576 lines
13 KiB
Markdown
576 lines
13 KiB
Markdown
|
|
**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. |