Automating data entry between systems: stop re-keying
By Keith Hamilton · · 4 min read
Automating data entry removes one of the most common jobs in a small business: reading something on one screen and typing it into another. An order arrives by email and is keyed into the stock system, or a new customer fills in a web form and someone copies the details into the CRM and then again into the accounts. This guide explains how data entry automation works for a small business, where it goes wrong, and how to stop re-keying data without creating new problems.
Why is re-keying data worth fixing?
Re-keying data is worth fixing because its cost comes from volume and repetition, not from any single entry. Each entry takes a minute or two, so it is easy to overlook. The same details are typed several times, by different people, into systems that do not talk to each other.
It also lets systems drift apart. An address is updated in one place and not the other, and someone has to work out which version is right.
To put a number on it first, our guide to the real cost of manual work shows how to turn hours into pounds.
What are the common types of data entry between systems?
Data entry between systems falls into three patterns.
- Form to system. A web form, booking form or enquiry form is copied into a CRM, booking system or spreadsheet.
- Document to system. Details are read from an email, PDF or attachment, such as an order, a delivery note or an application, and keyed in.
- System to system. Data already held in one system is copied by hand into another, such as orders into the accounts or new starters into payroll.
The third pattern is the easiest to automate, because the data is already structured. Most business systems pass records to each other directly, so connecting business systems is usually a matter of switching on a built-in connection or setting up a link between them.
The second pattern is where AI data extraction adds the most. AI reads documents in different layouts and pulls out the details that matter.
How does automating data entry work?
Automating data entry works in four steps.
- Pick up the data from where it arrives: an inbox, a form, a folder or another system.
- Read and tidy it. Pull out the fields you need and put them in a consistent format, such as dates, postcodes and product codes.
- Check it. Compare it against rules you set. Does the customer already exist? Is the product code real? Is the quantity within a sensible range?
- Write it into the target system, and keep a record of what was written and where it came from.
Anything that fails a check is set aside for a person with a short note explaining why. That is what keeps bad data out of your systems.
How much time does data entry automation save?
The time saved is the volume multiplied by the minutes per entry, less the exceptions a person still handles. Here is a worked example.
A team receives 40 orders a day by email, and each takes four minutes to key into the order system.
- Time a day: 40 orders at 4 minutes is 160 minutes, or 2 hours 40 minutes.
- Time a week: 160 minutes over five days is 800 minutes, or 13 hours 20 minutes.
- Cost: at a loaded hourly cost of £20, that is about £267 a week, or about £13,900 a year over 52 weeks.
Automation does not remove all of it. Some orders are unclear, have missing product codes or need a phone call. Removing half the time releases about £6,930 a year of capacity, before running costs.
That becomes a cash saving only when it removes real spending, such as overtime or a planned hire.
Where does data entry automation go wrong?
Data entry automation goes wrong in three predictable places.
- Messy source data. When customers type product names in their own words, the automation needs a way to match them to your codes, and a person for the ones it cannot match.
- Duplicates. Without a check for existing records, automation creates the same customer three times faster than a person would.
- Silent failures. When a connection breaks and nobody is told, records stop flowing and the gap is found weeks later. Make sure failures send an alert to a named person.
Should a person still check the exceptions?
Yes. The goal is to stop people typing what a machine can copy, so they can deal with the cases that need judgement.
A good set-up shows exceptions in one place, says why each was flagged, and records who dealt with it. In the early weeks, check a sample of the records the automation has written, even the ones that passed every rule. That tells you how accurate it really is with your data.
Does UK GDPR apply to automated data entry?
Yes. Data moving between systems usually includes personal data, such as customer names, addresses and contact details, and UK GDPR applies to it.
Only move the fields each system actually needs, know where any tool you use stores and processes the data, and keep your records of processing up to date. Our UK GDPR and AI checklist covers the main points.
How do you start automating data entry?
Start with one high-volume task, not the whole business.
- List the places where your team copies data from one place to another.
- For each, note the volume, the time per entry and how often errors happen.
- Choose one with high volume, consistent inputs and a clear owner.
- Run the automated version alongside the manual one for a short period and compare.
Where Forwardcycle fits
We have built this kind of link in a proof of concept for a UK gardening company, where an AI agent takes phone orders and reconciles them in the distribution system, so nobody needs to re-key them. No results are claimed yet. Our cost reduction service finds the re-keying worth removing and measures the hours before and after. To see where it fits in your business, take the free AI Readiness Assessment.