5CO02 1 And 2
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LO2Lessonresources-20211215.zipPeople Analytics Factsheet
24 May 2021
People analytics Understand what people analytics is, why it’s important and how it’s used
Introduction People analytics is about gathering and analysing data about people in a workforce. It’s sometimes called HR analytics or workforce analytics. People data is found in HR systems, from other departments like IT and sales, and from external sources such as salary surveys. Using people data offers the opportunity to contribute to an organisation’s strategy by creating insights on what people can do to drive change.
This factsheet explores what people analytics is, why it’s important and how it’s used. It introduces key terms such as correlation, causation, predictive and prescriptive. It also discusses who is responsible for people analytics as well as the strategy and process.
Explore our viewpoint on people analytics in more detail, along with actions for government and recommendations for employers.
What is people analytics and why is it important? People analytics is about analysing data about people to solve business problems. It’s sometimes called HR analytics or workforce analytics. One academic paper defines it as ‘a number of processes, enabled by technology, that use descriptive, visual and statistical methods to interpret people data and HR processes. These analytical processes are related to key ideas such as human capital, HR systems and processes, organisational performance, and also consider external benchmarking data’.
Five reasons for using people analytics:
1. It can be used to measure a workforce, for internal and external stakeholders, in a range of areas such as performance, wellbeing, and inclusion and diversity. See more on workforce planning.
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2. It enables more effective evidence-based decisions on improving workforce and organisational performance.
3. It can demonstrate the impact of HR policies and processes on workforce and organisational performance.
4. It can be used to estimate the financial and social return on investment of change initiatives.
5. ‘Analytics and creating value’ is a core knowledge element in our Profession Map, with ‘people analytics’ as a specialist knowledge element.
People analytics can be applied to almost an aspect of HR activity. For example:
Enhancing employee morale: Organisations can measure the drivers of employee engagement and adapt their practices accordingly to enhance employee morale.
Improving retention: Organisations suffering from high turnover of key employee groups can use people analytics to anticipate areas with specific issues and tailor incentives to curb attrition. Find out more on turnover and retention.
There’re more case studies of people analytics in action in our Valuing your Talent web pages and in our research report Human capital analytics and reporting: theory and evidence.
Find out more about how HR and finance professionals are using people data in our report People analytics: driving business performance with people data in association with Workday, as well as the summary reports People analytics: international perspectives.
Does collecting data involve monitoring and surveillance?
Potentially. Technology makes it easy to seamlessly collect data about people. Websites visited, time spent on specific apps, comments made on the organisation’s social networking site. Organisations can monitor their workforce within the bounds of law where they operate. Even if it’s lawful, how an organisation collects and uses monitoring data can be contentious, particularly if employees feel that it’s irrelevant, unnecessary or too intrusive. Watch Don’t be creepy: how to use data for good by Dr Heather Whiteman of the University of California, Berkeley at ‘People Analytics & Future of Work’ 2020.
If introducing employee monitoring software, it’s important to:
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Be transparent. Explain clearly what you’re monitoring and why. Consult with employees to ensure the measures are relevant and necessary. Measures can be about ensuring compliance as well as helping employees become better at their jobs. Be mindful of cultural differences and monitor your system to make sure it does not discriminate against minority groups.
What is descriptive, predictive and prescriptive analytics? Descriptive, predictive and prescriptive analytics are terms which are often used to describe the maturity level of the people analytics capability in an organisation.
Level 1a – descriptive analytics: Uses descriptive data to show, for example absence and annual leave records, and attrition and recruitment rates. At level 1 data is used to describe a snapshot at particular point in time or a trend. See our factsheets which give commonly-used absence measures and employee turnover and retention measures.
Level 1b – descriptive analytics using multidimensional data: Combines different types of data to investigate a specific idea. Like combining leadership capability data with engagement scores to measure leadership effectiveness.
Level 2 – predictive analytics: Uses data to predict future trends. For example, looking at historical workforce data and external labour market trends to build a model that predicts the organisation’s future workforce needs. The data needs to be relevant, high quality and robust for predictions to be reliable.
