Figures Nobody Can Trust

1. At a Glance

Issue study · From the public record. Neither organisation described here was an IOanyT client, and IOanyT had no part in the events described. Everything about them comes from the public record.

Organisation type	A large retailer that rolled out a packaged ERP product, set beside one retail group’s account of its old in-house system and two rules in India that ask for data a firm can trust and find
Size band	The retailer and the group: large. Our reader: a mid-size retailer, brand or online seller whose stock, product and customer figures cannot be trusted
Region	The retailer and the group: not given here. The rules: India
The decision at stake	What to do about figures that are corrected by hand, cleaned every so often and wrong again, when nobody can say where in the systems they are made wrong
The public outcome	After a large retailer’s expansion into a new country over a decade ago, its supply chain failed, and most of the data in the new system was found to be wrong. An older case; the record does not say how the data came to be wrong
Why it matters to a mid-size retailer	The same figures are wrong again after every clean-up, and new rules in India on price cuts and on customers’ data touch the same records. The question is whether the cause is found on a plan, or the data is discovered in the first week of a new system

2. The Business Problem, in the Buyer’s Words

“Every meeting starts with an argument about whose stock figure is right. We close the stores in January and count everything, and by April the figures have drifted again. Marketing checks every list by hand before a send. Now we are told the shop must show the lowest price of the last thirty days beside a sale, and that customers can ask us to correct their details. I do not even know how many places a customer’s details are kept. And next year we are meant to be replacing the system.”

These words are imagined. They describe the decision as the research shows it, not a real conversation.

The firm is caught between figures nobody trusts and a cause nobody has looked for. A clean-up looks cheaper every year, and each one corrects the records while whatever made them wrong keeps running.

3. What We Found

Method. We went through the public record on retailers whose stock, product and customer figures could not be trusted: press reporting of one large retailer’s system roll-out, one retail group’s own account of its old supply chain system, a survey of UK retail leaders, a survey of US IT professionals, and two rules announced in India, from 2013 to 2026.

Most of the data was wrong. A large retailer, expanding into a new country over a decade ago, rolled out a packaged ERP product.

What	Public record
What was rolled out	A packaged ERP product
What happened	The supply chain failed
The data	Most of the data in the new system was found to be wrong
Who investigated	Not stated in the account we read
Why the data was wrong	Not recorded
Date	Over a decade ago, an older case, in a press account published in 2025

Late data. A large retail group described its in-house supply chain system, about a decade old, as out of date. It said that before the system was replaced it could not see its data in real time. That is about data arriving late, not data being wrong. This study does not describe what the group did next.

The conditions around it. In a survey of about a hundred senior retail and brand leaders in the UK, paid for by a firm that sells retail technology services, about seven in ten direct-to-consumer brands said their core systems were not yet fully integrated, and more than half of retailers said fragmented systems were affecting profitability and customer experience. In a survey of about five hundred IT professionals in the US, run by a firm that sells software services, about a third named data migration concerns among the reasons for not upgrading old systems; that survey is not about retail.

Two rules in India. The government announced on 10 September 2026 that the Consumer Protection (E-Commerce) (Amendment) Rules, 2026 “shall come into force from January 1, 2027”. Under them, “Where a price reduction is announced, the reduced price and prior price shall both be displayed”, the prior price being “the lowest price at which the goods or services were offered during the 30 days preceding the announcement”. The release names “e-commerce entities” generally, and “Marketplace e-commerce entities” for some other duties; we did not read whether that covers a retailer’s own shop. Separately, the Digital Personal Data Protection Rules, 2025 were notified on 14 November 2025, with “an eighteen-month period for phased compliance”; the government’s summary says Data Fiduciaries “are required to address all requests related to access, correction, updating or erasure within a maximum of ninety days”.

Our reading of why figures stay wrong. These four are our reasoning from the record above. None is a finding of any source.

1. Nobody knows where a wrong figure is made. A figure passes through many rules, overnight jobs and joins between systems, and each error is corrected on the screen while the step that produced it is never found.
2. The cause is left running. A clean-up corrects records; the rule that spoiled them runs again that night.
3. Bad entries are let in. Old screens accept blanks, duplicates and impossible values, which become next quarter’s errors.
4. Data is moved, not described. At a replacement, records are carried across with a count to show none was dropped, and what is in them is found after go-live.

Some errors begin on the shop floor, with a miscount or a box in the wrong place. They are not caused by software, and the people who count and correct every week are not the cause of the rest.

4. The Options a Firm in This Position Had

Option	When it fits	What it risks	Our view
Run another cleaning project	The errors have a known, one-off cause that has been dealt with	Whatever made the errors keeps running; the project is paid for again	Right only after the cause is fixed
Buy a data-quality tool and run it yourselves	Errors come from many outside sources, and the firm has staff to act on what the tool flags	It flags wrong records after they are made; the rule that made them is not opened	Right for some firms, as the client’s own choice; it does not reach the cause
Replace the system and expect the new one to put it right	The old system is due to go for other reasons, and its rules are well understood	The old data and the old manual steps move across, and the new system starts with them	We would advise against this as an answer to wrong data, for a firm that cannot say where its figures go wrong
Trace each wrong figure to where it is made, then rebuild that part with checks at the point of entry	The same errors return after every clean-up, and the systems were built or adapted for the firm	Costs money before anything changes; may find causes on the shop floor that no software change touches	Our recommendation

5. Our Recommendation, and the Decision It Asks of the Board

For the code a retailer built or had built that changes stock, price, product and customer figures, and the joins between its systems: trace each key figure to the rule, join or manual step that produces it. Then rebuild the parts that make figures wrong, module by module beside the old system, on technology that is still supported, with checks that refuse a bad entry at the point it is made, starting with the figures the business most needs to trust. Before any replacement, write down what each field of the old data means and where it is weak. The retailer’s own people correct the existing records, count stock and measure how far the systems disagree; IOanyT reads the code and the structure of the database and does none of those things.

