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@@ -13,12 +13,12 @@
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               "icon": "lucide-file",
-              "title": "Weekly Review Template"
+              "title": "# Organizational Data & Analytics Operating Model"
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@@ -198,12 +198,14 @@
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diff --git a/20 Work/Ideas/# Organizational Data & Analytics Operating Model.md b/20 Work/Ideas/# Organizational Data & Analytics Operating Model.md
new file mode 100644
index 0000000..c4203da
--- /dev/null
+++ b/20 Work/Ideas/# Organizational Data & Analytics Operating Model.md	
@@ -0,0 +1,635 @@
+
+**Date:** 2026-07-22  
+**Status:** 💡 Idea
+
+## The Idea
+
+Build a company-wide **Data & Analytics capability** where trusted data is directly accessible to authorized departments, while each business unit retains ownership and expertise over its own business domain.
+
+The Data & Analytics team would not "own all company data." Instead, it would provide the common capability that makes organizational data:
+
+- Accessible
+    
+- Trusted
+    
+- Consistent
+    
+- Integrated
+    
+- Documented
+    
+- Governed
+    
+- Understandable
+    
+- Actionable
+    
+
+Each department would retain its business expertise and ownership, while working closely with a dedicated member of the Data & Analytics team who understands that department, its website, projects, objectives, and data.
+
+The long-term goal is not simply to build more Power BI reports.
+
+It is to build the organization's **measurement system**, allowing departments and leadership to understand organizational performance from trusted data.
+
+## Problem / Opportunity
+
+The current organization has a relatively new data culture and historically operates through departmental silos.
+
+There is currently ambiguity around:
+
+- Who owns data
+    
+- Who can access data
+    
+- Who interprets data
+    
+- Who defines KPIs
+    
+- Who distributes information
+    
+- Which reports are authoritative
+    
+- Who decides whether performance is good or bad
+    
+
+Marketing has historically positioned itself as an intermediary for organizational analytics. Departments may be expected to go through Marketing to obtain or interpret their own performance data.
+
+This creates a risk of **information gatekeeping**.
+
+For example, even when centralized reports are available to departments directly, Marketing may prefer that departments request information through Marketing first.
+
+This model creates unnecessary dependencies:
+
+```text
+Department
+    ↓
+Marketing
+    ↓
+Data / Reports
+    ↓
+Marketing interpretation
+    ↓
+Department
+```
+
+It also creates a potential conflict when data reveals uncomfortable results.
+
+A recent cross-platform video analytics report made previously difficult-to-see performance information directly visible. Some departments were resistant to having poor performance exposed to upper management.
+
+This demonstrates why business ownership should not automatically mean control over whether organizational performance information can be seen.
+
+Another issue became clear when the VP asked:
+
+> "What are the KPIs?"
+
+The report successfully solved the **data availability problem**, but the organization had not yet clearly established which metrics actually indicate whether video performance is succeeding or failing.
+
+This represents an opportunity to move beyond reporting toward true Data Governance and performance measurement.
+
+## Why It Matters
+
+A strong Data & Analytics operating model would reduce departmental silos and make trusted information directly available to the people who need it.
+
+Instead of asking:
+
+> "Who controls the numbers?"
+
+the organization should be able to ask:
+
+> "What do the numbers tell us, and what should we do about them?"
+
+The business value includes:
+
+- Better executive decision-making
+    
+- Greater transparency
+    
+- Less information gatekeeping
+    
+- Consistent KPI definitions
+    
+- Increased trust in reports
+    
+- Faster access to information
+    
+- More effective self-service analytics
+    
+- Better collaboration between Data and business departments
+    
+- Better understanding of organizational performance
+    
+- Reduced duplication of reports and analysis
+    
+- Less dependence on individual departments to distribute information
+    
+- Better accountability when performance is poor
+    
+- Cross-departmental visibility
+    
+
+The long-term objective is for leadership to understand the health of the entire organization using trusted data.
+
+## Who Benefits?
+
+- Executive leadership
+    
+- News
+    
+- Education
+    
+- Marketing
+    
+- Finance
+    
+- HR
+    
+- IT
+    
+- Legal
+    
+- Digital / Product
+    
+- Other business departments
+    
+- Data & Analytics team
+    
+
+Ultimately, every department should benefit from having direct access to trusted information and an analytical partner who understands its business.
+
+## Possible Approach
+
+### 1. Business Domains Own Their Business
+
+Departments retain ownership of their respective business domains.
+
+Examples:
+
+```text
+HR          → HR / Employee domain
+Finance     → Financial domain
+Marketing   → Marketing domain
+News        → News domain
+Education   → Education domain
+IT          → Technology / Operations domain
+Legal       → Legal / Compliance domain
+```
+
+Business ownership means departments understand their operations and participate in defining what their business metrics mean.
+
+It does **not** automatically mean that they control who may see organizational performance information.
+
+Ownership, access, reporting, interpretation, and governance are separate responsibilities.
+
+### 2. Data & Analytics Provides the Shared Capability
+
+The Data & Analytics function should be responsible for establishing and maintaining the organization's shared analytics capability.
+
+Responsibilities could include:
+
+- Data integration
+    
+- Analytics architecture
+    
+- Power BI / reporting
+    
+- Data quality
+    
+- Data lineage
+    
+- Metadata
+    
+- KPI documentation
+    
+- Shared definitions
+    
+- Cross-domain analytics
+    
+- Analytical methodology
+    
+- Self-service analytics
+    
+- Data access implementation
+    
+- Data governance standards
+    
+
+The objective is not:
+
+> "All data belongs to Data & Analytics."
+
+Instead:
+
+> **Business units own their business domains. Data & Analytics makes organizational data trustworthy, integrated, consistently measured, understandable, and accessible to authorized users.**
+
+### 3. Dedicated Data / Technology Partners
+
+Each department already has a dedicated team member who works closely with it on its website, projects, technology, and evolution.
+
+This relationship should evolve into an **embedded Data / Technology partnership**.
+
+```text
+                   Data & Analytics
+                         │
+          ┌──────────────┼──────────────┐
+          │              │              │
+      Team Member    Team Member    Team Member
+          ↕              ↕              ↕
+        News         Education      Other Dept.
+```
+
+These team members should develop enough domain knowledge to understand and interpret the department's analytics.
+
+They should not merely provide numbers.
+
+They should be able to:
+
+- Explain what happened
+    
+- Identify patterns
+    
+- Investigate changes
+    
+- Challenge assumptions
+    
+- Understand the department's digital environment
+    
+- Connect technical events with analytics
+    
+- Recommend further investigation
+    
+- Work with departmental experts to understand why something happened
+    
+
+The business department still retains accountability for business decisions.
+
+### 4. Shared Interpretation
+
+Interpretation should not automatically belong to Marketing.
+
+Instead:
+
+**Data & Analytics asks:**
+
+> What happened?
+
+> Where did it happen?
+
+> What patterns exist?
+
+> Is the data reliable?
+
+**Data + Department together ask:**
+
+> Why did it happen?
+
+The department contributes business context because it understands its operations.
+
+The embedded Data/Technology partner contributes analytical and technical expertise because they understand both the data and the department.
+
+**Department / Leadership decides:**
+
+> Is this performance acceptable?
+
+> What should we do about it?
+
+Marketing can contribute Marketing expertise where relevant, but should not automatically become the interpretation layer for News, Education, or other departments.
+
+### 5. Self-Service by Default
+
+Authorized users should not need to request basic information from Marketing or Data if trusted reports already exist.
+
+The desired model is:
+
+```text
+             Trusted Data & Analytics
+                      │
+        ┌─────────────┼─────────────┐
+        ↓             ↓             ↓
+      News        Education      Marketing
+        ↕             ↕             ↕
+  Data Partner   Data Partner   Data Partner
+```
+
+Not:
+
+```text
+Department → Marketing → Data → Marketing → Department
+```
+
+The goal is not to replace Marketing as the gatekeeper with Data as the new gatekeeper.
+
+The goal is to **remove unnecessary gatekeeping entirely**.
+
+### 6. Establish KPI Governance
+
+Reports should distinguish between:
+
+**Metrics**
+
+Numbers that describe what happened.
+
+and:
+
+**KPIs**
+
+Metrics specifically selected to determine whether the organization is succeeding against an objective.
+
+For example, a video analytics report may contain dozens of metrics.
+
+Leadership may ultimately decide that only a small number represent organizational success.
+
+The process should become:
+
+```text
+Business Objective
+        ↓
+Agreed KPI
+        ↓
+Business Definition
+        ↓
+Documented Calculation
+        ↓
+Trusted Data Source
+        ↓
+Power BI / Analytics
+        ↓
+Department Interpretation
+        ↓
+Leadership Decision
+```
+
+Data & Analytics should facilitate this process but should not independently invent corporate KPIs.
+
+Questions to establish for important KPIs:
+
+- What business objective does this KPI measure?
+    
+- Who owns its business definition?
+    
+- How exactly is it calculated?
+    
+- Which systems contribute data?
+    
+- What are its limitations?
+    
+- How frequently is it refreshed?
+    
+- Who may access it?
+    
+- Who approves changes?
+    
+- Where is the authoritative version?
+    
+- What supporting metrics explain changes?
+    
+
+### 7. Position Marketing's Data Analyst Correctly
+
+Marketing having a Data Analyst is not inherently a problem.
+
+The important question is the analyst's mandate.
+
+A Marketing Data Analyst could appropriately specialize in:
+
+- Marketing campaigns
+    
+- Acquisition
+    
+- Audience segmentation
+    
+- Marketing effectiveness
+    
+- Advertising performance
+    
+- Marketing KPIs
+    
+
+The concern would be if the role evolves into:
+
+> The organizational analyst responsible for interpreting News, Education, Digital, Streaming, and other departments.
+
+Marketing should be one business domain within the broader Data & Analytics ecosystem, not the mandatory gateway to organizational information.
+
+Do not make the organizational argument:
+
+> "Marketing shouldn't have an analyst."
