This is the pillar page on People Analytics – the starting point for all in-depth articles.
TL;DR summary: Everything about People Analytics
People Analytics is the use of data, statistics, and AI to make HR decisions smarter and more effective. It makes behavior and processes measurable and supports better decision-making about people and organizations. People Analytics is not a hype but a strategic tool that enables better, faster, and fairer HR policies.
More and more organizations use People Analytics to achieve strategic goals, reduce costs, make reliable predictions, optimize HR processes, and increase objectivity in decisions. It goes beyond reporting—think predictive models, algorithms, and dashboards. The core is an iterative process in 8 steps: start with a clear business question, gather and analyze relevant data, translate insights into policy, and measure the impact. Without a clear question, no meaningful analysis.
People Analytics requires collaboration between HR, analysts, and management. Technically, tools like Power BI, R, or Python are useful, but success depends mainly on vision, ethics, governance, and communication. Privacy is key: follow GDPR guidelines and consider the new EU AI Act. Combine data analysis with human insight—together, they are more powerful.
What is People Analytics?
People Analytics has become increasingly important over the past two decades. It involves using data, analytics, and AI to make better decisions about workforce management and to make processes more efficient and effective. This field combines knowledge from various disciplines such as human resources, psychology, business administration, and statistics. People Analytics helps organizations to make the most of all available data about employees in order to achieve the organization’s goals.
People Analytics includes simple reports on HR metrics as well as advanced predictive models, algorithms and AI tools. The aim is to translate developments within the human capital of the organisation into future consequences and to quantify their impact. This helps improve both individual and collective behavior and the performance of the organization.
What is an example of using analytics in HR?
In 2013, Hans Haringa, a member of Shell’s GameChanger team, discovered the company Knack, which uses serious games such as Dungeon Scrawl and Wasabi Waiter to map human skills. These games collect behavioral data that provides insight into creativity, tenacity, and social intelligence. Haringa used this method to more quickly identify talents who could be successful, which normally took two years. The experiment with Knack turned out to be successful, as the games were able to identify the most promising employees based on their gaming behaviour
Why is the use of data and AI for HR increasing?
The rise of information technology has led to an enormous amount of personnel data. Organizations are investing in HR software and systems to collect and analyze this data. Big data methods are increasingly being used to predict employee behavior and make better HR decisions. This helps organizations to optimize their HR policies and improve employee performance.
People Analytics can contribute to solutions for urgent HR themes such as sustainable employability, collaboration, working from home, leadership, absenteeism, and strategic personnel planning. It helps organizations to better understand the impact of their investments in human capital and to improve their HR policy.
People Analytics has evolved from a hype to an essential part of HR policy. More and more organizations are adopting People Analytics to make better decisions and improve the performance of their employees and the organization. This is supported by the growing availability of data and the development of advanced analytics tools.
What is the difference between People Analytics and business intelligence?
Business intelligence is all about collecting, analyzing, and applying data for better decision-making. People Analytics is a subdiscipline within this that focuses on personnel data. The better this intelligence is set up, the stronger the insights and competitive position of the organization. People Analytics provides decision-supporting information that is crucial for strategic decisions.
Why should HR use People Analytics at all?
People Analytics enables HR to make policies, interventions, and strategies more effective. According to Bassi, the real question is not why HR would use it, but why not. Organizations like Google show that data-driven HR is crucial for growth and innovation. By using algorithms to select applicants, Google was able to identify top talent faster and more reliably. This approach was followed, including through Harver’s TalentPitch.
What are the top five benefits of People Analytics?
People Analytics offers tangible benefits:
- it contributes directly to strategic goals,
- saves time and costs,
- makes reliable predictions,
- provides insight into internal processes
- and increases the reliability of decisions.
By replacing subjective judgments with data-driven insights, HR becomes more effective and consistent in decision-making.
Why is evidence-based HR policy important?
In any organization, people’s assessments influence countless decisions. People Analytics helps base these assessments on facts rather than assumptions. It makes policy more reliable and reduces subjectivity, which is essential for achieving organizational goals.
Scientific research offers valuable insights, but is not always applicable to specific organizations. People Analytics uses data from its own context, making predictions more accurate and relevant. Models can also be continuously adjusted based on current results.
How does People Analytics enable proactive HR policy?