6. Benchmark: Where You Are Now, and Where You Should Be

These are targets, not results. In an engagement the starting points are replaced by the retailer’s own figures, taken in the first review.

Measure	Typical starting point	Where it should be	How it is checked
Key figures (stock, price, product, customer)	Known to be wrong, not traced	Each traced to the rule, join or manual step that produces it	The specification
The causes of wrong figures	”The data is bad”	Each listed, and marked as in the system or on the shop floor	The risk register
Checks at the point of entry	An old screen accepts blanks, duplicates and impossible values	Each rebuilt part refuses an entry that makes no sense	The specification and the traceability matrix, for modules IOanyT rebuilds
The old data’s weak fields	Unknown until go-live	Each described before any move: what it means and where it cannot be relied on	The specification and the risk register
Disagreement between systems on a key figure	Noticed, never measured	Measured by the retailer’s own staff on a sample they choose, and falling as each part is rebuilt	The staff’s own count, reported at every review
Where past prices and a customer’s records are kept	Not known	Written down for each pricing rule and each place a customer is held	The specification

The traceability matrix shows that each old function has its replacement. It does not show that every record is right; that is why disagreement between systems is a separate measure, counted by the retailer’s own staff.

7. How IOanyT Would Work

1. We listen: a free Reliability Review. A first conversation about the figures you trust least, the clean-ups and what keeps coming back. You leave with a short written note of what we found. You can start with a conversation about the system; we ask for read-only code access only when the assessment begins.
2. We research. We research your sector, the regulator’s position and what the public record says about firms in your situation. This study is an example of that work.
3. We read the system and propose: the Legacy System Specification Assessment. Here the system is the code your firm built or had built that changes stock, price, product and customer figures, and the joins between your systems. It produces a map of the system as it stands, a written specification of what it does, a risk register, a cost comparison between keeping and replacing the system, and a staged plan with a fixed quote. AI does a first pass, our senior engineers review it and trace it to the code, and your own experts confirm it. Code access is read-only and logged, never used for training, and deleted within 30 days of the evaluation ending unless we engage.
4. If you approve, we build. We replace a system one module at a time while the old one keeps running. Here that means the parts that make figures wrong, rebuilt beside the old system on technology that is still supported, with checks at the point of entry. We never do a single cutover. A traceability matrix links every old function to its replacement. The work is billed by milestone, each milestone tied to a module you accept, and you can pause after any accepted module.
5. We review against the benchmark. Before work starts we set the benchmark. After each module we show you where each measure stands, and the filled-in benchmark is yours at the end.

The Review is free. Every paid step is fixed price, agreed at scoping. Stop after any step. The findings are yours. The assessment fee is credited in full against the first build stage if it starts within 90 days of the report.

8. What the Client Would Own

• The map and the specification: the rules the systems apply to stock, price, product and customer figures, in writing, and, if the delivery team confirms it, a description of what the old data means and where it is weak.
• The risk register: the causes of wrong figures the assessment found, listed before any price is set.
• The traceability matrix: for every module IOanyT rebuilds, every old function linked to its replacement, with how it was checked.
• The code: the rebuilt modules, with their entry checks, are the client’s. No runtime or platform fee.
• The filled-in benchmark: each measure, where it started and where it stands, including the disagreement between systems as the retailer’s own staff counted it.

9. What This Approach Does Not Cover

• IOanyT does not clean, correct, merge or reconcile records, and does not count stock. The retailer’s own people do. IOanyT does not move data.
• IOanyT reads the code and the structure of the database. It does not examine customer or stock records, and its published terms on handling cover code.
• IOanyT has no data-quality product.
• IOanyT does not install, upgrade or move a packaged shop platform or a packaged ERP product, which is what the main public case involved, and cannot change the rules inside another supplier’s packaged product.
• Errors that begin on the shop floor, such as a miscount or a box in the wrong place, are not caused by software and are not solved by this approach. Nothing here puts the blame on store staff.
• IOanyT does not host or secure the retailer’s systems.
• IOanyT does not offer a compliance, audit or legal service, and says nothing about what consumer-protection, data protection or card-payment rules require of a particular firm. Meeting the amended e-commerce rules or the data protection rules is the firm’s own work.
• The older technologies IOanyT reads include VB6, Delphi, AngularJS, PHP 4 to 8, Java 8, .NET Framework and classic ASP.
• Nothing here makes data free of errors. This study does not say IOanyT would have prevented the events described, or that IOanyT has done this work for a retailer. IOanyT has no published case of this work for a retailer.

About this study: IOanyT did not work on this case. It is drawn from published findings, cited below.

10. Sources

Source	Used for
Trade press, 2025	A large retailer’s roll-out in a new country
Trade press, 2023	A large retail group’s remark on late data
News report of a vendor-sponsored survey, July 2026	UK retail leaders
A vendor’s survey, 2025	Data migration concerns
PIB, Ministry of Consumer Affairs, 10 September 2026	The amended e-commerce rules and the prior price
PIB backgrounder, 17 November 2025	The data protection rules and the ninety days

IOanyT is a consultancy with its own engineering team: we study the problem, recommend, and prove it by building only what you approve. We start with where the wrong number is made, because that is the part a clean-up never reaches.

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