Empowering HR | A HR Dashboard Approach by M. Firlus

Intro

The following article will showcase an interactive HR Dashboard based on a fictional dataset. The main purpose of that article is to present a way:

1. How to kickstart the journey of creating a single source of truth solution for HR data, by having most of the core HR metrics in one user friendly dashboard.
2. How to create a proof of concept prototype, based on Power BI, as one potential core tool to steer HR initiatives.
3. To have a discussion with HR leaders, HR professionals and data people like you about the challenges of (a) getting HR data-driven, (b) keep it data-driven and (c) the improvements for your organization, which you could accomplish, by walking the data-driven path.

It’s especially interesting for small and medium sized companies, where HR Leaders already realized that data will help to justify decision making and may leverage the HR impact on the business.

“[] data-driven HR can help companies improve employee engagement, reduce attrition, and increase productivity. For example, companies that use predictive analytics to identify flight risks can reduce voluntary turnover by up to 30%.” (McKinsey 2019)

“Organizations that use workforce analytics have a 28% higher sales per employee and a 17% higher profit margin than those that do not.”(Aberdeen Group 2014)

2. Advice for Readers

For those of you who simply want to have a look at the dashboard, feel free to jump directly to section 12. Use the provided dashboard-link. If you have questions, read the article in a reverse way, or leave me a comment.

For those of you, who have some time meanwhile a lunchbreak, enjoy my journey which looks like this:

3. Contents

4. How to Kickstart

5. What Do You Need?

6. Technical Fundamentals

7. Visualization Best Practices

8. Preliminary Conclusion

9. The Prototype Creation

10. The Dashboard

11. User Tips

12. The Dashboard - Link

13. Summary

14. Final Thoughts

15. Sources

16. Special Thanks

17. Final Questions

4. How to Kickstart

In my humble opinion, answering the following question is crucial to make sense of a dashboard.

What is the main purpose of your HR function and which initiatives do you own to fulfill that purpose?

It may sound obvious, but if you don’t have a clearly defined purpose, how do you define goals to fulfill that purpose? How do you want to measure something if you don’t know what it’s good for?

Based on my experience, it’s sometimes a good thing to question even the most fundamental beliefs, to adjust to that wonderful but rapidly changing world we are living in.

In one of my previous blog articles I introduced “The Nine HR-Theses Framework for Holistic Thinking People”. This may offer a source of inspiration, if you are in the process of formulating your HR function’s core purpose, mission & vision.

4.1 Where Is My Data?

Allright, let’s fast forward. We have our HR-departments purpose. Now it’s about identifiying the sources of relevant data. Depending on your company’s maturity, you may have some of the following HR tools in place:

• Application tracking system (ATS)
• Human ressource information system (HRIS)
• A tool to keep track of your employees objectives and key results (OKR)
• Absence & time tracking tools
• Payroll and survey tools
• Some spreadsheets etc.

4.2 A Common Scenario in the Beginning

Multiple HR tools, multiple dashboards: All your different HR tools may have their own reporting’s and dashboards. Some will be good, some will be ok, and some will be just not worth it to call it a dashboard.

Limitation in customization: Some of your single tool dashboards, you may customize on your own, some customizations aren’t possible because it’s an all for one solution and for some customizations you simple lack the coding skills. On top of that,some tools may have KPI formulas simply not matching your definition of a specific KPI and you are unable to change calculation on your own.

Lacking the one, having too many: But the worst part, you have multiple datasets, reports, and dashboards, so you are lacking a single dataset and report, from which you build all your different other reports.

Copy & paste: Based on that you may copy and paste the data from each single tool report into multiple spreadsheets to send to multiple stakeholders. Quite a messy, time-consuming, and error-prone process in creation and maintenance. Maybe something from Figure 1 is common to you.

4.3 An Advanced Scenario

Single source of truth: Just imagine one single source of truth showing data from your ATS, HRIS, survey tools etc. as well as the progress of all your initiatives.

