How accurate and robust is your staff data?  That matters when you enter data to the national FE workforce data service, SIR Data Insights, for two reasons:

  1. If you enter bad data or miss data out, your management dashboards, which you get for free in SIR Data Insights, will be far less useful for your own internal decision making.
  2. The national evidence base suffers. The data you submit is used to provide a picture of the FE workforce for government policy and the plans of representative sector bodies, as well as providing useful benchmarks for you to compare your operation with similar providers.

So, in short, it makes sense to check and remove errors from your data submission.  To make your job as easy as possible, SIR Data Insights provides you with two useful tools:

  • Staff Error Report
  • Data Quality Overview Report 

When you upload your data file, it will flag up that you have errors. To help you identify which records have errors, click on the 'View Error Report' option. This takes you to your Staff Error Report where you will see a list of the errors in your upload. If you print off the report, you can work through the errors more easily.

In the report, errors on each row of your data upload are split into two types:

  • Staff Level Errors
  • Contract Level Errors

 An example of a Staff Level Error is shown below:

 An example of a Contract Level Error might be:

There are many different types of error. Some may include interdependencies between field values and some may require in-depth investigation.

You may also find that similar types of errors are repeated within your data, such as here:

You can get a quick overall view of the quality of your SIR data submission by going to the Data Quality Overview option on the Manage SIR tab.  This gives you a neat visual summary of the errors in your data.

The top row of boxes show you the overall position with your staff and contract records.

The next box picks out broader issues with your data. For example, in the instance below, the data shows no new starters for the whole academic year, which seems unusual.

Or, here is another example:

The bottom graph in the Data Quality Overview Report summarises errors and shows them by categories of fields:

  • Basic category - the fields that we would expect all organisations to provide
  • Standard - a further set of fields that we think most organisations should be able to extract from their HR system
  • Expert - the fields you should also try to submit if you want to get the most from the data dashboards and national benchmarks

The graph shows each field with green for good data, red for errors and yellow for missing data.  In the example below, there are errors in field 20 which is 'Date of leaving', one of the basic category fields.

In addition to the Staff Error Report and the Data Quality Overview Report, there may be some errors that only you will spot by taking a fresh look at your submission when completed. Errors such as: 

Stray additional zeros at the end of numbers, or 00 or 000 entered instead of leaving a blank field where you have no data to enter.

The same value provided for a series of fields, such as annual salary.

An abnormally high or low number of staff in a category of work, for example, 50 category 009 Chief Executives.

An abnormally low number of staff in one category compared to a related category; for example, a very low number of learning support staff compared to teaching staff.

For a step-by-step guide and more detailed information about improving data quality, please see our video guide 'How to correct data errors'.

If you have any queries, please contact our helpdesk service on email: or tel: 0345 833 9040.

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