What re-keying actually costs a UK small business (and why nobody can tell you)

TL;DR

Nobody believes productivity statistics any more. That is not because owners are complacent. It is because many of the most repeated numbers do not deserve to be believed.

Re-keying — typing the same information into a second system by hand — is a perfect example. Every small business owner recognises the problem. Bank receipts copied into a spreadsheet. Emailed instructions retyped into a job system. Job sheets turned into invoices. Timesheets copied into payroll. Nobody needs a statistic to know that processes go stale, software ages, and workarounds quietly become part of the payroll.

The trouble is that the statistics used to prove this obvious point are often worse than useless. They are usually real figures, but with the labels torn off: date, country, sample, sponsor, method, and caveat. Restore those labels and the claims become much less useful for a UK small business.

Nobody directly measures what re-keying costs UK SMEs. Not the ONS, not HMRC, not the Department for Business and Trade. We searched the main official sources extensively. They measure wages, admin burdens, tax compliance, digital adoption, software use, and business population. They do not measure the thing itself.

Into that gap, five numbers circulate endlessly: “2.5 hours a day searching for information”, “$3.1 trillion a year”, “$12.9 million a year”, “120 working days on admin”, and “7–18% productivity improvement per tool”. Almost every one traces back to a real document. What has been stripped away in circulation is the context that lets you decide whether it applies to your firm.

A decade of that has bought the opposite of urgency. Owners have learned — reasonably — to shrug at the whole subject.

So this piece does two things. First, it puts the labels back on the five statistics you are most likely to meet. Second, it shows you how to produce the one number that actually matters: your own, timed in your own office, using the same basic arithmetic HMRC uses when it values admin time — hours multiplied by an ONS median wage.

As a scale marker: five hours a week of re-keying at a book-keeper’s loaded wage is about £4,300 a year. That is not a productivity vibe. That is a payroll line.

Why re-keying exists in the first place

Re-keying is rarely caused by laziness. It is usually caused by history.

A firm buys payroll software one year, a CRM three years later, a job-management tool after that, and a finance system somewhere in between. The bank has its own export format. HMRC has its own gateway. A supplier insists on a portal. A client sends work by email because that is how they have always done it. Then someone builds a spreadsheet to bridge the gap, and the spreadsheet becomes a department.

Nobody chose the system as it exists today. It accreted.

That matters, because it changes the solution. If re-keying were just a discipline problem, the answer would be “train people better”. Sometimes that helps. But most re-keying is not a people problem. It is a systems problem. The same piece of information has to move from one place to another, and the business has not given it a pipe, so a person becomes the pipe.

That is why the cost is so easy to miss. The work is not dramatic. It is ten minutes here, twelve minutes there, a copied address, a retyped amount, a job status moved from one tab to another. In isolation, each loop looks too small to fix. In aggregate, it becomes a hidden subscription.

The question is not whether re-keying exists. The question is whether it is worth removing. And that cannot be answered by a marketing statistic.

The claim landscape: five famous numbers, relabelled

Type “cost of manual data entry” into a search engine and every result on the first page is written by a company selling the cure. That is not a complaint. It is a measurement; we ran the searches. Official sources simply do not appear, because official sources have no direct figure for re-keying.

Into that vacuum, five numbers circulate endlessly. Here is where each one actually comes from.

“Knowledge workers spend 2.5 hours a day searching for information.” This traces to an IDC white paper from July 2001 — sponsored, it says on the back page, by Inktomi, an enterprise-search vendor. The paper is honest about what the number is: “We use a general estimate that the typical knowledge worker spends about 2.5 hours per day… searching for information. This number also needs to be adjusted to reflect the circumstances of each specific enterprise.”

It was an assumption, plugged into a costing scenario, in the year Wikipedia launched. IDC’s own later work drifted steadily downwards — “between 15% and 30% of their time” by 2004, and by 2011 an IDC survey figure of 8.8 hours per week, roughly a third lower than the 12.5 hours a week the 2001 assumption implied. The 2.5-hours figure has now outlived the company that sponsored it, circulating as a “finding” it never was.

