This blog post is a first contribution pointing to the use of analytics in the field of Human Resources. In my work as a consultant towards HRM decision making, I saw that analysis tools and methods are mainly applied to financials and not so much to the field of Human Resources. In my work, I created for Human Capital Controlling (HCC) high-level models, proven concepts and hands-on recipes. I would like to share some of them for this audience.
There are many checklists around that help in determining corporate human resources maturity. Describing several stages of characteristics they help to classify the current situation in the company. They all start at a low level point, maybe «most basic KPI like salary sum and number of employees are regularly calculated». Then step by step they increase the requirements. Today they probably end up with use of AI and predictive models.
Many of those approaches stay within the field of HRM when they increase the maturity level. And many focus on technical aspects like the use of AI, thinking that the use of more technology would automatically lead to higher business competence.
All those approaches will fail to leverage the importance of HRM for the company and will not help HRM to gain «a place at the big table». This is exactly the goal we would like to reach: that HRM can play the role in the company it should play to move the company forward.
To achieve those goals, HRM has to cross borders and integrate with business. Todays HR Business Partner concepts are a step in the right direction, but they still have weaknesses. They are often «on-demand», are HR centric and driven by people that know a lot about HRM but only few about business. And they are missing reliable facts about almost everything. This is, where Business Intelligence or Analytics are entering the playfield.
To summarize:
- the endpoint of a mature HRM is not the use of sophisticated technologies, but the integration with business;
- to gain a place at the big table, HRM has to understand, how the company’s business works;
- before talking about predictive analysis methods, we must recognise, how difficult and time-consuming it is, to only get the basics up and running;
- HRM must cross its borders in a pro-active way by providing solutions to business, based on in-depth data analysis.
As a maturity roadmap towards business integration, I would like to draw these steps:
| Level 0: no use of HR data in HRM, occasional on-demand delivery of hand-crafted data collections to third-parties like managers or statistical offices. |
| Level 1: regular delivery of HR related data to target groups; loosely defined job functions and roles in HRM for basic analytics; setup of irregular data stores for analytics, e.g. spreadsheets with macros. |
| Level 2: Regular analysis of HR data in selected fields like time recording or salary; focus development and recognising areas of improvement; stronger analysis processes over the data lifecycle; corporate use of HR data in specific contexts; forming a common language for and a common understanding of KPIs. |
| Level 3: on-demand support for line managers; corporate strategy support; ongoing automatisation of data processing and report publishing; taking core process data into account. |
| Level 4: pro-active recognition of problem fields; providing HR programs and processes to line management; strong strategic orientation; integration of core business and HR data. |
Please note that in this summary, aspects might be missing, are not mentioned, you might encounter gaps, based on your own history. Each company is different and there is no one-fits-all approach. Maybe a specific aspect can be realised earlier here and later there in the process. I want to point on the key findings:
- build a strong basis in means of strategy, structure and systems;
- start at the beginning, then take one step after the other;
- for HRM to integrate with business and to reveal its full power to the company you need the resources: time, money, people with HR background that understands the business and last but not least data that integrates with core process data.
If you have questions about HR analytics, how to build an analytics solution, on KPIs and, in general, how to move HRM closer to business using hard facts, feel free to contact me.
#Global Business Analytics