By analyzing trends and behavioral patterns, HR can predict future needs. Think of flexible work or learning needs. This allows HR to respond to changes in a timely manner and provide employees with more targeted support, similar to how marketers predict customer behavior.
How does People Analytics help reduce subjectivity?
Human decisions are often influenced by intuition, prejudices and social context. People Analytics provides an objective basis for choices, reducing randomness. For example, structured selection procedures reduce the premature departure of new employees.
Modern behavioural science shows that people do not always act rationally. Unconscious processes often determine our behavior (system 1 thinking). People Analytics helps overcome this irrationality by basing decisions on measurable behavioral data instead of assumptions (system 2 thinking).
According to Kahneman, system 1 works quickly and intuitively, while system 2 is slow and rational. In practice, we often rely on system 1, even in complex decisions. People Analytics helps to correct these automatic responses with objective data.
People Analytics makes behavior and processes measurable, making organizations less dependent on intuition. By analyzing data, patterns can be recognized and predicted, leading to more informed and strategic HR decisions.
How does People Analytics contribute to the optimization of HR processes?
People Analytics is often applied for operational-level improvements, such as introducing or improving HR tools. Think of adding notifications to processes or using algorithms for better decision-making. In this way, HR is organized more efficiently and effectively.
How can you work and manage in HR in a data-driven way?
Technological innovations, such as the introduction of the iPhone, are forcing organizations to adapt quickly. For example, Vodafone had to attract new types of employees to develop innovative services. People Analytics helps organizations to anticipate such changes in a timely manner by substantiating personnel policy in a data-driven way and strategically linking it to organizational goals.
How does People Analytics support the HR policy cycle?
The HR policy cycle translates the organizational strategy into HR goals. People Analytics helps to substantiate these goals with data and makes it possible to continuously evaluate and adjust policy. It connects to the broader planning and control cycle of the organization and strengthens the coherence between strategy and execution.
How do you make HR measurable?
HR has been increasingly quantified through key and control figures, benchmarking and HR scorecards. People Analytics builds on this by analyzing connections and causes with statistical methods. This leads to deeper insights and better predictions. Qualitative data, such as employee satisfaction, are also made measurable through surveys and polls.
How do you visualize results effectively in dashboards and reports?
Dashboards should provide quick insight, preferably on one screen, without distraction. Good visualization tools include sparklines for trends and bullet graphs for target comparison. Context is crucial: figures only become meaningful compared to historical values, benchmarks or target values.
What is the role of HR scorecards and strategy cards?
The HR scorecard translates the organizational strategy into measurable contributions from HR, for example in the areas of learning, processes and customer satisfaction. Strategy maps show the cause-and-effect relationships of strategic objectives and help choose corresponding KPIs.
Which KPIs are relevant and how do you determine them?
A good KPI dashboard contains only a few control numbers per result area. These must cover quantity, quality and costs. When recruiting, think of the number of vacancies filled (quantity), applicant satisfaction (quality), and advertising costs (costs). KPIs must be valid, reliable and practically measurable. You determine the right target value using historical trends, benchmarks and predictive analyses.
Why measure the value of employees?
The employee lifetime value (ELV) provides insight into the individual value of an employee. Models such as the Individual Asset Multiplier (IAM) or Markov models project future revenues and costs. This helps HR focus on value creation instead of just cost control.
What is the impact of a data-driven HR policy cycle?
A fully data-driven HR policy cycle increases the organization’s returns. By making HR processes measurable and optimizing them with the help of data analysis, organizations can better respond to changes and achieve their strategic goals. This leads to higher productivity and better decision-making.
What do the eight steps of an HR analysis look like?
The People Analytics cycle consists of eight steps: from formulating a business question to measuring results. This cycle helps HR to improve policies and processes based on data. It is an iterative process that can be continuously adjusted and can be applied at both strategic and operational levels.
- Step 1: formulate the business question. The HR manager/business partner and the general manager (of the business unit or the department concerned) will identify one or more business questions, i.e. problems and/or opportunities for the organisation, that are relevant and urgent for the organisation and HR.
- Step 2: translate the business question into an analytics issue. If the relevant business question or questions are clearly formulated, they should be focused (possibly with the help of an analysis tree) and translated into one or more People Analytics questions.
