A critical thinking essay
Hi! This story is based on my critical thinking assignment on a Havard Business Review article. Click here to read the article.
Subject Matter
The article (Competing on Talent Analytics, 2010) talks about how organizations are striving to improve their edge against competitors through the use of talent analytics. Talent analytics is an analytics platform that produces insights into the workforce (Morphy, 2018) to gain deeper insights into ways to acquire, manage and keep its talent helping organizations make better-informed decisions to maximize talent productivity and engagement.
The article explores the different applications of talent analysis which are human-capital facts, analytical Human Resource (HR), human-capital investment analysis, workforce forecasts, talent value model, and talent supply chain and how they benefit organizations. It also highlights factors to effectively apply talent analytics which are data, enterprise, leadership, targets, and analysts.
Other Perspectives on Subject Matter
With the advancements in technology, there are new methods in analytics such as artificial intelligence (AI) using predictive analytics and organizational network analytics.
Predictive analysis is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning (Edwards, 2019). It can be used to estimate the odds of an employee leaving the organization, map out what runs employee engagement and find optimal tests that identify candidates who can best drive performance. For example, IBM’s AI can predict with very high accuracy which employees are going to leave the organization through predicting employee flight risk and advising measures to engage employees (Rosenbaum, 2019).
Organizational network analytics identifies informal relationships between employees within and outside the organization (What Is Organizational Network Analysis? — HR Daily Advisor, 2020). This allows the organization to identify unseen networks that may be significant drivers of performance and take measures to strengthen such relationships. For example, General Motors (GM) tapped into organizational network analytics in the area of team results (Disrupt or Be Disrupted: Advice from GM’s Chief Talent Officer | Jacob Morgan, 2018). One study found that teams with higher cohesion scores tend to have better response quality and higher response rates. They then relocated employees such that they were ‘facing each other’ and introduced ‘huddles’ which allowed employees to communicate regularly. With such measures, GM saw a 25% increase in productivity in some teams.
Additionally, the article focuses heavily on individuals. However, the article should also focus on teams, through the use of people analytics, as they are the new norm as organizations are moving towards a more team-based structure (The Changing Nature of Organizations, Work, and Workplace | WBDG — Whole Building Design Guide, 2016). For example, Google’s Project Aristotle aimed to find what makes effective teams (Re:Work, 2011). They studied and collected data from over 180 low and high-performing teams within Google and identified psychological safety as the most significant factor, amongst others. They then were able to evaluate the degree of psychological safety within teams using surveys and foster a team culture of psychological safety to improve effectiveness. Hence, this shows that analytics is not only applicable to individuals but also to teams.
Strengths and Weaknesses of Evidence
The article did not define the term ‘talent’. Talent can take on many meanings (“(1) (PDF) What Is the Meaning of ‘talent’ in the World of Work?,” 2013). There is a lack of consensus as to what talent means and which aspects of talent are of greatest concern to employers. Talent can be understood as all the employees, high performers, employees with high potential and even characteristics. Hence, it is important to define what talent means in this article as it allows readers to better understand what is being referred to and gives a better understanding to whether talent analytics is exclusive to those persons with high potential and performance or inclusive of all employees.
Quoting the article, “Leading-edge companies are increasingly adopting sophisticated methods of analyzing employee data to enhance their competitive advantage”. This shows that companies are more interested in the application of analytics in HR. However, according to a Deloitte article (People Analytics: Recalculating the Route, 2017), 71% of the companies they surveyed see analytics as a high priority but the progress of applying it has been slow. This could be attributed to the lack of skills and knowledge in such analytics, the issue of what data they need and how to collect them to support talent analytics functions, and also due to financial constraints (Why Is HR So Slow to Adopt People Analytics? — The HR Gazette, 2017).
One strength of this article is the many examples given of how organizations are changing their HR processes through the use of analytics. It gives readers a greater understanding of how and why organizations are transforming their processes. To further give a modern example for this subject, IBM has transformed its processes by abandoning the annual performance review (Rosenbaum, 2019). They replaced it with quarterly feedbacks that assess employees based on their skills growth. Through the power of AI, it provides career feedback to employees and helps them identify skills they need to grow. This then fosters a culture of constant learning allowing employees to grow and in turn, better contribute to the organization.
Key Assumptions
The author often gave examples of big organizations such as Google, AT&T, and Netflix applying talent analytics and thus, gives the assumption that such methods are only applicable to such well-established and data-driven organizations. However, even small-medium enterprises (SMEs) can incorporate analytics to improve their business (Chhavi Tyagi, 2018). They are able to make use of Big Data analytics to obtain better insights into how they run their business and how to improve it. Big Data refers to all the data that is generated by the business which is too complex for traditional data-processing techniques to analyze. Hence, SMEs do not have to worry about not having enough data as the massive readily available data may have useful insights in improving the business. With the limited purchasing power of a SME, they are still able to afford Big Data analytics tools from Oracle and IBM which used to be exclusive to large enterprises. This is due to cloud technology enabling products to be accessed online which makes it more affordable. Hence, SMEs are able to incorporate analytics despite their constraints.
Conclusion
In conclusion, I believe that the article is informative but lacks a well-rounded view. The article talks about the various uses of talent analytics mainly from the perspective of individuals and how well-established organizations are applying analytics. Hence, I explored different views such as how analytics can be used on teams, modern applications of analytics, and how small-medium enterprises can apply analytics to their operations.
Thank you for reading! :)