Independence from tool providers: Wouldn’t it be much easier to monitor all your initiatives being independent from your specific tool provider in terms of visualization and KPI calculation?

Customized stakeholder dashboards: Having customized reports/dashboards for each stakeholder, which can be simply bookmarked in the browser. No need for sending e-mails no more.

Data refresh: Having a simple refresh button to update all the reports when you want them to be refreshed, having the lates data in place.

Mobile view: Imagine the option that you and your stakeholders can access reports and dashboards via mobile devices in a responsive view.

5. What Do You Need?

Data know-how, data protection and people you trust: To do so you need a basic understanding of the data infrastructure and architecture as well as support from your internal Business Intelligence (BI)/data people or an external partner you trust. You deal with heavily sensitive data and compliance with data protection regulations is a must have, because violating i. e. GDPR regulations can cause severe penalties.

6. Technical Fundamentals

As an HR Leader you are probably not the person in charge of implementing the technical part, but it’s recommended to have a basic understanding of data concepts to be sure to delegate that technical part to the right people, being able to have a conversation on a bird’s eye perspective with them.

6.1 Connecting Your Data Sources

Connect all your data sources via API to a HR Datawarehouse (including commercial data like revenue, margin, COGS etc. to have the full picture).

6.2 Datawarehouse (DWH)

Now you may ask, a HR Datawarehouse, why? Well, your tool provider may implement changes which may jam your data extraction, transformation, and load routines by e.g. adding new columns, changing names of the columns simply switching off features. The DWH guarantees that at least the historical data you already have is safe, and reports and dashboards will still be working.

6.3 API’s

Creating a DWH being connected to all your different data sources is heavy engineering. You may need a developer or data engineer to do so. Also, you need someone who owns the maintenance of API’s and DWH. Ask your internal product managers, data engineers or developer who are responsible for API maintenance, they will tell you stories. 😊

6.4 ETL Routines

Extract, Transform, Load. What does it mean? Well, it simply means you extract your data from some data source. You probably need to clean the data, rearrange, rename columns etc. which is called transforming the data. Afterwards you want to do something with that data, so you load it somewhere, probably in your visualization tool where you want to build your dashboards.

The ETL routine can be done in your DWH. So, you have cleaned data for your next step, which is to extract it to your visualization tool. In Power BI the extraction can be done by a little SQL script, M-code or simply by using the ribbon of the user interface. Power BI has its own ETL Engine on board called Power Query. The same tool is also available in Excel. Just FYI.

Figure 2 shows a data architecture which can suit our needs.

6.5 Data Modeling & KPI/Measure Creation

The basic buzzwords you should know in terms of data modeling are: #star schema, #fact table and #dimension tables. It’s less complicated than you may think. The #fact table(s) has all the information you want to analyze. Start dates, termination dates, salary, promotion dates etc. The #dimension tables are the filters you want to slice and dice the fact tables data with. E.g., you have a dimension table called ‘Date’ including Years, Quarters, Month, Days. A simple use case: You want to see the number of hires and terminations over time. Other dimension tables could be age groups, attrition reasons and so on. The #star schema is simply a recommended form to relate all your tables with each other in the form of a star; in the middle the fact table surrounded by your dimension tables. This is your star schema. In the Power BI world, the data model is mostly recommended as the foundation of creating visuals (Ferrari, Alberto: 2022) 

KPI’s/ Measures: In the Power BI world you create your KPI’s with a formula language called #DAX. KPI’s are called measures. Just FYI, a measure could be e.g. headcount or attrition rate.

6.6 Sharing Dashboards with Data Access Limitations

You probably need a visualization solution with an option to share your reports with different stakeholders, making sure that access is limited to a specific group of users. Also, you may share the same report to different users, but with different data. Let’s imagine you want to share a salary or employee survey report to your VP Marketing, VP Sales, and VP Engineering. Let’s imagine that the reports shall be confidential to each single VP. You can build the same report multiple times. If you do so, you need to maintain three reports. Wouldn’t it be much more efficient to:

1. have just one main report,
2. a rule set of data access permissions,
3. to share just one report to multiple users? With this approach, you
4. need to implement new features just into one report which decreases maintenance efforts.

