UK

United Kingdom · Learning and development · Lead (8-12 years)

Lead Learning Analytics Consultant

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.

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  • Experience bandLead (8-12 years)
  • Direct reports3-8 reports
  • Reports toDirector, Learning & Workforce Analytics
  • UK framework levelUsually a manager, or the deepest specialist in a team

Also advertised as Principal Learning Data Scientist · Senior Manager, Learning Insights · Analytics Lead, People Development

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

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
NOCN Level 4 certificate for Data Analysts NOCN · Level 4 · covers 6 of 10 standards
OTHM Level 7 Diploma in Data Science OTHM Qualifications · Level 7 · covers 4 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

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

The units you would start with

Data Analytics Pearson BTEC Level 5 Higher National Diploma in Computing for England
Data Analytics Primer NOCN Level 4 certificate for Data Analysts
Data Analysis and Visualisation OTHM Level 7 Diploma in Data Science
Data Analysis Highfield Level 3 Diploma for Team Leaders and Supervisors (RQF)

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
Systems Fluency seeing the whole picture
Trust Fluency keeping the digital world safe
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Your Plan Illustration

Built for Lead Learning Analytics Consultant

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

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

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

The new skill this role is being asked for

  • Ethical AI & Algorithmic Fairness in Learning
Why each one is rising

The future skills this job leans on

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 Lead Learning Analytics Consultant 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.

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3What this role really is

This job turns learning data into clear findings that senior leaders can act on. It involves measuring how learning programmes affect behaviour and business results, and presenting that analysis in a form that leaders can use.

4What you'd actually use

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

Power BI / Tableau Advanced

Designing, building, and deploying complex, interactive dashboards from scratch. Using advanced DAX/LOD calculations. Managing data sources and ensuring optimal performance for self-service analytics.

LMS/LXP APIs (e.g., Cornerstone OnDemand, Workday Learning, Degreed) Advanced

Extracting data programmatically via REST APIs. Understanding the nuances of xAPI/cmi5 data structures to build robust data pipelines. You'll be guiding your team on how to get the most out of these APIs.

HRIS Custom Reporting (e.g., Workday Report Writer, SuccessFactors BIRT) Advanced

Creating custom reports and calculated fields in HRIS systems. Understanding core HCM data objects and their relationships to integrate with learning data at a deeper level. You'll be advising on best practices here.

SQL (PostgreSQL, T-SQL) Advanced

Writing complex multi-join queries, Common Table Expressions (CTEs), and window functions to transform raw data from various sources. You'll be reviewing your team's SQL and optimising queries.

Python (pandas, NumPy, scikit-learn) Advanced

Using scripts for advanced data cleaning, transformation, statistical analysis, and building predictive models. You'll be setting coding standards and leading the development of analytical scripts.

Qualtrics / SurveyMonkey Advanced

Implementing complex survey logic, branching, and automated triggers. Analysing text data from open-ended feedback using advanced techniques. You'll be designing feedback strategies and guiding analysis.

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 choiceComing in Where you are nowThe step above
Analytical Methodology for a new programme Proposes a simple methodology (e.g., pre/post survey) to supervisor for review and approval. Selects appropriate methodology for routine problems (e.g., A/B test design for a course variant), escalates novel approaches for review. Designs and implements complex methodologies (e.g., quasi-experimental design, regression analysis) for critical programmes, consulting with Director on potential risks.
Budget Allocation for Analytical Tools Identifies a need for a new tool, researches options, and presents a business case to supervisor. Recommends specific tool purchases up to £1K, with justification, to manager for approval. Manages a small project budget (up to £5K) for ad-hoc tool licenses or data subscriptions. Recommends larger investments to Director.
Data Governance Standards Follows existing data entry and cleaning protocols. Flags data quality issues to supervisor. Proposes minor improvements to existing data quality checks or documentation standards. Designs and implements new data validation rules and documentation templates for specific data sources. Leads data quality initiatives for a workstream.
Hiring for the Analytics Team No involvement in hiring decisions beyond potentially being interviewed by a candidate. Provides feedback on technical skills during interviews for junior roles. May participate in initial screening calls. Leads technical interviews for junior and mid-level analysts. Provides strong recommendations on candidate suitability to the hiring manager.

