Human Capital Metrics and Reporting

Human capital is the term for the collective capability, knowledge and skills of the people that are employed by an organisation. In an increasingly complex knowledge-based economy, the measurement of human capital provides a data-driven approach to identifying people management practices, which, if done well, can help ensure that value creation is long-term and sustainable.


What is human capital?

The term ‘human capital’ is widely used in HR to describe people at work and their collective knowledge, skills, abilities and capacity to develop and innovate. Human capital reporting aims to provide quantitative, as well as qualitative, data on a range of measures (such as labour turnover or employee engagement levels) to help identify which sort of HR or management practices will drive business performance.

It’s now commonly accepted that the value of organisations is drawn from a mixture of tangible assets in the form of equipment, money, land or other physical objects together with intangibles in the form of brand, reputation, knowledge and, of course, people – critically important in an increasingly knowledge-based economy.


Measuring human capital

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 human capital measurement: 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 the different aspects of HR they refer to. Some data groups are cross-cutting and can combine 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: includes data on the compliance of employees to established standards and guidelines for working practices in particular disciplines
  • organisation development and design: includes data on spans of control, skills mix and talent pipelines.

Read more on our Human capital metrics and reporting page

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 make it 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 contribution of people to business performance.

Key issues to consider

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

  • 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 outcomes, are:

Action

Operational data analysis:

  • Collect basic input measures such as absence data, turnover data.
  • Identify useful data already available.
  • 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 not just on how to implement processes, but with accompanying information on why they are important and what they can achieve.

Insights driving performance:

  • Identification of the drivers of business performance.
  • Information that will enable better-informed decision-making internally and externally.


Reporting human capital

Stakeholder groups

Different types of information will be of value to different stakeholder groups:

  • Leaders are interested in understanding how effective employees are at creating value for the organisation, and whether people enable the organisation to be sustainable over the long term.
  • Shareholders seek information on the employee attributes or behaviours that are likely to influence short- or long-term financial performance.
  • Investors are interested in knowing 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 themselves and their skills.
  • Managers require information on which actions they can take to improve the performance of their business units.
  • Regulators and policy makers are interested in understand whether organisations are operating within the correct 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 becoming more able to capture and report on data, and thus expectations from external stakeholders are that reporting will become standardised and may even become compulsory. Some examples of different types of reporting are:

Method: Unstructured voluntary
Explanation: Isolated pilot studies are initiated by individual enterprises or consultancy companies/ researchers develop and promote approaches and 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.


What is HR analytics?

HR analytics is a HR practice enabled by information technology that uses descriptive, visual and statistical analyses of data related to HR processes, human capital, organizational 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.


Why is HR analytics important?

HR analytics enables HR and their major stakeholders to measure and report key workforce concepts, such as performance, well-being, productivity, innovation and alignment. This in turn enables more effective evidence-based decisions by strategic business functions. HR analytics enables HR teams to demonstrate the impact that HR policies and processes have on workforce and organisational performance, and can be used to demonstrate return-on-investment and social-return-on-investment for HR activity. Business managers are increasingly interested in how to use HR concepts more effectively, and so HR analytics is an important way in which HR teams can evaluate and improve people and business performance.


What is HR data?

HR data is information about any aspect of employees or the HR management system. Data comes in many forms, and may be quantitative or qualitative.

Quantitative

Quantitative data can be measured and illustrated through numbers

  • Objective
  • How many? How much?
  • Facts are value-free / unbiased
  • Measurable
  • Report statistical analysis. Basic element of analysis is numbers
  • ‘Counts the beans’
  • Examples: number of employees, remuneration rates, productivity.

Qualitative

Qualitative data can’t be measured and are often subjective assessments representing an individual’s view of something.

  • Subjective
  • What? Why?
  • Facts are value-laden and biased
  • Interpretive
  • Report rich narrative, individual; interpretation. Basic element of analysis is words/ideas.
  • Provides information as to ‘which beans are worth counting’
  • Examples: employee opinion survey feedback, appraisals and performance reviews, learning and development outcomes

For example, a fondness for chocolate is qualitative data as it relates to an individual’s preference towards chocolate, while the dimensions of a chocolate bar is quantitative data as it relates to its numerical size (length, width and height). In HR, an individual’s age or performance rating is quantitative data, whereas their engagement data (such as job satisfaction) is qualitative.

Data is held in many places in an organisation but ideally should be managed by a specific data owner who is responsible for maintaining it, keeping it secure and ensuring management in line with the data protection policy. Only those responsible for the HR data should be able to change any aspect of the HR data itself (for instance changing terminology or definitions for specific HR indicators).

Correlation and causation

HR analytics can help identify cause and effect relationships, by investigating the relationship between two sets of data to be investigated, and determining whether the relationship is correlational or causal.

  • Correlation is when two or more things or events happen around the same time which might be associated with each other, but they aren’t necessarily related in a cause-effect relationship. It implies a mathematical relationship between two things which are measured, and is often described numerically with a value between 0 and 1, where 0 is no relationship and 1 is a fully predictive relationship. For example, there is a 0.01 correlation between eye colour and height, so knowing someone’s eye colour does not mean you know how tall they are. They are virtually independent. But there is a 0.8 correlation between smoking and incidence of lung cancer, so it’s possible to say that smokers are more likely to develop lung cancer. However, this doesn’t necessarily imply smoking causes lung cancer in every case.
  • Causation is when one event or thing happens and as a result of it happening, another event or thing happens. If the first event did not happen, then the second does not happen. There is not a mathematical/probabilistic relationship between the two, but instead a time-based cause-effect relationship.

Just because two things correlate, it doesn’t necessarily mean there’s a causal (or cause-effect) relationship between them. For example, an increase in sales in a team which also has high engagement does not necessarily mean that engagement causes more sales. Other factors, such as improved training, or increase in customer-facing staff, may also be causing an increase in sales. Therefore, it’s important to analyse as much data as possible before drawing conclusions.


How does HR analytics work?

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 are able to do level 1 only, very 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: Uses 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 requires high quality and robust data, and specialist technology and capability.
  • Level 3 – prescriptive analytics: Applies mathematical and computational sciences 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 analytics strategy

HR managers running analytics activity should tie the outputs of their analytics process into both the HR strategy and the 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 to demonstrate the return on investment (ROI) of HR. The type of data used will depend on the strategy and operations of the organisation, so for example 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 a particular aspect of the organisation that business leaders should be informed about to help them make decisions.
  • 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 in delivering against its objectives.


HR analytics process

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

  1. Plan: Develop the goals and purpose for the analytics activity. Map the requirements of the customer and plan questions/queries which will be answered by the analytics process.
  2. 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.
  3. Data audit: Map the data which is currently available and grade its quality. This will illustrate where any gaps in data may be, which should be filled before progressing.
  4. Design the process: Define roles and set objectives for team members. Define resource requirements and map stakeholders for the project.
  5. Design the data collection strategy: Design the collection and processing stages of the analytics activity.
  6. 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).
  7. 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.
  8. Report data: Report in a clear and simple way illustrating a solution to their issue, or further areas of investigation if further data is required.
  9. Evaluate: Review the data-analytics-insights process and evaluate impact. Review and update process as required.


Example of HR analytics in action

HR analytics can be applied to virtually any aspect 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 offer to employees accordingly. Career-development planning, and learning and development for high performers are both 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 which is suffering from high turnover of key employee groups can use HR analytics to anticipate areas with specific issues and can then tailor their incentives to curb attrition accordingly. Better measuring the impact of HR activity on turnover can illustrate the specific needs of certain employee groups, for example adapting incentives for senior leaders to meet their needs if they have specific requirements to keep them from leaving.

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