A Price History and a Ninety-Day Answer: What Waits on Retail Data Nobody Trusts.

In January the business closed every store for a day and counted everything. Duplicated customers were merged and old products retired. For a few weeks the figures were a pleasure to read.

It is now April. The stock report is drifting. There are new duplicates in the customer list. A product retired in January has come back with a price of zero.

Someone suggests another clean-up before the season. Nobody asks why the first one did not last.

What We Found

We looked at two rules announced in India that touch on prices and customers’ data, and at the systems retail data comes from.

A rule on showing price cuts in India comes into force on 1 January 2027. 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” is “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.

The government’s summary gives ninety days for requests about personal data. 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 of them says that Data Fiduciaries “are required to address all requests related to access, correction, updating or erasure within a maximum of ninety days”.

The systems behind the data do not share. In a vendor-sponsored survey of about a hundred senior retail and brand leaders in the UK, 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.

The Work That Waits on Data Nobody Trusts

Data nobody trusts is usually treated as a cost of doing business. Our reading is that it also holds back work the business needs to do.

The prior price. Our reading, and only that: a system that overwrites a price when it changes keeps no record of last week’s price. Showing the lowest price of the past 30 days beside a cut needs that record, and needs it to be right, even where prices are also changed by an overnight job, a supplier file or a correction typed by hand. Where the rule that changes a price lives in old code nobody has read, this is the change that waits.

The ninety days. Our reading again: to answer a customer who asks to see, correct or erase their data, a firm has to find every place that customer is held. Where the same customer sits in the tills, the website, the loyalty scheme and a marketing list under different spellings, that is slow. And a correction made in one place can be undone by a nightly job that copies the old value back from another.

Whether either rule applies to your firm, and what it requires of it, is for you and your advisers.

This is our reading of the evidence. It is not a measured market figure, and we do not put a rupee value on it here. That figure has to come from your own business.

Four Ways to Answer Data That Keeps Going Wrong, Weighed

Option	When it fits	What it risks
Run another cleaning project	The errors have a known, one-off cause, such as a bad import, and it has been dealt with	Whatever made the errors keeps running; the project is paid for again, and the evidence of the cause is corrected away
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
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 manual steps move across, and the new system starts with them
Trace each wrong figure to where it is made, then rebuild that part with checks at the point of entry	The same kinds of error return after every clean-up, and the systems were built or adapted for the firm	It is slower to show a result than a clean-up. A screen that refuses bad entries annoys people at first. Old and new run side by side, and for a time you pay for both

Where You Are Now, and Where You Should Be

Before the next clean-up is approved, we would set a benchmark for the decision itself.

Measure	Typical starting point	Where it should be	How it is checked
The cost of living with wrong data	A clean-up budget alone	The clean-ups, the checking by hand and the staff time, in one place	The cost comparison
The cost of fixing the causes	Never priced	A price for each stage	The staged plan and quote
Where past prices are kept	Not known	Known, and written down for each pricing rule	The specification
Where one customer’s records are held	Spread across systems nobody searches together	Every place listed	The specification
The cause of each kind of wrong figure	”The data”	Named: the rule, join or manual step	The risk register
The size of one commitment	A clean-up every year, or a whole new system	One module, with the freedom to pause after any accepted module	The staged plan

The starting points above are what we expect to find. In an engagement they are replaced by your own position, taken in the first review. The targets are what we would work towards with you. They are not results we have already achieved for a retailer.

How We Work With You

1. We listen: a free Reliability Review. A first conversation about the clean-ups, the corrections 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 article 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 prices, stock and customer records, and the joins between your systems. It produces 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. You get the options and our recommendation.
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 part that makes a figure 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. 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.

What you need to bring: a budget for the assessment; read-only access to the code once it begins; people who can say what each rule should do and accept each module; and your own staff to correct the existing records once each cause is fixed.

What we do not do: we do not clean, correct, merge or reconcile records, and we do not count stock; that work stays with your people. We have no data-quality product, and we do not examine customer or stock records. We cannot change another supplier’s packaged product, and we do not install, upgrade or move packaged shop platforms or packaged ERP products. Errors that begin on the shop floor are untouched, and nothing here blames the people who work there. Meeting the amended e-commerce rules or the data protection rules is your own work: we do not offer a compliance or legal service, and we do not say what any rule requires of a particular firm. We have no published case of this work for a retailer.

What changes for the business:

Role	What they gain
CEO	The yearly clean-up stops being a ritual, because the money goes to the cause.
CTO or CIO	The rule that spoiled the figure is replaced by tested code, and a bad entry is refused at the door.
CFO	Cleaning stops being a cost that returns every year with no end in sight.
COO	A count, once done, stays true for longer.
Head of marketing	What shoppers are shown is less often wrong for reasons inside the system.

The cases in this article, and what IOanyT does about that kind of problem:

The case	What IOanyT does
India’s government announced on 10 September 2026 that, under its amended e-commerce rules, in force from 1 January 2027, “Where a price reduction is announced, the reduced price and prior price shall both be displayed.”	Our assessment shows where a firm’s pricing rules live and whether past prices are kept, and we can rebuild the part that changes prices. Meeting the rules is the seller’s own work.
The government’s summary of India’s data protection rules, published on 17 November 2025, says Data Fiduciaries “are required to address all requests related to access, correction, updating or erasure within a maximum of ninety days”.	Our assessment shows where a customer’s records are held and which rules change them. Answering requests and correcting records is the firm’s own work.

IOanyT is a consultancy with its own engineering team: we study the problem, recommend, and prove it by building only what you approve. A clean-up corrects what was made wrong; we would rather fix what keeps making it.

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