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.

Impact Measurement Accuracy
The degree to which your analytical models accurately link learning interventions to specific business outcomes (e.g., revenue, retention, efficiency).
Target · 85% of key learning programmes have a robust, statistically sound impact measurement framework in place.

After your analysis, we can confidently say that our new sales onboarding programme contributes to a 10% faster ramp-up to quota attainment, with a statistical significance of p<0.05. You'll be able to show the workings and defend the methodology.

Self-Service Analytics Adoption
The percentage of key business stakeholders (e.g., HRBPs, L&D Managers) actively using the self-service dashboards and reports you've designed to answer their own questions.
Target · Increase active user base by 30% year-on-year, with a minimum of 20 unique users accessing dashboards weekly.

Your new 'Manager Effectiveness' dashboard sees 45 unique HRBPs using it monthly to identify coaching needs, reducing direct ad-hoc requests to your team by 15 hours a week.

Data Model Reliability & Performance
The stability and speed of the underlying data models and ETL pipelines your team manages, ensuring data is fresh and accurate for all reporting.
Target · 99.9% uptime for critical data pipelines; less than 0.1% data discrepancy rate between source systems and analytics platform.

The 'Skills Taxonomy' data model you built consistently refreshes overnight, meaning the Product team always sees up-to-date skill gaps in their quarterly planning meetings, without any manual intervention or data errors.

Team Mentorship & Development
The growth and progression of your direct reports, measured by their skill development and ability to take on more complex work independently.
Target · At least 2 junior analysts promoted to the next level within 18-24 months, with demonstrable increase in technical and communication skills.

One of your analysts, who started with basic SQL, is now independently building complex Python scripts for data cleaning and presenting their findings directly to L&D VPs, showing a clear path of development under your guidance.

Strategic Partner Influence
How often senior leaders seek your input and analytical guidance *before* making decisions about learning strategy or talent development.
  • You're invited to L&D leadership strategy meetings, not just to present data, but to help shape the agenda. Your recommendations are frequently adopted into programme design or budget shifts. You're seen as the 'go-to' person for 'what does the data say?' before major initiatives kick off.
Clarity of Data Storytelling
The ability to translate complex analytical findings into compelling, easy-to-understand narratives that resonate with non-technical executive audiences.
  • Senior leaders consistently praise your presentations for their clarity and actionable recommendations. They don't get lost in the numbers
  • they understand the 'so what?' Your reports are often shared widely within the leadership team because they're so digestible and impactful. You're able to simplify complex statistical concepts without dumbing them down.
Proactive Problem Solving
Identifying potential data issues, analytical gaps, or business questions before they become urgent problems, and proposing solutions.
  • You're flagging potential data quality issues from a new LMS integration before anyone else notices. You're suggesting new metrics to track for an upcoming programme, rather than waiting to be asked. You're anticipating the next set of questions from leadership and preparing the analysis in advance.

7Would you like it

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

What people enjoy
Solving Complex Puzzles

You'll spend a good chunk of your week untangling messy datasets, figuring out how to join disparate sources, and designing experiments to prove causality. It's like being a detective, but with numbers.

A new sales training programme launched, and the sales numbers went up, but so did the market. Your job is to figure out how much of that uplift was actually due to the training, not just the market. That's a proper puzzle.

Driving Real Business Impact

Your analysis won't just sit in a report; it will directly inform decisions about where we invest our learning budget, how we design programmes, and ultimately, how we develop our talent. You'll see your work make a tangible difference.

Your analysis on the effectiveness of our onboarding programme leads to a redesign that reduces 90-day attrition by 5%, saving the business hundreds of thousands of pounds in recruitment costs. That's real impact.

Building and Mentoring a Team

You'll be leading a small team of analysts, guiding their technical development, helping them solve tricky problems, and shaping their career paths. Seeing them grow and succeed under your leadership will be a big part of your satisfaction.

One of your junior analysts is struggling with a complex SQL query. You'll sit with them, walk through the logic, and help them debug it, not just give them the answer. You're building capability, not just delivering solutions.

