Report Samples

The samples below were created with a small, anonymised data set using PowerBI.

I will be adding and improving these over the next few weeks, as I expand my BI use.

The general claim I would make about reporting is that:
If the data exists in some format (ideally in one place), I have the skills and experience to pull and append the data into information

Absences

The four graphs below show the same value, "Days Lost due to absence" but in different interpretations.

Overall, the 4 graphs can be read as:

Top Row = by Division
Bottom Row = by Month
Left Column = Total Days
Right Column = Total Days/Potential workdays (as a Percentage)

Gender Pay Gap

A simplified Gender Pay gap, using calculations to determine:

The Quartiles
Headcount by Quartile
Mean and Median by Quartile

Because the figures are derived from calculations on data that can be updated, monthly, the snapshot can be chosen up to and including the last reporting period.

Turnover

I have created, initially, 3 visuals below, consisting of 6 graphs altogether. For the unitiated: Turnover is designed to show the proportion of leavers against the organisation's population. The basic calculation is:

Leavers/Headcount

The two graphs show The turnover via two different means:

Monthly = Leavers for the Month/Headcount for the Month
Trend = Total Leavers over 12 Months / Average Headcount over 12 Months.

Overall, the differences between the two visuals:

TurnoverMonth:
- A lower overall percentage
- Very "spiky", as each month is shown independently
Turnover12Mth
- a higher overall percentage
- less spiky, but shows trends over time

Generally, although the monthly report has it's place, the 12 monthly trend report gives more information about the health of the organisation. What happens in one month is rarely indicative, but over the course of a year will be more illustrative, although there is correlation between the two. It is also the approach preferred by most monitoring/advisory groups, including the CIPD.

This first visual is all leavers, with no further modifiers.

This set of metrics has been modified to show turnover for resignations only. Again this is a standard reporting metric

What the graphs below show is turnover that is considered "Preventable". What could be considered preventable or not will be subjective to each organisation. In the fictitious organisation, preventable has:

Excluded resignations that don't reflect on the organisation, ie
- Moving away
- long term health problems
Included dismissals that might have been avoided with better management/mentorship
- Poor timekeeping
- Poor performance

As I stated, this is extremely subjective and would only be possible if the categories to filter were agreed upon and, crucially, available within the dataset.