- Step 3: collect data. From the People Analytics question obtained in this way, it can be deduced which data is needed to answer the question.
- Step 4: prepare data. In many cases, the collected data cannot simply be used for analyses, but must be cleaned and enriched.
- Step 5: analyze the data and translate it into insights and algorithms. Analyses are carried out on the prepared data. This step consists of describing, explaining and predicting with the help of data analysis, statistics and data mining. The results obtained in this way are brought together and translated into insights to substantiate decisions or into a mechanism (a model or an algorithm) that can be used to improve operational processes, solve problems or seize opportunities.
- Step 6: presentation, reporting and visualization. The insights or algorithms obtained must now be effectively and convincingly visualized and reported for the benefit of management and/or other directly involved parties.
- Step 7: translate the insights into HR policies, processes and interventions and/or implement the algorithms.
- Step 8: measure and monitor the results achieved. In order to check the effectiveness and progress of the HR policy implemented in this way, the new or adapted processes and the associated interventions, these are expressed in key and control figures and measured and monitored.
What is a business question and why is it important?
An analysis project does not start with data, but with a question. Without a clear business question, there is no reason for analysis. A well-formulated business question makes it possible to develop targeted policy or make concrete decisions. As John Tukey said, an approximate answer to the right question is more valuable than an exact answer to the wrong one.
What makes a business question relevant to HR?
From an HR perspective, a business question must do justice to the dual role of employees: as a production factor and as a stakeholder. Models such as the INK management model and Rhineland thinking emphasise that employees are not only means, but also ends. A good business question therefore takes into account both the return and the well-being of employees.
How do you formulate a good business question?
A business question is in fact a decision that has to be made. This often arises from a complication in a stable situation, such as increasing absenteeism or a planned merger. Analysis questions are supportive, but without a clear business question, there is no support for action.
What is the ISBK method and how does it help?
The ISBK method (IST, SOLL, Barriers, Key Question) helps to find the core of the problem. By describing the current and desired situation and naming the blockages between them, the business question automatically arises. Interviews with various stakeholders are essential to get this into focus.
What are common pitfalls and success factors when formulating a business question?
A common mistake is to come up with solutions too quickly or to start with analysis questions without knowing the core question. Personal interests of internal customers can also influence the process. The analyst must be critical, tenacious and honest to arrive at the right question. Without a clear business question, there is a good chance that the analysis will not yield anything.
How do you set priorities for multiple business questions?
Use a matrix in which you compare the added value of an analysis with the feasibility. Start with analyses that are both high-yielding and easy to implement. This will help you build confidence in the process and increase the chances of impactful results.
How do you get from a business question to an analytics issue?
A business question is the starting point, but only by translating it into a concrete analytics issue will it be possible to perform targeted analyses. Analytics questions help to collect the right data and discover connections that lead to actionable insights and actions.
A business question such as “how do line managers get more time for talent management?” can be broken down into analysis questions about time use, undesirable activities and efficiency. These questions form the basis for an optimization model that helps to take targeted measures.
How do you determine the analytics issue?
The analytics issue includes the analysis questions, the scope of the research, the intended output and the preconditions. Presuppositions must be explicitly named and sometimes tested first. This prevents analyses from being based on incorrect assumptions.
Broad questions, such as in strategic workforce planning, should be broken down into sub-questions. Tools such as PIOFACH or JCOMPAFITHS help to highlight all relevant organizational aspects. This creates a complete picture of what needs to be investigated.
What analysis methods are there?
There are three main forms: hypothesis-driven analysis, structured data mining, and unstructured data mining. Hypothesis-driven analysis starts with a theoretical model, while data mining discovers patterns in large datasets. Machine learning goes even further and improves itself based on new data.
What is an analysis tree and how do you use it?
An analysis tree visualizes the relationship between analysis questions and possible causes. It helps to start the analysis in a targeted way and prevents important factors from being overlooked. Out-of-the-box thinking prevents tunnel vision. Tools like the fishbone diagram help to explore broader causes, even beyond the direct influence of HR.
How do you determine the scope of the analysis?
The scope determines what you do and do not investigate. This depends on resources, relevance and feasibility. It is important to test the scope with internal customers and existing studies.
How do you tailor the analysis to the intended output?
The output can range from reports to predictive models. The form chosen influences the approach, presentation and privacy requirements. Profiling requires extra care.
