My People Analytics and HR-Tech reading list on Kindle includes +30 items! Find here inspiration, practical guidance, validation for practices, new ideas and innovative tools, an “open door” to a professional community.
To face both technical and social difficulties related to AI, every HR leader should start understanding 5 themes: What AI is – or isn’t? How accurate is AI? Why AI prone to bias? How people react to AI? How legal frameworks deal with AI? This part discusses the first 3 themes.
You can’t evaluate AI solutions without understanding the basics of practical machine learning and predictive analytics. You don’t have to be a data scientist for that. It’s like driving a car – you don’t need to be a mechanical engineer to buy or drive your car.
The gender pay gap analysis in this article is straightforward. HR managers with a B.A. education can handle it, with a little help from a data scientist. I encourage HR practitioners who start their journey in People Analytics to practice it. The data is available, and the insights may be vital.
In a public talk I challenged myself to describe the state of People Analytics in five sentences. Each point I made implies a myth. HR leaders should be aware of the following five misconceptions, or otherwise, continue to let these false ideas inhibit their advancement.
HR data is a mess! Nevertheless, there is so much that HR leaders can do to cope with this challenge, starting today, based on six recommendations included in this article, in a mixture and volume that depend on the phase in the journey to data-driven HR.
People Analytics and dashboards of HR Analytics deal with Performance. However, each practice has a different approach: Dashboards enable us to present KPIs, and to answer questions such as: Did we reach our goals? However, by using dashboards, we can’t answer the question: Why?
Employees and candidates will judge employers, in addition to Employee Experience perceptions, by employer ethics in data management, and when feeling secure, they’ll be more receptive and enthusiastic to participate and cooperate with AI and ML to influence their career path.