UK

United Kingdom · Technology and data · Senior (5-8 years)

Senior Analytics Support Analyst

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 bandSenior (5-8 years)
  • Direct reportsNo direct reports
  • Reports toAnalytics Support Manager
  • UK framework levelUsually a manager, or the deepest specialist in a team

Also advertised as Senior BI Support Analyst · Analytics Specialist · Data Operations Analyst

Built on 43,079 real UK job descriptions we analysed · grounded in what real qualifications cover

Two short quizzes about you

1From this job to a course

The qualifications matched to this job

NCC Education Level 5 Extended Diploma in Computing with Data Science NCC Education Limited · Level 5 · covers 4 of 11 standards
Pearson BTEC Level 5 Higher National Diploma in Computing for England Pearson Education Ltd · Level 5 · covers 4 of 11 standards
Pearson BTEC Level 5 Higher National Diploma in Digital Technologies Pearson Education Ltd · Level 5 · covers 2 of 11 standards
NOCN Level 5 Certificate in Productivity Improvement Practice NOCN · Level 5 · covers 1 of 11 standards
NOCN Level 4 certificate for Data Analysts NOCN · Level 4 · covers 7 of 11 standards
OTHM Level 7 Diploma in Data Science OTHM Qualifications · Level 7 · covers 4 of 11 standards

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

The units you would start with

Introduction to Data Science and Big Data NCC Education Level 5 Extended Diploma in Computing with Data Science
Data Analytics Pearson BTEC Level 5 Higher National Diploma in Computing for England
Data Analysis in support of Productivity Improvement Projects NOCN Level 5 Certificate in Productivity Improvement Practice
Data Analytics Primer NOCN Level 4 certificate for Data Analysts
Data Science Foundations 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
Automation Fluency building AI into how work runs
Systems Fluency seeing the whole picture
Your Plan Illustration

Built for Senior Analytics Support Analyst

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

  1. Introduction to Data Science and Big Data NCC Education Limited · covers 4 of 11 standards Level 5
  2. Data Analytics Pearson Education Ltd · covers 4 of 11 standards Level 5
  3. Data Analysis in support of Productivity Improvement Projects NOCN · covers 1 of 11 standards Level 5
  4. Data Analytics Primer NOCN · covers 7 of 11 standards Level 4
  5. Data Science Foundations OTHM Qualifications · covers 4 of 11 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 Senior Analytics Support Analyst?

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
  • Advanced Data Observability & Alerting
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 Senior Analytics Support Analyst plan as you set up, starting with the free Automation Fluency module. No card.

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

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Keep this read. One email with what AI is changing in this job, the new skills it asks for and where to start free. Nothing else follows unless you confirm your address.

3What this role really is

You fix things when the numbers don't add up or a dashboard breaks. You work out why it broke, stop it happening again, and help others understand the data.

4What you'd actually use

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

SQL (PostgreSQL, T-SQL) Advanced

Writing complex multi-table `JOIN`s, CTEs, and window functions to debug data discrepancies, validate report outputs, and perform ad-hoc data investigations. You'll also be optimising queries written by others.

BI Platforms (Tableau, Power BI, Looker) Advanced

Debugging complex calculation errors, data source connection failures, and permission issues. You'll build diagnostic dashboards to monitor BI tool health and train business users on advanced self-service features.

Ticketing Systems (Jira Service Management, Zendesk) Advanced

Identifying trends in ticket data to pinpoint systemic issues, creating new automation rules and SLA policies, and mentoring junior analysts on efficient ticket handling and escalation procedures.

Knowledge Base (Confluence, Notion) Advanced

Authoring new, comprehensive knowledge base articles, detailed runbooks, and user-facing guides. You'll also be establishing and maintaining documentation standards for the team.

Data Warehouse (Snowflake, BigQuery, Redshift) Intermediate

Navigating complex schemas, understanding the impact of data modelling decisions on query performance, and tracing data lineage from the warehouse to the dashboard to identify data flow issues.

Spreadsheets (Excel, Google Sheets) Expert

Using Power Query for data cleaning and transformation, debugging complex formulas, and connecting sheets to live data sources for advanced ad-hoc analysis. You'll understand the limitations and risks of spreadsheet-based reporting.

