vault backup: 2026-08-04 16:51:01
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**Date:** 2026-07-22
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**Status:** 💡 Idea
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## The Idea
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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.
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The Data & Analytics team would not "own all company data." Instead, it would provide the common capability that makes organizational data:
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- Accessible
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- Trusted
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- Consistent
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- Integrated
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- Documented
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- Governed
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- Understandable
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- Actionable
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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.
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The long-term goal is not simply to build more Power BI reports.
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It is to build the organization's **measurement system**, allowing departments and leadership to understand organizational performance from trusted data.
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## Problem / Opportunity
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The current organization has a relatively new data culture and historically operates through departmental silos.
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There is currently ambiguity around:
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- Who owns data
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- Who can access data
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- Who interprets data
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- Who defines KPIs
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- Who distributes information
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- Which reports are authoritative
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- Who decides whether performance is good or bad
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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.
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This creates a risk of **information gatekeeping**.
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For example, even when centralized reports are available to departments directly, Marketing may prefer that departments request information through Marketing first.
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This model creates unnecessary dependencies:
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```text
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Department
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↓
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Marketing
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↓
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Data / Reports
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↓
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Marketing interpretation
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↓
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Department
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```
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It also creates a potential conflict when data reveals uncomfortable results.
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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.
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This demonstrates why business ownership should not automatically mean control over whether organizational performance information can be seen.
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Another issue became clear when the VP asked:
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> "What are the KPIs?"
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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.
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This represents an opportunity to move beyond reporting toward true Data Governance and performance measurement.
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## Why It Matters
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A strong Data & Analytics operating model would reduce departmental silos and make trusted information directly available to the people who need it.
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Instead of asking:
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> "Who controls the numbers?"
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the organization should be able to ask:
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> "What do the numbers tell us, and what should we do about them?"
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The business value includes:
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- Better executive decision-making
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- Greater transparency
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- Less information gatekeeping
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- Consistent KPI definitions
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- Increased trust in reports
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- Faster access to information
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- More effective self-service analytics
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- Better collaboration between Data and business departments
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- Better understanding of organizational performance
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- Reduced duplication of reports and analysis
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- Less dependence on individual departments to distribute information
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- Better accountability when performance is poor
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- Cross-departmental visibility
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The long-term objective is for leadership to understand the health of the entire organization using trusted data.
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## Who Benefits?
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- Executive leadership
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- News
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- Education
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- Marketing
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- Finance
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- HR
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- IT
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- Legal
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- Digital / Product
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- Other business departments
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- Data & Analytics team
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Ultimately, every department should benefit from having direct access to trusted information and an analytical partner who understands its business.
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## Possible Approach
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### 1. Business Domains Own Their Business
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Departments retain ownership of their respective business domains.
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Examples:
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```text
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HR → HR / Employee domain
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Finance → Financial domain
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Marketing → Marketing domain
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News → News domain
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Education → Education domain
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IT → Technology / Operations domain
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Legal → Legal / Compliance domain
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```
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Business ownership means departments understand their operations and participate in defining what their business metrics mean.
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It does **not** automatically mean that they control who may see organizational performance information.
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Ownership, access, reporting, interpretation, and governance are separate responsibilities.
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### 2. Data & Analytics Provides the Shared Capability
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The Data & Analytics function should be responsible for establishing and maintaining the organization's shared analytics capability.
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Responsibilities could include:
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- Data integration
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- Analytics architecture
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- Power BI / reporting
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- Data quality
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- Data lineage
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- Metadata
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- KPI documentation
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- Shared definitions
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- Cross-domain analytics
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- Analytical methodology
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- Self-service analytics
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- Data access implementation
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- Data governance standards
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The objective is not:
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> "All data belongs to Data & Analytics."
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Instead:
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> **Business units own their business domains. Data & Analytics makes organizational data trustworthy, integrated, consistently measured, understandable, and accessible to authorized users.**
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### 3. Dedicated Data / Technology Partners
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Each department already has a dedicated team member who works closely with it on its website, projects, technology, and evolution.
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This relationship should evolve into an **embedded Data / Technology partnership**.
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```text
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Data & Analytics
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│
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┌──────────────┼──────────────┐
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│ │ │
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Team Member Team Member Team Member
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↕ ↕ ↕
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News Education Other Dept.
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```
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These team members should develop enough domain knowledge to understand and interpret the department's analytics.
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They should not merely provide numbers.
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They should be able to:
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- Explain what happened
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- Identify patterns
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- Investigate changes
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- Challenge assumptions
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- Understand the department's digital environment
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- Connect technical events with analytics
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- Recommend further investigation
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- Work with departmental experts to understand why something happened
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The business department still retains accountability for business decisions.
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### 4. Shared Interpretation
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Interpretation should not automatically belong to Marketing.
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Instead:
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**Data & Analytics asks:**
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> What happened?
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> Where did it happen?
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> What patterns exist?
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> Is the data reliable?
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**Data + Department together ask:**
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> Why did it happen?
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The department contributes business context because it understands its operations.
