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At 21.7 per cent, staff attrition within the Australian Information Technology (IT) sector is unsustainably high. Staff recognition can be defined as the action or process of recognising employees for the work completed through words and gratitude1. Over the past five years, globally, organisations have increased their focus and investment on employee reward and recognition.
However, despite this increased focus, research shows that recognition is not occurring as often as it should be, as only 61 per cent of employees feel appreciated in the workplace1. Research also shows that even when recognition is provided for employees, it is not executed well or enacted correctly 1/3 of the time.
Organisational development and human resource studies demonstrate that reward and recognition programs commonly do not resonate or hit the mark for employees, if they are: not authentic and sincere2, only provided in a single context, or are based on award criteria that is overly complex or unattainable3.
This paper covers how leaders and organisations can recognise and then subsequently avoid these three common pitfalls, to maximise the investment into employee reward and recognition programs and efforts.
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Australian organisations in both public and private sectors enthusiastically identify and implement best practices from around the world. After considerable time and effort has been allocated to implementing these processes and the associated tools the results are all too often less than satisfactory. There are many best practices, frameworks and tools to assist in the optimisation of IT but there are two key problems areas that if overcome, can make a significant difference in the benefits that organisations will derive from best practice implementation.
Conclusion: Machine learning operations (MLOps) adapts principles, practices and measures from developer operations (DevOps), but significantly transforms some aspects to address the different skill sets and quality control challenges and deployment nuances of machine learning (ML) and data engineering.
Implementing MLOps has several benefits, from easing collaboration among project team members to reducing bias in the resulting artificial intelligence (AI) models.
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