Owner or manager of a 5-50 person company
You need to know where reported figures come from, who approves them and how to explain discrepancies.
21 text lessons · 7 XLSX workbooks · lifetime access
Put your data sources, metrics and access in order
Data Governance: A Practical Introduction for SMEs helps you map data sources, agree definitions for important metrics, and review access, retention and the basic GDPR and AI duties that affect a small company. You receive editable workbooks and a 90-day work plan. The pace and final scope depend on your business.
- 7 modules
- 21 lessons
- 7 implementation workbooks
- about 110 min of reading plus workbook exercises
- quizzes · final exam · certificate
- 20+
- years in technology, analytics and business
- 600+
- completed projects
- 14
- countries served
- 1×
- payment, lifetime access
01
Who the course is for
Marketer responsible for reporting
GA4, CRM and spreadsheets disagree. You need shared definitions, UTM rules and a documented reporting method.
The part-time analyst
You manage reports or data alongside another role and need templates a small team can actually maintain.
02
What you finish with
- aA data source register with a business owner role and current person for each key source.
- bA metric glossary with 15 examples and room for the definitions your company uses.
- cA method for diagnosing differences between GA4, CRM, ERP and spreadsheets.
- dAn access matrix, retention table and offboarding checklist.
- eA ROPA template, consent mode checklist, AI use policy and AI use-case register.
- fA 90-day work plan with dates, owners and outcome measures.
03
Syllabus
01Why SMEs need Data Governance
The cost of poor data, the scope of governance and a workable split of responsibilities in a small company.
- 1.1 What Data Governance is, and is not, in a company with 5-50 people
- 1.2 What poor data costs: three SME scenarios
- 1.3 A minimum role model: who makes decisions about data
Module workbook
Data maturity self-assessment: 12 scored questions02Map your data sources
Inventory every source that produces figures, trace data flows and deal with shadow IT.
- 2.1 Register systems and sources: GA4, CRM, ERP, ad platforms and spreadsheets
- 2.2 Data flows: how a figure reaches a report
- 2.3 Shadow IT: private spreadsheets, exports and local files
Module workbook
Data source register with a worked example03Agree definitions: the metric glossary
Why GA4, CRM and spreadsheets show different figures, and how to document the definitions used in reports.
- 3.1 Why GA4, CRM and Excel show different figures
- 3.2 How to write an unambiguous metric definition
- 3.3 Approval and change control for the glossary
Module workbook
Business glossary template with 15 example definitions04Data quality in practice
Quality dimensions, clean data collection and reviews triggered by a regular schedule, changes or incidents.
- 4.1 Quality dimensions: completeness, accuracy, consistency, timeliness and uniqueness
- 4.2 Collection hygiene: UTM rules, tagging and CRM deduplication
- 4.3 Review rhythm: scheduled checks and updates after change
Module workbook
Data quality review checklist and UTM convention05Access, retention and security
Who can see what, how accounts are removed after someone leaves, and how long each dataset should be kept.
- 5.1 Access matrix: who can see what, and why
- 5.2 Onboarding and offboarding: accounts that outlive people
- 5.3 Retention in GA4, CRM and email
Module workbook
Access matrix and minimum retention policy06Compliance: GDPR, consent and AI
A record of processing activities, the effect of consent mode on analytics and basic rules for using AI at work.
- 6.1 Consent mode and analytics: what you can and cannot see
- 6.2 GDPR minimum: a record of processing activities for an SME
- 6.3 Company data and duties when using AI
Module workbook
ROPA template, AI use policy and AI use-case register07Your 90-day work plan
Sequence the work, divide responsibility and measure whether the company changed after one quarter.
- 7.1 Sequence the work: week 1 and the full quarter
- 7.2 Help the team maintain the rhythm
- 7.3 Measures: how to tell whether governance works
Module workbook
90-day implementation plan, week by week∎Final exam and certificate
Pass all seven module quizzes at 75% or above, then take the 12-question final exam. Nine correct answers unlock a surowiecki.org completion certificate that you can save as PDF. This is a course completion certificate, not a professional certification or accredited qualification.
04
How you work through the course
DG Academy43% complete
Module 3 · lesson 2 of 3Why GA4, CRM and Excel show different figures
Lesson outcome: separate differences in definition, time and measurement scope.
Module outcomeA glossary of 15 company metrics
Certificate of completionData Governance: A Practical Introduction for SMEsParticipant name7 modules · final exam · save as PDF
- Mark progress after each lesson and move to the next task.
- Every module names a specific working document or outcome.
- Quizzes test scenario understanding and the exam covers all modules.
05
Sample lesson
Module 1 · Why SMEs need Data Governance · full lesson available without purchase
What Data Governance is, and is not, in a company with 5-50 people
Distinguish Data Governance from data engineering and tool procurement, then identify the four minimum working documents.
Before you continue: write down which company figures you trust without checking, why you trust them, where they come from and who can explain the calculation.
Data Governance sets responsibility, decision rights and rules for data. A company with 5-50 people does not need a large governance organisation, but it does need a clear record of which data matters, who makes decisions about it and which rules apply.
This course starts with four working documents:
- Data source register: what produces company figures and who manages it.
- Metric glossary: what terms such as conversion, customer and revenue mean.
- Access matrix: who can see what and why.
- Review schedule: routine quality checks plus updates after an important change or incident.
Data engineering covers integrations, warehouses and pipelines. Governance decides who can make decisions about the data and under which rules. A platform does not replace agreed definitions, ownership or access control.
Example: if a monthly report requires someone to find the source and renegotiate the revenue definition, the source register and metric glossary show what must be agreed. Measure improvement by comparing report preparation time before and after the change.
Scope: these four documents are a starting point, not a complete governance model. Expand the scope as systems, legal risk and the number of data users grow.
06
Taught from practical reporting and data work
Academy access
Buy the course
One payment and lifetime access. Payment is handled by Stripe Checkout.
- 7 modules and 21 text lessons, about 110 minutes of reading plus workbook time
- 7 XLSX files: self-assessment, source register, metric glossary, quality and UTM review, access and retention, ROPA and AI rules, 90-day plan
- Module quizzes, final exam and a completion certificate to save as PDF
- Downloadable implementation playbook
- Lifetime access, no subscription and no account
- Invoice available during checkout
- Lesson progress stays in your browser and does not sync between devices. Exam completion is saved with your purchase. Reactivate access to download your certificate on another device.
- You may copy and adapt the workbooks for internal use within the buyer's company. Resale and distribution outside that company are not permitted.
- Open the course using the email access link. Each purchase stores one exam result. The certificate confirms course completion. Implementing the documents in your company requires separate work and is not assessed by the exam.
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