Hello my name is Christopher.
In this video presentation, the subject is Human Resources.
The source material content originates from Microsoft Power BI blog article by Michele Hart
The HR department has the same reporting model across different companies, even when they differ by industry or size.
This sample looks at new hires, active employees, and employees who left and tries to uncover the trends in the hiring strategy.
The main objectives are to understand:
Who the company hires? Biases in our hiring strategy? Trends in voluntary separations?
This sample is part of a series that illustrates how you can use Power BI with business-oriented data, reports, and dashboards.
This is real data from obviEnce (www.obvience.com) that has been anonymised.
Let’s explore new hires first.
1. In the left navigation pane, I’ll select the Human Resources dashboard.
2. I’ll select the New Hire Count, New Hires Same Period Last Year.
This actives year on year percentage (YoY %) Change by Month tile.
The Human Resources Sample report opens to the New Hires page.
Notice the following: The New Hire Count, New Hires Same Period Last
Year (SPLY) and Actives year on year percentage (YoY %) Change by Month combo chart.
This shows the company hired more people every month this year compared to last year — significantly more people in some months.
In the combo chart, New Hire Count and Active Employee Count by Region and Ethnicity.
I see the company hired fewer people in the East region.
The New Hires year on year (YoY) Variance (Var) by Age Group waterfall chart shows the company is hiring mainly younger people.
This may be due to the mainly part-time nature of the jobs.
The New Hire Count by Gender pie chart shows a pretty even split.
I can you find more insights — for example, a region where the gender split is not even?
I can select different age groups and genders in the charts to explore the relationships between age,
gender, region, and ethnicity group.
Next I’ll select Power BI in the top navigation bar to return to the dashboard.
Let’s compare current active and former employees.
I can explore data for current active employees and employees who no longer work for the company.
On the dashboard, I’ll select the Active Employee Count by Age Group tile.
The Human Resources Sample report opens to the Active Employees vs. Separations page.
The items of interest for me are: Combo charts on the left show year-over-year change for active employees and separates.
I can see the company has more actives this year due to rapid hiring, but also more separates than last year.
In August I see more separates compared to other months.
I select the different age groups, genders, or regions to see if I can find any outliers.
Looking at the pie charts, I notice I have a pretty even split in the company’s active employees by gender and age groups.
I’ll select different age groups to see how the gender split differs by age.
The question is ‘do we have an even split by gender in every age group’?
Another question is reasons for separation Let’s look at the report in Editing View.
I’ll select Edit report in the upper-left corner.
I’ll change the pie charts to show Separates data instead of Actives.
1. Select the Active Employee Count by Age Group pie chart.
2. In Fields, I’ll select the arrow next to Employees ﻿to expand the Employees table.
I’ll clear the check box next to Active Employee Count﻿ to remove that field.
3. I’ll select the check box next to Separation Count in the Employees table to add it to the Values box in the field well.
4. Next I’ll select the Voluntary bar in the Separation Count by Separation Reason bar chart.
This highlights those who left voluntarily in the other visuals in the report.
5. I’ll click the 50 plus slice of the Separation Count by Age Group pie chart.
Look at the Separations by Reason line chart in the lower-right corner.
This chart is filtered to show voluntary separations.
Notice the trend in the 50 plus age group?
During the latter part of the year more employees over the age 50 are leaving voluntarily.
This would be an area to investigate further with more data.
6. I can follow the same steps for the Active Employee Count by Gender pie chart too,
changing it to separations instead of active employees.
I could look at the voluntary separation data by gender to see if I find any other insights.
7. I’ll click Power BI in the top navigation bar to return to the dashboard.
I can save the changes I’ve made to the report it I chose to.
Let’s look at bad hires.
Bad hires are defined as employees who didn’t last for more than 60 days.
The company’s hiring rapidly.
Is the company hiring good candidates?
1. I’ll select the Bad Hires as percentage of Actives by Age Group dashboard tile.
This opens the report to page 3, “Bad Hires”.
2. I’ll select the Northwest﻿ check box in the Region slicer on the left and the Male slice in the Bad Hire Count by Gender donut chart.
More male bad hires than females and lot of Group A bad hires.
3. Looking at the Bad Hires by Gender donut chart and clicking through the Region
slicer I notice that East is the only region with more female than male bad hires.
4. I’ll click Power BI in the top navigation bar to return to the dashboard.
Let’s ask a question in the Q&A box.
The Q&A question box is where I can type a question using natural language.
Q&A recognises the words I type and figures out where in your dataset to find the answer.
1. I’ll click in the Q&A question box. Notice before I even start typing, the Q&A box contains suggestions:
2. I can pick one of those suggestions, or type: show age group, gender and bad hires where region is east.
Notice most of the female bad hires are under 30.
Thank you for viewing this video, hopefully the presentation intrigued you enough to want to read Michele Hart, article in full.
This video presentation can only at the very best give you an overview so why not explore the data for yourself.
Have a fabulous day.