Posted in Human Resources Articles, Total Reads: 1637
, Published on 17 November 2014
“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 et al (2009) while assessing the impact of Evidence-Based Management (EBM) on performance of business and related management practices.
But w.r.t. EBM, HR related decisions, usually, are not supported by rigorous data analysis. Very few organisations can measure the real value their workforce adds to their business (Mayo, 2008). HR still concentrates descriptive metrics whereas it should concentrate predictive analytics (Ulrich, 2010). To achieve the same, HR departments should take the EBM approach in Human Capital analytics.
Image Courtesy: freedigitalphotos.net, Stuart Miles
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).
Human Capital analytics is context-specific (Baron 2011). Relevance of Human Capital analytics varies from industry to industry. Generally, Human Capital analytics helps an organisation to understand and measure the effect of HR practices and policies on organisational performance and subsequently to influence business strategy (Lawler et al, 2004).
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.
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
Human Capital Analytics Framework:
To identify the relevant factors influencing Human Capital practices in an organisation, Paauwe (2004) developed a contextually based human resource theory and came up with the following Human Capital analytics framework:
1. Competitive isomorphism: PMT (Product/Market/Technology) impact on Human Capital analytics which suggests “demands arise from relevant product market combinations and appropriate technology” which shapes the HR policies and practices and thus influences the use of Human Capital analytics.
2. Institutional Isomorphism: SCL (Social/Cultural/Legal) influence on HRM practices and hence Human Capital analytics in the form of societal values (fairness, legitimacy) and legislations.
3. Configuration of Company: Organizational/Administrative/Cultural heritage of an organisation influence the HR practices such as Human Capital analytics.
These coalition of the above-stated three dimensions affects the degree and nature of application of Human Capital analytics in a company.
Factors affecting Human Capital Analytics:
Based on the contextual factors affecting the HR analytics framework, the following relevant factors are determined which affect the way Human Capital analytics is used in organisations:
1. Competitive mechanisms: Human Capital 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 Human Capital analytics.
2. Institutional mechanisms: Companies are implementing Human Capital analytics either by imitating their competitors or to prevent themselves not being seen as outdated. So, the no. of competitors going for Human Capital analytics influences the use of same in an organisation in the same industry.
3. Configuration: The older the company, the more formalised is its behaviour (Mintzberg, 1979). So, Human Capital analytics should be applied more rigorously in older organisations to support formal approach towards decision-making.
4. Organisation Structure: Nowadays most organisations are showing organic growth. So, to gain information about workforce performance drivers and increase organisational effectiveness, Human Capital analytics approach is the way forward (Boudreau & Ramstad, 2006).
5. Labour-capital Ratio: Companies with a high labour-capital ratio, should maintain better workforce related information (King 2010). So, greater application of HR analytics can be observed in knowledge-intensive industries.
6. Financial Health: The better the financial health of a company, more the use of Human Capital analytics and vice-versa (Lawler et al, 2004).
7. Innovation-orientation: The more an organisation is inclined towards innovation, the more is the chance of it to involve itself in Human Capital analytics to thrive continuous improvement and generation of new ideas and practices.
8. Size of Organisations: Planning and control systems of a larger organisation should be more sophisticated (Mintzberg 1979). What more sophisticated than HR analytics can help a larger organisation (by size) to implement sophisticated systems.
In this section, we’ll look into the current Human Capital analytics practices that are prevalent in the industry:
1. Correlation: Correlating people data and business is definitely the future of analytics. However, care must be taken not to use the same for major decision-making as correlation can, sometimes, identify only mere coincidences.
2. Benchmarking: Benchmarking, a powerful data collecting tool, should be used as a way of looking at data, and should not be considered as an analysis procedure.
3. Cause-Effect Analysis: In order to perform cause-effect analysis in Human Capital analytics, Structural Equation modelling methods are being used.
4. Regression Analysis: Regression as a statistical tool helps to view multiple facets of data simultaneously and enables the user prioritize 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 suggests that the companies were capable to execute HR analytics project. However, most did not have any institutionalized HR analytics as a function. 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. To this effect HR analytics is a useful way to justify the actual business case of the aforementioned processes. Therefore, two strategies primarily emerges 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 are 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?”
This article has bee authored by Shaon Banerjee & Sib Sankar Datta from XIMB
1. Baron, A. (2011). Measuring human capital. Strategic HR Review, 10(2): 30-35
2. Boudreau, J. W., & Ramstad, P. M. (2006). Talentship and HR Measurement and Analysis: From ROI to Strategic Organizational Change.
3. Briner, R. B., Denyer, D., & Rousseau, D. M. (2009). Evidence-Based Management: Concept Cleanup Time?
4. CAHRS Topical Working Groups (2011).State of HR Analytics: Facts and Findings, Cornell University
5. Harris, J. G., Craig, E., & Light, D. A. (2010). The New Generation of Human Capital Analytics.
6. Joerik van Dooren (2012). HR Analytics in practice
7. King, Z. (2010). Human Capital Reporting: What information counts in the city
8. Lawler, E. E., Levenson, A., & Boudreau, J. (2004). HR Metrics and Analytics Uses and Impacts.
9. Levenson, A., Boudreau, J., & Lawler, E. (2005). Survey on HR analytics and HR transformation.
10. Mayo, A. (2006). Measuring and reporting: The fundamental requirement for data.
11. Mayo, A. (2008). Financial statements and human capital.
12. Mintzberg, H. (1979). The Structuring of Organizations
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