Dr. Karani Magambo FIHRM

In today’s global environment, organisational ecosystems are undergoing significant changes to stay competitive. In this context, Human Resources plays a vital support role, responsible for making many key decisions. This article aims to provide an overview of data and analytics, emphasising their practical use, importance, applications, and challenges in HR decision-making. Furthermore, the article will explore the applications of HR analytics across various areas, including talent acquisition, performance management, employee engagement, learning and development, and retention strategies, while also highlighting data sources relevant to HR analytics.

Operationalisation

From a historical perspective, using data and analytics to inform decisions is not new. Recently, HR practitioners used simple data to make administrative decisions, like tracking staffing, training, and industrial relations. Nevertheless, with the growth of big data and powerful computers, leaders have shifted from relying on intuition, observation, and suspicion-based choices to a technology-focused, evidence-based approach, utilising quantitative HR metrics to gain valuable insights.

Data Points

Workforce planning and acquisition: This is a strategic process designed to ensure the organisation maintains optimal staffing levels. Key metrics include analysing past data to identify trends, such as recruitment success rates, sources of successful candidates, and the quality of interviews. The insights presented here can help HR make predictions about candidate availability and develop prescriptive models for recruitment, career advancement, and retention.

Learning and development: A critical process that equips employees with the competencies needed for their roles. By analysing employee performance, skills, competencies, and career goals, HR can identify gaps and take a proactive approach to align interventions with each person’s career aspirations and work preferences.

Performance Management: Performance management systems have often been seen as subjective and judgmental; however, technological advancements have made them more objective and comprehensive. This boosts trust by providing clear key performance indicators, such as completion rates, defects, NPS, revenue per employee, and profits per FTE. Such an approach helps organisations evaluate performance accurately, identify gaps, and plan interventions.

Employee engagement: Employee engagement refers to an individual’s commitment and willingness to contribute to an organisation’s success, as evidenced by their discretionary efforts. Key indicators include engagement rates, performance review reports, turnover rates, and satisfaction scores. By utilising predictive analytics, HR can identify the primary drivers of employee engagement and staff turnover.

Talent Management: Talent management is crucial for HR success because it focuses on identifying, developing, and retaining future leaders. Metrics such as the Net Promoter Score, which categorises promoters, passives, and detractors, along with the cost per hire and the strength of the slate, provide valuable insights into staff quality and help inform and improve talent strategies.

Significance

Organisations that practice Data-Driven Decision Making might potentially benefit from the following:  

• Strategic Alignment:

HR analytics should align with organisational strategic goals, such as business growth targets. For example, a 10% quarterly increase in new business and 5% organic growth. As such, leaders can align teams and individual KPIs and develop metrics to monitor progress. Analysing these metrics can provide insights into whether the company is on track, thereby enabling necessary adjustments.

• Cost Management

Uncontrolled costs can harm an organisation; however, this can be mitigated through effective budgeting, control, and monitoring. For example, HR data can indicate whether to retain or modify specific initiatives, such as wellness programs, reveal how turnover and absenteeism affect performance, and inform decisions on retention strategies and flexible or remote working arrangements.

• Employee Experience

An employee experience (EX) encompasses how employees perceive and navigate their journey within a company. Data from engagement, NPS, and satisfaction surveys can provide valuable insights into employees’ reasons for staying, help improve corporate culture, boost morale, and foster a sense of belonging.

• Risk Management and Compliance

The HR department must comply with legal and regulatory requirements. For instance, diversity, equity, and inclusion (DEI) go beyond equal employment opportunity (EEO), but are now essential legal and best practices. Such data can help organisations evaluate compliance with laws like the Constitution of Kenya 2010, Articles 27, 41, and 232, which emphasise equality and protection from discrimination, as well as the Persons with Disabilities Act, among others. Additionally, HR analytics can provide insights into whether the company adheres to its own HR policies and standard operating procedures.

Challenges and Solutions

Although the use of data and analytics has been integrated into decision-making, several challenges still exist. Some notable drawbacks include a lack of sufficient data knowledge among HR professionals, ethical concerns related to using personal data outside of data privacy laws, the vital importance of data quality for effective integration, and a lack of executive support. However, as noted by the Society for Human Resource Management (2023) and Workpay (2023), the success of HR analytics depends on HR professionals’ ability to interpret and apply data insights. This requires developing skills in algorithms, collaborating with the legal team to establish clear policies for ethical data collection, and investing in HR information systems. 

Enhancing Data-Driven Decision-Making Journey

Initiation and Implementation: It is essential to start by identifying the organisation’s key pain points. These are areas that leaders recognise as critical, requiring quantitative evidence to justify resource allocation. The data presented will include the rationale, clearly defined problem, and the objectives to be addressed. The data presented here should help in developing an executive management pitch deck that emphasises key points, benefits, and a call to action.

The next step is to identify specific HR-supported business strategic objectives and metrics that will help achieve these goals, such as aiming for 95% business retention. Relevant metrics can include data from quarterly sales, customer service, and individual performance against key performance areas and indicators.

Data collection: Data can be collected internally and externally, depending on the goals and metrics to analyse. Common data sources include performance reviews, HR information systems, engagement surveys, learning management systems, payroll data, and exit interviews. Data integration is also important at this stage to build a complete dataset that includes all data from different sources.

Data analysis: Data analysis can be conducted using statistical and analytical tools. Such tools can help identify specific patterns and correlations within the collected data. Depending on the data type to be analysed, one could choose to use different analytical techniques, such as descriptive, prescriptive, or predictive, and then present data through charts, tables, and graphs. 

Results and Implementation: The presented data will require HR to use statistical skills to interpret the results. Based on the data, HR can then make decisions grounded in evidence.  

The rise of big data and advanced analytics has allowed HR professionals to gain deeper insights into their workforce. HR analytics, which goes beyond simple data, supports evidence-based decision-making aligned with strategic goals. It helps HR shift from reactive to proactive management by analysing data from various sources to identify patterns that inform recruiting, talent development, engagement, and retention strategies. This data-driven approach enhances HR accuracy and elevates its strategic role in driving organisational success.

Dr. Magambo is the Managing Partner at Ceal Consulting Limited.

Employee engagement: Employee engagement refers to an individual’s commitment
and willingness to contribute to an organisation’s success, as evidenced by their
discretionary efforts. Key indicators include engagement rates, performance review
reports, turnover rates, and satisfaction scores. By utilising predictive analytics, HR can
identify the primary drivers of employee engagement and staff turnover.
Talent Management: Talent management is crucial for HR success because it focuses
on identifying, developing, and retaining future leaders. Metrics such as the Net
Promoter Score, which categorises promoters, passives, and detractors, along with the
cost per hire and the strength of the slate, provide valuable insights into staff quality and
help inform and improve talent strategies.

Significance
Organisations that practice Data-Driven Decision Making might potentially benefit from
the following:
 Strategic Alignment:
HR analytics should align with organisational strategic goals, such as business growth
targets. For example, a 10% quarterly increase in new business and 5% organic growth.
As such, leaders can align teams and individual KPIs and develop metrics to monitor
progress. Analysing these metrics can provide insights into whether the company is on
track, thereby enabling necessary adjustments.
 Cost Management
Uncontrolled costs can harm an organisation; however, this can be mitigated through
effective budgeting, control, and monitoring. For example, HR data can indicate whether
to retain or modify specific initiatives, such as wellness programs, reveal how turnover
and absenteeism affect performance, and inform decisions on retention strategies and
flexible or remote working arrangements.
 Employee Experience
An employee experience (EX) encompasses how employees perceive and navigate
their journey within a company. Data from engagement, NPS, and satisfaction surveys
can provide valuable insights into employees’ reasons for staying, help improve
corporate culture, boost morale, and foster a sense of belonging.
 Risk Management and Compliance
The HR department must comply with legal and regulatory requirements. For instance,
diversity, equity, and inclusion (DEI) go beyond equal employment opportunity (EEO),

but are now essential legal and best practices. Such data can help organisations
evaluate compliance with laws like the Constitution of Kenya 2010, Articles 27, 41, and
232, which emphasise equality and protection from discrimination, as well as the
Persons with Disabilities Act, among others. Additionally, HR analytics can provide
insights into whether the company adheres to its own HR policies and standard
operating procedures.

Challenges and Solutions
Although the use of data and analytics has been integrated into decision-making,
several challenges still exist. Some notable drawbacks include a lack of sufficient data
knowledge among HR professionals, ethical concerns related to using personal data
outside of data privacy laws, the vital importance of data quality for effective integration,
and a lack of executive support. However, as noted by the Society for Human Resource
Management (2023) and Workpay (2023), the success of HR analytics depends on HR
professionals’ ability to interpret and apply data insights. This requires developing skills
in algorithms, collaborating with the legal team to establish clear policies for ethical data
collection, and investing in HR information systems.
Enhancing Data-Driven Decision-Making Journey
Initiation and Implementation: It is essential to start by identifying the organisation’s
key pain points. These are areas that leaders recognise as critical, requiring quantitative
evidence to justify resource allocation. The data presented will include the rationale,
clearly defined problem, and the objectives to be addressed. The data presented here
should help in developing an executive management pitch deck that emphasises key
points, benefits, and a call to action.

The next step is to identify specific HR-supported business strategic objectives and
metrics that will help achieve these goals, such as aiming for 95% business retention.
Relevant metrics can include data from quarterly sales, customer service, and individual
performance against key performance areas and indicators.
Data collection: Data can be collected internally and externally, depending on the goals and
metrics to analyse. Common data sources include performance reviews, HR information
systems, engagement surveys, learning management systems, payroll data, and exit interviews.
Data integration is also important at this stage to build a complete dataset that includes all data
from different sources.
 

Data analysis: Data analysis can be conducted using statistical and analytical tools. Such tools
can help identify specific patterns and correlations within the collected data. Depending on the
data type to be analysed, one could choose to use different analytical techniques, such as
descriptive, prescriptive, or predictive, and then present data through charts, tables, and
graphs. 
Results and Implementation: The presented data will require HR to use statistical skills to
interpret the results. Based on the data, HR can then make decisions grounded in evidence.  
The rise of big data and advanced analytics has allowed HR professionals to gain
deeper insights into their workforce. HR analytics, which goes beyond simple data,
supports evidence-based decision-making aligned with strategic goals. It helps HR shift
from reactive to proactive management by analysing data from various sources to
identify patterns that inform recruiting, talent development, engagement, and retention
strategies. This data-driven approach enhances HR accuracy and elevates its strategic
role in driving organisational success.

Dr. Magambo is the Managing Partner at Ceal Consulting Limited.