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The embedded Data/Technology partner contributes analytical and technical expertise because they understand both the data and the department.
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**Department / Leadership decides:**
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> Is this performance acceptable?
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> What should we do about it?
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Marketing can contribute Marketing expertise where relevant, but should not automatically become the interpretation layer for News, Education, or other departments.
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### 5. Self-Service by Default
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Authorized users should not need to request basic information from Marketing or Data if trusted reports already exist.
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The desired model is:
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```text
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Trusted Data & Analytics
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│
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┌─────────────┼─────────────┐
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↓ ↓ ↓
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News Education Marketing
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↕ ↕ ↕
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Data Partner Data Partner Data Partner
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```
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Not:
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```text
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Department → Marketing → Data → Marketing → Department
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```
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The goal is not to replace Marketing as the gatekeeper with Data as the new gatekeeper.
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The goal is to **remove unnecessary gatekeeping entirely**.
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### 6. Establish KPI Governance
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Reports should distinguish between:
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**Metrics**
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Numbers that describe what happened.
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and:
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**KPIs**
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Metrics specifically selected to determine whether the organization is succeeding against an objective.
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For example, a video analytics report may contain dozens of metrics.
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Leadership may ultimately decide that only a small number represent organizational success.
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The process should become:
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```text
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Business Objective
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↓
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Agreed KPI
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↓
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Business Definition
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↓
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Documented Calculation
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↓
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Trusted Data Source
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↓
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Power BI / Analytics
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↓
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Department Interpretation
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↓
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Leadership Decision
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```
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Data & Analytics should facilitate this process but should not independently invent corporate KPIs.
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Questions to establish for important KPIs:
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- What business objective does this KPI measure?
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- Who owns its business definition?
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- How exactly is it calculated?
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- Which systems contribute data?
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- What are its limitations?
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- How frequently is it refreshed?
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- Who may access it?
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- Who approves changes?
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- Where is the authoritative version?
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- What supporting metrics explain changes?
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### 7. Position Marketing's Data Analyst Correctly
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Marketing having a Data Analyst is not inherently a problem.
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The important question is the analyst's mandate.
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A Marketing Data Analyst could appropriately specialize in:
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- Marketing campaigns
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- Acquisition
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- Audience segmentation
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- Marketing effectiveness
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- Advertising performance
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- Marketing KPIs
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The concern would be if the role evolves into:
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> The organizational analyst responsible for interpreting News, Education, Digital, Streaming, and other departments.
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Marketing should be one business domain within the broader Data & Analytics ecosystem, not the mandatory gateway to organizational information.
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Do not make the organizational argument:
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> "Marketing shouldn't have an analyst."
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Instead establish the broader model in which that analyst naturally becomes a **Marketing domain specialist**.
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### 8. Expand Beyond Audience Analytics
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The current video analytics work can become the foundation rather than the final destination.
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Long term, gradually integrate authorized information from additional domains.
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```text
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ORGANIZATIONAL HEALTH
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Financial Audience People Operations
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│ │ │ │
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Revenue Reach Headcount SLA
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Budget Viewing Turnover Uptime
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Costs Retention Hiring Delivery
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│ │ │ │
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└──────────────┴──────┬──────┴──────────────┘
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↓
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Executive Decisions
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```
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Potential domains include:
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**Finance**
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- Revenue
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- Budget
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- Actual vs forecast
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- Costs
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**HR**
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- Headcount
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- Hiring
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- Turnover
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- Workforce trends
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**IT**
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- SLA
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- Incidents
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- Uptime
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- Project delivery
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- Service requests
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**Legal**
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- Compliance indicators
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- Contracts
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- Rights / licensing where applicable
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**Audience / Content**
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- Reach
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- Consumption
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- Engagement
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- Retention
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- Content performance
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The goal is not one enormous Power BI dashboard.
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The goal is an interconnected **organizational data ecosystem**.
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### 9. Respect Sensitive Data Ownership
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Expanding into HR, Finance and Legal should not mean requesting unrestricted access to everything.
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These departments may legitimately need strict controls around sensitive information.
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Instead:
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|
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> The department retains ownership and appropriate access control while participating in the organization's governed analytics ecosystem.
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|
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For example, HR may expose approved workforce metrics without exposing individual salaries or unnecessary personal information.
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|
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This reduces resistance and avoids presenting Data & Analytics as an attempt to take control away from departments.
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## Effort
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**Estimated complexity:** High
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|
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This is not primarily a Power BI or technical project.
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|
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It requires:
|
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|
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- Organizational change
|
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|
||||
- Executive sponsorship
|
||||
|
||||
- Data Governance
|
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|
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- Departmental cooperation
|
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|
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- Clear responsibilities
|
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|
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- Trust
|
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|
||||
- Political navigation
|
||||
|
||||
- Technical architecture
|
||||
|
||||
- Gradual cultural change
|
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|
||||
|
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Implementation should therefore be incremental rather than presented as one large transformation project.
|
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|
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## Risks / Questions
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|
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- Marketing currently has strong relationships with the VP and CEO.
