Article (March-2018)


HR Analytics : the next frontier for workplace transformation

Dr. Debendra P. Kar

Designation : -   Associate Professor-HR

Organization : -  Institute of Management Technology, Hyderabad


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"In God we trust. All others bring data." - Barry Beracha, former CEO, Sara Lee Bakery Group.
This affinity of c-suite employees towards data is supported by the works of Pfeffer and Sutton (2006) and Briner, Denyer and Rousseau (2009) while assessing the impact of Evidence - Based Management (EBM) on performance of business and related management practices. According to their works, data should be the basis of all decisions.
HR Analytics can be defined as "a methodology for understanding and evaluating the causal relationship between HR practices and organizational performance outcomes (such as customer satisfaction, sales or profit), and for providing legitimate and reliable foundations for human capital decisions for the purpose of influencing the business strategy and performance, by applying statistical techniques and experimental approaches based on metrics of efficiency, effectiveness and impact" (Lawler, Levenson & Boudreau, 2004; Boudreau & Ramstad, 2006).
Purpose of HR analytics
According to Baron (2011), HR analytics is context - specific. Relevance of HR analytics varies from industry to industry, for 
e.g. : Human capital is valued more in knowledge - driven industries like IT. Hence, this paper focuses on the applicability of HR analytics with reference to contextual factors.
Generally, HR Analytics helps an organisation to understand and measure the effect of HR practices and policies on organisational performance and subsequently to influence business strategy. Therefore, it's a very powerful tool in the hands of the HR department to add value to their organisations (Lawler, Levenson & Boudreau, 2004).
Applications of HR analytics
According to Harris, Craig and Light (2010) there are five different categories of human capital analytical applications, which are as follows :
  • Identify and manage critical talent (e.g., high performers, high potentials, pivotal workers).
  • Manage critical workforce segments accordingly (e.g., underperforming units are identified and helped to improve).
  • Predict employee preferences and behaviours and tailor HR practices to attract and retain talent.
  • Forecast business requirements and staffing requirements (e.g., workforce skills needed in different business scenarios).
  • Adapt rapidly and scale recruiting supply channels and targets to meet changing business conditions, objectives, and competitive threats.
Some of the other uses HR analytics may have are :
1. Leadership succession planning: Data can be used to identify the next leader through analytics that goes beyond the simple financial performances and outcomes. Behavioural indicators can be processed to make sure that the performance achieved is in sync with the culture of the company.
2. HR risk management: The HR risk is of two types : Known and Managed. Known risk refers to the cyclical fluctuation of talent availability in the manpower supply chain of an organization. Managed risks are those which have been identified specifically as focus area and a framework to manage the same. 
3. Identifying the hidden factors of success: Diversity, generational and other differences helps the understanding of varying growth drivers in an organization. Staging HR interventions, promotion decisions and policy change can be made more data driven through the results.
Metrics in HR analytics
Metrics, in general, are of three types (Lawler et al., 2004) : 
  • Efficiency metrics
  • Effectiveness metrics
  • Impact metrics
Efficiency metrics measure performance of the HR department, for e.g. performing basic administrative jobs. Effectiveness metrics measure the effect of HR programs on employees, for instance measuring skill development through trainings instead of number of trainings attended. Impact metrics measure to what extent HR can provide a sustainable competitive advantage, for example defect reduction after relevant training.
On the other hand, Mayo (2006) had proposed seven metrics for HR analytics : 
  • Workforce statistics,
  • Financial ratios relating to people and productivity, 
  • Measures of people's values, 
  • Measures of people's engagement, 
  • Measures of efficiency of the HR function, 
  • Measures of effectiveness of people processes,
  • Measures of investment in one-off initiatives and programs.
After data is collected using the above mention metrics, statistical tools and techniques can be used to show the casual relationship between HR practices and organisation performance metrics like profitability, customer satisfaction, sales etc. which forms the basis of HR analytics.
Contextual factors related to HR analytics
Successful implementation or application of HR analytics in an organisation depends on the internal and external context (Van de Ven & Drazin, 1985). Following contextual factors might affect the same : 
  • Size of the organisation.
  • No. of years the organisation is in the industry.
  • The industry in which the organisation is a part of.
  • Competition in the industry.
  • Generic and specific regulations and legislations for the industry.
Factors affecting HR analytics
1. Competitive mechanisms: As evident from above discussion, HR analytics helps to increase efficiency and generate better business results (Harris et al, 2010), we can assume more competitive environment pushes an organisation towards greater application of HR analytics.
2. Institutional mechanisms: Similar to the interest of HR scorecard implementation among competitors to account for HR importance in a better way (Paauwe, 2004), companies are implementing HR analytics either by imitating their competitors or to prevent themselves not being seen as outdated. So, the no. of competitors going for HR analytics influences the use of HR analytics in an organisation in the same industry.
3. Configuration: Keeping in mind an organisation's heritage, the older the company the more formalised its behaviour (Mintzberg, 1979). Companies with more formal approach towards decision making should apply HR analytics more rigorously. So, combining the above two statements, HR analytics should be applied more in older organisations.
4. Organisation Structure: In today's world, most organisations are showing organic growth due to unpredictable, dynamic environment, high differentiation and little standardization among task. In such a backdrop, to gain information about workforce performance drivers and increase organisational effectiveness, HR analytics approach is the way forward (Boudreau & Ramstad, 2006).
5. Size of Organisations: Mintzberg (1979) argues planning and control systems of a larger organisation should be more sophisticated. So, what more sophisticated than HR analytics can help a larger organisation (by size) to implement sophisticated systems.