What frustrates people
  • The 'Causality vs. Correlation' Battle: You'll constantly be explaining to senior leaders that just because sales went up after a training programme doesn't mean the training caused it. You'll spend half your life trying to isolate variables and manage expectations about what data can truly prove.
  • Garbage In, Garbage Out: Your analysis is only as good as the data you get. Expect to spend 60% of your time cleaning, merging, and validating incredibly messy data from the LMS, HRIS, and a dozen other systems that were never designed to talk to each other. It's not glamorous, but it's essential.
  • The Quest for the 'One ROI Number': Leadership will relentlessly ask for a single, simple ROI figure for all L&D spending. You will relentlessly explain why this is a methodological nightmare and a misleading metric, but they'll still ask for it next quarter. It's a recurring theme.
  • Data Silos & Gatekeepers: Begging the Finance department for performance data or the Sales Ops team for quota attainment data is a full-time job involving politics, horse-trading, and endless meetings. You'll need to build strong relationships to get access.
  • 'Flavor of the Month' Metrics: A new CHRO arrives, and suddenly everything needs to be measured in terms of 'Learning Velocity' or some other buzzword, forcing you to abandon your carefully constructed analytics roadmap and pivot to a new, often ill-defined, measurement framework.
  • LMS Reporting Sucks: The built-in reporting module of nearly every major Learning Management System is inflexible and inadequate. You'll be building complex, brittle workarounds just to get basic data out, and it's a constant source of frustration.
What this role does not give you
  • A perfectly structured, predictable work environment with consistent data sources.
  • Immediate gratification on every project; some initiatives take months or even years to show full impact.
  • A purely technical, heads-down role; you'll be spending a lot of time talking to people and managing expectations.

8Who you work with

This role directly shapes our organisation's ability to measure, understand, and improve its human capital. Your work will inform multi-million-pound investment decisions in learning, directly impacting employee performance, retention, and the overall skill profile of our workforce. You're essentially building the intelligence layer for how we develop our people, which is pretty critical for a business that relies on its talent.

Inside the business
  • L&D Leadership Team (VPs, Directors)
  • HR Business Partners (HRBPs)
  • Heads of Sales, Marketing, Product, and Engineering
  • IT and Data Engineering teams
  • Finance Business Partners
Outside the business
  • Learning Platform Vendors (e.g., Cornerstone, Degreed)
  • External Consulting Partners (for specialist projects)

9What you need before you start

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

  • Proven track record of designing and delivering complex analytical projects from start to finish, ideally within a people or learning analytics context.
  • Demonstrable experience leading or mentoring junior analysts, including code reviews and technical guidance.
  • Advanced proficiency in SQL for complex data manipulation and Python (with pandas/NumPy) for statistical analysis.
  • Expert-level skills in at least one major BI tool (Tableau or Power BI) for dashboard design and deployment.
  • Strong understanding of statistical concepts (e.g., hypothesis testing, regression, correlation) and their application in business contexts.
  • Experience working with large, messy datasets from multiple disparate sources (e.g., HRIS, LMS, survey platforms).
  • Excellent verbal and written communication skills, with a proven ability to present complex data to non-technical audiences.

10What to practise next

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

MLOps for Learning Models

As we move beyond one-off analyses to deploying predictive models (e.g., for attrition risk, skill recommendation) into production, you'll need to manage their entire lifecycle: deployment, monitoring, retraining, and version control. This ensures models remain accurate and useful over time.

Model Deployment & API Integration · Model Monitoring & Drift Detection · Automated Retraining Pipelines · Version Control for Models & Data

  • This month: Explore cloud platforms like AWS SageMaker, Azure ML, or Google AI Platform. Understand their basic MLOps capabilities.
  • Next quarter: Work with the IT/Data Engineering team to deploy one of your existing predictive models (e.g., a simple attrition predictor) as a test API.
  • Month 3-6: Implement basic monitoring for your deployed model, tracking its performance and identifying any degradation.
  • Month 6-12: Design an automated retraining pipeline for a key learning model, ensuring it updates regularly with fresh data.

Quick win: Start by using Git for version control on all your analytical scripts and model code. It's a fundamental MLOps practice and easy to implement today.

11Staying current once you are in

What people here do to keep up
  • Actively participate in online communities or forums dedicated to learning analytics, people analytics, or data science (e.g., LinkedIn groups, Kaggle).
  • Attend industry conferences or webinars focused on the future of work, HR technology, or advanced analytics.
  • Dedicate time each quarter to learning a new advanced statistical technique or a new feature in our core analytical tools.
  • Mentor junior colleagues or participate in internal knowledge-sharing sessions to solidify your own understanding and build leadership skills.

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.

Ethical AI & Algorithmic Fairness in Learning

As we use more predictive models (e.g., for identifying 'at-risk' learners or recommending personalised learning paths), ensuring these algorithms are fair, unbiased, and transparent becomes absolutely critical. Regulatory scrutiny and public trust demand it.

  • Bias Detection & Mitigation
  • Explainable AI (XAI)
  • Data Privacy & Security in AI
  • Human-in-the-Loop Systems

13What you’ll use

What you’ll use

Skills this role draws on

Technical

  • Kirkpatrick Model of Evaluation (Level 4 focus)
  • Learning Transfer Analysis
  • Skills Taxonomy & Ontology Design
  • Predictive Modelling
  • Data Storytelling & Narrative Design
  • Learning Experience Data Governance

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. 1

    Senior Learning Data Analyst (L3) to Lead Consultant (L4)

    2-4 years as a Senior Analyst

    Skills to master

    • Moving from owning individual projects to designing broader analytical frameworks, mentoring others, and influencing senior stakeholders. It's less about doing all the analysis yourself and more about architecting and guiding it.

    You're ready to move on when

    • Consistently delivering complex analytical projects independently, with minimal supervision.
    • Proactively identifying new analytical opportunities and proposing solutions to leadership.
    • Demonstrable experience mentoring junior team members and providing constructive feedback.
    • Strong communication skills, with experience presenting to mid-level management and influencing decisions.
  2. 2

    Data Scientist / Senior Analyst (from another domain) to Lead Consultant (L4)

    3-5 years in a similar analytical role, plus 1-2 years focused on people/learning data.

    Skills to master

    • Translating core data science skills into the specific context of learning and human capital. This means understanding learning science, HR data nuances, and the unique challenges of measuring human behaviour. You'll need to quickly get up to speed on our internal systems and stakeholder landscape.

    You're ready to move on when

    • A strong portfolio of data science projects, demonstrating advanced analytical and modelling capabilities.
    • A clear passion for learning & development and human capital challenges.
    • Ability to quickly learn new domain knowledge and adapt analytical approaches to a new field.
    • Excellent communication skills, with an ability to bridge the gap between technical and HR/L&D language.
  3. 3

    Experienced L&D Professional with Strong Analytical Skills to Lead Consultant (L4)

    5-8 years in L&D, plus 2-3 years dedicated to developing advanced analytical capabilities.

    Skills to master

    • Deepening technical skills in SQL, Python, and BI tools to match analytical rigour, while leveraging existing L&D domain expertise to design highly relevant and impactful measurement strategies. This path is about adding technical depth to existing domain knowledge.

    You're ready to move on when

    • Proven experience designing and delivering learning programmes, with a strong understanding of their objectives and desired outcomes.
    • Demonstrable self-taught or formal training in advanced data analytics (SQL, Python, statistics).
    • A strong network within the L&D community and an understanding of common challenges and opportunities.
    • A clear drive to move into a more data-centric role and lead analytical initiatives.

15Where this role leads

The long view: This work can lead to director-level analytics roles overseeing learning and workforce data, or to a VP position setting the strategy for talent analytics across a company.

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 Lead Learning Analytics Consultant is actually changing.

Where it leads

Roles this leads to

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, worked against Lead Learning Analytics Consultant.

Data Analytics Level 5

Applied to your work in Lead Learning Analytics Consultant

The objective of this unit is to enable learners to understand and apply data analytics techniques for decision-making. Learners will be able to apply descriptive, predictive, and prescriptive analytic methods, utilising statistical methods, to convert raw data into actionable insights and determine the best course of action.

