:
Data lineage turns mystery into traceability.

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

#DataGovernance
#DataLineage
#BusinessIntelligence
#DataQuality
#DataAnalytics
#DataManagement
#LearningByDoing

Data Governance Check #05

🏛️ Master Data Management

Data Governance Check #05: Master Data Management

Some data should have one trusted version.

Business problem:
The same customer, employee, vendor, product, or location may appear differently across systems.

Example:

System      | Customer Name
CRM         | ABC Medical Center
Billing     | ABC Med Ctr
Support     | A.B.C. Medical
Excel List   | ABC Medical Center Inc.

Governance check:
Identify master data entities.

Common examples:
Customer
Employee
Vendor
Product
Location
Department
Asset
Student
Patient

Why it matters:
Without master data rules, duplicates and inconsistent names spread across reports.

Common mistake:
Trying to fix master data only inside dashboards.

BI impact:
The same customer may appear as four different customers, causing wrong totals, fragmented history, and poor decisions.

Key idea:
Master data should be managed at the source of truth, not patched endlessly in reports.

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

#DataGovernance
#MasterDataManagement
#BusinessIntelligence
#DataQuality
#DataManagement
#DataAnalytics
#LearningByDoing

Data Governance Check #06

🔐 Data Access Governance

Data Governance Check #06: Data Access Governance

Data governance and data security work together.

Business problem:
People need access to data, but access must match business responsibility.

Example:

Role              | Access Needed
Sales Analyst     | Sales totals and customer segments
HR Manager        | Employee records
Finance Analyst   | Revenue and expenses
Operations Lead   | Service requests
Public Viewer     | Aggregated open data only

Governance check:
Create access rules by role.

Ask:

Who should see this data?
At what level of detail?
Can they export it?
Can they edit it?
Should they see personal information?
Should access expire?

Why it matters:
Governance defines what is appropriate.
Security enforces it.

Common mistake:
Granting access case by case without a policy.

BI impact:
Reports may expose too much detail or give the wrong audience access to sensitive information.

Key idea:
Access should be governed by role, purpose, and level of detail.

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

#DataGovernance
#DataSecurity
#AccessControl
#BusinessIntelligence
#DataPrivacy
#DataManagement
#LearningByDoing

Data Governance Check #07

✅ Data Quality Rules

Data Governance Check #07: Data Quality Rules

Data quality should not depend on someone noticing a problem by accident.

Business problem:
Important datasets need defined quality rules.

Example rules:

Field             | Rule
customer_id       | Must not be null
order_id          | Must be unique
amount            | Must be greater than or equal to zero
status            | Must be an approved value
closed_date       | Cannot be before opened_date

Governance check:
Create quality rules for critical data elements.

Ask:

Which fields are critical?
What makes a value valid?
What makes a value suspicious?
Who reviews exceptions?
How often are checks run?
Where are issues documented?

Why it matters:
A rule turns a vague concern into a repeatable control.

Common mistake:
Saying “the data should be clean” without defining what clean means.

BI impact:
Dashboards may look polished but still be built on inconsistent, incomplete, or invalid data.

Key idea:
Data quality improves when rules become explicit.

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

#DataGovernance
#DataQuality
#BusinessIntelligence
#DataValidation
#DataAnalytics
#DataManagement
#LearningByDoing

Data Governance Check #08

🔄 Data Change Management

Data Governance Check #08: Data Change Management

A small data change can break many reports.

Business problem:
A source system changes a column name, status value, table structure, or business rule.

The dashboard may still refresh.
But the meaning may be different.

Example change:

Old status values:
OPEN
CLOSED
PENDING

New status values:
NEW
IN_PROGRESS
RESOLVED
CANCELLED

Governance check:
Create a change communication process.

Ask:

What changed?
When did it change?
Who approved it?
Which reports are affected?
Which users need to know?
Was historical data impacted?
Do definitions need updating?

Why it matters:
Data changes should not surprise analysts, report owners, or decision-makers.

Common mistake:
Treating data structure changes as purely technical changes.

BI impact:
A KPI can shift overnight because the definition changed, not because the business changed.

Key idea:
Every meaningful data change needs communication and impact review.

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

#DataGovernance
#ChangeManagement
#BusinessIntelligence
#DataQuality
#DataManagement
#DataAnalytics
#LearningByDoing

Data Governance Check #09

🏷️ Metadata Management

Data Governance Check #09: Metadata Management

Metadata is data about data.

Business problem:
Tables and columns often exist without context.

Example:

Column Name       | What does it mean?
cust_id           | Customer ID
dt                | Which date?
status            | Status of what?
amt               | Gross or net amount?
region            | Sales region or service region?

Governance check:
Document metadata.

Useful metadata includes:

Table description
Column description
Data type
Business meaning
Source system
Owner
Sensitivity level
Refresh frequency
Known issues
Allowed values

Why it matters:
Without metadata, every analyst has to rediscover meaning from scratch.

Common mistake:
Assuming column names are self-explanatory.

BI impact:
Reports may be built on misunderstood fields.

Key idea:
Metadata helps people use data correctly without guessing.

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

#DataGovernance
#Metadata
#BusinessIntelligence
#DataManagement
#DataQuality
#DataAnalytics
#LearningByDoing

Data Governance Check #10

🚦 Governance Before Reporting

Data Governance Check #10: Governance Before Reporting

A report is not ready just because it looks good.

Business problem:
Dashboards are often reviewed for design, performance, and visuals.

But governance questions are sometimes skipped.

Pre-reporting governance checklist:

Is the metric defined?
Is the source trusted?
Who owns the data?
Are quality rules documented?
Is sensitive data protected?
Is the refresh frequency clear?
Are filters and calculations understood?
Are users seeing the correct level of detail?
Is there a known limitation?
Who approves the final report?

Why it matters:
A beautiful report can still create confusion if the underlying governance is weak.

Common mistake:
Publishing first and asking governance questions later.

BI impact:
Bad governance can lead to conflicting numbers, low trust, wrong decisions, and rework.

Better approach:
Review governance before publishing.

Key idea:
A dashboard is not finished when it is visual.
It is finished when it is trusted, understood, governed, and useful.

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

#DataGovernance
#BusinessIntelligence
#DataQuality
#DataManagement
#DataAnalytics
#PowerBI
#LearningByDoing