Power BI is offering such a solution with Power Bi Services.

7. Visualization Best Practices

There are a lot of discussions going on, about what are the best practices of dashboard designs. Keep the following in mind.

7.1 The Purpose

Why do you need the dashboard? Ask yourself and your stakeholders what they want and decide what they need. Consider the reading behavior.

7.2  Reading Order

For example, in the “western” world. Readers read from the left to right and from the top to the bottom. The dashboards visuals should be sorted by priority in the common reading order.

7.3  Keep It Simple

Think of a main dashboard with core metrics and limited interaction. Focus on the most crucial ones. Too many visuals on one page can create user confusion. You may divide your dashboard into a main page and if needed, further pages with more detailed analysis. Bernardita Calzon described 23 design principles and best practices. The link you will find at the end of this article. Let’s make sure that you don’t end up like in Figure 3.  

8. Preliminary Conclusion

If you are still reading that article, I’m proud of you. It’s probably not the most common HR topic and quite technical. Maybe you have a coffee break, if it’s in the evening, have a drink, have a smoke and let’s continue. 😊

9. The Prototype Creation

Well, if you have the resources available, go with the advanced scenario. Save your time reading this. The following content is for the HR people who have budget limitations and maybe want to know:

• how to get a prototype in production
• to have a working use case
• to get the resources they need for further development.

So let me showcase how I created a scalable HR dashboard prototype by using the free Power Bi Desktop application and Power Bi Pro to share the showcase with you.

9.1 Purpose

• First, my motivation was pure curiosity. Am I able to create a dashboard like this? Yes, I can. And if I can, you can do as well!
• Second, to have a discussion with you, how do you handle the dashboarding in your HR department?
• Third, what were your challenges and how did you solve them?

9.2 Data Source

It’s obvious, I couldn’t use real life data. I’m grateful, that I found a free to use fictional HR dataset published on Kaggle by Dr. Carla Patalano and Dr. Rich Huebner.

Some parameters of the data set

• CSV format
• 311 distinct rows
• Each row holds the record of a unique employee with unique employee identification number
• Timespan: 2006-2019
• I focused on the years: 2006-2018

Side note: The dataset doesn’t include promotions or any kind of target values.

9.3 Technical Set Up

Oriented on the advanced scenario but given that we are on our own, we have a CSV file available. Probably even the most basic HRIS allows a CSV export. Create a folder on your companies’ online storage. Ideally you create a folder for each data source.

Why? If you have a folder for each data source, you can store data updates in a CSV format in that specific folder and tell Power Bi to refresh from that source. Depending on the number of data sources it can be a bit messy, but unless Power Bi and its ETL Engine Power Query doesn’t have Connectors (APIs) suiting to your specific source system; it’s one way to go.

There are also vendors out there who provide ready-made API’s for almost everything but check their data protection agreements and have in mind the extra costs and additional dependencies.

If you have a data engineer who can help with the API’s make her/him your best friend.

We skip the details around the ETL Process, Data Modeling and KPI creation. This would blow up this article.

Figure 4 shows the infrastructure I worked with.

9.4 What Kind of Data to Display?

My approach is simply to show HR-core metrics, which I assume are useful for HR leaders. Unfortunately, the used dataset doesn’t have goal values in place like: targeted headcount, hiring or termination goals. But sure, if you have them, please add them to get a real traction on your data driven HR approach. What I did was a descriptive year to year comparison.

10. The Dashboard

This may be overwhelming at first glance, but I will walk you through it, step by step, from left to right and from the top to the bottom.