How do you brief the HR analyst properly?
A good brief includes background information, purpose, expected output, ideas, and budget. This ensures a targeted and effective analysis.
How do you get data for People Analytics?
Data is the foundation of any analysis. The HR analyst must carefully select data sources, assess their quality, and determine the order in which they are consulted. It is important to work from the outside in: first external sources, then internal ones.
Which external data sources are relevant?
The Netherlands has reliable institutions such as Statistics Netherlands, TNO, UWV and SCP that provide valuable secondary data. In addition, there are trend reports from consultancy firms, blogs from HR specialists and knowledge platforms such as ‘Healthy in Business’. These sources help with the initial imaging and the sharpening of analysis questions.
Sometimes existing information is sufficient, for example in the case of labour market research abroad. Conclusions can be drawn by deduction if one’s own situation meets the conditions of previous research. This is an example of evidence-based working.
What internal data sources are available?
Internal sources include employee records, performance data, training history, absence registrations, compensation data, recruitment information and more. These can be supplemented with business data such as customer satisfaction, turnover and productivity.
What is primary research and when do you use it?
If internal or external data is missing, primary research offers a solution. This can be qualitative (interviews) or quantitative (surveys). Well-known examples are employee satisfaction surveys and eNPS measurements.
How do you use big data in HR?
Big data offers new insights thanks to its volume, speed and variety. Examples include IoT data about workplaces, game-based assessments, video analysis and sentiment measurements via email. These sources allow for real-time monitoring and predictions.
How do you assess the quality of data sources?
The reliability of a source depends on the expertise of the sender, the definitions used and the consistency of the data and any conclusions. Critical assessment prevents analyses from being based on incorrect assumptions.
Collecting personnel data requires care. Legal and ethical frameworks must be respected. Transparency, consent and cooperation with employee participation are essential to maintain trust.
What is data management and data preparation?
Before data can be used for analysis or reporting, it must be cleaned, enriched and merged. This applies to both primary and secondary data. A good data management plan is indispensable, especially with repeatable analyses or periodic reports. Data preparation is therefore not an afterthought, but a prerequisite for reliable insights.
Why data management and data governance?
Effective data management starts with structure and ownership. Organizations have ever-increasing amounts of data, but without clear agreements about quality, access and use, value remains untapped. Data governance provides the framework: who is responsible, which rules apply, and how do we guarantee compliance and transparency?
A well-designed governance model ensures consistent definitions, reliable reports and a grip on risks. It enables teams to use data in a targeted way, with confidence in the outcomes. Think of clear data standards, metadata management and lifecycle management. In this way, data is not only managed, but used strategically. Data management and governance are therefore not IT themes, but organization-wide preconditions for data-driven working.
What is the difference between raw and aggregated data?
Raw data is individual data, as it comes from systems. Aggregated data are summaries, such as averages. The more individual the data, the more analysis possibilities are. But raw data is often polluted and needs to be cleaned up first. Aggregation errors can lead to wrong conclusions, as Simpson’s paradox shows.
How do you organize data smartly?
Ideally, data is stored on a data (management) platform, such as a data warehouse or data lake(house). These platforms make it possible to link data from different sources, preserve historical data and perform analyses without burdening the operational systems. They also help in complying with privacy guidelines.
Why is a data definition library important?
Different systems often use different definitions for the same concepts. A library of data definitions prevents confusion and inconsistency. By explicitly naming and documenting multiple definitions, analyses can be carried out correctly and transparently.
How do you approach data cleaning?
Data cleaning takes place in three steps: structure, content and quality. You check whether the data is logically structured, whether the content is complete and whether the quality is adequate. Missing or incorrect data is detected and corrected, for example by replacement or estimate. During analysis, it often turns out that additional variables or corrections are needed. Therefore, data preparation is not a one-time step, but an iterative process that is constantly reflected in the analytics project.
How do you merge data?
Data can be merged through various methods such as Inner Join, Half Join, or Outer Join. The chosen method determines which records are retained and how the data is combined. Key variables are essential to link datasets correctly.
How do you enrich data?
Data can be enriched with external sources, such as consumer data or labour market information. This makes it possible to segment employees or analyze their mobility opportunities. Enrichment increases the depth of analyses.
What are derived variables?
Derived variables are calculated from existing data, such as age from date of birth. They make it possible to better explain or predict behaviour. Often several variables are derived from one source variable. Sometimes new variables are not enough and new tables are needed, such as period files or aggregate tables. These help to analyze trends over time or to summarize data at the right level.
How do you analyze data?
Once the data has been cleaned and prepared, the analysis begins. The goal: to develop actionable insights or algorithms that lead to action. The methods of analysis are divided into four levels:
- Descriptive analytics – describes what happened.
- Diagnostic analytics – explains why something happened.
- Predictive analytics – predicts what is likely to happen.
- Prescriptive analytics – advises which action will yield the best result.
This structure helps organizations to grow from insight to decision-making.
How do you apply HR analyses in practice?
A practical example from the municipality of The Hague shows how subjective data, such as work experience, combined with objective data (such as absenteeism figures) leads to new insights. By means of multivariate analysis methods such as tree analysis and random forest, risk groups were identified and modifiable factors were named. This resulted in models that not only showed correlations, but also had predictive value for policy adjustment.
What steps do you go through in data analysis?
The analysis starts with describing trends and patterns. This is followed by investigating the relationship between variables via correlation and regression analyses. Predictive models are then built, for example to estimate sentiment based on email traffic. The final step is prescriptive analytics, where simulations help to make the best decisions based on expected outcomes.
Why is the measurement level of data important?
The type of data determines which analyses are possible. There are four levels of measurement:
- Nominal: categories without order (e.g. gender).
- Ordinal: Categories in order (e.g., satisfaction scores).
- Interval: equal distances, no absolute zero point (e.g. temperature).
- Ratio: equal distances with absolute zero point (e.g. salary).
Numerical data are suitable for calculations; Categorical data especially for counts. In practice, ordinal data are sometimes treated as intervals, for example in the case of report marks.
What is descriptive analytics?
Descriptive analytics is the foundation of any data analysis. More than half of an HR analyst’s work consists of simple counts, averages, and percentages. These analyses are not complex, but essential to get to know the data and assess which variables are relevant for further analysis. This prevents irrelevant data, such as a uniform bonus scheme, from being unnecessarily included in predictive models.
How do you get to know your data with frequency tables?
An effective first step is to draw up frequency tables. This shows you how often certain values occur. For categorical data, you use the raw values, for numeric data, you group them into classes. This gives you quick insight into the distribution of, for example, departments or age groups within an organization.
Why is visualization important in data analysis?
Visualizations quickly reveal patterns and anomalies in the data. Graphs show the distribution of variables and help recognize outliers. They give context to the figures and make it easier to identify trends.
Which key figures do you use in descriptive analytics?
Statistical key figures provide insight into the central trend (such as mode, median and mean) and the spread (such as standard deviation, interquartile range and dispersion width). Each key figure has its own application. In the case of skewed distributions, such as salaries, the median is often more reliable than the average. The mode can help discover subgroups.
How do you deal with outliers in your data?
Outliers can disrupt analyses and must therefore be detected early. With normally distributed data, values that deviate by more than two or three standard deviations are suspect. For skewed distributions, use the interquartile range as a benchmark. Recognizing outliers helps to keep analyses pure and reliable.
What is a time series analysis and why is it relevant for HR?
Time series analyses help to understand developments in HR data over time. They form the bridge between descriptive and explanatory analyses. By plotting data over time, you can discover trends, seasonal patterns and abrupt changes. Think of absenteeism that peaks annually in the winter or a decrease in staff after the introduction of a new HR system. Time series make these patterns visible and help to recognize trend breaks.
How do you recognize trends and seasonality in HR data?
An easy way to spot trends is to add a trend line to a chart. This line shows the general direction of the data. For more accurate analysis, you can use moving averages or exponential smoothing. Seasonal influences can be recognized by analyzing the deviation from the trend: are these deviations constant, relative or dependent on the trend direction? Visualization is essential here.
What is diagnostic analytics and how does it help explain?
Diagnostic analytics focuses on answering the question: why is something happening? By combining variables in cross-tabs or graphs, you discover relationships. For example: younger employees appear to work more often in Customer Support. Filters help to analyze subgroups, such as differences between men and women.
How do you statistically substantiate relationships between variables?
