United Kingdom · Learning and development · Mid-Level (2-5 years)

Global Learning Analytics Manager

Here is the whole job, in plain words. What it is, what you decide, how you're judged, how people get here and where they go next. Then the part no course gives you: twelve AI tutors who learn your work.

Start my Global Learning Analytics Manager plan, free

Free to start: a plan built for this role, and a Future Fluency module to try first. No card.

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Where should we send it?

One email: what AI is changing in this job, and where to start free. With a link back here. Nothing else follows unless you confirm your address.

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Experience bandMid-Level (2-5 years)
Direct reportsNo direct reports
Reports toSenior Global Learning Analytics Manager
UK framework levelUsually a coordinator, or early in a professional job

Also advertised as Learning Data Analyst · People Analytics Specialist (Learning) · L&D Data Scientist (Junior)

Built on 43,079 real UK job descriptions we analysed · grounded in qualifications employers recognise

Two short quizzes about you

1From this job to a course

The qualifications matched to this job

NOCN Level 4 certificate for Data Analysts NOCN · Level 4 · covers 6 of 10 standards

Pearson BTEC Level 4 Higher National Certificate in Computing for England Pearson Education Ltd · Level 4 · covers 3 of 10 standards

Highfield Level 3 Diploma for Team Leaders and Supervisors (RQF) Highfield Qualifications · Level 3 · covers 2 of 10 standards

Pearson BTEC Level 5 Higher National Diploma in Computing for England Pearson Education Ltd · Level 5 · covers 4 of 10 standards

Pearson BTEC Level 5 Higher National Diploma in Digital Technologies Pearson Education Ltd · Level 5 · covers 2 of 10 standards

OTHM Level 7 Diploma in Data Science OTHM Qualifications · Level 7 · covers 4 of 10 standards

Matched from the standards this job is measured against: a suggestion, not an official ruling.

The units you would start with

Data Analytics Primer NOCN Level 4 certificate for Data Analysts

Data Analytics Pearson BTEC Level 5 Higher National Diploma in Computing for England

Data Analysis Highfield Level 3 Diploma for Team Leaders and Supervisors (RQF)

Data Analysis and Visualisation OTHM Level 7 Diploma in Data Science

The future skills this job leans on

AI Fluency working with AI as a thinking partner

Data Fluency turning numbers into a decision

AI Governance Fluency using AI responsibly

Start my Global Learning Analytics Manager plan, free

Your Plan Illustration

Built for Global Learning Analytics Manager

4 units that map to this job, from the qualifications that cover it.

Data Analytics Primer NOCN · covers 6 of 10 standards Level 4
Data Analytics Pearson Education Ltd · covers 4 of 10 standards Level 5
Data Analysis Highfield Qualifications · covers 2 of 10 standards Level 3
Data Analysis and Visualisation OTHM Qualifications · covers 4 of 10 standards Level 7

These are the real units behind this job, in the order they rank for it. Nothing here is marked done, because this plan has not been started by anyone yet. Yours would fill in as you go.

2AI and this work

Will AI change your work as a Global Learning Analytics Manager?

Nobody can say exactly how. Here is what this job is being asked for next, and where to start.

The new skills this role is being asked for

Prompt Engineering & LLM Integration (for Analysts)
Basic Ethical AI & Data Bias Awareness

Why each one is rising

The future skills this job leans on

AI Fluencyworking with AI as a thinking partner
Data Fluencyturning numbers into a decision
AI Governance Fluencyusing AI responsibly

We'll only ever tell you what we can actually back up. No hype, no scare tactics.

What happens when you start

Next, a free account. We build your Global Learning Analytics Manager plan as you set up, starting with a Future Fluency module on using AI in your job. No card.

Your first Future Fluency module is free. After that, your AI tutor is £70 a month.

Start my Global Learning Analytics Manager plan, free Ready to pay? Subscribe for £70 a month

3What this role really is

This job involves analysing learning data to find out what training works and what doesn't. It means pulling, cleaning and analysing data from learning platforms and HR systems, then presenting findings clearly. It suits someone who likes digging into numbers to solve problems.

4What you'd actually use

The tools this job runs on, and how well you'd need to know each one.