10.1 Branding

For branding reasons, include your company’s logo. Also recommended, color grading related to your company’s CI guidelines. Ask your Marketing team. As I’m working with a fictional data set, I was looking for a way to add some character to the dataset. I imagined a fictional company around it. Welcome to Nibbler Inc. It’s a fictional, American based, company founded in 2006. The core product is a kind of neuro enhancer fully complying with FDA regulations or maybe not because the dataset is limited to 2019. Never heard from that company again. 😉

10.2 Slicer

It’s convenient. By default, the dashboard shows all data from 2006 to 2018. You can choose a single year or by keeping CTRL/STRG pushed, you can select the years you are interested in. By Selecting “Select All” you get the default setting. Almost all charts and tiles are connected to the slicer except Tenure & Satisfaction. Those are fixed values.

10.3 Headcount

This tile displays the current headcount for 2018. PY is an abbreviation for “Previous Year”, in our case the headcount in 2017. The percentage (rounded) shows a change compared to the previous year. In the background you see an indication of the headcount development based on selection of years by slicer setting. Headcount calculation (yearly): The dataset doesn’t provide a starting date, so I took the next best suiting date: “Hiring Date” being aware of the difference. I count those employees per year who have a:

• Hiring Date” before or equal to a specific year &
• “Termination Date” is after a specific year &
• as well as “Termination Date” has a blank (no value given).

An alternative calculation would be the average headcount per year, YTD (Year To Date) or FTE (Full Time Employee) calculation. It majorly depends on your specific needs. At the end it’s just a formula you can fully adjust to your needs. Later, I will mentioned further details to consider for calculating the headcount.

8.4 Gender

As diversity matters, it simply shows the headcount distinct by two gender categories: male and female. The dataset doesn’t include diverse entries.  

10.4 Attrition

Simply the ratio of leavers in a specific year divided by headcount of a specific year. You may think the percentages are wrong, but this impression is caused by the rounding. A good point to discuss rounding rules.

8.6 Age Groups

Another measure for diversity as well as for recruiting purposes. Depending on your local & individual retirement age regulations, you can set notification alerts when you should start recruiting for roles held by elderlies, to refill the ranks if needed.  

10.7 Average Tenure

The average timespan an employee is employed by the company. The value is hard coded. In our case: from 2006 to 2018 the average timespan an employee was employed = 4,6 years. If you break this measure down:

• (1) to role level (i.e., developer or sales manager), modifying
• (2) the calculation to tenure per role,
• you can include this measure to optimize your recruiting efforts.

 Meaning, if you are constantly hiring certain roles/positions and you:

• (1) have your average time to hire & average tenure for that specific role available,
• (2) you know when to proactively approach the hiring manager whether you should start a recruiting process in time.

(Of course, the tenure calculation should be dynamic, but based on time resources, I leave it hardcoded for now)

10.8 Employee Satisfaction

The dataset provided a column with survey data regarding employee satisfaction on a scale from 1-5. The exact date of the survey wasn’t mentioned. The participation rate was 100%. We all know that would be a dream in real life.

The plain hypothesis behind it is: If your employees are happy, their productivity is good. Really? You will know it’s a bit more complex. Employee satisfaction should be correlated to the level of accomplished OKR’s, satisfaction with direct manager and different other things. But if you need one single question, use the usual eNPS question:

“Would you recommend your current employer to a friend or acquaintance?” (Don’t judge me on the exact formulation).

10.9 Yearly Salary Costs

Depending on your business, the payroll/ salary costs can be one of your highest, or even the highest investment you make to achieve your company targets. It’s valid to include it in a HR dashboard. Based on the used dataset it’s unclear whether it’s the gross or net salary. In real life, this should be considered too. Salary costs alone are good to know and monitor but the real impact you can get is if you also have your revenue data (which wasn’t in the data set). Then you can calculate the return on investment per employee (ROIpE).

Old-school controllers will now start thinking. How much can I  squeeze out of my people? Well, in times of workforce shortage you better not squeeze too much, elsewise your attrition rate rises. Keep the balance, my lovely number crunchers. We are talking about people, not robots.[

The ROIpE is also part of the calculation to compute Cost of Vacancy. Which is a valid measure to prioritize staffing efforts and allocation of recruiting budgets.

Calculation of salary costs: The data set provided the annual salary with no specification of net/gross characteristics, so I calculated the monthly given salary by dividing the annual salary by 12.

10.10 Average Yearly Salary Costs

This measure can be an indicator regarding the distribution of your salary bands. Consider the Average’s weakness: It’s sensitive towards outlier.

Short example: Imagine you have one employee with a yearly salary of 100.000 €. Your Average = 100.000 €. Let’s assume, you hire an intern with a yearly salary of 18.000 €. The Average = (100.000+18.000)/2 = 59.000 €. I guess, it’s clear what I mean.

In further analysis the Minimum and Maximum should be added as well as the Median to have a better overview of the distribution. As a more advanced visualization I would recommend a box plot diagram. Anyway, we leave it for now as it is.

Especially for recruiting purposes the salary band per role is more relevant because besides benefits... surprise, surprise, salary is one of the main drivers for quitting a job.

10.11 Headcount Development & Growth

While most tiles of the dashboards are showing a specific KPI compared to the previous year, as well as a rough trend (including the option of color grading and arrows to indicate whether the KPI is in good or bad health) bar charts allow a visual comparison over multiple years. Also, you can insert lines to put additional KPI’s in relation to the KPI shown as bars.

Figure 16 is showing the absolute headcount development by blue bars with orientation to the left axis (Headcount) and the Headcount Growth in percentage compared to the previous year oriented to the right axis (Growth in %)

Details: The great thing about Power BI is: you can hover over each single bar or line and you will get a detailed pop up for those (Figure: 17). You can also click on each bar to highlight it and blur out other portions of the visualization to get a more focused view (Figure: 18).

Hiring Progress: Based on a yearly headcount planning and monthly/quarterly review, you may exclude the Headcount Growth line, adding another line instead, to visualize the status of your specific needed target.You probably will do this on a department or even role basis. In the further development of a dashboard, you can add an extra report on a different page drilling down from a yearly view to a quarterly or monthly view as well as from a company level to a department or even job role view.

Beneficial for recruiters and onboarding topics: This can be quite useful for recruiters to either increase or decrease their efforts and will help everybody who is involved in onboarding fundamentally As well as for the workplace managers, by having the physical seats and tables available for new joiners. The IT managers, having the needed devices and applications in place or the HR Business Partners, who may have an eye on the leadership capacity of the hiring managers in terms of onboarding capacity or proposing organizational changes to keep the number of direct reports in a manageable scope.

Headcount is a diva: As HR professionals know, the Headcount can be a diva. You may ask 3 different people and you will get 4 different answers. Finance may relate to all employees who are still on the payroll and will include people taking their over hours and remaining holidays bundled before they finally leave. Department managers may just count on the workforce, who is available in the moment to accomplish great things. Some folks may not make a clear cut between employees and contractors and others get confused of the difference between FTE and absolute Headcount which simply counts the heads being on duty.

If you need different headcount measures, then well, go ahead! But make sure you name them differently, as well as that every dashboard user is aware of the specific definition and context of usage.

10.12 Hires and Terminations

While the headcount is an “aggregated” measure of active employees, new hires, and leavers (terminations), it doesn’t show you specifically the hires and terminations. This can be of importance to measure your recruiting or lay-off efforts. In a further dashboard development, you may drill down on a department or even role level to investigate where the main part of involuntary attrition is happening to identify the root causes.

10.13 Terminations and “Bad Hires”

A short description: a zero (light blue bar) means “No Bad Hire” and a 1 (dark blue bar) means “Bad Hire.”

Definition of Bad Hire: My definition of a Bad Hire is an employee tenure equal or smaller than being 6 months on duty. The dataset doesn’t include information about the contract type (limited/unlimited). So, I treat all hires as if they would have an unlimited contract. Why 6 months? Well, at least in Germany a probation period of 6 month is common if you hire someone with an unlimited contract. Adjust the timespan to your specific needs.

In real life and for further analysis the difference between contract types(limited/unlimited) shall be considered in your Bad Hire analysis. Elsewise your analysis results may get distorted.