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
Technical Approach to Issue Resolution Follows prescribed runbooks; consults senior analysts on deviations. Chooses best approach from known methods; escalates novel problems. Designs novel solutions for complex issues; defines best practices for the team; consults manager on strategic implications.
Prioritisation of Support Tickets Prioritises based on SLA and manager guidance. Manages own queue based on SLA, impact, and existing workload. Prioritises own queue and helps junior analysts with theirs; identifies and escalates P0/P1 issues to manager; influences overall team prioritisation.
Process Improvement Suggestions Identifies minor inefficiencies and suggests them to manager. Proposes and implements small-scale process improvements within own scope. Designs and leads the implementation of significant process improvements across the team; influences cross-functional process changes; recommends tools/technologies up to £5K.
Mentoring & Training Receives training and asks questions. Provides informal guidance to new joiners on specific tasks. Formal mentoring of 1-2 junior analysts; develops training materials; leads internal knowledge-sharing sessions.

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.

Recurring Ticket Reduction
The percentage decrease in tickets related to specific, known issues after you've implemented a fix or created new documentation.
Target · Reduce recurring tickets for top 3 issues by 50% per quarter.

If 'Dashboard X data missing' generated 20 tickets last quarter, you'd aim for 10 or fewer this quarter after your fix and new knowledge base article.

Knowledge Base Contribution
The number of new or significantly updated knowledge base articles, runbooks, or user guides you've authored to empower self-service.
Target · Author/update 10+ high-quality KB articles per quarter.

You create a new guide on 'How to interpret the Sales Funnel Dashboard' and update the 'Common Data Lag Issues' article with new troubleshooting steps.

Mentee Progression
The successful onboarding and skill development of junior analysts you've mentored, evidenced by their increased autonomy and resolution rates.
Target · Successfully onboard and mentor 1-2 junior analysts per year, with measurable improvement in their FCR (First Contact Resolution) rates.

A junior analyst you've mentored increases their FCR rate from 50% to 75% over six months, taking on more complex tickets independently.

Complex Issue Resolution Time
The average time it takes you to resolve tickets that have been escalated from L1/L2 support or are categorised as high complexity.
Target · Average resolution time for escalated tickets under 48 hours.

A P1 data integrity issue that usually takes 3 days to fix is resolved by you in 1.5 days due to your methodical approach.

Proactive Problem Solving
Moving beyond just fixing bugs to identifying systemic issues and proposing solutions before they become widespread problems.
  • You're flagging potential data quality issues before users notice them. You're suggesting improvements to data pipelines or BI platform configurations. You're not just reacting, you're anticipating.
Stakeholder Satisfaction & Trust
How much business users and internal teams (like Data Engineering) trust your expertise and the data you help manage.
  • Business users specifically ask for your help on tricky issues. Data Engineering consults you on potential upstream changes. You're seen as a reliable source of truth and a helpful partner, not just a ticket resolver.
Effective Knowledge Transfer
Your ability to clearly explain complex technical issues to non-technical users and to effectively train junior team members.
  • Junior analysts consistently improve after your guidance. Business users understand your explanations and feel empowered. Your documentation is regularly referenced and praised for its clarity.
Process Improvement & Automation
Your contributions to making our support processes more efficient, scalable, and less reliant on manual effort.
  • You've identified a repetitive task and suggested an automation. You've streamlined an existing runbook. You're always looking for ways to make things smoother for everyone.

7Would you like it

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

What people enjoy
Solving Puzzles

You get a real kick out of taking a vague, complex problem – 'the dashboard is broken' – and systematically breaking it down until you find the exact line of code or data point that's causing the issue. That 'aha!' moment is what you live for.

Spending an afternoon tracing a data point through three different systems and a dozen SQL queries until you find the tiny ETL bug causing a discrepancy in the weekly revenue report.

Helping People

You genuinely enjoy being the person who brings clarity and relief to someone who's frustrated or confused by data. Seeing a user's face light up when you've fixed their problem or taught them how to find the answer themselves is incredibly rewarding.

Guiding a marketing analyst through a complex report, not just fixing their filter error, but showing them *why* it was wrong so they can avoid it next time.

Building Better Systems

You're not content with just patching things up. You're always thinking, 'How can we prevent this issue from happening again?' This means you enjoy creating robust documentation, automating repetitive tasks, and suggesting improvements to our data tools and processes.