Level 3 – prescriptive analytics: Uses the results of descriptive and predictive analytics to automatically recommend options. For example, an online learning platform that recommends courses for a learner based on their interests, career goals and past courses.
Most organisations can do descriptive analytics but few as yet can do prescriptive analytics. This is changing though as more apps offer analytics out of the box. Having a mature people analytics capability expands what you can analyse and automate. However, as discussed in People analytics effectiveness: developing a framework, using more advanced analytics doesn’t always bring more value to the organisation. Valuable insights can come from descriptive analytics.
What is quantitative and qualitative data,
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correlation and causation? People analytics can help identify whether one or more things can reliably predict something else. To do this, we use quantitative and/or qualitative data to build a predictive model. If the model reliably predicts something, we say there is a correlation and describe the strength of the relationship as a number. But correlation does not imply causation.
Quantitative data: is quantifiable and objective. It can be described in numbers. The number of employees, average age and salary range are examples.
Qualitative data: describes the qualities observed by someone and is subjective. It is useful for understanding the ‘what’, ‘why’ and ‘how’ of something. Employee engagement, performance appraisals and exit interview notes are examples of qualitative data. Qualitative data can be turned into quantitative data. For example, a performance appraisal can be summarised as a performance rating.
Correlation: is when two or more things that happen around the same time might be associated with each other. For example, a survey reviewed in In a Nutshell issue 106 found a link between employee perceptions of corporate social responsibility (CSR) and their work engagement. But the survey cannot prove that positive perceptions of CSR result in high work engagement.
Causation: is when something happens, it causes something else to happen. For example, during school holidays more employees with school-aged children go on leave. To prove causation, you usually need to analyse data from different points in time.
Remember that organisations are not closed systems. It’s important to look beyond the analytics and consider other factors that can’t easily be measured before drawing conclusions. When analysing race data, for example, consider where structural discrimination can hide. A lack of diversity in frontline staff might reflect a long-term lack of investment in public transport and residential segregation.
Who is responsible for people analytics and managing people data?
It varies. Large organisations may have a centralised people analytics team that provide insights to stakeholders in the organisation. Some organisations prefer a decentralised approach where individual HR analysts within small centres of expertise provide insights within their specialist domain. Others prefer to outsource their analytics. In practice,
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organisations usually take a hybrid approach.
Although data is held in many places in an organisation, it should ideally be managed by a specific data owner. The data owner is responsible for ensuring that data is maintained and kept secure according to the organisation’s data protection policy. Only those responsible for the people data should be able to change the structure of the people data itself, such as the definitions for specific HR indicators. Employees and managers can view and update some of their personal data through self-service. Find out more about data protection in the UK.
What are the aims of a people analytics strategy? People analytics projects should align to both the business and the HR strategy. Solving a critical business issue is likely to create the most value for the business and create further demand to create insights from people data.
A people analytics strategy should have three aims:
1. Connect people data with business data to inform business leaders and help them make decisions.
2. Enable HR leaders to use insights from the analytics to design and implement appropriate HR activities.
3. Measure HR’s effectiveness in delivering against its objectives. A sizeable minority of the people profession find this part challenging. Almost a quarter of respondents to our People Profession Survey 2020 said that they don’t have clear measures of success for measuring their impact.
Our practitioner’s guide explores the first steps to building a people analytics strategy, developing simple analytics capabilities. In our research report Human capital analytics and reporting: theory and evidence, we summarise key academic concepts to apply in a people analytics strategy.
What is a people analytics process? The people analytics process should follow nine steps from planning through to evaluation. In practice, the process can be shorter. For example, a recent data audit can be reused, or when analysis and reporting have been automated.
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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 existing data sets (for example, absence records) or collect new data (for example, by running an engagement survey).
7. Analyse data: Analyse data and create insights, in line with the stakeholders’ requirements.
8. Report data: Report a solution to the problem clearly and recommend further areas of investigation if needed.
9. Evaluate: Review the process and evaluate impact. Update process as required.
Further reading
Books and reports
EDWARDS, M. and EDWARDS, K. (2016) Predictive HR analytics: mastering the HR metric. London: Kogan Page.