Interpretation of a Bad Hire: It’s a first indication of organizational recruiting performance and some Bad Hires are part of real life. Nevertheless, the numbers shall be as low as possible to not waste resources of HR, hiring managers and other functions involved in the onboarding.

Organizational recruiting performance: I consider the focus on organizational recruiting performance as really important because recruiting is a team sport. Don’t blame it on the hired newbie alone or the recruiter because a hiring committee or hiring manager made the decision to hire. His/her superior gave him/her the responsibility to hire, the management defined a salary grid, and the recruiter was sent out to get the job done. As you all know, Bad Hires can have multiple reasons and are valid to investigate whether:

• there was a mismanagement of expectations between the recruiter, newbie and hiring manager?
• The assessment process or onboarding process was inadequate.
• The hiring managers’ leadership scope was overstretched.
• Or the salary grid isn’t matching the market demand to hire the needed people to stand the expectations and so on.

To sum it up: Bad Hires, based on my experience, are organizational failures with multiple people involved and to be fair, a bad hire rate of zero from the beginning to the end of a company’s lifecycle is a kind of out of this world scenario.

10.14 Termination by Cause

A short description: This chart simply shows the terminations clustered

• by employees where the company initiated the termination (“Terminated for a cause”; light blue bar)
• and employees who initiated the termination by themselves (“Voluntarily Terminated”; dark blue bar).

As you may notice, the number of employees who voluntarily left the company is much higher than the ones who were terminated by the company. For further analysis you may be interested in the reasons why and differences of distribution per department so you can  implement initiatives to decrease the number of voluntarily leaving people. For sure, if you are on a financial stretch in the unhappy need to execute lay off waves, voluntarily leavers are good, to avoid potential lawsuits. It’s obvious each analysis must take relevant circumstances into consideration.

That’s it. that’s the Dashboard 😊

11. User Tips

Attached are some tips for your dashboard consumption.

11.1 Recommended Devices

Desktop + 2 Screens. Open the dashboard on one screen and the article on the other. This will have the best user experience. Unfortunately, the Power BI Publish to Web Option I used doesn’t deliver a mobile view which splits the dashboards in suitable visuals adjusted to your smartphone device. You can still use it, but it will be small.

11.2 Recommendation for Publishing Data

Note: Never use the Power BI Publish to Web Option with sensitive data, because everybody with the link will have access to the dashboard. Also, the link may be indexed to Google & co. In my case that’s exactly what I want because I’m working with 100% fictional data on a 100% real dashboard, which I want to share with you.

11.3 Screen Size Scaling

You can scale the dashboard by the common usage of two fingers on your smartphone or using the options to scale on the right down corner of the display.

11.4 Mobile View

If you share your dashboard inside your organization, you luckily have access to a mobile view editor and view itself, cool, right? Just imagine you have a suitable view of your dashboard always available on your business smartphone. As well as your stakeholders.

12. The Dashboard - Link

Either you skipped the whole part between introduction and this part, or you belonged to the brave ones fighting your way through here. No matter to which group you belong. Hopefully this article was a bit edutaining for you and valid to read through.

Important: I’m currently using a Trial Version of Power BI Pro. Still in the process of evaluation whether a purchase would make sense. Long story short, in a couple of days the link may expire.

Enjoy the dashboard:

13. Summary 

The main purpose was to showcase

(1) how a data-driven HR Approach can benefit from a single source of truth interactive dashboard solution. (2) How to create a prototype. (3) Have a discussion about it.

If you want to kickstart a HR-Analytics function, I recommend the following way.

