A little background …
Big data analytics and AI has seen applications in many fields such as finance, trade, and health sectors. Recently, HR and talent domains started enjoying the benefits of these technologies, where it is called “people analytics.” This article begins by questioning the status quo of the current talent management practices and highlight its flaws, thereafter it paves the path for the rise of people analytics. Then, people analytics contemporary position is outlined and investigated. Next, we examine applications of people analytics in the talent management domain. Finally, I will share with you my personal opinion on this approach. Yours is surely welcomed 🙂
Contemporary Talent Management Approach and Rise of “People Analytics”
HR and talent management are continuously challenged due to their subjective nature rather than objective nature compared to other business functions. This is mainly because talent and HR science derive directly for physiology, a soft social science field with difficult metrics to measure and quantify, even if done, the relationship between personality and organizational variables is not entirely understood. However, over the past years, science has seen significant improvements in how we can measure and understand personality characteristics and cognitive abilities. The Large corporation such as Google and AT&T was known previously to recruit top performers from Ivy League schools and using metrics such as GPA to screen candidates. However, such an approach proved inaccurate and might result in screening out other qualified candidates; thus Google started moving towards looking for “digital trails” from KMS and social networks to understand candidates better. For example, Google found that blogging employees are more engaged and satisfied by 10% than other employees. Moreover, before the recent digital explosion and social media networks flood, it is hard to get a holistic view of how a candidate or employee will behave and perform, leading to analysis to the only subset of actual information, such as CVs, references and university records. By using people analytics, the manager can make accurate talent management decisions that are directly connected to business needs.
There are other factors led to the rise of people analytics such as (i) The impact of technology on the labor market; The way how candidates look for jobs and recruiters look for candidates changed, workers with hot skills receive ample flow of opportunities from social networks, forums and mobile applications, (ii) Decreasing employees’ tenure; nowadays, employees loyalty and commitment is at least, where job switching occurs frequently. These factors are pushing towards a more profound and rich understanding of employees, paving the way towards “people analytics” more objective, fact-based assessment rather than subjective, impression based assessment of talent.
People Analytics Background and Status Quo
Big data and AI have proven usefulness in various industries such as finance, retail, insurance, and education. However, it is agreed that HR and talent are among the laggards in adopting Big data and AI, and it started getting attention in these domains only last few years ago. Recently, HR domain started realizing the importance of moving away from subjective, gut-driven judgments to more objective analytics-driven judgments. This movement from “soft” measures towards more “hard” measures is widely emphasized in nowadays. Additionally, it is agreed that the focus of people analytics should be strategic forward-looking rather than reflective and backward-looking.
Research suggests that talent analytics is driven by large corporates such as Bloomberg, Google, and Microsoft. Typical usages scenarios include: handling huge applicants pool, identifying top performers, identifying best incentives, and matching the skills with the future business need. The major applications of talent analytics include: Tracking employees competence, identifying key employees, focused HR investments where critical employees’ segments are identified, customizing EVP (Employee Value Proposition) to tailor HR practices to what best fits the most valuable employees, workforce planning where the staffing needs are identified with business needs, and finally to talent supply chain where recruitment channels are optimized and scaled to hunt the best candidates, and onboarding and cultural fit and retention.
After All: What Is People Analytics?!
A commonly used definition for talent analytics is: “the integration of disparate data sources from inside and outside the enterprise that
What can people analytics provide?
We find it useful to base our analysis on (Isson & Harriott, 2016) seven pillars of people analytics as depicted in Figure 1.
- Workforce planning analytics
By mining historical data, market forecasts and industry trends it is possible to provide valuable inputs to analyze turnover and succession planning. It helps to answer questions such as: What business units should I scale up and what should I scale down?
- Sourcing Analytics
Sourcing analytics merely is about using big data to help in staffing resources after workforce planning. It includes both the employer decision journey and the candidate selection journey. Sourcing analytics addresses questions such as: What are the best place to find STEM competence? What are the essential candidate considerations to pursuing a career?
- Acquisition\Hiring Analytics:
The availability of big data increased the number of employee and organizational variables that can be used to evaluate the talent selection process. Thus, metrics such as the talent satisfaction rate can be directly assessed by applying big data and AI to the new hire profile. It helps to answer questions such as: What should I ask during an interview? What is the correlation between interview performance and job performance?
- Onboarding, culture fit and engagement analytics:
Analytics from engagement activities can generate business value by creating the positive first impression for the candidate, tailoring onboarding experience based on the candidate profiled personality and avoiding employee and company mismatch and thus increasing loyalty. It can help on answering questions such as Does the new employee fits me? What budget should I set for onboarding this talent?
- Performance assessment and development and employee lifetime value analytics:
Engagement analytics can help employers to understand the drivers and the motives of employees to decrease turnover. It also provides insight on improving current communication channels such as performance assessment. It can help in answering questions such as: What are the main drivers behind employees’ engagement? How does it affect our bottom-line?
- Employee churn and retention analytics:
Big data provides insights and knowledge of sources of frustration and disappointment of employees and hence to address their retention issues. By using market data, company data and employee data to a deeper understanding of staff retention can be reached. It helps to answer questions such as: Who is most likely will quit? What is the cost of losing top performers?
- Employee wellness, health, and safety analytics:
Big data analytics helps to reveal employees stress, identifying “workaholics” to address work-life balance issues, and addressing emergent issues that can impact employees’ satisfaction. It contributes to addressing questions such as: Why and when employees are stressed? What is the relation between employee satisfaction and customer satisfaction?
Well … What I personally think
As humans, we are prone to cognitive traps such as confirming bias and stereotyping. By objectifying the subjectivities and soft hardening measures, people analytics help us to move from impression-based decision making to a fact-based decision making on HR and talent-related decisions. For example, it is quite common to ask an employee for his Github (software development platform) or StackOverflow (programming Q&A platform) accounts, by inspecting and checking these accounts, employers can form an “impression” on how this candidate performs in communities and the quality of his contribution. However, people analytics state a stronger promise, it aims to objectify such unstructured information in a hard way. For the previous example, one would expect people analytics to provide a clear factual metric representing technical community contribution, and assign a particular score of the candidate based on the significance of contribution, the frequency of contribution and the reputation of the community. This way,
People analytics entails ethical questions; issues such as data privacy and confidentiality are always fired against big data and AI. What a company might consider “social media analysis to determine employee satisfaction rates” can be considered “spying and stalking” from an employee perspective. Even worse, employees may feel threatened if they express something against their employers. It is essential that companies obtain the acceptance of their employees before performing any social media analysis, and inform if they are doing analysis on the internal collaboration portal as to maintain transparency and trust with their employees. However,, I would argue that the total removal of subjectivity remains a dream due to two reasons. First, to convert from subjective to an objective measure, we will need somehow to assign a certain factor to these subjective measures, this assignment will entail some subjectivity. Secondly, though impression based assessment could be unfair, it allows the reviewer to capture the whole context. An objective measure will typically be based on a certain closed set of factors that might not cover all aspects. Even though, people analytics remains an “equal” though not “fair” way to assess employees by eliminating biases.
Finally, that is it for today! I made easy to comment and discuss by using your social media login, please let me know what you think about talent analytics, you opinions are highly valued!