After fixing a recurring permission issue, you design a new, simpler process for access requests and write a clear Confluence guide, reducing future tickets by 80%.

What frustrates people
  • Being treated as a personal data concierge for senior leaders who need a 'quick number' for a meeting in 10 minutes, completely derailing your planned work.
  • Receiving incredibly vague tickets like 'The sales report is broken' with no context, screenshots, or explanation of what they expected to see.
  • Spending hours investigating a 'critical bug' only to discover the user had applied the wrong date filter or didn't understand a basic dashboard feature.
  • When an engineering team changes an API, a source table, or a field name without telling the analytics team, causing a cascade of failures in dashboards downstream.
  • Being caught between two departments (e.g., Sales and Finance) who disagree on a metric's definition, and being pressured to make the numbers match one side's view.
  • Having to patiently explain why a 'simple' request requires a complex data model change that will take two weeks, not two hours.
  • Being held responsible for the accuracy and uptime of dashboards, but having no direct control over the source data quality or the engineering pipelines that feed them.
What this role does not give you
  • A perfectly predictable day-to-day routine; urgent issues pop up constantly.
  • The ability to always see your work directly result in a new product feature or a massive business decision.
  • Complete control over data definitions or upstream data quality; you'll often be reacting to issues caused elsewhere.
  • A quiet, uninterrupted environment for deep, heads-down coding (you'll be interacting with people a lot).

8Who you work with

This role directly underpins the reliability and trustworthiness of our entire data ecosystem. Your work ensures business users can make data-driven decisions confidently, reducing operational friction and preventing costly errors. You'll help improve data literacy across the organisation and directly contribute to the efficiency of our BI and data platforms.

Inside the business
  • Analytics Support Manager (for strategic alignment)
  • Junior Analytics Support Analysts (for mentoring and guidance)
  • BI Developers (to report bugs and suggest platform improvements)
  • Data Engineering Team (to flag upstream data issues and schema changes)
  • Product Teams (to understand new feature impact on data)
  • Business Users (Sales, Marketing, Finance – your primary 'customers')

9What you need before you start

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

  • At least 5 years of hands-on experience in an analytics support, BI support, or data operations role, where you were regularly debugging complex data issues.
  • Demonstrable advanced proficiency in SQL – you should be able to write and debug complex queries with confidence, not just simple SELECT statements.
  • Proven experience with at least one major BI platform (Tableau, Power BI, Looker) at an advanced level, including troubleshooting calculations and data connections.
  • Experience in mentoring junior team members or leading small technical projects.
  • A track record of identifying recurring problems and implementing solutions that reduce future support tickets.
  • Strong ability to communicate complex technical issues clearly to non-technical business users, both verbally and in writing.

10What to practise next

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

Advanced Data Warehousing Concepts

As our data grows, understanding the underlying architecture of our data warehouse (Snowflake, BigQuery) becomes crucial. You'll need to move beyond just querying data to understanding how data is stored, indexed, and partitioned, and how these decisions impact query performance and cost.

Star Schema vs. Snowflake Schema · Indexing and Partitioning Strategies · Materialised Views and Caching · Data Security and Access Controls · Cost Optimisation in Cloud Data Warehouses

  • This month: Spend an hour weekly reviewing our data warehouse documentation and schema diagrams.
  • Month 2: Shadow a Data Engineer during a data modelling session or a performance tuning exercise.
  • Month 3: Take an online course on advanced data warehousing concepts specific to our platform (e.g., Snowflake Data Warehousing Fundamentals).
  • Month 4: Propose and implement a small optimisation (e.g., a new index or a materialised view) for a frequently slow query.

Quick win: Use your BI tool's query history to identify the top 5 slowest queries and investigate their execution plans in the data warehouse.

BI Platform Governance & Administration

As our BI platform (Tableau, Power BI) grows, simply building dashboards isn't enough. You'll need to understand how to manage the platform itself, ensuring it's secure, performant, and easy for users to navigate. This is about being a steward of the platform, not just a user.