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|
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- Marketing has already received approval to hire a Data Analyst focused on interpretation without consultation with the existing Data & Analytics function.
|
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|
||||
- Marketing may perceive direct self-service analytics as reducing its organizational influence.
|
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|
||||
- 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]]
|
||||
@@ -0,0 +1,576 @@
|
||||
|
||||
**Date:** 2026-07-22
|
||||
**Status:** 💡 Idea
|
||||
|
||||
## The Idea
|
||||
|
||||
Create a formal framework connecting the organization's strategic pillars and objectives to measurable indicators of success.
|
||||
|
||||
The organization already has:
|
||||
|
||||
1. Strategic pillars
|
||||
2. Strategic objectives
|
||||
3. Large amounts of operational and audience data
|
||||
|
||||
What appears to be missing is the layer connecting strategy to measurement:
|
||||
|
||||
Strategic Objective
|
||||
→ Definition of Success
|
||||
→ KPI
|
||||
→ Target
|
||||
→ Supporting Metrics
|
||||
→ Data Source
|
||||
→ Business Owner
|
||||
→ Reporting / Analysis
|
||||
|
||||
The objective is not to have Data & Analytics independently decide what the company's KPIs are.
|
||||
|
||||
The objective is to establish a process where business owners and leadership define what success means, while Data & Analytics ensures those definitions can be measured consistently, reliably and transparently.
|
||||
|
||||
## Problem / Opportunity
|
||||
|
||||
The organization has five strategic pillars:
|
||||
|
||||
1. Offer content reflecting communities and their reality
|
||||
2. Become the educational reference for Francophones in minority contexts
|
||||
3. Build a technological and operational ecosystem supporting a digital-first model
|
||||
4. Become an employer of choice
|
||||
5. Diversify revenue sources to ensure sustainability
|
||||
|
||||
There are also strategic objectives under each pillar.
|
||||
|
||||
However, many of the objectives are directional rather than directly measurable.
|
||||
|
||||
Examples:
|
||||
|
||||
- "Devenir la référence éducative"
|
||||
- "Adapter nos contenus aux besoins du public"
|
||||
- "Optimiser notre écosystème technologique"
|
||||
- "Être un employeur de choix"
|
||||
- "Diversifier les sources de revenus"
|
||||
|
||||
These are valid strategic directions, but they do not by themselves answer:
|
||||
|
||||
> How will we know whether we succeeded?
|
||||
|
||||
A recent example exposed this gap.
|
||||
|
||||
A cross-platform video analytics report was created using data from several platforms.
|
||||
|
||||
When the VP Technology saw the report, one of his first questions was:
|
||||
|
||||
> "What are the KPIs?"
|
||||
|
||||
At the time, I interpreted the question as asking which metrics were available.
|
||||
|
||||
In retrospect, the more important question was:
|
||||
|
||||
> Which of these measures tell us whether the organization is succeeding?
|
||||
|
||||
The report solved the data availability problem.
|
||||
|
||||
It did not solve the strategic measurement problem.
|
||||
|
||||
## Why It Matters
|
||||
|
||||
Without clearly defined KPIs:
|
||||
|
||||
- Reports can contain many numbers without showing whether the organization is succeeding.
|
||||
- Departments may select different metrics to describe success.
|
||||
- A department may emphasize numbers that make its performance look favourable.
|
||||
- Leadership cannot easily compare actual results against strategic objectives.
|
||||
- Different analysts may interpret success differently.
|
||||
- Strategic plans can become difficult to evaluate objectively.
|
||||
- Data exists without necessarily supporting decisions.
|
||||
|
||||
A KPI framework would connect strategy directly to measurement.
|
||||
|
||||
Instead of leadership seeing:
|
||||
|
||||
> 1.4M views
|
||||
|
||||
leadership should eventually be able to understand:
|
||||
|
||||
> Our objective is X.
|
||||
>
|
||||
> We measure success using Y.
|
||||
>
|
||||
> Our target is Z.
|
||||
>
|
||||
> Current performance is A.
|
||||
>
|
||||
> These supporting metrics explain why.
|
||||
|
||||
## Who Benefits?
|
||||
|
||||
- CEO
|
||||
- Executive leadership
|
||||
- Content leadership
|
||||
- News
|
||||
- Education
|
||||
- Marketing
|
||||
- Technology
|
||||
- HR
|
||||
- Finance
|
||||
- Data & Analytics
|
||||
- Other business units
|
||||
|
||||
## Possible Approach
|
||||
|
||||
### 1. Start With Strategy, Not Data
|
||||
|
||||
Do not begin by asking:
|
||||
|
||||
> What metrics do we have?
|
||||
|
||||
Begin with:
|
||||
|
||||
> What are we trying to accomplish?
|
||||
|
||||
Then:
|
||||
|
||||
> What does success look like?
|
||||
|
||||
Only then determine the appropriate KPI.
|
||||
|
||||
Example:
|
||||
|
||||
Strategic objective:
|
||||
Increase the relevance and consumption of our content.
