Key definitions for People Analytics professionals
People analytics
People analytics is an interdisciplinary field that draws upon elements from organisational psychology, data science, and psychometrics to gain insights into human behaviours in organisations. It involves assessing workplace behaviours and connecting them to an organisation’s wider strategy.
Organisational behaviour (OB)
Organisational behaviour (OB) is the study of how people’s behaviour within the workplace can impact an organisation’s effectiveness. It involves looking at individual behaviour within the organisation, how groups work together, how the organisation itself behaves, and how all of these are interconnected.
Human resource management (HRM)
Human resource management is the strategic approach to the effective management of people in an organisation. Its primary aim is to maximise employee performance and help an organisation achieve its various aims.
HRM practices
HRM practices are a set of processes (e.g. recruitment and selection, training and development, and performance appraisal) designed to attract, develop, and retain employees in order to improve an organisation’s effectiveness.
Data analytics
Data analytics is the process of analysing raw data in order to draw conclusions about that information. Data analytics can help organisations to strategically integrate different functional areas and get new insights into managerial behaviour and HR practices, such as how hiring managers make recruitment decisions and how organisations communicate with their staff.
Psychometrics
Psychometrics is the field of psychology concerned with the design, delivery, and interpretation of tests that measure human responses. In people analytics, psychometric testing is used to measure experiences and behaviours in the workplace, such as job satisfaction, organisational commitment, and leadership styles.
Classical test theory
In psychometrics, classical test theory is the measurement of the performance of an individual taking a test and the difficulty level of the test, with the aim of improving test result reliability.
Item response theory
In psychometrics, item response theory is the measurement of an individual’s performance in a test, and how it relates to individual items in a test. The aim of item response theory is to improve the reliability of test results.
Organisational psychology
Organisational psychology is the study of the structure of an organisation and the ways in which the people in it interact. The focus of the field is on increasing workplace productivity and related issues such as employee wellbeing.
Quantitative research methods
Quantitative research methods are concerned with collecting and analysing numerical data. In people analytics, quantitative research methods such as surveys and polls are used to quantify attitudes, opinions, and behaviours to generalise results from a large sample population.
Qualitative research methods
Qualitative research methods are concerned with collecting and analysing non-numerical data. In people analytics, qualitative research methods such as focus groups, individual interviews, and observations are used to gain an understanding of employees’ opinions and motivations.
Psychological constructs
Psychological constructs are tools used to facilitate understanding of human behaviour. They can’t be observed directly and are not simple to measure. They include personality traits (e.g. extraversion), emotional states (e.g., happiness), attitudes, and abilities. In organisations, they can include organisational climate, culture, commitment, and turnover.
Data processing
Data processing is the collection and manipulation of items of data to produce information that can be read by a computer. Data processing is essential for organisations to make accurate decisions to improve their effectiveness.
Data engineering
The process of refining the data into something useful for modelling purposes. In general, it includes transformation of variables into vectors, normalisation of variables to keep values in the same range and strategies for dealing with missing data.
Features
Features, in machine learning, are the same as variables in scientific research, but they might include a combination of separate variables.
Feature engineering
Feature engineering is the preparation of variables/features so they become compatible with machine learning algorithm requirements. It might include imputation, transformation, grouping or feature split, and seeks to change the original features to improve the performance of machine learning models.
Data wrangling
Data wrangling is the process of transforming and mapping data from one raw data form into another format, so that it can be used for other purposes. Data wrangling can be performed manually or via scripts in languages such as Python or R. Visual data wrangling systems have also been developed and are more accessible to non-programmers.
Data visualisation
Data visualisation involves taking data and placing it into a visual context, such as a map or graph. This makes it easier to detect patterns, trends, and outliers in groups of data. For people analytics professionals, being able to visualise data can help you explain your insights to your organisation’s key decision-makers and make suggestions for improving performance.
People Analytics can transform an organisation's performance - find out how in our short guide to this growing field:
