Given the complexity of organisations and the various approaches to managing human capital, measurement can be challenging. The context of the organisation is also a fundamental aspect of measuring human capital: the emphasis for measurement is no longer on absolute measures of human capital, but instead context specific information to enable informed decision making.

HR data groupings

Human capital data can be grouped according to their relation to different aspects of HR. Some data groups are cross-cutting and can be combined to create additional data groups. HR data tends to fit into the following groupings:

• workforce composition: demographics data including age, gender and ethnicity
• recruitment and retention: number of resignations/vacancies/applications, length of service
• skills, qualifications and competencies: levels of expenditure on training, types of training provided, length of time to reach competence levels, data on training needs
• performance management: performance management results, productivity and profitability data, targets set and met, levels of customer satisfaction, customer loyalty
• employee relations and voice: findings from employee attitude surveys
• pay and benefits: overall wage bill costs, distribution of individual performance-related pay awards, level of total reward package
• regulatory compliance: data on the compliance of employees to established standards and guidelines for working practices in particular disciplines
• organisation development and design: data on spans of control, skills mix and talent pipelines

Measurement challenges

• The contribution of people is difficult to isolate from other factors such as the economic situation, market forces and customer or social trends.
• The value of people is often expressed in qualitative rather than quantitative terms that is difficult to represent in traditional accountancy models.
• HR data has traditionally been collected for administrative rather than evaluation purposes.
• HR practitioners do not always have the skills or resources to interpret or explain data to evaluate the contributions of people to business performance.

Different levels of data collection

There are three distinct levels of human capital data collection and analysis:

• Operational data analysis – simple monitoring data with no analysis, for example, reporting absence and retention data.
• Basic insights – basic data is analysed and correlations are explored between types of data to draw simple human capital insights.
• Insights driving performance – human capital data is triangulated with other business data to identify performance drivers; and may be used to illustrate how organisations can leverage human capital to drive performance more effectively.

The different levels of data collection that might take place, with their likely outcomesare illustrated below:

• Action
  • Operational data analysis:
    • Collect basic input measures such as absence data, turnover data.
    • Identify readily available useful data.
    • Use this data to communicate essential information to managers.
  • Basic insight:
    • Design data collection for specific human capital needs.
    • Look for correlations between data – for example, whether high levels of job satisfaction occur when certain HR practices are in place, such as performance management, career management or flexible working.
  • Insights driving performance:
    • Identify key performance indicators relating to the business strategy, and design data collection processes to measure against them.
    • Communicate data in ways that are meaningful to differing audiences.
• Outcome
  • Operational data analysis:
    • Basic information for managers on headcount and make-up of the workforce.
  • Basic insight:
    • Information to help design the HR model most likely to contribute to performance.
    • Communication to managers to implement processes with accompanying information on their importance and expected outcomes.
  • Insights driving performance:
    • Identification of the business performance drivers.
    • Information that will enable informed decision-making internally and externally.

Stakeholder groups

Different types of information are valuable to different stakeholder groups:

• Leaders are interested in understanding how effective employees are in creating value for the organisation, and how they contribute to the long-term sustainability.
• Shareholders seek information on the employee attributes or behaviours that are likely to influence short- or long-term financial performance.
• Investors are interested to know how organisations value and grow their pools of talent, and whether long-term decision making takes place with people in mind.
• Customers wish to know if they will get good service and after-sales support.
• Employees want to know their jobs are secure and how they can develop and enhance their skillsets.
• Managers require information on actions to improve the performance of their business units.
• Regulators and policy makers are interested to understand whether organisations are operating within the ethical, moral, social and environmental governance boundaries.

External reporting

Reporting is typically done in one of four ways:

• unstructured voluntary
• systemised voluntary
• voluntary for reward
• compulsory

Organisations are better at capturing and reporting data. Thus, external stakeholders expect reporting to become standardised and compulsory. Some examples of different types of reporting are:

Method: Unstructured voluntary
Explanation: Isolated pilot studies initiated by individual enterprises or consultancy companies/ researchers to develop and promote new approaches/methods
Example: social reports, knowledge accounts, human resource audit, holistic balance sheet, intellectual capital statements.

Method: Systemised voluntary
Explanation: Develop a consistent framework which can be operational across sectors and countries and promote this at large scale through the inherent rewards and image gains.
Example: ISO 9000 standards, benchmark programmes.

Method: Voluntary for reward
Explanation: Develop a consistent framework supported by rewarding mechanisms once it is introduced and approved at enterprise level.
Example: Investors in People (UK), European Label for Innovative Projects in Language Learning (EU).

Method: Compulsory
Explanation: Identify disclosure on human capital as a public concern and prepare (inter-)national regulations and standards.
Example: Green accounts (Denmark)

Internal reporting

Internal reporting is far more prevalent than external reporting, as this is important in the evaluation of the effectiveness of HR interventions and guiding future HR strategy, while also protecting business confidentiality where desired. It takes a number of forms.

