Given the popularity acquired by Workforce/HR Analytics in a short period of time and the lack of professional standards, an important problem many are facing is to be able to understand if certain professional activities are done indeed under this heading. One or more of the criteria written below might be missing. In my opinion it is a real problem worth thinking and doing something about it.
My intention is to create herewith benchmark criteria for all interested parties. Luckily a very thought provoking book titled “Predictive Analytics for Human Resources” from Jac Fitz-Enz and John R. Mattox II comes to the rescue with inspiring ideas.
1. Access to internal and external HR data
The person in charge of Predictive Analytics should have access to a vast amount of internal HR data. Ideally the person has super power authorisations to access HR data from the entire ERP system of its own company.
The person should have access also to a vast amount of externally available HR data about its own organisation. Those data can be purchased from companies like LinkedIn, Glassdoor, Joberate or hiQ Labs or collected by the expert itself.
2. Unlimited creativity
Since most likely certain HR data are difficult to access, the expert has to come up with some creative ideas on how to extract HR data from the organisation. An “In-house LinkedIn”, as explained in my previous post, seems to me an ideal source for new HR data. Staff shall be allowed to endorse each other and propose new competences and skills. Staff engagement questionnaires might need to become more frequent and shorter to capture the pulse of the organisation. Exit questionnaires might be needed too.
3. Hierarchical location
The expert needs to be located hierarchically close to the manager of its own organisation, who can sponsor the work. At a maximum of one or two hierarchical levels higher. The sponsor needs to have responsibility for business, financial and HR aspects of the organisation.
4. Burning talent management issues
The organisation needs to have some burning talent management issues and the ambition to remain leader in its field. Examples for talent management issues are identifying high and low performers, issues in competency gaps, desire to increase competencies, engagement or reduction of turnover.
5. Cultural embrace
Key influencers in the organisation need to embrace taking HR data driven decisions in the real spirit of Workforce/HR Analytics. This will avoid the typical flawed approach.
6. Patience and encouragement from sponsor
The expert in Predictive Analytics will need a very patient high-level sponsor who constantly encourages and provides guidance. The interaction should not be dominated by fear of failure. If you’re aiming at something, then you’ve got a better chance of hitting it. It takes time to collect, check and clean HR data, which most likely will be coming from different sources. The expert might be even get paralysed from all these preparatory tasks. The expert has to be allowed time to produce actionable results. Workforce/HR Analytics is a journey of several iterations between sponsor and expert, as illustrated in the book mentioned above.
7. Knowledgeable in statistics
The expert needs to be knowledgeable in statistics. Have you implemented cutting-edge algorithms and solutions for identifying patterns within and across large, multi-dimensional datasets in HR? If you not, you probably need an expert in the field.
8. Knowledgeable in business processes
The expert in Analytics needs to be knowledgeable in business processes that drive its own organisation. The expert should have done some work similar to a Lean Six Sigma expert, have analysed and optimised business processes in its own organisation. Legal, procurement and compliance are other aspects of its own business he/she should be knowledgeable of.
9. Knowledgeable in HR matters
The expert in Workforce/HR Analytics needs to understand key HR processes, e.g. selection, performance management, compensation and training. Most importantly he/she needs to understand how HR processes can drive business outcomes.
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