How the thinking builds
  1. 1 Remember
  2. 2 Understand
  3. 3 Apply
  4. 4 Analyse
  5. 5 Evaluate
  6. 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 Lead Learning Analytics Consultant plan, free
See Your Progress Grow Illustration
Lead Learning Analytics Consultant
  • Kirkpatrick Model of Evaluation (Level 4 focus)
  • Learning Transfer Analysis
  • Skills Taxonomy & Ontology Design
  • Predictive Modelling
  • Data Storytelling & Narrative Design
  • Learning Experience Data Governance
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

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

  1. Global Learning Analytics Manager (L5)

    3-5 years as a Lead Learning Analytics Consultant

    This is a step into formal management, where you'll be responsible for the entire learning analytics function, managing a larger team (10-25 people, including other leads/managers), setting the strategic roadmap, and owning the relationship with IT and Data Engineering.

    • Enterprise Data Architecture Making strategic decisions on data warehousing solutions for learning data.
    • Platform Integration Strategy Architecting data flow between multiple learning platforms (e.g., post-merger).
    • Strategic Planning Tools Using tools like Anaplan or Workday Adaptive Planning to model L&D budget impact and forecast skill gap closure.
Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, a huge chunk of a Learning Analytics Consultant's time goes into repetitive tasks. Imagine getting back a full day or two every week to focus on the truly strategic, high-impact work. That's not a pipe dream; it's what AI can do for you right now.

We're not talking about replacing your brain; we're talking about giving you a seriously powerful co-pilot. AI tools are already transforming how we handle data, analyse feedback, and even draft our executive summaries. For a Lead Learning Analytics Consultant, this means less time wrestling with messy data and more time building impactful stories and leading your team.

Automated Data Harmonisation

Use AI-powered scripts to automatically clean, map, and merge disparate datasets from your LMS (Cornerstone), HRIS (Workday), and engagement surveys (Qualtrics). It'll flag anomalies for your review, saving you hours of tedious manual reconciliation and ensuring your data is always pristine.

Thematic Feedback Analysis

Feed thousands of open-ended comments from course feedback surveys ('smile sheets') into a Large Language Model (LLM). It will instantly identify and quantify key themes, sentiment, and actionable suggestions, replacing days of manual reading and categorisation with a concise, data-driven summary.

Pre-Meeting Research Synthesis

Preparing for a high-stakes meeting with business leaders? Ask an AI assistant to 'Summarise the top 5 academic papers from the last 3 years on measuring learning transfer for soft skills' or 'Outline the key challenges in skills taxonomy design'. You'll get instant domain expertise, making you look like a research guru in minutes.

Executive Summary Drafting

After you've built a complex dashboard in Tableau, you can paste screenshots or key data points into an LLM and prompt it to 'Write a 3-bullet point executive summary for a CHRO explaining the key insights and recommended actions from this data, focusing on business impact.' It's a massive time-saver for crafting compelling narratives.

Common questions

Common questions

How do you become a Lead Learning Analytics Consultant?

Common routes in include Senior Learning Data Analyst to Lead Consultant (2-4 years as a Senior Analyst), Data Scientist / Senior Analyst (from another domain) to Lead Consultant (3-5 years in a similar analytical role, plus 1-2 years focused on people/learning data.) and Experienced L&D Professional with Strong Analytical Skills to Lead Consultant (5-8 years in L&D, plus 2-3 years dedicated to developing advanced analytical capabilities.). Times vary with prior experience.

Where can a Lead Learning Analytics Consultant progress to?

This role can lead on to Global Learning Analytics Manager (3-5 years as a Lead Learning Analytics Consultant), depending on the skills you build.

What new skills matter most for a Lead Learning Analytics Consultant?

Increasingly, Ethical AI & Algorithmic Fairness in Learning. These are the skills our record of this role lists as growing.

Free tools

Want a read on where you stand?

Three free tools, no account needed. Each one ends with a role, a level and a way in.

Read the Zavmo manifesto to understand how you will be taught Meet your tutors

Or try another one

17Where to go from here

Other roles at Level 5

Same depth of qualification, different job. Useful if the work appeals but this particular role does not.

 More jobs in learning and development

Stay in the field you know and move sideways rather than up.
If you leave this industry

The skills involved apply beyond one sector. Data analysis and reporting can transfer into people analytics roles in industries such as FinTech or healthcare, or into data science and analytics leadership roles outside HR. Turning data into business insight is valued across many fields.

This role profile is © 2026 Growth Engineering Technologies Ltd. Built from UK occupational standards and regulated qualification data, and written for Zavmo.

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 Lead Learning Analytics Consultant 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.