1. Define the purpose of HR, find common ground with your stakeholders.
2. Create theses, hypothesis and try to validate or falsify.
3. Do this by a lean set of KPI’s with a clear understanding of what they do, being aware of the strengths and weaknesses of each KPI and their dependencies to each other.
4. Work in iterations or sprints.
5. Speak the language of commercial people; data; prove your arguments.
6. Comply with data protection regulations. Talk to your legal people.
7. Evaluate the quality and quantity of your data sources.
8. When designing a dashboard, stay user focused. Deliver what is needed.
9. Start with a prototype below the radar, use it, show it and get funding for data specialists to help you in terms of maintenance, automation and further development.
10. There are many tools which can be of support. I simply chose[Ga86]  Power BI, because of my Excel background.
11. Have a basic knowledge of data infrastructure, do´s and don’ts, to be able to talk to your data people.
12. As an HR Leader, you may have a lot of stuff on your plate, like the everyday firefighting. Data projects may be threatened by reprioritization. But still have in mind:

“Organizations that use workforce analytics have a 28% higher sales per employee and a 17% higher profit margin than those that do not.”(Aberdeen Group 2014)

14. Final Thoughts

Data is everywhere and available in different forms. The showcase is mainly focusing on quantitative data. But we all know that formal and informal conversations with colleagues and the newest rumors are also valid data to include in decision making. Try to build thesis and hypothesis around that, as well as try to quantify. Use the qualitative data you gather, pre-evaluate by your intuition, instinct, empathy & experience and use quantitative data to pitch your initiative towards the C-Suite having one goal in mind:

Let your company flourish, while having fun with your people in the process of reaching that goal.

Thanks a lot for your attention.

15. Sources

• Aberdeen Group (2014): "The Value of Workforce Analytics"
• Calzon, Bernardita (2021): 23 Dashboard Design Principles & Best Practices To Enhance Your Data Analysis: https://www.datapine.com/blog/dashboard-design-principles-and-best-practices/
• Enterprise DNA:  https://forum.enterprisedna.co
• Ferrari, Alberto (2022): Why star schema is your best friend in Power BI. https://www.youtube.com/watch?v=KBqRC6JWy9A
• McKinsey (2019): "The Future of Work in Europe: Managing Talent in a Digital Age"
• Microsoft | Learn (2022): Human Resources sample for Power BI: Take a tour: https://learn.microsoft.com/en-us/power-bi/create-reports/sample-human-resources#get-the-pbix-file-for-this-sample
• Patalano, Carla, Dr. & Huebner, Rich, Dr.: Human Resources Data Set, Version 14 CC-BY-NC-ND This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
• Power BI Community: https://community.powerbi.com  
• Russo, Marco & Ferrari, Albert (2019): The Definitive Guide to DAX: Business intelligence for Microsoft Power BI, SQL Server Analysis, and Excel
• sqlbi: https://www.sqlbi.com/
• Treacy, Mynda (2019): Interactive Excel Dashboard: https://www.youtube.com/watch?v=rsx43g7TBBs

16. Special Thanks

Special thanks to Holger Pannhorst, who showed me a long time ago, how to store data in the right way, even at a time when I wasn’t even able to create a pivot table. This was the beginning of my data driven approach.

Also, a long time ago I appreciated conversations with Laurens Hubert M. Mauquoi who introduced me to the funnel logics of Online-Marketing and transferring this to recruiting.

I also learned a lot from the Berlin Power Bi Group and it’s hosts Artur König and Christoph Spieles, realizing that the Power Bi Community is one of the most helpful communities I ever have experienced.

Finally, professional love to all the Business Intelligence people I met like: Akash Rathod, Dr. Elena Ossella, Sina Tabar and Andrea Fiallos. You make your company see clearly; you are providing the eyes with ultrazoom. Without you, leadership would be blind.

Finally, thank you Ingo Thomas for proof reading, even if I may have rejected some of your recommendations. 😉

17. Final Questions

 Would you consider this approach useful?

What are your challenges to get more and more data driven in your daily work?

In which further analysis would you be interested?

Maria Maximino Özlem Arslan Chris Brown Julia Martínez Benito, Ph.D. Veronika Weigang Marcus Schmermer Matthias Schmeißer Sarah Griesche - CIPD Claudia Schüßler Tobias Wille Jonas Probst Benjamin Birkner Astrid Fischer Stefan Echinger Mathias Mengel Kooku Recruiting GmbH - Interim Recruiting & Recruiting on Demand

#hranalytics #peopleanalytics #dashboard #strategicanalysis