|
||||
|
||||
Definition of success:
|
||||
More members of our target audience consume and return to our content.
|
||||
|
||||
Candidate KPI:
|
||||
Viewing hours
|
||||
|
||||
Target:
|
||||
To be determined by leadership/business owner
|
||||
|
||||
Supporting metrics:
|
||||
- Reach
|
||||
- Starts
|
||||
- Completion rate
|
||||
- Average viewing time
|
||||
- Returning audience
|
||||
- Platform
|
||||
- Content type
|
||||
|
||||
### 2. Distinguish Metrics From KPIs
|
||||
|
||||
A metric measures something.
|
||||
|
||||
A KPI measures something specifically selected to indicate whether an important objective is succeeding.
|
||||
|
||||
Example:
|
||||
|
||||
Views = metric
|
||||
|
||||
If the organization's objective is to increase digital content consumption and leadership agrees that viewing hours represents success:
|
||||
|
||||
Viewing Hours = KPI
|
||||
|
||||
A report may contain dozens of metrics while leadership only needs a small number of KPIs.
|
||||
|
||||
### 3. Distinguish KPI From Target
|
||||
|
||||
KPI:
|
||||
|
||||
Monthly Active Educators
|
||||
|
||||
Target:
|
||||
|
||||
Increase Monthly Active Educators by 15% YoY
|
||||
|
||||
The target determines the desired performance.
|
||||
|
||||
The KPI determines what is being measured.
|
||||
|
||||
### 4. Establish Ownership
|
||||
|
||||
Data & Analytics should not independently decide what success means for every department.
|
||||
|
||||
The appropriate business domain should participate in defining success.
|
||||
|
||||
Examples:
|
||||
|
||||
Education owns the business context of Education.
|
||||
|
||||
News owns the business context of News.
|
||||
|
||||
HR owns HR definitions.
|
||||
|
||||
Finance owns financial definitions.
|
||||
|
||||
Marketing owns Marketing's business context.
|
||||
|
||||
Data & Analytics should facilitate the measurement framework and ensure that:
|
||||
|
||||
- definitions are documented
|
||||
- calculations are consistent
|
||||
- sources are known
|
||||
- data quality is understood
|
||||
- historical information is preserved
|
||||
- reports use the same definition
|
||||
- authorized departments can access the information
|
||||
|
||||
Business ownership does not automatically mean exclusive control over reporting or distribution.
|
||||
|
||||
### 5. Interpretation Model
|
||||
|
||||
The desired model is collaborative.
|
||||
|
||||
Data & Analytics / embedded partner:
|
||||
|
||||
- What happened?
|
||||
- Where did it happen?
|
||||
- What changed?
|
||||
- Are there patterns?
|
||||
- Is the data reliable?
|
||||
- Which segments/platforms/content explain the change?
|
||||
|
||||
Business department + embedded Data partner:
|
||||
|
||||
- Why did it happen?
|
||||
- What operational/business factors contributed?
|
||||
|
||||
Business leadership:
|
||||
|
||||
- Is the result acceptable?
|
||||
- What should be done about it?
|
||||
|
||||
The dedicated team members assigned to departments should develop enough domain expertise to interpret performance with those departments.
|
||||
|
||||
Marketing should not automatically become the interpretation layer for News, Education or other business domains.
|
||||
|
||||
### 6. Candidate KPI Framework
|
||||
|
||||
These are candidates for discussion, not official KPIs.
|
||||
|
||||
#### Pillar 1: Content / Communities
|
||||
|
||||
Strategic questions:
|
||||
|
||||
- Are we reaching our target communities?
|
||||
- Are people consuming our content?
|
||||
- Are they returning?
|
||||
- Does the content actually represent the communities named in the strategy?
|
||||
|
||||
Candidate KPIs:
|
||||
|
||||
- Audience reach
|
||||
- Viewing hours
|
||||
- Returning audience rate
|
||||
- Audience growth
|
||||
|
||||
Supporting metrics:
|
||||
|
||||
- Views
|
||||
- Starts
|
||||
- Average viewing time
|
||||
- Completion rate
|
||||
- Platform
|
||||
- Content category
|
||||
- Geography
|
||||
|
||||
Available sources:
|
||||
|
||||
- YouTube
|
||||
- JW Player
|
||||
- Roku
|
||||
- Apple TV
|
||||
- Amazon Fire TV
|
||||
- GA4
|
||||
- Numeris
|
||||
- Numeris ETAM
|
||||
|
||||
Important gap:
|
||||
|
||||
Digital analytics can show what people consume.
|
||||
|
||||
It cannot by itself prove that content reflects the needs and realities of Francophone minority communities.
|
||||
|
||||
Other research, survey or qualitative measures may be required.
|
||||
|
||||
#### Pillar 2: Education
|
||||
|
||||
Strategic questions:
|
||||
|
||||
- Are we becoming more widely used by educators?
|
||||
- Are educators returning?
|
||||
- Are we expanding outside existing markets?
|
||||
- Are educational resources actually being consumed?
