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:
- 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.
- 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.
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:
of a Contract Level Error might be:
many different types of error. Some may include interdependencies between field
values and some may require in-depth investigation.
also find that similar types of errors are repeated within your data, such as
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
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: email@example.com
or tel: 0345 833 9040.