Power BI / Tableau Intermediate

Creating basic charts from clean datasets, applying filters, exporting data, and building simple, interactive dashboards for L&D programme managers. You'll use pre-built dashboards but also create your own from scratch for specific requests.

Cornerstone OnDemand / Workday Learning / Degreed (LMS/LXP) Intermediate

Running standard reports directly from the UI, understanding the basic data schema (users, courses, completions, scores), and extracting raw data for further analysis. You know where to find the numbers.

Workday HCM / SAP SuccessFactors (HRIS) Intermediate

Pulling standard reports on employee demographics, performance ratings, and job roles. You'll often manually join this with learning data in Excel or Power BI to get a richer picture.

Excel (Power Query, XLOOKUP, Pivot Tables) Advanced

Your go-to for data cleaning, transformation, and initial analysis for datasets under, say, 1 million rows. You're a wizard with Power Query for automating repetitive steps and using XLOOKUP for merging data.

SQL (PostgreSQL, T-SQL) Intermediate

Writing basic to intermediate multi-join queries to extract and transform raw data from our data warehouse. You can filter, aggregate, and join tables to get exactly the data you need for your analyses.

Python (pandas, NumPy) Basic

Using Python scripts with pandas for more complex data cleaning, transformation, and basic statistical analysis where Excel just won't cut it. You're comfortable writing and adapting scripts for these tasks.

Qualtrics / SurveyMonkey Intermediate

Building simple surveys from templates, implementing basic survey logic, and exporting response data for analysis. You know how to get feedback from learners and make sense of it.

Jira / Confluence Intermediate

Updating tickets for your analytical requests, viewing project boards to track progress, and reading/contributing to documentation for data models and processes. It's how we keep track of our work.

5What you get to decide, and how that grows

Power in a job isn't your title. It's what you're allowed to decide. Here's how it grows as you move up.

The choice	Coming in	Where you are now	The step above
Data Cleaning Methodology	Follows explicit instructions from supervisor; escalates any deviations.	Chooses appropriate cleaning methods for routine data issues; consults manager on complex or novel data quality problems.	Designs and implements data cleaning best practices for entire datasets; defines data quality standards for the team.
Dashboard Design & Publication	Updates existing dashboards with new data under direct supervision; cannot publish new dashboards independently.	Designs and publishes simple dashboards for specific business units or programmes, adhering to established design principles; manager reviews before wider release.	Designs and deploys complex, interactive dashboards and reports for leadership; establishes dashboard governance and user access protocols.
Analytical Approach for Ad-Hoc Requests	Executes pre-defined queries and analyses as instructed.	Selects appropriate analytical methods (e.g., descriptive stats, basic comparisons) to answer ad-hoc questions; consults manager on more advanced statistical needs.	Defines the analytical approach for complex business questions; recommends appropriate statistical models and methodologies.
Stakeholder Communication	Communicates findings to immediate supervisor; may draft emails for review.	Communicates findings directly to L&D Programme Managers and HRBPs; seeks manager review for executive-level communications.	Presents complex findings and recommendations directly to senior business leaders and executives; represents the team in cross-functional meetings.

6How you'll be judged

The scoreboard, and what is not on it. The hard targets, how often each is actually looked at, and the quiet signals that never reach a dashboard.

Data Refresh Accuracy

How often your scheduled data refreshes and reports are free from errors or inconsistencies.

Target · 99.5% accuracy on all scheduled data refreshes

If you're refreshing the monthly learning engagement dashboard, we'd expect the numbers to match the source systems exactly, every single time. A single mismatch in 200 data points would be a miss.

Ad-hoc Request Fulfilment SLA

The percentage of urgent, one-off data requests you complete within our agreed service level agreement.

Target · 90% of ad-hoc data requests fulfilled within 48 hours

An L&D Programme Manager needs data on course completions for a board report by Friday. If they ask on Wednesday, you'll aim to have it ready by Friday morning, no excuses.

Manual Data Cleaning Time Reduction

The amount of time you save by automating repetitive data cleaning tasks, especially for recurring reports.

Target · Reduce manual data cleaning time for quarterly reports by 10 hours within 12 months

If the Q1 report took 20 hours of manual Excel work, by Q4, you should have automated enough of that process to get it down to 10 hours or less. That's real time back for more interesting analysis.