|
||||
|
||||
Candidate KPIs:
|
||||
|
||||
- Active educators
|
||||
- Returning educator rate
|
||||
- Educational resource consumption
|
||||
- Geographic adoption
|
||||
- Growth outside Ontario
|
||||
|
||||
Sources:
|
||||
|
||||
- Drupal API
|
||||
- GA4
|
||||
|
||||
#### Pillar 3: Digital-First Transformation
|
||||
|
||||
Strategic questions:
|
||||
|
||||
- Is more consumption occurring digitally?
|
||||
- Are our digital services reliable?
|
||||
- Are technology operations improving?
|
||||
- Are we delivering strategic projects effectively?
|
||||
|
||||
Candidate KPIs:
|
||||
|
||||
- Digital audience / consumption
|
||||
- Platform adoption
|
||||
- Service availability
|
||||
- SLA attainment
|
||||
- Strategic project delivery rate
|
||||
|
||||
Supporting metrics:
|
||||
|
||||
- Jira tickets
|
||||
- SLA breaches
|
||||
- Incident counts
|
||||
- Sentry errors
|
||||
- Uptime
|
||||
- Project delivery
|
||||
- Resolution time
|
||||
|
||||
Sources:
|
||||
|
||||
- Jira
|
||||
- Sentry
|
||||
- Uptime monitoring
|
||||
- GA4
|
||||
- JW
|
||||
- OTT platforms
|
||||
- YouTube
|
||||
|
||||
#### Pillar 4: Employer of Choice
|
||||
|
||||
Strategic questions:
|
||||
|
||||
- Are we retaining employees?
|
||||
- Are employees voluntarily leaving?
|
||||
- Is the organization able to attract and retain talent?
|
||||
|
||||
Candidate KPIs:
|
||||
|
||||
- Employee retention
|
||||
- Voluntary turnover
|
||||
- Headcount evolution
|
||||
- Employee engagement if available
|
||||
|
||||
Sources:
|
||||
|
||||
- Dayforce
|
||||
|
||||
Potential supporting data:
|
||||
|
||||
- Employee counts
|
||||
- Departures
|
||||
- Leaves
|
||||
- Hiring
|
||||
- Tenure
|
||||
|
||||
HR should own the business definitions while Data & Analytics supports consistent measurement.
|
||||
|
||||
#### Pillar 5: Revenue Diversification
|
||||
|
||||
Strategic questions:
|
||||
|
||||
- Are revenues increasing?
|
||||
- Are revenues becoming more diversified?
|
||||
- Is the organization less dependent on a small number of sources?
|
||||
|
||||
Candidate KPIs:
|
||||
|
||||
- Revenue by source
|
||||
- Revenue growth
|
||||
- % of revenue by source
|
||||
- % of revenue from new/non-core sources
|
||||
- Advertising revenue
|
||||
- Commercial revenue
|
||||
- Philanthropic revenue
|
||||
- Revenue outside Ontario
|
||||
|
||||
Sources:
|
||||
|
||||
- Finance
|
||||
|
||||
Current gap:
|
||||
|
||||
Data & Analytics currently has limited financial data.
|
||||
|
||||
The first step should be a conversation with Finance about how they currently measure revenue diversification.
|
||||
|
||||
### 7. Current Data Inventory
|
||||
|
||||
#### Video / Streaming
|
||||
|
||||
- YouTube
|
||||
- JW Player
|
||||
- Roku
|
||||
- Apple TV
|
||||
- Amazon Fire TV
|
||||
|
||||
#### Websites
|
||||
|
||||
- GA4
|
||||
- Umami planned
|
||||
- Main website
|
||||
- News website
|
||||
- Education website
|
||||
|
||||
#### Education
|
||||
|
||||
- Drupal
|
||||
- API access available
|
||||
|
||||
#### Linear TV
|
||||
|
||||
- Numeris
|
||||
- Numeris ETAM upcoming
|
||||
|
||||
#### HR
|
||||
|
||||
- Dayforce
|
||||
- Employee retention
|
||||
- Employee counts
|
||||
- Leaves
|
||||
|
||||
#### IT / Technology
|
||||
|
||||
- Jira
|
||||
- Sentry
|
||||
- Uptime monitoring
|
||||
- Tickets
|
||||
- SLA
|
||||
- Project information
|
||||
|
||||
#### Finance
|
||||
|
||||
Limited current integration.
|
||||
|
||||
Requires discussion with Finance.
|
||||
|
||||
#### Marketing
|
||||
|
||||
Marketing currently manages its own reporting environment, including Google Looker.
|
||||
|
||||
This creates a broader question around:
|
||||
|
||||
- organizational ownership of historical data
|
||||
- long-term retention
|
||||
- shared definitions
|
||||
- integration with company-wide analytics
|
||||
|
||||
The governance question should not initially be whether a specific tool should be eliminated.
|
||||
|
||||
The requirement should be:
|
||||
|
||||
> Strategic KPI history must be retained for sufficient time to evaluate long-term organizational performance.