Year	Figure	What it actually was
2001	2.5 hrs/day (= 12.5 hrs/wk)	An in-text “general estimate” in a vendor-sponsored white paper
2011	8.8 hrs/wk	IDC’s own survey figure
2026	2.5 hrs/day, still	Quoted as a “finding” in current marketing

“Bad data costs the US $3.1 trillion a year.” The conduit is a 2016 Harvard Business Review piece by Thomas Redman — a consultant who sells data-quality work — who attributed the figure to IBM. IBM’s source was an infographic — since deleted; the address now redirects to a general IBM AI page — whose entire sourcing was one undifferentiated list of nine organisations, with no per-figure attribution.

The earliest traceable appearance of $3.1 trillion is a 2011 marketing press release, also without methodology. Redman himself, in a 2023 update, wrote that IBM was “pretty cagey about their methodology, but they stood by the number”, and that his own objective had been “to grab people’s attention with an outrageously big number”. Today, IBM’s only live publication on the cost of bad data uses a different figure entirely — Gartner’s. Even taken at face value, the $3.1 trillion claim is US-only, not UK SME evidence.

“Poor data quality costs organisations an average of $12.9 million a year.” This one has a real, named source: Gartner’s 2020 Magic Quadrant for Data Quality Solutions. The primary text says the figure comes from a survey of reference customers “identified by each vendor” — 154 organisations supplied by the 16 software firms being rated, asked to estimate their own costs.

That is useful context if you sell enterprise data-quality systems. It is much less useful if you run a small UK service business. Enterprise customers of data-quality vendors, self-estimating in 2020, are not a representative sample of all businesses. Gartner has published no update since. The number now circulates as if it were a measured average across all organisations — and in at least one live article as “$12.9 billion”, a thousand-fold mutation that hyperlinks, without apparent embarrassment, to the Gartner page that says million.

“Small businesses spend 120 working days a year on admin.” Real research: a 2017 study by Plum Consulting for Sage, with fieldwork by FTI Consulting across roughly 300 SMEs in each of eleven countries. But 120 days is the cross-country average; the UK-specific finding was 5.6% of staff time.

The mutations started at the launch. Sage’s own CEO foreword says “120 days” and then, four paragraphs later, “120 hours”. One UK finance-news outlet covered the launch the very next day; its page title today reads “Sage research reveals UK SMEs spending 120 hours a year on admin tasks” — wrong country scope and a days-to-hours shrink in a single line. The hours version is still live on a UK small-business site today.

“Technology adoption lifts productivity 7–18% per tool.” The origin is a genuinely good 2018 study by the Enterprise Research Centre — of micro-businesses: firms with one to nine employees, trading three years or more. Cloud computing was associated with 13.5% higher sales per employee, CRM with 18.4%, web accounting software with 11.8%.

By July 2025 that range had migrated into the ministerial foreword of a GOV.UK taskforce report as “firm-level productivity improvements of 7 to 18 per cent per technology” for SMEs in general — credited to the ERC by name, but with no citation or footnote anywhere in the document, and with 5.7 million firms inheriting a finding about the smallest sliver of them.

The pattern across all five is the same, and it is not fabrication. The date falls off first. Then the country. Then the population. Then the hedge — “up to”, “we estimate”, “micro-businesses” — sands away, and a modelling assumption from 2001 walks around in 2026 wearing the clothes of a fact.

The uncomfortable part is that the mutations keep starting inside the publishers’ own documents: Sage’s foreword; the ministerial foreword; a 2023 whitepaper that states the finding correctly on its opening pages — 53% of technology adoption efforts rated unsuccessful — then restates it, pages later in the same document, as “53% of SMEs fail”.

Nobody launders these numbers to us. They arrive pre-laundered.

The claim as it circulates	Where it was born	The labels that fell off
“2.5 hours a day searching”	IDC white paper, 2001, sponsored by a search vendor	An in-text “general estimate”, never a finding; IDC’s own 2011 survey said 8.8 hrs/week
“Bad data costs the US $3.1trn a year”	2011 press release → deleted IBM infographic → HBR, 2016	No methodology ever published; US-only; the HBR author wanted “an outrageously big number”
“$12.9m a year per organisation”	Gartner Magic Quadrant, 2020	Self-estimates by 154 enterprise reference customers supplied by the vendors being rated; never updated
“120 working days a year on admin”	Sage/Plum Consulting, 2017	Cross-country average, not UK (the UK finding: 5.6% of staff time); mutated to “120 hours” in Sage’s own foreword
“7–18% productivity per tool”	Enterprise Research Centre, 2018	Micro-businesses (1–9 staff) only; associations, not guarantees; uncited in the GOV.UK foreword that popularised it

What the evidence actually says

Strip the genre back to what survives verification and you get a smaller, stranger, more useful picture.