Content Lifecycle Management · User Permissions and Row-Level Security · Performance Monitoring & Optimisation · Data Source Governance · Platform Upgrades & Maintenance

  • This month: Get access to the BI platform's admin console and spend time exploring its features and settings.
  • Month 2: Review our current BI governance policies and identify areas for improvement.
  • Month 3: Take an advanced course on BI platform administration (e.g., Tableau Server Certified Associate).
  • Month 4: Lead a project to clean up stale dashboards or optimise a frequently used data source.

Quick win: Work with your manager to get temporary admin access to our BI platform and explore the usage statistics to identify underutilised or problematic content.

11Staying current once you are in

What people here do to keep up
  • Regularly participate in industry webinars and online courses on advanced SQL, data warehousing, and BI platform administration.
  • Attend local or virtual meetups for data professionals to network and learn about new trends and best practices.
  • Contribute to open-source data projects or build personal projects to hone your technical skills.
  • Seek out opportunities to mentor junior colleagues or lead internal knowledge-sharing sessions.
  • Read books and articles on data governance, data quality, and data observability to deepen your theoretical understanding.

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

This isn't just a buzzword; it's already here. Competitors are using tools like ChatGPT and Claude to draft reports in 10 minutes that used to take 2 hours. Analysts who figure out how to effectively use these Large Language Models (LLMs) will outproduce their peers by a significant margin. It's about working smarter, not harder.

  • Context Windows and Token Limits
  • Temperature Settings for Different Tasks
  • RAG (Retrieval Augmented Generation) Architectures
  • Output Validation and Hallucination Detection
  • Prompt Chaining for Complex Analysis

Advanced Data Observability & Alerting

Moving from reactive support to proactive prevention is the holy grail. As data volumes and complexity grow, manually checking everything is impossible. The next step is building sophisticated systems that automatically detect data quality issues, performance bottlenecks, or unexpected changes before they impact users. This shifts your role from 'firefighter' to 'early warning system designer'.

  • Data Quality Dimensions
  • Anomaly Detection Algorithms
  • Monitoring Tool Integration
  • Data Contract Enforcement
  • Service Level Objectives (SLOs) for Data

13What you’ll use

What you’ll use

Skills this role draws on

Technical

  • Root Cause Analysis (RCA)
  • Stakeholder Triage & Management
  • Data Lineage Tracing
  • Technical Documentation for Non-Technical Audiences
  • User Acceptance Testing (UAT) Facilitation
  • Data Quality Monitoring

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

    Internal Promotion (Analytics Support Analyst L2)

    2-3 years at L2

    Skills to master

    • Mastering complex ticket resolution independently, proactively identifying recurring issues, beginning to mentor new joiners, and taking ownership of small process improvements.

    You're ready to move on when

    • Consistently exceeds SLA targets for complex tickets.
    • Has identified and helped resolve at least 3 recurring issues.
    • Regularly assists and guides newer team members.
    • Proactively suggests improvements to support processes or documentation.
    • Demonstrates strong communication with both technical and non-technical stakeholders.
  2. 2

    External Hire (Senior Data Analyst / BI Developer with Support Focus)

    Direct entry with 5-8 years experience

    Skills to master

    • Deep understanding of data warehousing, advanced SQL, BI platform administration, and a proven track record of debugging and optimising data systems while supporting business users.

    You're ready to move on when

    • Can demonstrate complex SQL query writing and optimisation.
    • Has experience troubleshooting end-to-end data pipelines.
    • Can articulate examples of leading process improvements or significant issue resolutions.
    • Proven ability to communicate with diverse stakeholder groups.
    • Experience working with ticketing and knowledge base systems.
  3. 3

    Data Analyst (Mid-Level) Transition

    3-5 years as a Data Analyst + 1-2 years focused on support

    Skills to master

    • Developing strong troubleshooting skills, understanding data lineage, mastering BI tool administration, and honing communication skills for user support. This path requires a shift from pure analysis to a support-oriented mindset.

    You're ready to move on when

    • Demonstrated interest in data quality and system reliability.
    • Experience with data validation and reconciliation.
    • Strong SQL and BI tool skills, with a desire to apply them to problem-solving.
    • Enjoys helping others and explaining technical concepts.

15Where this role leads

The long view: This work can lead into managing an analytics support team, directing BI strategy across an organisation, or working as a specialist data quality engineer.