Dashboard Usage & Adoption

How often the dashboards you build are actually viewed and used by your target audience.

Target · Average 50 unique monthly viewers for your primary dashboards

You build a new dashboard showing leadership programme impact. We'd expect to see at least 50 different people (L&D, HRBPs, business leaders) logging in to check it out each month, not just your manager.

Clarity of Insights

How well you can explain complex data findings to people who aren't data experts, making it easy for them to understand the 'so what?'

Stakeholders consistently say your reports are easy to understand. They can repeat your key findings back to you. You're asked to present your findings in team meetings, not just send a report.

Proactive Problem Identification

Your ability to spot potential issues or interesting trends in the data before someone else asks you to look for them.

You bring up a potential dip in compliance training completion rates before the Head of L&D notices. You suggest a new way to look at engagement data that no one had considered. You're not just reacting to requests, you're looking ahead.

Data Quality Improvement

Your efforts to identify and help fix underlying data quality issues in our source systems.

You've documented 3-5 recurring data errors and worked with IT or the LMS admin to get them resolved. You've implemented new data validation checks in your processes that catch errors early. The overall 'cleanliness' of your source data improves over time.

Informal Mentorship & Support

How you help other team members (especially new joiners or less data-savvy colleagues) understand and use data.

New team members come to you with basic data questions. You've shown a colleague how to use a specific Excel function or Power BI filter. You're seen as a helpful resource for data-related queries within the team.

7Would you like it

Both sides. What people enjoy, and what grinds them down.

What people enjoy

Solving Puzzles & Discovering Insights

You love the challenge of taking messy, disparate data and making it tell a coherent story. That moment when a pattern finally emerges from the noise? That's what you live for.

Spending an afternoon wrestling with two different datasets that don't quite match, then finally figuring out how to join them to reveal a new trend in course completion rates.

Making a Tangible Impact

You want your work to actually be used, not just sit in a report. Knowing that your analysis helped a programme manager improve their course or influenced a budget decision is a big win for you.

Presenting data that clearly shows a specific module in a leadership course isn't effective, leading to that module being redesigned and seeing engagement improve in subsequent cohorts.

Continuous Learning & Skill Development

You're always keen to pick up a new technique, a different tool, or a better way to do things. The world of data analytics moves fast, and you enjoy keeping up and expanding your toolkit.

Taking the initiative to learn a new Python library to automate a data cleaning task that used to take hours in Excel, even if it's not explicitly asked of you.

What frustrates people

The 'Causality vs. Correlation' Battle: You'll spend a lot of time explaining to senior leaders that just because sales went up after a training programme doesn't automatically mean the training caused it. Isolating variables is tough, and you'll be fighting this battle regularly.
Garbage In, Garbage Out: Your analysis is only as good as the data you're given. Expect to spend a significant chunk of your time — maybe 60% — cleaning, merging, and validating messy data from the LMS, HRIS, and other systems. They often weren't designed to talk to each other, so it's a constant battle.
The Quest for the 'One ROI Number': Leadership will relentlessly ask for a single, simple ROI figure for all L&D spending. You'll explain why this is a methodological nightmare and often a misleading metric, but honestly, they'll probably still ask for it next quarter.
Data Silos & Gatekeepers: Getting access to specific data, say from the Finance department for performance metrics or the Sales Ops team for quota attainment, can feel like a full-time job in itself. It involves politics, building relationships, and often, endless meetings.
'Flavour of the Month' Metrics: A new CHRO arrives, and suddenly everything needs to be measured in terms of 'Learning Velocity' or some other buzzword. This can sometimes force you to put your carefully constructed analytics roadmap on hold to chase the latest trend.
LMS Reporting Sucks: Let's be real, the built-in reporting modules of most major Learning Management Systems are pretty inflexible and often inadequate. You'll likely find yourself building complex, sometimes brittle, workarounds just to get basic data out.

What this role does not give you

A perfectly clean, pre-structured dataset ready for immediate analysis.
The ability to make high-level strategic decisions without input or approval.
A 'set it and forget it' routine; priorities and data sources will shift.
Direct management of a team (at this level).

8Who you work with

This role directly improves the effectiveness and efficiency of our global learning programmes. By providing solid data, you help us identify which training delivers real value, where we need to adjust, and how we can better support our employees' development. Ultimately, your work helps us build a more skilled and capable workforce, which directly impacts our business performance and employee retention.

Inside the business

L&D Programme Managers
HR Business Partners (HRBPs)
Talent Acquisition Team
Business Unit Leaders (e.g., Sales, Product)
IT & Data Engineering Teams

Outside the business

LMS/LXP Vendors (for data queries)
Survey Platform Providers (e.g., Qualtrics support)

9What you need before you start

Not a wish list. The things you would be expected to already have.

At least 2 years of hands-on experience in a data analyst role, ideally within HR, Learning & Development, or a related people-focused function.
Demonstrable experience with data cleaning, transformation, and manipulation using Excel (Power Query) and SQL.
Proven ability to build and maintain dashboards in Power BI or Tableau.
A solid understanding of basic statistical concepts (e.g., averages, medians, standard deviation, correlation).
Experience working with data from HRIS (e.g., Workday, SuccessFactors) and/or LMS/LXP platforms (e.g., Cornerstone, Degreed).
The ability to explain data findings clearly to non-technical audiences, both in writing and verbally.

10What to practise next

Where the job is going, and what to do about it starting this week.

Advanced SQL & Data Modelling

As our data warehouse grows and our analytical needs become more complex, you'll need to write more efficient and sophisticated SQL queries. This means moving beyond basic joins to things like Common Table Expressions (CTEs) and window functions to build more robust data models.

Common Table Expressions (CTEs) · Window Functions · Basic Data Model Design

This quarter: Pick one recurring report you build and try to rewrite the underlying SQL query using CTEs.
Next quarter: Find an online course or tutorial specifically on SQL window functions and apply them to a real-world problem.
Within 6 months: Proactively suggest improvements to existing SQL queries or data structures to your Senior Manager.

Quick win: Start by refactoring one of your most frequently used SQL queries to make it more readable and efficient. Even small improvements add up.

Basic Statistical Modelling (Regression/Classification)

To move beyond 'what happened' to 'what might happen' or 'why it happened,' you'll need to start applying basic predictive techniques. This means understanding when to use simple regression or classification models to forecast outcomes or identify key drivers.

Linear Regression (Basic) · Logistic Regression (Basic) · Model Evaluation Metrics (R-squared, Accuracy)

This quarter: Complete an online course on introductory statistics for data science, focusing on regression.
Next quarter: Try to build a very simple linear regression model in Python (using scikit-learn) on a learning dataset, even if it's just for practice.
Within 6 months: Propose a small project to your Senior Manager where a basic predictive model could add value, even if it's just a proof of concept.

Quick win: Start by simply understanding the difference between correlation and causation really well. That's the first step to good modelling.

11Staying current once you are in

What people here do to keep up

Actively participate in online data analytics communities (e.g., Reddit's r/dataanalysis, LinkedIn groups) to learn from peers and stay updated on trends.
Take online courses (Coursera, Udemy, DataCamp) to deepen your skills in Python (pandas), advanced SQL, or introductory statistics.
Attend webinars or virtual conferences on people analytics or learning technologies to broaden your understanding of the field.
Proactively seek feedback from your manager and senior colleagues on your analytical approaches and data storytelling.

12The rising capability

The rising capability

Zavmo analysis

What's rising in its place

This is where the work is heading, and the higher pay with it. Get fluent here and the shift stops being a threat and starts being your edge.

Prompt Engineering & LLM Integration (for Analysts)

Frankly, competitors are already using tools like ChatGPT and Claude to draft reports in 10 minutes that used to take 2 hours. Analysts who figure out how to use these tools effectively will outproduce their peers significantly. It's not just a 'nice to have' anymore; it's becoming essential for efficiency.

Effective Prompting for Data Summaries
Data Validation with LLMs
Code Generation & Debugging (SQL/Python)
Understanding LLM Limitations ('Hallucinations')

Basic Ethical AI & Data Bias Awareness

As we use more AI in analytics, understanding its ethical implications and potential for bias becomes crucial. Our insights need to be fair and responsible, especially when dealing with people data. You'll need to recognise when an AI model might be perpetuating existing biases in the data.