Typing errors are rarer than the horror stories say — and the everyday cost is hours, not blunders. The best evidence on manual entry accuracy is an NIH-funded meta-analysis published in 2025, pooling studies of trained clinical data staff from 1978–2008: single typing passes ran at 0.29% errors per field; double entry halved that to 0.14%. A 1992 clinical trial found double entry caught more errors but took 37% longer — for a difference that was not statistically significant.

So the pantomime-villain version of re-keying — “1–4% error rates!”, usually cited to studies that do not say that, or to nothing — fails. Two honest cautions survive, though. These are trained operators; a distracted office on a Friday is likely worse, and nobody has measured that cleanly. And rare errors carry tail risk: a 2011 experiment found eyeballing your typing, or “visual checking”, produced roughly thirty times the errors of double entry, and that a single wrong cell can flip an analysis.

A wage formula prices the hours. It cannot price the one bad cell that misprices a job.

UK small firms have software; what they do not have is software that talks to itself. The government’s current survey of SME technology — DBT/Ipsos, published July 2025 — finds accountancy software widespread: 47% in the online sample, 72% by telephone. But ERP, the category that exists to make systems share data, sits at 6% and 4%.

The British Chambers of Commerce, in a September 2025 report with Intuit’s sponsorship disclosed, found just 11% of firms use technology “to a great extent” to automate or streamline operations. The closest thing to a measured UK admin benchmark sits adjacent to re-keying rather than on it: the Federation of Small Businesses, surveying 1,436 owners in summer 2024, puts tax compliance at 44 hours and £4,500 a year for the average small firm — a figure that explicitly includes software subscriptions and accountants’ fees, not just time.

Here is the detail that justifies this whole article: the DBT report — the best official evidence we have — never quantifies a single hour or pound saved. It records that 40% of tech-using SMEs say technology saved time. How much? Nobody asked, or nobody could answer.

Category	Online sample (n=2,000)	Telephone sample (n=1,001)
Accountancy software	47%	72%
ERP (systems that share data)	6%	4%

The hours numbers that do exist are beliefs, honestly labelled as such — until they circulate. ABBYY, with fieldwork by Opinium in November 2020 among 1,000 UK office workers, found people believe — their word — they lose about 1 hour 23 minutes a day to automatable tasks.

Starling Bank, using Opinium again in October 2019 with 1,009 micro-businesses, found the average micro firm puts 15 of its 79 weekly working hours into financial admin — a firm-level figure, promptly garbled by a trade outlet covering it, whose URL still reads “15 hours a day”.

These vendor surveys are not worthless. They are just not measuring the same thing. One is per person; one is per business. One is all automatable tasks; one is financial admin. Neither can tell you what re-keying costs your firm.

And the counter-evidence cuts against easy automation promises, including ours. A 2020 Be the Business/McKinsey survey found 53% of UK SMEs’ technology adoption attempts were rated unsuccessful by the businesses themselves. The Enterprise Research Centre’s October 2025 analysis of nationally representative data concludes technology benefits “are not automatic”: gains are tool-specific, and some combinations associate with lower productivity.

Even HMRC’s record is two-sided. Its 2025 final evaluation of Making Tax Digital for VAT found 41% of the smaller businesses mandated in 2022 felt benefits outweighed costs — but 23% felt the reverse. Its March 2026 impact note prices the income-tax expansion at a net ongoing cost of roughly £104 per person per year, using our division of its £101m across 970,000 people.

Typing into government-approved software is not the same as systems that cooperate. Any honest costing of re-keying has to admit that removing it also costs something, and does not always pay off.

The evidence hierarchy

The mess of statistics above is not an argument against measurement. It is an argument for better measurement.

There are roughly four levels of evidence in this subject.

At the bottom are marketing claims with no usable method. These are the “$3.1 trillion” style numbers: memorable, repeatable, and mostly impossible to apply.

Above those are vendor surveys. Some are perfectly legitimate, but they usually measure beliefs, intentions, or broad categories like “admin” and “automation”. They tell us something about business sentiment. They do not tell one business what to do next.

Above those are official datasets: ONS wages, HMRC admin-cost modelling, DBT adoption surveys, Business Population Estimates. These are more reliable, but they still measure around re-keying, not re-keying itself.

