In a world where statistics, big data, artificial intelligence and the like are driving business outcomes, one corporate department across industries stands out for its relative lack of analytics use: human resources (HR). Indeed, a 2015 report by the Harvard Business Review indicated that managers across other business areas believe that a major obstacle facing HR offices stems from their lack of analytical application, impeding their ability to make efficient and strategic decisions. Specifically, less corporate experience, the tendency to not speak in business terms about employee talent, and a trend of not providing relevant data or understanding the financial effects of decisions were identified as characteristics inherent to many HR departments.
However, in a space where recruiting and training is often dependent on face-to-face interaction, and variables such as individual personalities and work styles complicate decision-making, the widespread use of hard data has historically been regarded as a less-than-perfect fit. Another challenge cited by the report is that information compiled on the labour force is generally “imprecise, inconsistent or hard to grasp, thus requiring manual manipulation”.
In Colombia, very few companies have leveraged the true value of data when it comes to recruitment and ongoing development of human capital, but HR departments globally are slowly beginning to realise the potential of technology to help build a competitive workforce. There is no single definition for the concept of HR analytics, but its essential function is to support strategic decision-making on matters related to securing and nurturing talent in an environment where continuous training and flexible roles is the new normal.
To achieve this, HR managers can benefit not only from acquiring the technology that will help them generate consolidated, useful information, but also by analysing the data in a way that allows them to identify an appropriate course of action for training and hiring gaps in their organisations. Robust human capital development programmes can lead to improved financial results, greater operational efficiency and more targeted market orientation.
Undoubtedly, a heightened use of technology and data in regards to talent will be one of the most important changes companies can adopt if they wish to remain competitive. Utilising information in real time as a central element in the decision-making process will allow HR departments to keep pace with the human capital needs of other company areas, such as IT, finance and engineering.
However, this goal will only become a reality if those in charge of HR departments know the right questions to ask of data sets, and how to act upon such information to help move the business forward. Drawing conclusions to identify changes needed in company culture, recruitment practices and mentorship programmes, for example, will allow HR managers to implement initiatives without engaging in the time-consuming task of speaking one on one with large groups of people.
The Right Approach
It is often not necessary for companies to invest a significant amount of time and money in software and hardware updates, nor in hiring dozens of data scientists; if HR managers have clear goals for human capital development programmes, a good database administrator and basic software can be all that is needed to provide the data a company requires to shape policies and identify ways to attract and retain top talent.
A number of companies have been able to leverage the use of data analytics to improve their decisions regarding talent. Google has been developing its People Operations Department for over a decade in order to answer questions such as “Are engineering managers adding value?” and “What are the characteristics of a highly effective team?” Utilising data to answer specific questions such as these can help to drive productivity, create effective work teams and identify gaps in company talent.
Another example is US low-cost carrier JetBlue. The airline employed an analytical study by Wharton Business School to improve its recruiting patterns for flight attendants and to profile for attributes that have the biggest impact on passenger satisfaction. This ultimately led to an enhanced customer experience for JetBlue travellers.
Hard data can also be utilised to tackle general issues that any company may suffer from, such as high employee turnover. Analysing records that detail metrics such as working hours, and vacation days allotted and taken, can identify employee burnout and unhappiness with working conditions. Industry-wide data comparison can be used to ensure a company is attracting and retaining top talent by studying factors like average pay, benefit packages and professional development opportunities.
Nevertheless, caution is necessary when considering how to best use gathered data, as not all information transfers well across industries. In this regard, Amazon, one of the world’s most successful online retailers, was criticised about how it applied its expertise to the acquisition of Whole Foods, a US supermarket chain known for high-end products and a strong service-oriented culture. When Amazon tried to leverage its know-how to standardise processes and reduce costs, it was revealed that the entirely different business model employed by Whole Foods – where customers enjoyed personalised interactions at point-of-sale counters and around the store – could not be easily replicated by machines. Indeed, the digital user experience of Amazon customers, which is based on computer algorithms suggesting products similar to previous purchases, does not have much similarity to the experience at a traditional supermarket. This approach alienated both customers and employees, demonstrating that standardisation does not have to drive every decision, and that data is best tailored to organisational and employee culture.
The revolution of analytics in the field of talent management enables a path to the creation of diverse and specialised corporate teams. There are opportunities to find algorithmic approaches to recruitment and promotion, generate data streams in real time, track performance feedback and monitor the organisational climate by means of employee surveys. Moreover, digital trace analysis can be performed to map and shape informal organisational networks with help from nodes configured by internal opinion leaders. Creating connections among employees beyond the direct manager-subordinate relationship would help support a new generation of employees who ask for and appreciate additional feedback, and who value personal as well as professional development.
However, there are legal and ethical risks to using data analytics in the human capital sphere, as many countries have laws about the dissemination of personal information within a company.
There is also the risk of employing discriminatory practices based on age, gender, education and race as a consequence of existing biases in the design of algorithms and the sources of data they use. For instance, Amazon’s recruiting system – which utilised artificial intelligence – discriminated against women based on the university they attended and rewarded the use of certain words more common in men’s resumes. The company was unable to correct the system’s tendency to make recommendations to hiring managers that were unfair towards women and ultimately abandoned the tool in October 2018.
Additional considerations to bear in mind when applying data analytics to talent management are outlined in a 2017 report by IBM Watson Talent. An important one is that analytics are not always required in the field of HR, and that standard problems typically only require a well-informed, experienced individual. Also, the utility of data is more important than its novelty, as the latter is not a substitute for sound judgement or the convenience of using more conventional measurements.
Furthermore, although HR departments are moving towards greater use of data in their day-to-day work, ultimately their job is managing people. Given individual personality traits, not all relevant information to a decision can be codified; HR departments would therefore benefit from getting to know employees when helping them improve their performance and grow in the company. Lastly, if a firm embarks on designing its own computerised hiring and evaluation system, managers should remember that easily captured characteristics such as education, years of experience and numerical-based performance grades are only one part of the picture. Equally important factors like perseverance, time management and creativity are difficult to measure.
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