6. Labour-capital Ratio: King (2010) pointed out that companies, in which people contributes more to the total value i.e. with a high labour - capital ratio, should maintain better workforce related information. So, greater application of HR analytics can be observed in knowledge - intensive industries.
7. Financial Health: According to Lawler, Levenson and Boudreau (2004), the better the financial health of a company, more the use of HR analytics (due to more resource available for the same) and vice-versa (as HR analytics improves organisational performance).
8. Innovation-orientation: The more an organisation is inclined towards innovation, the more is the chance of it to involve itself in HR analytics to thrive continuous improvement, generation and improvement of new ideas and practices.
What is not HR analytics
While HR analytics can be used for an array of usages, the impact of HR analytics may be maximized if the usage is meaningful. This section identifies the common usages of HR analytics which do not justify the cost invested for the execution:
1. Efficiency Metrics or Workforce Score cards: Tracking of metrics per se is not analytics contrary to the popular opinion. Unless it is established that there are causative and correlative factors present in the indicators being tracked there is no analysis.
2. Alignment: A popular opinion about the usage of HR analytics is to create alignment with the line of business. However, it would indeed seem to be very strange if HR actually was out of alignment with the business in the first place.
3. Gap analysis: A common practice regarding HR analytics is to identify gaps between departments through interpretation of survey scores. Again, the mere existence of gap has no meaning on its own unless the business impact of the gaps are calculated.
HR analytics tools: Present practices
In this section we'll look into the current HR analytics practices that are prevalent in the industry:
1. Correlation: Correlating people data and business is definitely the future of analytics. However, it also has to be considered that mere correlation cannot be used as a tool to make major decisions as it can also identify mere coincidences and hence, has a high propensity of not being able to stand scrutiny.
2. Benchmarking: Benchmarking is a powerful data collecting tool. However, it may not be evident if doing well or lagging behind on a criteria being benchmarked is connected to a business outcome. It is best used as a way of looking at data, it should not be considered to be an analysis procedure.
3. Cause-Effect Analysis: In order to perform cause - effect analysis in HR analytics processes, structural equation modelling methods are being used. The benefits of this method are:
a. Concurrently measures multiple independent & dependent factors.
b. Identify cause - effect relationships.
c. Robust ROI calculation.
d. Fix measurement errors.
4. Regression Analysis : Regression as a statistical tool helps to view multiple facets of data simultaneously and enables the user prioritizes the facets of people data that impact business outcomes.
Barriers to HR analytics
The major impediments to the application of HR analytics identified are (Van Dooren 2012):
  • Inconsistent and inaccessibility of data,
  • Data quality issues,
  • Lack of standard/generic methodologies to analyse HR data,
  • Executive buy-in,
  • Skill gap in analytical knowledge & experience,
  • Funding issues,
  • Wrong or not targeting the right analytical opportunities,
  • Problems in initiating the project,
  • Improper timing.
These factors are true for countries like India, where companies are trying to develop HR analytics capability. The framework to implement an integrated talent management metric or a HR business driver analytics requires the usage of advanced statistical tools beyond the usual univariate statistical tools (means, quartiles and percentiles). Dooren in his findings questioned the objectives of using HR analytics in a company beyond its basic usages when more than 73.6% of the surveyed organizations admitted of having capability to utilize only the basic univariate statistical tools. His finding suggests that the major impediment in developing HR analytics capabilities is the perceived skill gap in the industry to analyse data using standard research methods (2012).
The CAHR partner meeting on HR analytics came up with some interesting findings (2011). Of the 15 fortune 500 companies that took part, all admitted to have been using HR data for some basic reporting purposes. 80% of these respondents were of the view that there exists a dash - board or a score - card that's is a ready source of HR data. They were also confident of having in-house expertise in quantitative data techniques. The findings broadly suggest that the companies were capable to execute HR analytics project. However, most did not have any institutionalized HR analytics as a function. A respondent summed up the issue that they felt was a major impediment to this effect as:
"We have not institutionalized an HR analytics function, so that says something. Our sources of expertise are scattered and there is no specific strategy that is championed by our leadership. We are evolving". The findings also suggested that only 20% of the organizations in the meeting had trust on the reliability and accuracy of organization data.
The way forward
HR processes have now come to a stage of maturity in nearly all organizations. So much so that these processes like 360 Feedback and Employee satisfaction surveys have come to be accepted as a regular process within the organization with properly identified stake-holders. To this effect HR analytics is a useful way to justify the actual business case of the aforementioned processes. Therefore, two strategies primarily emerge as viable if the organization is serious about maximizing the effects and influence of HR analytics in the organization.
First, HR analytics should be used to connect the following HR processes to business outcomes:
On boarding, Selection, Work - life Balance Initiatives, Employee Opinion Surveys, 360 Assessments, Competencies, Performance Management and Leadership Development.
Each of these processes should be analysed in order to demonstrate ROI/NPV of the processes. This is relevant because once the viability of these processes is conveyed to the management it will become comparatively easy to drive action with urgency across the organization based on the impact perceived.
Secondly, HR analytics can be instrumental in combining the key HR drivers of the business derived from the process analytics approach described above and integrated into a business focussed strategic plan. For example, succession planning consists of indicators from various business processes. So, HR analytics should be used in tandem with other related business processes in the organisation.
So, as far as business analytics is concerned, before taking any decision, HR professionals should ask the same question as Garry Loveman (CEO, Caesars Entertainment Corporation) "Do we think this is true? Or do we know?"