In addition to visual analysis, you can use statistical tests to confirm connections. Depending on the type of variables, you use:
- Chi-square test for two categorical variables.
- T-test or analysis of variance for combinations of categorical and numeric.
- Correlation analysis for two numerical variables. A high correlation indicates a strong linear relationship, but does not automatically mean causation. For more complex relationships, the Predictive Power Score (PPS) offers a solution, which can also detect non-linear relationships.
What is the difference between correlation and causation?
A common mistake in data analysis is confusing correlation with causality. Two variables can change together without one being the cause of the other. A well-known example is the (humorous) correlation between chocolate consumption and the number of Nobel Prize winners. Although there was a statistical correlation, this did not mean that chocolate leads to scientific achievements. The same is true for the relationship between ice cream sales and homicide rates: Both rise in the summer, but have no direct correlation to each other.
Why is causality important in People Analytics?
If you only analyze relationships, you can predict, but not influence. For example: the moment of first absence can be a good predictor of long-term absenteeism, but it does not offer any starting points to prevent absenteeism. If you want to actually steer towards improvement as an organization, you need to know what causes absenteeism. Only then can you develop effective policy.
How do you demonstrate causality?
Statistics can measure relationships, but not show causal relationships. This requires additional considerations. Sir Austin Bradford Hill formulated nine criteria to assess causality, including strength of the relationship, consistency, time sequence, and plausibility. The more criteria apply, the more plausible the causal relationship.
What is an interrelationship diagram and how does it help with cause-and-effect analyses?
An interrelationship diagram is a visual aid to explore cause-and-effect relationships between multiple factors. All factors are placed in a circle and each pair is determined which factor is the cause. The direction is indicated by arrows. Factors with only outgoing arrows are the drivers; Factors with only incoming arrows are symptoms. This helps to find the right knobs to turn.
What is predictive analytics?
Predictive analytics focuses on predicting future outcomes based on historical data. The first step is often regression analysis, which quantifies relationships between variables. Importantly, predictions are only reliable within the range of the original data. Moreover, there is always a degree of uncertainty, because not all variation is explained by the model.
How do you predict future developments?
Time series analyses help predict trends, for example for strategic workforce planning. In Excel, you can extend trend lines or use functions such as =PREDICT. More advanced models, such as ARIMA, also take into account seasonality and margins of uncertainty.
Pay attention to external influences such as trend breaks (e.g. pandemics), use benchmarks to compare your organization, and work both top-down and bottom-up. Combine forecasts with other variables to plan more accurately, such as by deriving staffing needs from projected sales figures.
What is prescriptive analytics?
Prescriptive analytics goes a step further than predictions: it makes recommendations based on predictions. By analyzing scenarios and calculating the probability of outcomes, it helps choose the best course of action. Think of self-driving cars that continuously make decisions based on expected situations.
How does prescriptive analytics work in practice?
It combines statistical models (for forecasting) with mathematical models (for optimizing). These models include possible actions, their consequences and the likelihood of them. This creates a simulation model that helps to calculate scenarios and choose the optimal route to a desired result.
What are HR examples of prescriptive analytics?
A concrete example is strategic workforce planning. Models like Markov’s allow you to simulate future staffing needs. This allows you to respond in a timely manner to expected shortages or surpluses, and make informed decisions about inflow, throughput and outflow.
How do you deal with data from employee surveys?
Many HR analyses work with samples: a selection from the total population, for example in employee satisfaction surveys or labour market surveys.
How do you deal with uncertainty in sample results?
Because a sample represents only a part of the population, results may differ from reality. Therefore, margins (intervals) are used when interpreting results. The larger the sample, the smaller the margin. But the desired reliability and the distribution of the trait in the population also play a role. When comparing measurements over time, it is important to see if the margins overlap. If not, there is a significant difference.
How do you determine the required sample size?
The required sample size depends on the desired accuracy, reliability, and population size. In general, the smaller the margin of error and the greater the reliability, the larger the sample should be.
When is a sample representative and valid?
A sample is representative if the distribution of important characteristics (such as sex or age) corresponds to that of the population. If this is not the case, results can be weighed. Representativeness is independent of the sample size. Validity means that you measure what you want to measure. For example: satisfaction is not measured by salary, even though there may be a connection.
How do you draw correct conclusions from data?