+
+Instead establish the broader model in which that analyst naturally becomes a **Marketing domain specialist**.
+
+### 8. Expand Beyond Audience Analytics
+
+The current video analytics work can become the foundation rather than the final destination.
+
+Long term, gradually integrate authorized information from additional domains.
+
+```text
+                 ORGANIZATIONAL HEALTH
+
+ Financial      Audience       People       Operations
+     │              │             │              │
+ Revenue          Reach        Headcount        SLA
+ Budget          Viewing       Turnover        Uptime
+ Costs          Retention       Hiring         Delivery
+     │              │             │              │
+     └──────────────┴──────┬──────┴──────────────┘
+                           ↓
+                  Executive Decisions
+```
+
+Potential domains include:
+
+**Finance**
+
+- Revenue
+    
+- Budget
+    
+- Actual vs forecast
+    
+- Costs
+    
+
+**HR**
+
+- Headcount
+    
+- Hiring
+    
+- Turnover
+    
+- Workforce trends
+    
+
+**IT**
+
+- SLA
+    
+- Incidents
+    
+- Uptime
+    
+- Project delivery
+    
+- Service requests
+    
+
+**Legal**
+
+- Compliance indicators
+    
+- Contracts
+    
+- Rights / licensing where applicable
+    
+
+**Audience / Content**
+
+- Reach
+    
+- Consumption
+    
+- Engagement
+    
+- Retention
+    
+- Content performance
+    
+
+The goal is not one enormous Power BI dashboard.
+
+The goal is an interconnected **organizational data ecosystem**.
+
+### 9. Respect Sensitive Data Ownership
+
+Expanding into HR, Finance and Legal should not mean requesting unrestricted access to everything.
+
+These departments may legitimately need strict controls around sensitive information.
+
+Instead:
+
+> The department retains ownership and appropriate access control while participating in the organization's governed analytics ecosystem.
+
+For example, HR may expose approved workforce metrics without exposing individual salaries or unnecessary personal information.
+
+This reduces resistance and avoids presenting Data & Analytics as an attempt to take control away from departments.
+
+## Effort
+
+**Estimated complexity:** High
+
+This is not primarily a Power BI or technical project.
+
+It requires:
+
+- Organizational change
+    
+- Executive sponsorship
+    
+- Data Governance
+    
+- Departmental cooperation
+    
+- Clear responsibilities
+    
+- Trust
+    
+- Political navigation
+    
+- Technical architecture
+    
+- Gradual cultural change
+    
+
+Implementation should therefore be incremental rather than presented as one large transformation project.
+
+## Risks / Questions
+
+- Marketing currently has strong relationships with the VP and CEO.
+    
+- Marketing has already received approval to hire a Data Analyst focused on interpretation without consultation with the existing Data & Analytics function.
+    
+- Marketing may perceive direct self-service analytics as reducing its organizational influence.
+    
+- Departments may resist transparency when data reveals poor performance.
+    
+- HR and Finance may resist participation because of concerns around control and sensitive information.
+    
+- Departments may interpret "central Data & Analytics" as an attempt to take ownership of their data.
+    
+- KPI ownership is currently unclear.
+    
+- Leadership may not yet recognize the distinction between metrics and KPIs.
+    
+- There may be no formal Data Governance mandate.
+    
+- Data access and business ownership may currently be confused.
+    
+- Embedded team members need sufficient analytical and business knowledge to become credible partners.
+    
+- Who ultimately resolves disagreements over KPI definitions?
+    
+- Who should sponsor Data Governance at the executive level?
+    
+- How should cross-department KPIs be owned?
+    
+- What should the formal mandate of Data & Analytics become?
+    
+
+## Next Step
+
+Use the existing **cross-platform video analytics report** as the first practical example of this operating model.
+
+Do not initially attempt to restructure the organization or challenge Marketing's analyst position.
+
+Instead, follow up on the VP's question:
+
+> **"What are the KPIs?"**
+
+Work with leadership and the relevant departments to identify:
+
+1. What are our business objectives for video?
+    
+2. Which metrics indicate whether we are succeeding?
+    
+3. Who owns the business definition of each KPI?
+    
+4. How should each KPI be calculated?
+    
+5. Which supporting metrics help explain performance?
+    
+
+Document the agreed definitions and implement them consistently in the existing report.
+
+This creates a small, concrete example of Data Governance and demonstrates the value of the broader operating model.
+
+From there, gradually extend the model to additional domains rather than trying to win the abstract argument over **"Who is Data?"**
+
+The long-term strategy is:
+
+> **Don't fight to be called Data. Build the organizational Data & Analytics capability until its role becomes self-evident.**
+
+## Related
+
+- [[Data Governance]]
+    
+- [[Data Quality]]
+    
+- [[Data Lineage]]
+    
+- [[Semantic Layer]]
+    
+- [[Business Glossary]]
+    
+- [[KPI]]
+    
+- [[Data Owner]]
+    
+- [[Data Steward]]
+    
+- [[Power BI]]
+    
+- [[Data Culture]]
+    
+- [[Self-Service Analytics]]
+    
+- [[CTO]]
\ No newline at end of file