Algorithmic Bias in Data
Data Privacy in AI Contexts
Transparency & Explainability (Basic)

13What you’ll use

What you’ll use

Skills this role draws on

Technical

Kirkpatrick Model of Evaluation
Learning Transfer Analysis (Basic)
Data Storytelling & Narrative Design (Developing)
Learning Experience Data Governance (Basic)

14The pathway

The pathway

How you actually get there, here

How you become one varies far more by country than what one does. This is the UK route. Most people take one of these ways in; the right one depends on where you're starting from.

1
Junior Data Analyst / Data Associate (any domain)
1-2 years
Skills to master
- Core data cleaning, Excel proficiency (Power Query), basic SQL, understanding of data visualisation tools (Power BI/Tableau).
You're ready to move on when
- Consistently delivers accurate data extractions and basic reports.
- Can independently troubleshoot common data quality issues.
- Demonstrates curiosity about the business context of the data.
2
HR Data Specialist / HRIS Analyst
2-3 years
Skills to master
- Deep understanding of HR data structures (Workday/SuccessFactors), experience with HR reporting, exposure to people-related metrics.
You're ready to move on when
- Can pull complex HR reports and explain what the fields mean.
- Understands the sensitivity and privacy requirements of people data.
- Has a good grasp of basic HR processes and how they generate data.
3
L&D Coordinator with Data Focus
2-4 years
Skills to master
- Familiarity with LMS/LXP platforms, understanding of learning programme lifecycles, strong Excel skills, basic analytical mindset.
You're ready to move on when
- Can run standard LMS reports and identify initial trends.
- Asks 'why' when numbers don't look right in learning data.
- Shows initiative in automating manual reporting tasks.

15Where this role leads

The long view: This work can lead to managing a team of analysts, or moving into a director role overseeing wider talent and workforce analytics.

The ten Future Fluencies

Zavmo analysis

The credential is what you can do today. These are what keep you valuable.

A qualification proves you can do the job as it's defined today. These ten are what decide whether you're still the obvious person for it in five years. They're the capabilities employers are now writing into senior roles faster than people are learning them. Zavmo weaves them through whatever you study, so you come out with both: the credential and the fluency.

The highlighted ones are the Fluencies your role leans on hardest, from how Global Learning Analytics Manager is actually changing.

How you would learn this

One to one, on the work you already do

A tutor sets the task against your own job and marks the reasoning, at your pace. There is no cohort to fall behind.

A lesson from Data Analytics Primer, worked against Global Learning Analytics Manager.

Data Analytics Primer Level 4

Applied to your work in Global Learning Analytics Manager

This unit aims to equip learners with a foundational understanding of data analytics, including its applications and the stages of the data analysis lifecycle. Learners will explore various data types and structures, understand the role of data within an organisation, and recognise the importance of GDPR and compliance requirements in data handling.

How the thinking builds

1 Remember
2 Understand
3 Apply
4 Analyse
5 Evaluate
6 Create

An illustration of a Zavmo lesson, built from this role’s own route. The unit, its objective and every criterion above are the awarding body’s own words, not an example.

It is £70 a month, billed monthly. Your free first module is Future Fluency, and you can cancel any time.

Start my Global Learning Analytics Manager plan, free

See Your Progress Grow Illustration

Global Learning Analytics Manager

Kirkpatrick Model of Evaluation
Learning Transfer Analysis (Basic)
Data Storytelling & Narrative Design (Developing)
Learning Experience Data Governance (Basic)

This is your Mind Palace on learn.zavmo.ai. Every skill above comes from this role's own record, not an example borrowed from another job. A node lights up when you evidence it, and what you build stays yours between jobs. That is the part a course cannot do.

16The detail, folded away

Everything else the record holds

The career branches in full, how AI is already showing up in the day-to-day, and the questions people ask about this job. Here when you want them, out of the way while you decide.