|
||||
|
||||
Technology decisions should follow that requirement.
|
||||
|
||||
## Effort
|
||||
|
||||
**Estimated complexity:** High
|
||||
|
||||
The technical work is achievable.
|
||||
|
||||
The more difficult work involves:
|
||||
|
||||
- agreeing on definitions
|
||||
- establishing business ownership
|
||||
- obtaining executive sponsorship
|
||||
- aligning departments
|
||||
- creating targets
|
||||
- changing the organization's data culture
|
||||
- avoiding information silos
|
||||
- establishing the mandate of Data & Analytics
|
||||
|
||||
This should therefore be implemented incrementally.
|
||||
|
||||
## Risks / Questions
|
||||
|
||||
- There is currently no clear organization-wide KPI framework.
|
||||
- Departments may define success differently.
|
||||
- Departments may resist measurement when results are unfavourable.
|
||||
- Marketing currently has strong influence with executive leadership.
|
||||
- Marketing is building its own analytical capability.
|
||||
- HR and Finance may be hesitant to expose data outside their departments.
|
||||
- Business ownership may be confused with exclusive control of data and reporting.
|
||||
- Data & Analytics does not yet appear to have a formal enterprise-wide governance mandate.
|
||||
- Targets do not appear to be clearly defined for many strategic objectives.
|
||||
- Some strategic objectives cannot be measured using digital analytics alone.
|
||||
- Cross-platform unique audience measurement may be difficult because users cannot necessarily be deduplicated across platforms.
|
||||
- Who ultimately approves corporate KPIs?
|
||||
- Who resolves disagreements between departments?
|
||||
- How much historical data should be retained for strategic measurement?
|
||||
|
||||
## Next Step
|
||||
|
||||
Use the existing Content and Education strategy as the first pilot.
|
||||
|
||||
Rather than proposing an organization-wide KPI framework immediately, discuss with the VP Content:
|
||||
|
||||
> How do we currently know whether the Content and Education strategic objectives are succeeding?
|
||||
|
||||
Use the discussion to identify:
|
||||
|
||||
1. What success means
|
||||
2. Which measures demonstrate success
|
||||
3. Who owns the definitions
|
||||
4. Which data is currently available
|
||||
5. What data is missing
|
||||
6. Which targets should eventually be established
|
||||
|
||||
Then discuss the broader framework with the VP Technology, using his previous KPI question as the starting point.
|
||||
|
||||
If leadership sees value in the approach, expand the framework to:
|
||||
|
||||
HR → Finance → IT → other business domains.
|
||||
|
||||
## Related
|
||||
|
||||
- [[Organizational Data & Analytics Operating Model]]
|
||||
- [[Data Governance]]
|
||||
- [[Data Quality]]
|
||||
- [[Data Lineage]]
|
||||
- [[Semantic Layer]]
|
||||
- [[Business Glossary]]
|
||||
- [[KPI]]
|
||||
- [[Data Owner]]
|
||||
- [[Data Steward]]
|
||||
- [[Self-Service Analytics]]
|
||||
- [[Data Culture]]
|
||||
- [[CTO]]
|
||||
|
||||
## Update - 2026-07-27
|
||||
|
||||
Discussed the concept with CTO.
|
||||
|
||||
The organization currently has many metrics and reports, but we have not consistently defined which KPIs demonstrate whether strategic objectives are being achieved.
|
||||
|
||||
CTO agrees that this is worth pursuing.
|
||||
|
||||
### Approach
|
||||
|
||||
This needs to be handled carefully because KPI definition touches organizational priorities and executive accountability.
|
||||
|
||||
Rather than immediately proposing a new KPI framework, prepare a presentation that:
|
||||
|
||||
1. Demonstrates the distinction between metrics and strategic KPIs.
|
||||
2. Shows the current gap using concrete examples.
|
||||
3. Connects KPIs to existing strategic objectives.
|
||||
4. Proposes an approach for defining them collaboratively.
|
||||
|
||||
### Next Step
|
||||
|
||||
- [ ] Prepare presentation for discussion after CTO returns from vacation.
|
||||
@@ -0,0 +1,245 @@
|
||||
This is **not** the meeting where I would pitch the entire company-wide architecture.
|
||||
|
||||
She's responsible for **Marketing + News + Education**, so use _her own strategic objectives_.
|
||||
|
||||
And because she's close to the Marketing director, I would **completely avoid framing this as Marketing vs your team**.
|
||||
|
||||
That would immediately make the conversation political.