The highest level, for one business, is direct measurement: time the loop, count the occurrences, price the labour, then compare that number with the cost and risk of fixing it.

That is the only level where the decision becomes practical.

What we measured

The measured evidence I can put against all this comes from the most recent of the systems I have built — fifteen years of them, from child-protection casework software to an organisational social network, recruitment analytics, and credit-note automation.

The latest is the one with publishable numbers: two years rebuilding the systems of one UK firm, which stays unnamed here. In that time:

• five separate software subscriptions replaced by one integrated system;
• around 80 automated jobs running every night;
• more than 20 external systems connected;
• over 2,000 screens and endpoints built.

Every one of those is countable in the codebase, which is the standard this article has been applying to everyone else’s numbers.

I will not dress those up as a benchmark. Measured numbers from one deployment are one deployment’s worth of evidence, and the honest literature above says outcomes vary. But the context matters.

This is a firm operating in the “great extent” band that the BCC found contains 11% of the firms it surveyed, in the integration category — ERP-like, systems-sharing-data — that the government survey found at 6% and 4%. And McKinsey’s 2017 activity analysis — a modelled ceiling, US data, but the most careful of its kind — found collecting and processing data to be the most automatable of office activities: 64% and 69% technical potential, against 9% for managing people.

Eighty nightly jobs live precisely there. The re-keying those jobs replaced did not show up in any national statistic. It showed up in payroll.

What it means for your Tuesday

You now know why no article can tell you what re-keying costs you. The honest answer does not exist in any dataset.

But it exists in your office, and it takes about an afternoon to extract. The method is the one HMRC used when it valued digital record-keeping: hours of admin time, multiplied by an ONS median wage, with your own stopwatch supplying the hours.

1. List the loops. Anywhere the same information is typed twice: bank statement lines into the job spreadsheet, emailed instructions into the job system, completed job sheets into invoices, timesheets into payroll.

2. Time one occurrence of each — with a clock. Do not estimate. Every failed number in this article started life as somebody’s estimate.

3. Count a normal week’s occurrences. The diary, sent-items folder, bank feed, job system, and invoice list will usually tell you.

4. Multiply out: minutes ÷ 60 × weekly occurrences × 46 working weeks × the loaded hourly wage of whoever does it.

Loaded wage = the ONS April 2025 median for the role, plus 15% employer National Insurance. Medians: data-entry administrator £14.45/hr, book-keeper or payroll clerk £16.33, office manager £19.20.

A worked example, for a 12-person service firm:

• bank receipts into the job ledger: 25 minutes, four times a week;
• emailed instructions into the job system: 10 minutes, fifteen times a week;
• job sheets into invoices: 12 minutes, ten times a week.

That is 370 minutes — about six hours — a week. At a book-keeper’s loaded wage, it is a little over £5,300 a year. For one member of staff’s loops.

If you would rather not do the arithmetic by hand, the re-keying calculator does the same sum with the same verified wage data — free, no email required.

Role (ONS ASHE April 2025 median, all employees)	Hourly	Loaded (+15% NI)	5 hrs/wk × 46 wks
Data-entry administrator (SOC 4152)	£14.45	£16.62	£3,820/yr
Book-keeper / payroll clerk (SOC 4122)	£16.33	£18.78	£4,320/yr
Office manager (SOC 4141)	£19.20	£22.08	£5,080/yr

Two honesty clauses before you act on your number.

First, it prices the hours only — not the invoice that went out wrong, which the error studies say is rare per keystroke but real in consequence.

Second, a priced cost is not a captured saving. The 53%-of-attempts finding and the ERC’s “not automatic” verdict both say the payback depends on fixing one process properly and measuring it, not buying a platform and hoping.

That is why I build one module at a time, and measure each one.

The direction of travel

Two verified facts before the opinion, because the opinion leans on them.

HMRC is extending quarterly digital reporting for income tax down a published timetable — from April 2028 it reaches sole traders and landlords with incomes of £20,000 and above.

And in the British Chambers’ 2025 survey, the share of SMEs actively using AI rose from 25% to 35% in a single year.

Read those however you like; the direction only points one way. The systems around your business — your competitors’, your suppliers’, the tax authority’s — are being re-invented on a schedule, whether yours is or not.

