“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 the performance of business and related management practices. According to their works, data should be the basis of all decisions. But HR related decisions are more often than not supported by rigorous data analysis. As a result, nowadays, even though HR is recognised as a strategic partner to any business, 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 on predictive analytics (Ulrich, 2010). To achieve the same, organisational HR departments should take the EBM approach. This article highlights the importance of HR analytics and leveraging technology for HRM.
Metrics in HR Analytics Metrics, in general, are of three types (Lawler et al., 2004): Efficiency, Effectiveness and 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. 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 organisational performance metrics like profitability, customer satisfaction, sales etc. which forms the basis 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 w.r.t. Contextual Factors. Generally, HR Analytics helps an organisation 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 et al, 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
- Manage critical workforce segments accordingly Predict employee preferences and behaviours, and tailor HR practices to attract and retain talent
- Forecast business requirements and staffing requirements
- 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:
- Leadership Succession Planning
- HR risk management
- Identifying the hidden factors of success
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
- Number 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.
To identify the relevant factors, both internal and external, influencing HR practices in an organisation, Paauwe (2004) applied the Institutional Theory to develop a contextually based human resource theory. Since, HR analytics is nowadays an integral part of HR practice in organisations, this theory can be used to derive the required contextual factors.
Institutional Theory This theory explains the change in organisational behaviour due to market pressures, actions of regulatory agencies, expectations from society and measures taken by industry leaders. DiMaggio and Powell (1983) extended this theory to come up with the idea of isomorphism: organisations within an industry apply similar practices and systems due to market pressures, regulatory agencies, society’s expectations etc. According to the authors, isomorphism is of two types: competitive and institutional. HR analytics is influenced more by institutional isomorphism than by competitive isomorphism. Institutional isomorphism is of 3 types:
- Coercive (labour legislations, trade union pressure etc.)
- Mimetic (imitations of practices of industry leader like implementation of HR scorecards in many organisations)
- Normative (impact of professional networks on HR practices)
Besides isomorphism, competitive forces cause an organisation to differentiate from its competitors to enjoy a competitive advantage. Companies can only become successful by striking a balance between the isomorphic forces and competitive forces described above. According to Paauwe & Boselie (2007), organisations can only be viable by addressing both contextual demands (efficiency, innovation, effectiveness, etc.) and institutional demands (demands of employees and society at large).
HR Analytics Framework Considering the above theories, Paauwe (2004), came up with his framework which includes:
- Competitive isomorphism: PMT (Product/Market/ Technology) impact on HR 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 HR analytics.
- Institutional Isomorphism: Social/Cultural/Legal (SCL) influence on HRM practices and hence HR analytics in the form of societal values (fairness, legitimacy) and legislations.
- Configuration of Company: Organizational/ Administrative/Cultural heritage of an organisation influence influences the HR practices such as HR analytics.
These coalition of the above-stated three dimensions affects the degree and nature of application of HR analytics in a company, which forms the framework for HR analytics in an organisation.
Factors affecting HR Analytics Based on the contextual factors affecting HR analytics, Institutional Theory and the HR analytics framework, the following relevant factors are determined which affect the way HR analytics is used in organisations:
- Competitive mechanisms: HR analytics help 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.
- Institutional mechanisms: Companies are implementing HR analytics either by imitating their competitors or to prevent themselves not being seen as outdated. So, the no. number of competitors going for HR analytics influences the use of HR analytics in an organisation in the same industry.
- Configuration: HR analytics should be applied more in older organisations as application of HR analytics leads to formalised decision making and older companies behave in a more formalised manner (Mintzberg, 1979).
- Organisation Structure: Today most organisations are showing organic growth due to unpredictable, dynamic environment, high differentiation and little standardization among task. So, to gain information about workforce performance drivers and increase organisational effectiveness, HR analytics approach is the way forward (Boudreau & Ramstad, 2006).
- Size of Organisation: Mintzberg (1979) argues planning and control systems of a large organisation should be more sophisticated. So, what can be more sophisticated than HR analytics in helping a larger organisation to implement sophisticated systems.
- Labour-capital Ratio: King(2010) pointed out that companies 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.
- Financial Health: According to Lawler et.al. (2004), the better the financial health of a company, more the use of HR analytics and vice-versa.
- Innovation-orientation: The more an organisation is inclined towards innovation, the more is the chance of it to involve itself in HR analytics to drive continuous improvement, generation and improvement of new ideas and practices.
HR Analytics Tools: Present Practices In this section we’ll look into the current HR analytics practices that are prevalent in the industry:
- Correlation
- Benchmarking
- Cause-Effect Analysis
- Regression Analysis
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
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 stakeholders. In order to implement many of the HR processes, technology is leveraged to a huge extent in most of the organisations. 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.
Second, 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.
Author Information:
Shaon Banerjee is currently pursuing PDGM-HRM in XIMB. His interest includes HR Operations, Procurement Planning and Industrial Relations.
Shib Sankar Datta is currently pursuing PDGM-HRM in XIMB. His interest includes Industrial Relations, Compensation & Rewards Management and HR Analytics. Sources
