We are living in a world that’s changing fast and getting disrupted daily. A lot of businesses keep on making frequent structural changes to bring their human resources in line with the organization’s LOBs. This is where analytics is playing a crucial role! Not just in building smarter products but also in forming and managing a strong and streamlined team has analytics at its core. With cut-throat competition across industries, it’s become an imperative for organizations to maximise ROI in their human resource investments to increase profitability.
Here’s a round-up of four awesome curated blogs that will give you a hang of talent analytics and how smarter organizations are leveraging it to structure their teams and get an edge over their competitors.
This article provides you with a fairly solid framework on avoiding talent shortfalls by leveraging data. The question it raises are those which all HR personnel in this space should consider. Starting with the fundamental question “What is workforce planning”, Weisbeck takes you through the process, with insight into the changing workforce dynamic and ultimately segues into - turning business goal alignment into action.
Why do some companies struggle to get their People Analytics Programs up and running? Green shares 16 practices which companies that excel at people analytics follow. The article concisely lays out 16 defined practices and explains their impact on the program. A strong plus point - the sharp graphics and tables make it very easy on the eye.
A short article arguing the merits of “Holistically implementing talent analytics”. The article puts together the application of data science in various aspects of human resource management such as recruitment, talent management, compensation management and retention prediction.
This article is a really good read, showing us why even the most technically equipped workers need to rely on more than just numbers and data. HR professionals are accustomed to using “soft data” - “things like the electricity in the air at a political rally and the fear in the eyes of an executive faced with an unexpected threat”. However, data scientists who sit behind their computers may only focus upon the data in front of them. With examples ranging from oil fields to political rallies, Redman distinguishes the great from the good, as those who “know they must understand the larger context, the real problems and opportunities, how decision makers decide, and how their predictions will be used.”
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