Where it leads next, rung by rung

Where it leads

The career path, and where it branches

Global Learning Analytics Manager is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

Senior Global Learning Analytics Manager (L3)
3-5 years in current role

You'll move from owning specific analytical tasks to leading entire analytical projects from start to finish. You'll also start mentoring junior analysts and taking on more complex, predictive analysis.
- Predictive Modelling (Intermediate) Applying regression and classification models to forecast outcomes, like identifying attrition risk or predicting training effectiveness.
- Experimentation Design (A/B Testing) Designing and analysing A/B tests for learning interventions to measure their causal impact.
- Advanced Data Storytelling Crafting compelling narratives for executive audiences, focusing on strategic recommendations and business impact.
- Data Model Design (Intermediate) Contributing to the design of more robust data models for the learning analytics function.

Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, a lot of data work can be repetitive and time-consuming. But what if you could offload some of that grunt work to AI? We're not talking about replacing you, but giving you superpowers. Our AI Productivity Hub is designed to help you reclaim hours every week, so you can focus on the really interesting stuff: finding those 'aha!' insights.

For a Global Learning Analytics Manager, AI isn't just a buzzword; it's a practical tool that can seriously speed up your data cleaning, analysis, and reporting. Imagine spending less time wrestling with spreadsheets and more time crafting compelling stories from the data. That's the goal here.

Automated Data Harmonisation

Use AI scripts to automatically clean, map, and merge disparate datasets from our LMS (Cornerstone), HRIS (Workday), and engagement surveys (Qualtrics). The AI flags anomalies for your human review, cutting down on hours of manual reconciliation. Think of it as your super-efficient data assistant.

Thematic Feedback Analysis

Got thousands of open-ended comments from course feedback surveys (our 'smile sheets')? Feed them into an LLM to instantly identify and quantify key themes, sentiment, and actionable suggestions. This replaces hours of tedious manual reading and categorisation, giving you insights in minutes.

Pre-Meeting Research Synthesis

Need to quickly get up to speed on a new learning theory or analytical method? Ask an AI assistant to 'Summarise the top 5 academic papers from the last 3 years on measuring learning transfer for soft skills.' You'll get instant domain expertise, helping you prepare for high-stakes meetings with business leaders much faster.

Executive Summary Drafting

After you've built a complex dashboard in Tableau, you can paste screenshots or key data points into an LLM. Prompt it to 'Write a 3-bullet point executive summary for a CHRO explaining the key insights and recommended actions from this data.' This saves you valuable time crafting that perfect, punchy summary.

Common questions

How do you become a Global Learning Analytics Manager?

Common routes in include Junior Data Analyst / Data Associate (any domain) (1-2 years), HR Data Specialist / HRIS Analyst (2-3 years) and L&D Coordinator with Data Focus (2-4 years). Times vary with prior experience.

Where can a Global Learning Analytics Manager progress to?

This role can lead on to Senior Global Learning Analytics Manager (3-5 years in current role), depending on the skills you build.

What new skills matter most for a Global Learning Analytics Manager?

Increasingly, Prompt Engineering & LLM Integration (for Analysts) and Basic Ethical AI & Data Bias Awareness. These are the skills our record of this role lists as growing.

You're not behind. You're right on time. The shift is only just beginning. Your role won't look the same in two years. Be the one who leads the change, not the one it happens to. Start my Global Learning Analytics Manager plan, free What happens next: a free account, then we build your plan as you set up. No card. Build your plan, see your roadmap and meet the twelve tutors matched to you. All free. When you're ready to start learning, it's £70 a month, billed monthly. Cancel any time and billing stops.

Global Learning Analytics Manager

Where should we send it?

Two short quizzes about you

Will AI change your work as a Global Learning Analytics Manager?

What's rising in its place

Prompt Engineering & LLM Integration (for Analysts)

Basic Ethical AI & Data Bias Awareness

Skills this role draws on

Technical

How you actually get there, here

Junior Data Analyst / Data Associate (any domain)

HR Data Specialist / HRIS Analyst

L&D Coordinator with Data Focus

The credential is what you can do today. These are what keep you valuable.

Roles this leads to

One to one, on the work you already do

Everything else the record holds

The career path, and where it branches

Senior Global Learning Analytics Manager (L3)

Where AI is starting to help

Automated Data Harmonisation

Thematic Feedback Analysis

Pre-Meeting Research Synthesis

Executive Summary Drafting

Common questions

Want a read on where you stand?