Conclusions form the bridge between analysis and decision-making. They answer the analysis questions and thus the business question. It is important that conclusions are objective, based on facts and not opinions. By synthesizing: determining core concepts, establishing relationships, translating where necessary, and bringing everything together into an overarching insight. This requires critical thinking and combining different sources and perspectives. Work together with colleagues to arrive at well-founded judgments.
What are common pitfalls when drawing conclusions?
Look for mistakes such as:
- Confusing causality with correlation
- Cherrypicking only appropriate results
- Model overfitting
- Representativeness problems in samples
- Simpson’s paradox: wrong conclusions due to aggregation
- Survivorship bias: only looking at who ‘remains’
These errors can lead to misleading insights.
How do you effectively report on your HR data analysis?
The last step in the analysis process is to report the results clearly and convincingly. A good report answers the business question and encourages action. The HR analyst acts as an advisor here: he influences, but does not execute himself. So the message must not only be right, but also convincing.
What are the pyrimid principles and why do they work?
Reporting according to the Pyramid principles, developed by Barbara Minto, starts with the core message: the answer to the business question. This is followed by the substantiation in layers of arguments. This structure is in line with how people process information: from overview to detail. It helps the reader quickly get to the essence and makes the report clear and convincing.
How do you write a strong introduction to your story?
A good introduction contains three elements:
- The neutral context or initial situation.
- The complication or cause.
- The central question.
How do you reinforce your message with visualizations?
Visualizations make complex data understandable. Graphs and tables support the argumentation, provided they are well chosen and designed. Use contrast, recognizable patterns and limit the appeal to the reader’s memory. Choose a chart for trends and patterns, a table for detail comparisons.
What are the points of attention in storytelling?
People see what they expect and focus on contrast. Take into account selective attention, familiarity with shapes (such as time on the x-axis) and limited RAM. Tailor your visualizations accordingly to convey the message powerfully.
When do you choose a graph and when do you choose a table?
A table is ideal for looking up and comparing specific values, such as monthly figures or averages. A graph is better suited to visualize trends, patterns and deviations. The choice also depends on the target group: finance departments often prefer tables, while management gets insights faster through charts.
How do you determine the right chart?
You can choose the right chart in three steps:
- Determine the message: what do you want to convey?
- Identify the equation: is it a passage of time, a distribution, a coherence?
- Select the chart type: for example, a trend line chart, a ratio pie chart, or a correlation scatter chart.
So the message determines the form, not the data itself.
How do you make visualizations more effective?
Good visualizations are clear and goal-oriented. Avoid 3D effects, use color sparingly, and emphasize only what’s important. Make sure the main line or bar stands out visually. Take visual perception into account: people focus on contrast, recognize familiar patterns and have a limited working memory.
How do you make reports actionable?
An action-oriented report contains:
- Urgency: show the impact of the problem, for example in euros.
- Topicality: indicate where and when the problem occurs.
- Solution-oriented: give concrete recommendations, preferably with a chance of success and possible side effects.
- Assignment: Assign responsibilities to specific functions or departments.
What is the role of the HR analyst as a reporting advisor?
The HR analyst is not only a data expert, but also an advisor. This requires communication skills, substantive knowledge and the ability to anticipate resistance or political sensitivities. Only if insights are properly presented and accepted can they lead to policy and action.
How to implement results of a People Analytics study?
How does HR translate insights into policy and actions?
Based on People Analytics, organizations can develop targeted HR policies. For example, the Ministry of Foreign Affairs used data to identify inequalities between men and women and developed targeted interventions. Such insights are translated into concrete HR actions, such as new advancement measures or adapted job models.
What role does HR play in the organization in implementation?
The effectiveness of HR depends on its role. Modern HR functions are strategic partners that work together on business objectives. Ulrich distinguishes four HR roles: strategic partner, change agent, administrative expert and employee champion. Clear communication about the division of tasks between HR and line management increases the chance of successful implementation of insights.
What determines the content of HR interventions?
The business question is leading. Strategic questions lead to long-term policy measures, such as sustainable employability or cost control. Interventions can affect both HR functioning and the functioning of the organisation, for example by reviewing processes or developing digital learning platforms.
How do you measure and monitor the effect of HR policy and interventions?