Generally any human capital data reported internally should:

• be reliable and open to scrutiny
• be accompanied by adequate explanation
• be presented in a manner that is easily understandable for the audience
• be related to business needs
• enable managers to identify appropriate actions that will improve business performance.

HR analytics is a HR practice enabled by information technology that uses descriptive, visual and statistical analysis of data related to HR processes, human capital, organisational performance, and external economic benchmarks to establish business impact and enable data-driven decision-making.

Analytics may be used to look at the traits of the workforce, in particular its human capital: the value of individual knowledge, skills and experience of individuals and teams. This is also known as human capital analytics. When an organisation reports on the insights gathered through HR analytics, it’s often known as human capital reporting.

HR analytics uses workforce or HR data, either qualitative or quantitative, to investigate a certain concept with the help of computer programmes and modelling techniques. There are three main levels of HR analytics capability. Most organisations can achieve up to level 1 and few are able to complete level 3 analytics:

• Level 1a– descriptive analytics: Uses descriptive data to illustrate a particular aspect of HR, for example recording absence, annual leave, and attrition and recruitment rates. At level 1, no analysis is applied to the data beyond using it to describe a certain concept or illustrate its change over time (sometimes called trend analysis).
• Level 1b – descriptive analytics using multidimensional data: Combines different data sets, or types of data, to investigate a specific idea can help to uncover interesting relationships between different HR activities and processes. Using two different types of data to create an analytics output is known as multidimensional analytics (for example, combining leadership capability data with engagement scores to measure leadership effectiveness).
• Level 2 – predictive analytics using data to predict future trends can help HR professionals to plan for future events and scenarios, and ensure they are able to deliver to the business. Predictive analytics for forecasting require high quality and robust data, and specialist technology and capability.
• Level 3 – prescriptive analytics applying mathematical and computational science models to suggest decision options to take advantage of the results of descriptive and predictive analytics. Prescriptive analytics specifies both the actions necessary to achieve predicted outcomes, and the interrelated effects of each decision.

HR managers running analytics activity should tie the outputs of their analytics process into both the HR and business strategy. Taking a strategic and planned approach to HR analytics, for example tackling a specific business issue, is likely to create the most value for the business and create further demand for HR insights.

Connecting HR data with the strategic objectives of the business can help HR managers demonstrate the return on investment (ROI) of HR activities. The type of data used will depend on the strategy and operations of the organisation, for instance a sales-driven organisation is more likely to collect performance data such as sales per employee to differentiate between employees and reward them accordingly.

The HR analytics strategy should have three aims:

• Connect HR data with business data to demonstrate an aspect of the organisation that business leaders can use in decision making.
• Enable HR leaders to design and implement HR management activity in an efficient and effective manner.
• Allow the business and HR to measure the effectiveness of HR policies against their planned objectives.

The HR analytics process should follow nine steps from planning through to the evaluation of the process:

• Plan: Develop the goals and purpose for the analytics activity. Map the requirements of the customer and plan questions/queries which will be answered during the analytics process.
• Define critical success factors: Define the measures that will show if the project has been a success. Examples of what these can be based on include: delivery on time, impact of project, feedback from users.
• Data audit: Map the data which is currently available and grade its quality. This will illustrate data gaps to be filled before progressing.
• Design the process: Define roles and set objectives for team members. Define resource requirements and map stakeholders for the project.
• Design the data collection strategy: Design the collection and processing stages of analysis.
• Data collection: Collect data from data sources. This can be from drawing on established data sets (for example. absence records) or running new data collection processes (for example, engagement survey).
• Analyse data: Depending on the customer requirements, analyse the data and develop insights in the form of recommendations and guidance for the users of the data.
• Report data: Report in a clear and simple way illustrating a solution to issues, or further areas of investigation if further data is required.
• Evaluate: Review the data-analytics-insights process and evaluate impact. Review and update process as required.

HR analytics can be applied all aspects of HR activity. For example:

• Enhancing employee morale: instead of absorbing the costs of losing key employees, organisations can mitigate against increased attrition rates by measuring the happiness and well-being of their employees and adapting their offers to employees accordingly. Career-development planning, and learning and development for high performers are two ways in which HR departments can use HR data to help improve the morale of the workforce.
• Driving business performance: HR analytics can help to address performance issues by identifying workers with strong leadership skills and flagging those which do not mix with the culture of the team or organisation. By better matching job applicants or future successors to the right positions, organisations can improve their overall performance.
• Improving retention: An organisation suffering from high turnover of key employee groups can use HR analytics to anticipate areas with specific issues and tailor their incentives to curb attrition accordingly. measuring the impact of HR activity on turnover can identify the specific needs of certain employee groups, for example adapting incentives for senior leaders to meet their needs to keep them from leaving.