|
||||
|
||||
Your opening could be:
|
||||
|
||||
> **« Je travaille beaucoup dernièrement sur comment mieux relier nos données à nos objectifs stratégiques. En fait, une question de [VP Tech] m'a fait réaliser quelque chose. Quand je lui ai montré notre rapport vidéo, sa première question a été : "c'est quoi les KPI?" Sur le coup, je n'ai pas vraiment compris la question comme il la posait. Mais en y repensant, j'ai réalisé qu'on a énormément de métriques, mais qu'on n'a pas nécessairement défini lesquelles nous permettent de dire si on atteint réellement nos objectifs stratégiques. »**
|
||||
|
||||
Then connect it directly to her area:
|
||||
|
||||
> **« Par exemple, on dit qu'on veut devenir la référence éducative pour les francophones en contexte minoritaire. Mais comment est-ce qu'on détermine qu'on est en train de le devenir? Est-ce le nombre d'enseignants actifs? Leur taux de retour? L'utilisation des ressources? Notre pénétration hors Ontario? Je ne pense pas que ce soit à mon équipe de décider ça seule, mais je pense qu'on pourrait aider à structurer la discussion et ensuite s'assurer que les données permettent de le mesurer correctement. »**
|
||||
|
||||
That's strong.
|
||||
|
||||
You're asking her to **define success**, not defending your territory.
|
||||
|
||||
---
|
||||
|
||||
## Then Content
|
||||
|
||||
You can say:
|
||||
|
||||
> **« Même chose pour le contenu. On a maintenant YouTube, JW, Roku, Apple TV, Fire TV, Numeris, bientôt ETAM, GA4. On peut montrer énormément de chiffres. Mais parmi tous ces chiffres, lesquels représentent réellement notre succès? La portée? Les heures de visionnement? La rétention? Plusieurs peuvent être utiles, mais tant qu'on n'a pas défini ce qu'on considère comme le succès, on reste avec beaucoup de métriques plutôt qu'avec de vrais KPI. »**
|
||||
|
||||
Then the really important sentence:
|
||||
|
||||
> **« Ce que j'aimerais construire, ce n'est pas simplement un autre dashboard. C'est le lien entre les objectifs stratégiques et les données qui permettent de savoir si on les atteint. »**
|
||||
|
||||
That's probably the core sentence for Monday.
|
||||
|
||||
---
|
||||
|
||||
# Don't make Monday about Marketing
|
||||
|
||||
Even though Marketing is a huge part of why you've arrived here.
|
||||
|
||||
I would **not say**:
|
||||
|
||||
> Marketing shouldn't interpret News.
|
||||
|
||||
or:
|
||||
|
||||
> Marketing is gatekeeping.
|
||||
|
||||
or:
|
||||
|
||||
> We should own Analytics instead.
|
||||
|
||||
or:
|
||||
|
||||
> Their new analyst duplicates my team.
|
||||
|
||||
Not yet.
|
||||
|
||||
Get the operating principle accepted first.
|
||||
|
||||
Something like:
|
||||
|
||||
> **« Idéalement, chaque secteur devrait pouvoir accéder directement aux indicateurs qui le concernent, avec une personne de mon équipe qui connaît déjà bien son environnement pour l'aider à les analyser. Le secteur apporte son expertise métier, et nous apportons l'expertise analytique et technique. »**
|
||||
|
||||
That implicitly describes the model you want without attacking Marketing.
|
||||
|
||||
If she says:
|
||||
|
||||
> "Marketing can do the interpretation."
|
||||
|
||||
Then you have the opening:
|
||||
|
||||
> **« Marketing peut évidemment contribuer lorsque l'analyse touche son expertise. Mais pour moi, l'interprétation devrait se faire avec le secteur concerné. Une personne d'Éducation comprend beaucoup mieux la réalité d'Éducation, tout comme News comprend sa réalité. Notre rôle serait de travailler avec eux pour connecter cette connaissance métier aux données. »**
|
||||
|
||||
Calm. Very defensible.
|
||||
|
||||
---
|
||||
|
||||
# Your VP Technology
|
||||
|
||||
This is actually the person I'd have the **bigger conversation** with.
|
||||
|
||||
Because his innocent:
|
||||
|
||||
> "What are the KPIs?"
|
||||
|
||||
is what uncovered this entire thing.
|
||||
|
||||
I'd literally tell him that.
|
||||
|
||||
> **« Ta question sur les KPI quand je t'ai montré le rapport vidéo m'est restée en tête. Sur le coup, je n'avais pas compris ce que tu cherchais. Je pensais surtout aux métriques disponibles. En y revenant, j'ai réalisé que ta question touchait quelque chose de beaucoup plus gros. »**
|
||||
|
||||
Then:
|
||||
|
||||
> **« On a des piliers stratégiques. On a énormément de données. Mais je ne vois pas encore de couche formelle qui relie les deux. Pour chaque objectif stratégique : comment définit-on le succès, quel KPI le mesure, quelle est la cible, qui est propriétaire de la définition et quelle est la source officielle? »**
|
||||
|
||||
That's excellent because you're giving **him credit** for triggering the insight.
|
||||
|
||||
Then show him:
|
||||
|
||||
```
|
||||
Objectif stratégique
|
||||
↓
|
||||
Définition du succès
|
||||
↓
|
||||
KPI
|
||||
↓
|
||||
Cible
|
||||
↓
|
||||
Source de données
|
||||
↓
|
||||
Rapport
|
||||
↓
|
||||
Décision
|
||||
```
|
||||
|
||||
And then:
|
||||
|
||||
> **« Je pense que c'est quelque chose que mon équipe pourrait faciliter. Pas décider les KPI pour les secteurs, mais travailler avec eux pour les définir, les documenter, les mesurer de façon cohérente et s'assurer qu'on utilise les mêmes définitions partout. »**
|
||||
|
||||
This is where I'd start discussing your **Data & Analytics mandate**.