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 Senior Analytics Support Analyst 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 Introduction to Data Science and Big Data, worked against Senior Analytics Support Analyst.

Introduction to Data Science and Big Data Level 5

Applied to your work in Senior Analytics Support Analyst

The objective of this unit is to provide learners with a systematic understanding of Data Science and Big Data concepts, including their characteristics and applications. Learners will develop proficiency in data collection, design, and modelling techniques, and will be able to select appropriate tools for data pre-processing and apply analytical techniques to generate insights from data.

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.

See Your Progress Grow Illustration
Senior Analytics Support Analyst
  • Root Cause Analysis (RCA)
  • Stakeholder Triage & Management
  • Data Lineage Tracing
  • Technical Documentation for Non-Technical Audiences
  • User Acceptance Testing (UAT) Facilitation
  • Data Quality Monitoring
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

Senior Analytics Support Analyst is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. Lead Analytics Support Analyst (L4)

    3-5 years in Senior role

    This is a step up into a more strategic and leadership-focused role, where you'll be designing support systems and potentially managing a small team.

    • Support System Architecture Designing the entire support workflow, including automation and integration with other systems.
    • Advanced Data Governance Contributing to enterprise-wide data governance policies.
    • Budget Management Managing a small budget for tools and resources.
    • Performance Reporting Reporting on team performance metrics (CSAT, TTR) to senior leadership.
Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, a big chunk of an Analytics Support Analyst's day can feel like Groundhog Day: answering the same questions, debugging similar issues, or trying to explain complex data concepts. But what if AI could handle some of that heavy lifting, freeing you up for the really interesting, complex stuff?

We're not talking about replacing you; we're talking about giving you a superpower. Imagine having an intelligent assistant that helps you triage tickets, diagnose problems faster, and even draft documentation. That's the future we're building, and you'll be at the forefront of using these tools to make your job more impactful and less repetitive.

Automated Ticket Triage & Routing

Imagine an AI model that reads every incoming ticket, instantly identifies keywords like 'Tableau permissions' or 'slow query', automatically categorises the issue, assigns priority, and routes it to the right queue. It can even suggest relevant knowledge base articles to the user before you even see the ticket. This means fewer 'easy' tickets hitting your queue and more time for complex investigations.

Root Cause Analysis Accelerator

When a critical dashboard fails, an AI agent can scan system logs, query histories, and recent code check-ins across related data systems. It then provides you with a ranked list of probable causes, like 'Upstream ETL job `load_sales_fact` failed at 3:15 AM' or 'Query runtime increased 500% after yesterday's deployment'. This cuts down hours of manual digging into minutes.

Natural Language Query Explanation

Ever had to explain a complex, 200-line SQL query to a non-technical stakeholder? Just paste it into an AI tool and ask it to 'explain this to a sales manager'. It'll generate a simple, bullet-pointed summary of what the query does (e.g., 'This finds all customers in the UK who bought Product X but not Product Y in the last 90 days'), saving you loads of time and frustration.

First-Draft Documentation Generator

Creating user documentation for a new dashboard or a complex data definition can be a real drag. Point an AI tool at a new dashboard and ask it to 'create user documentation'. It analyses the charts, filters, and underlying data fields to generate a structured first draft, explaining each component, its purpose, and the definitions of key metrics. You just review and refine.

Common questions

Common questions

How do you become a Senior Analytics Support Analyst?

Common routes in include Internal Promotion (Analytics Support Analyst L2) (2-3 years at L2), External Hire (Senior Data Analyst / BI Developer with Support Focus) (Direct entry with 5-8 years experience) and Data Analyst (Mid-Level) Transition (3-5 years as a Data Analyst + 1-2 years focused on support). Times vary with prior experience.

Where can a Senior Analytics Support Analyst progress to?

This role can lead on to Lead Analytics Support Analyst (3-5 years in Senior role), depending on the skills you build.

What new skills matter most for a Senior Analytics Support Analyst?

Increasingly, Prompt Engineering & LLM Integration and Advanced Data Observability & Alerting. 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 technology and data

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

Skills such as advanced SQL, BI tools, data lineage, root cause analysis and stakeholder management apply across most industries that use data. This includes FinTech, e-commerce, healthcare and consultancy work.

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 free Automation Fluency module 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.