Effectiveness requires monitoring. The impact of policy is assessed through measurable indicators. HR must constantly test whether goals are being achieved within the set frameworks. Measuring and monitoring results is the last – and sometimes first – step in the People Analytics process. This is done through key figures and key performance indicators (KPIs), which show the extent to which goals have been achieved. Examples are the absenteeism rate or the time-to-hire. KPIs are defined SMART: specific, measurable, achievable, realistic and time-bound. In addition to backward-looking key figures, predictive indicators are also used to steer proactively.
How do you evaluate the effect of an intervention?
A baseline measurement and an effect measurement (possibly with a control group) are used to determine whether goals have been achieved. ROI measurements look at the benefits versus investments of interventions. Pitfalls such as the Cobra effect or regression to the mean can distort the interpretation.
How do you successfully implement data-driven working in your organization?
A common concern among HR managers is that People Analytics can overshadow the human side of HR. It is essential to emphasize that data analysis and the human approach can actually reinforce each other. Good managers deliver measurably better results; By analyzing those successes, organizations can develop goal-oriented leadership. Case studies from Shell and Billy Beane illustrate how data-driven working actually increases human effectiveness. At the same time, it is important not to blindly rely on technology. Just like a pilot, an HR professional must also learn to rely on his instruments, but intuitive signals remain important. Data should always be assessed in the context of professional norms, values and experience.
How do you ensure a workable start with People Analytics?
The practical implementation starts with determining the current maturity level. A simple first step helps to gain experience, create support and make results visible quickly. Small, clear analyses lead to acceptance in the organization faster than complex processes. The growth model of People Analytics has five phases: from opinion-driven (phase 1) to fully data-driven (phase 5). Each phase requires specific actions such as improving data quality, developing internal competencies and integrating analyses into decision-making.
What do you need in terms of technology and systems for People Analytics?
A solid ICT structure is crucial for People Analytics. Systems must be able to combine, store and analyze data from different sources. Appropriate tools are needed for every maturity level. In the early stages, Excel or Power BI may suffice for simple reporting. Advanced analytics and predictive modeling require tools like R or Python and specialized data warehouses. Increasingly, organizations are working with cloud-based systems/platforms for maximum flexibility. A separate data infrastructure (such as a data lake house) offers the most opportunities for future growth.
Who is involved in People Analytics and what is their role?
People Analytics requires collaboration between HR professionals, analytics translators, and HR analysts. Each step requires specific expertise. The HR professional and line manager formulate the business question, the HR analyst collects and analyzes data, and the insights are translated into policy and processes by the HR professional and analytics translator.
What roles and competencies are needed within People Analytics?
Successful application of People Analytics requires diverse roles and skills. The HR business partner acts as a bridge between data and policy; the HR analyst performs analyses and develops models. The People Analytics manager or translator provides vision, connects stakeholders and translates insights into actions. In addition, data managers, visualization experts, and change managers are also needed. Not only analytical skills are important, but also knowledge of HR themes, organizational sensitivity and strong communication skills. Multidisciplinary teams – or even central enterprise intelligence teams – are proving to be very effective.
What does the implementation of People Analytics at the organizational level require?
The transition to data-driven working is a change process, not a project. It requires a vision, roadmap and continued involvement of the board and management. Leadership is crucial: if top management does not propagate data-driven working, the organization will not keep up. Investments are needed in technology and in people. Successful organizations provide ambassadors, internal training, clear governance and cooperation with ICT and finance. Where necessary, expertise can be hired.
What about privacy, personal data and the AI Act?
The processing of personal data for People Analytics is permitted provided that legal and ethical conditions are met. The GDPR requires that data is used lawfully, purposefully and proportionately. Special personal data (such as health or race) may only be processed under strict conditions. Anonymizing or pseudonymizing data helps to limit risks. The new European AI Act (from August 2024) imposes additional requirements on AI systems, especially in high-risk applications such as recruitment. Human supervision is mandatory for these systems. Organizations must make choices not only technically and legally, but also ethically. The key question remains: is it desirable?
How can you apply People Analytics responsibly?
People Analytics can greatly improve the quality of decision-making, provided that the balance between data and people is maintained. It requires careful steps, clear governance, good leadership and the right competencies. Technology and data are the foundation, but success is determined by the people who work with it. With a clear roadmap and an ethical compass, People Analytics can develop into a powerful strategic function within the organization.