My view is this: it should be stating the obvious that a process needs re-inventing every now and then. Nothing else in a business is expected to run untouched for a decade — not the van, not the wiring, not the price list.

A process left alone does not hold its position, because everything around it moves. It rots in place, and re-keying is what the rot looks like from the inside.

That does not mean every manual loop should be automated. Sometimes the fix costs more than the friction. Sometimes a spreadsheet is the right answer. Sometimes the process is too rare, too messy, or too dependent on judgement to justify building anything around it.

But if your loops resemble the worked example, you are paying something like £5,000 a year for typing that may no longer need a person in the middle — and the £5,000 is only the part payroll can see. The part it cannot see is the distance opening up between how your firm works and how the firms you compete with will.

A decade of laundered statistics made all of this easy to shrug at. When the numbers are junk, the whole subject reads as sales patter.

Your own timed number takes the shrug away.

Sitting on the old system is not always caution. Sometimes it is a subscription to the problem, and the price is no longer just the typing.

If you would rather someone else held the stopwatch: book a free process audit and we will time the loops together.

Sources and method

Every claim above is hyperlinked where it is made; the notes below restore the labels — sponsor, sample, fieldwork dates — and add an archived copy of each source, checked 2 July 2026. Where a live page has died, or where the point is a mutation the owner may later fix, the body links to the archived capture.

• IDC / Feldman & Sherman, “The High Cost of Not Finding Information”, July 2001. Vendor white paper, sponsored by Inktomi; the 2.5 hrs/day is stated in-text as “a general estimate” used in costing scenarios. Mirror (byte-identical to a 2020 Wayback capture).
• Martin White, “Time spent searching: a chronology of a myth”, LinkedIn Pulse, May 2020. Documents IDC’s own revisions (2004: 15–30% of time; 2011 survey figure: 8.8 hrs/week). Archive.
• Thomas C. Redman, “Bad Data Costs the U.S. $3 Trillion Per Year”, Harvard Business Review, 22 Sep 2016 (author is president of Data Quality Solutions, a data-quality consultancy), plus his June 2023 update. Mirror PDF · 2023 repost · the companion “50%” figure self-cites his 2013 HBR piece, where it is “up to 50%” from unnamed studies.
• IBM “Four V’s of Big Data” infographic, c.2013–16 (deleted). Sole sourcing: an undifferentiated nine-organisation list. Archived image.
• Hollis Tibbetts press release, “Dirty Data Costs the US Economy $3.1 Trillion Yearly”, 11 Sep 2011. Earliest traceable instance; no methodology. Archive.
• Gartner, Magic Quadrant for Data Quality Solutions, 27 Jul 2020 (G00389794). “Organizations estimate the average cost… $12.9 million” — survey of 154 reference customers identified by the 16 rated vendors. Licensed-reprint mirror. No newer Gartner figure exists as of July 2026.
• Fluxygen, “Impact of human error rates”, Dec 2023. Carries “$12.9 billion” while linking to Gartner’s million. Archive.
• Plum Consulting for Sage, “Sweating the Small Stuff”, Sept 2017. Fieldwork FTI Consulting, Jul–Aug 2017, ~300 SMEs in each of 11 countries; 120 man-days is the cross-country per-company average; the UK figure is 5.6% of staff time (implied loss £39.9bn). Report PDF · launch release.
• The two mutation carriers. globalbankingandfinance.com, 13 Sep 2017 — current page title: “Sage research reveals UK SMEs spending 120 hours a year on admin tasks” (archive) · smallbusiness.co.uk, “UK small businesses are still wasting time on admin” (Sep 2017, modified Jan 2024, still live) — carries “average of 120 working hours a year on administrative tasks” (archive).
• Enterprise Research Centre, State of Small Business Britain 2018. Micro-business Britain Survey: CATI, Jan–Apr 2018, firms with 1–9 employees established 3+ years; sales-per-employee associations (the report’s own text slides from “linked” to “leads to”). Live PDF · archive.
• DBT, SME Digital Adoption Taskforce final report, 31 Jul 2025. Ministerial foreword states “7 to 18 per cent per technology” for SMEs; the ERC is credited by name in the foreword, but there is no citation or footnote anywhere in the document. Live · PDF archive · HTML archive.
• McKinsey & Co and Be the Business, “The UK’s Technology Moment”, 2020 (Opinium, June 2020, N=1,476 SMEs; 53% = share of N=1,007 adoption efforts self-rated unsuccessful), plus the BtB/Amazon whitepaper, Sep 2023 (restates it as “53% of SMEs” against its own earlier, correct wording). 2020 archive · 2023 archive.
• Garza et al., “Error rates of data processing methods in clinical research…”, Int J Med Inform 195:105749, Mar 2025. NIH-funded systematic review/meta-analysis; 93 manuscripts, underlying studies 1978–2008, trained clinical staff; single entry 0.29% errors per field, double entry 0.14%. Free full text · archive.
• Reynolds-Haertle & McBride, Controlled Clinical Trials 13(6), 1992. 42,278 fields; 22 vs 15 errors per 10,000 (P=.09); double entry +37% time. PubMed · archive.
• Barchard & Pace, Computers in Human Behavior 27(5), 2011. 195 undergraduates; visual checking produced 2958% more errors (≈30×) than double entry; single errors can flip results. Abstract PDF, archived; corroborating APA 2008 poster at barchard.faculty.unlv.edu.
• DBT/Ipsos, “Understanding technology adoption among UK SMEs”, 31 Jul 2025. Online n=2,000 (Nov–Dec 2024) + telephone n=1,001 (Jan–Mar 2025), 1–249 employees excluding sole traders, weighted to BPE 2024; the modes diverge (4% vs 19% non-users), so figures are reported with their survey mode. Live PDF · archive.
• BCC Insights Unit with Intuit, “The Turning Point for SMEs”, Sep 2025. n=1,558 online, 23 Jun–18 Jul 2025; 93% SMEs; sponsorship disclosed in-report. 11% “great extent” automation (full scale 11/42/29/14). AI use 25% (2024) → 35% (2025), base n=1,630 weighted. Live PDF · archive.
• McKinsey Global Institute, “A Future That Works” (In Brief), Jan 2017. Modelled technical automation potential on US BLS activity data: collect data 64%, process data 69%, managing people 9%. Archive.
• ABBYY “COVID-19 Technology and Business Process Survey”, Dec 2020. Opinium, Nov 2020, 4,000 office workers (1,000 UK); self-reported belief, vendor-sponsored. Archived primary.
• Starling Bank, “Make Business Simple”, Jan 2020. Opinium, Oct 2019, n=1,009 UK micro-businesses; firm-level figures. Live report PDF · archive · the “15-hours-a-day” URL garble.
• HMRC, “Making Tax Digital: estimating the wider economic benefit”, 27 Feb 2025. Kantar Public/Verian survey, n=2,300; central estimate 33 hrs/business/yr (95% CI 26–40) valued at ASHE medians; below-threshold central 20 hrs/£382. Live · archive.
• ERC Research Paper 119 (exec summary), Oct 2025. LSBS 2022–23, nationally representative; “benefits are not automatic”; some technology bundles associate with lower productivity. Live PDF · archive.
• HMRC, MTD VAT final evaluation, 27 Feb 2025. 2022 cohort: 41% benefits-outweigh vs 23% costs-outweigh. Live · archive.
• HMRC, MTD Income Tax threshold TIIN, 24 Mar 2026. Mandation threshold reduced from £30,000 to £20,000 from April 2028; £380m transitional cost, £101m/yr continuing, 970,000 people; the ~£104/yr is our division. Live · archive.
• ONS, Annual Survey of Hours and Earnings 2025 (provisional), 23 Oct 2025. 1% PAYE sample, achieved n=174,000; occupation medians from dataset Tables 2.6a/14.6a (all-employees sheets), verified at cell level. Bulletin · Table 2 dataset · Table 14 dataset.
• GOV.UK, “Rates and thresholds for employers 2026 to 2027”. Employer Class 1 NI 15% above £5,000/yr; unchanged from 2025-26. Live · archive.
• DBT, Business Population Estimates 2025, 2 Oct 2025. 5,690,265 UK private sector businesses at the start of 2025 (Table C). Live · archive.
• FSB, “Taking a Toll”, 22 Apr 2025. Survey by Verve, n=1,436 (FSB members + wider self-employed, fieldwork 31 Jul–14 Aug 2024, self-selecting, membership-weighted); the £4,500 includes accountants’ fees and software subscriptions, not just time; the ~£25bn/242m-hour aggregates are gross-ups of the survey mean over the business population. Live report page · archive.