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  • Writer's pictureCaden Armstrong

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Strategies to minimize the burden of data entry

The value of automation is directly linked to the amount of time workers spend on repetitive tasks, and the reduction of human errors. Even in the most automated systems, humans will still need to be involved in the data entry process. In a study by SmartSheet “59% say they could save six or more hours a week if the repetitive aspects of their job were automated.” [1]. The value of reducing data entry is clear, but how can one reduce the burden of data entry? Well, here are three easy ways:

Hiring mice is a last resort solution


Automating data entry is about removing repetitive work. Not everything can be automated, but the next best thing is simplification. Reducing the workload of the task pays dividends in each repetition. Not every record is going to be completely unique, and a template is an easy way to input a lot of data very quickly. Traditional document templates (invoices, quotes, etc) are only the tip of the iceberg. Think more along the lines of mad libs and cookie cutters. The option to apply a template to a form or task gives an easy shortcut. Some examples include:

A doctor filling out electronic health records - A doctor will likely see a few illnesses regularly (flu, cold, infections, bronchitis, etc). The option to quickly bring up options for symptoms, prognosis, and prescribed treatment for fast recording could give a doctor more time to focus on treatment instead of data entry.

Sales call notes - A study of 10,000 salesforce users found that only 40% of sales updates are completed [2]. Categorizing and templating sales call records could lower the effort required to ensure calls don’t go missing in the pipeline.

Linked Data

Single Data Entry is the philosophy that data should only need to be entered once. Data that exists in multiple places can be connected, giving greater power to the database. Here are three benefits to linked data:

Linked Data Example
Linked Data Example

Reduced errors - In many cases of automation, data needs to match exactly. The name of a manufacturer, client, email, etc. Mistakes or inconsistencies not only make data automation difficult, but potentially create costly errors.

Faster - A single field generating X fields is X times faster, not even including the complexity of gathering the information for each field. The real speed gains comes from the automations possible with a linked database.

Greater Potential - Exploring the possibilities of data automation and a consistent, linked data system, your imagination becomes the limit.

Take a stroll down example lane, and imagine a build to order manufacturer. When the manufacturer receives an order for an assembly, they will generate a bill of materials, and a well linked data system could do any number of the following:

  • Create Purchase Orders and email them to Vendors

  • Update stock numbers in the warehouse

  • Schedule machines for production

  • Print documentation

  • Add the template workflow to project management and update sales with lead times

The workflow relies on data linked between the Bill of Materials and the automated steps. Part files need to have vendor names included, machining operation requirements, etc. But the time to enter the vendor in a common part pays off when you create dozens, or hundreds of purchase orders for that part.

The same workflow can be applied to other industries. Generating paperwork, emails, reports, and records is universal in business.

Data Validation

Data validation within any automation or entry software provides two key values: The data has been entered, and has been entered correctly. Fixing data can be as big a problem as entering data to begin with.

An example of forced data compliance is a date picker. No training is required, and no format mistakes can be made. The data is guaranteed to be consistent in format, which is a huge time save in an automated system. Only one format needs to be accounted for, and bad data does not exist that would otherwise need to either be accounted for (manually most likely) or fixed.

Date picker vs Date format chaos

As big an issue as getting data into the correct format is getting it to exist at all. It's not unreasonable for a busy worker to miss an important field. Quick work in a moment, a distraction in the office, or an interrupting call. But that missing data can prove to be an issue down the road. Validating completeness of data adds a layer of security against data debt.


And there you have it, 3 methods for reducing the burden of data entry. With these three strategies you can reduce the workload of data entry, mitigate errors, and expand the potential of your workflow.

If you are interested in discussing any of the topics in this article, feel free to send me an email:


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