|
||||
|
||||
---
|
||||
|
||||
# What I'd ask your VP Technology for
|
||||
|
||||
Not:
|
||||
|
||||
> Give me authority over everyone's data.
|
||||
|
||||
Instead:
|
||||
|
||||
> **« J'aimerais savoir si tu vois aussi ce manque et si tu serais à l'aise qu'on teste cette approche sur un ou deux objectifs, probablement contenu et éducation, avant d'essayer d'en faire quelque chose de plus large. »**
|
||||
|
||||
That gets you **sponsorship for a pilot**.
|
||||
|
||||
Much easier than:
|
||||
|
||||
> Let's launch Data Governance.
|
||||
|
||||
---
|
||||
|
||||
# CEO
|
||||
|
||||
Different conversation entirely.
|
||||
|
||||
The CEO does not need the architecture.
|
||||
|
||||
I'd give them the problem in about 45 seconds.
|
||||
|
||||
> **« En travaillant sur nos données, j'ai remarqué qu'on a trois éléments qui ne sont pas encore complètement reliés. On a nos piliers stratégiques, on a des objectifs sous chacun d'eux et on a énormément de données. Ce qu'il nous manque, selon moi, c'est une façon commune de mesurer si les objectifs sont réellement atteints. »**
|
||||
|
||||
Then example:
|
||||
|
||||
> **« Par exemple, on veut devenir la référence éducative des francophones en contexte minoritaire. Très bien. Mais quel indicateur nous permet, dans deux ans, de dire objectivement qu'on s'est rapprochés de cet objectif? »**
|
||||
|
||||
Then:
|
||||
|
||||
> **« J'aimerais qu'on arrive éventuellement à avoir quelques KPI clairs par objectif stratégique, avec une définition, une cible, un propriétaire et une source de données fiable. Comme ça, nos rapports ne servent pas seulement à montrer des chiffres. Ils permettent de suivre concrètement l'exécution de la stratégie. »**
|
||||
|
||||
And stop.
|
||||
|
||||
Seriously.
|
||||
|
||||
Don't explain Bronze/Silver/Gold.
|
||||
|
||||
Don't explain Data Governance.
|
||||
|
||||
Don't explain semantic layers.
|
||||
|
||||
Don't explain Marketing politics.
|
||||
|
||||
The CEO-level idea is:
|
||||
|
||||
> **Can we objectively tell whether our strategy is working?**
|
||||
|
||||
---
|
||||
|
||||
# And with all three audiences, this is the key distinction
|
||||
|
||||
You have accidentally found a very compelling way to describe your Data vision:
|
||||
|
||||
### Old pitch
|
||||
|
||||
> "I want to bring together HR, Finance, Marketing, IT, audience data, etc."
|
||||
|
||||
That can sound like:
|
||||
|
||||
> Amadou wants everybody's data.
|
||||
|
||||
### New pitch
|
||||
|
||||
> **"I want us to be able to measure whether our strategic objectives are succeeding."**
|
||||
|
||||
Then when they ask:
|
||||
|
||||
> How?
|
||||
|
||||
You naturally arrive at:
|
||||
|
||||
```
|
||||
Content data
|
||||
Education data
|
||||
Audience data
|
||||
HR data
|
||||
Finance data
|
||||
IT data
|
||||
Operational data
|
||||
↓
|
||||
Strategic KPIs
|
||||
↓
|
||||
Leadership
|
||||
```
|
||||
|
||||
Now HR/Finance/IT aren't being pulled into your ecosystem because **you want their data**.
|
||||
|
||||
They're there because **the corporate strategy itself requires measurements from those domains.**
|
||||
|
||||
That's a vastly stronger justification.
|
||||
|
||||
---
|
||||
|
||||
# One thing I would consciously stop saying
|
||||
|
||||
Even internally to yourself:
|
||||
|
||||
> **"I am Data."**
|
||||
|
||||
I know what you mean by it, and we've spent enough time discussing this that I understand the fight underneath it.
|
||||
|
||||
But I think you've found a much stronger identity:
|
||||
|
||||
> **My team is building the organizational Data & Analytics capability that connects strategy, business knowledge and trustworthy measurement.**
|
||||
|
||||
That's harder for Marketing to compete with by hiring one analyst.
|
||||
|
||||
Because the argument is no longer:
|
||||
|
||||
**Who gets to analyze the numbers?**
|
||||
|
||||
It's:
|
||||
|
||||
**How does the organization measure its strategy?**
|
||||
|
||||
And _that_ is the level where I think your CTO ambitions and what you're currently doing at work are starting to genuinely intersect.
|
||||
Reference in New Issue
Block a user