635 lines
16 KiB
Markdown
635 lines
16 KiB
Markdown
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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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> The department retains ownership and appropriate access control while participating in the organization's governed analytics ecosystem.
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For example, HR may expose approved workforce metrics without exposing individual salaries or unnecessary personal information.
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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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This is not primarily a Power BI or technical project.
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It requires:
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- Organizational change
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- Executive sponsorship
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- Data Governance
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- Departmental cooperation
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- Clear responsibilities
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- Trust
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- Political navigation
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- Technical architecture
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- Gradual cultural change
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Implementation should therefore be incremental rather than presented as one large transformation project.
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## Risks / Questions
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- Marketing currently has strong relationships with the VP and CEO.
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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.
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- HR and Finance may resist participation because of concerns around control and sensitive information.
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- Departments may interpret "central Data & Analytics" as an attempt to take ownership of their data.
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- KPI ownership is currently unclear.
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- Leadership may not yet recognize the distinction between metrics and KPIs.
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- There may be no formal Data Governance mandate.
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- Data access and business ownership may currently be confused.
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- Embedded team members need sufficient analytical and business knowledge to become credible partners.
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- Who ultimately resolves disagreements over KPI definitions?
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- Who should sponsor Data Governance at the executive level?
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- How should cross-department KPIs be owned?
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- What should the formal mandate of Data & Analytics become?
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## Next Step
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Use the existing **cross-platform video analytics report** as the first practical example of this operating model.
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Do not initially attempt to restructure the organization or challenge Marketing's analyst position.
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Instead, follow up on the VP's question:
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> **"What are the KPIs?"**
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Work with leadership and the relevant departments to identify:
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1. What are our business objectives for video?
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2. Which metrics indicate whether we are succeeding?
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3. Who owns the business definition of each KPI?
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4. How should each KPI be calculated?
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5. Which supporting metrics help explain performance?
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Document the agreed definitions and implement them consistently in the existing report.
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This creates a small, concrete example of Data Governance and demonstrates the value of the broader operating model.
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From there, gradually extend the model to additional domains rather than trying to win the abstract argument over **"Who is Data?"**
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The long-term strategy is:
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> **Don't fight to be called Data. Build the organizational Data & Analytics capability until its role becomes self-evident.**
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## Related
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- [[Data Governance]]
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- [[Data Quality]]
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- [[Data Lineage]]
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- [[Semantic Layer]]
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- [[Business Glossary]]
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- [[KPI]]
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- [[Data Owner]]
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- [[Data Steward]]
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- [[Power BI]]
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- [[Data Culture]]
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- [[Self-Service Analytics]]
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- [[CTO]] |