KHAN, N. and MILLNER, D. (2020) Introduction to people analytics. London: Kogan Page.
MARR, B. (2018) Data-driven HR: how to use analytics and metrics to drive performance. London: Kogan Page.
Visit the CIPD and Kogan Page Bookshop to see all our priced publications currently in print.
Journal articles
© Copyright Chartered Institute of Personnel and Development 2021, 151 The Broadway, London SW19 1JQ, UK Incorporated by Royal Charter, Registered Charity no. 1079797 123 Page 6 of 7
BASKA, M. (2018) Six ways analytics will future-proof HR. People Management (online) 6 June.
GARCIA-ARROYO, J. and OSCA, A. (2019) Big data contributions to human resource management: a systematic review. International Journal of Human Resource Management (online). 9 October. Reviewed in In a Nutshell, issue 93.
GREASLEY, K. and THOMAS, P. (2020) HR analytics: the onto-epistemology and politics of metricised HRM. Human Resource Management Journal, Vol 30, Issue 4, November. pp494-507. Reviewed in In a Nutshell, issue 103.
JEFFERY, R. (2019) Amazing insights you can learn from people analytics. People Management (online). 21 February.
RASMUSSEN, T. and ULRICH, D. (2015) Learning from practice: how HR analytics avoids being a management fad. Organizational Dynamics. Vol 44, No 3, July-September. pp236- 242.
CIPD members can use our online journals to find articles from over 300 journal titles relevant to HR.
Members and People Management subscribers can see articles on the People Management website.
This factsheet was last updated by Hayfa Mohdzaini.
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Week 2.pptx
5CO02 Evidence based practice
CIPD L5 Associate Diploma in People Management
1
Starter
Small group or 4-5 peeps
Prize for the team that’s wins!
HR News
Last week LO1
Understand strategies for effective critical thinking and decision-making;
The concept of evidence based practice and how this can be used to support decision making for people and organisational issues
Macro and micro analysis tools research activity
The principle of Critical thinking
Ethical theory/perspectives and how these inform and influence decision making
4
CIPD Profession Map – Professional Values
Principles led
Evidence led
Outcomes driven
Profession Map
Professional Value: Evidence based
What is evidence-based practice?
It is about making better decisions
Informing action that has the desired impact
Based on a combination of using critical thinking and the best available evidence
It makes decision makers less reliant on anecdotes, received wisdom and personal experience – sources that are not trustworthy on their own
It is important because of the huge impact management decisions have on the working lives and wellbeing of people in all sorts of organisations worldwide.
CIPD Video – Research Report
In search of the best available evidence
Video
8
Using evidence in HR decision making: 10 lessons from the COVID-19 crisis (June 2020)
Being evidence based is at the heart of the CIPD's new Profession Map and a vital skill for all people professionals to develop.
What can we learn from the Covid-19 crisis about making more evidence-based decisions?
Using evidence HR decision making
Sources of evidence
3.2 Task 2
9
Scientific literature (journals) on management, psychology and sociology – issues facing managers
Organisational data – internal/external, hard/soft
Expertise and judgement of practitioners, managers, consultants and business leaders – professional knowledge not opinion
Stakeholders – internal and external
Combining the evidence – collate from different sources
3.2 Task 2
10
Asking
Acquiring
Appraising
Aggregating
Applying
Assessing
See CIPD infographic
Infographic
Application - ideas
3.2 Task 2
11
Implement a performance management system – see factsheet page 6
Reward
Absence policy and procedure
Wellbeing
Enhancing employee morale
Tonight a look at LO2 Understand the importance of decision-making strategies to solve people practice issues
decision-making approaches that could be used to identify possible solutions to a specific issue relating to people practice. (2.3)
as a worked example to illustrate the points made in 2.3, take this same people practice issue, explain the relevant evidence that you have reviewed, and use one or more decision-making tools to determine a recommended course of action, explaining the rationale for that decision and identifying the benefits, risks and financial implications of the suggested solution. (2.2 & 2.4)
2.2 Review relevant evidence to identify key insights into a people practice issue. Identification of relevance; relevant evidence might include statistical data; evidence relating to processes (for example process documentation, records of errors, complaints or shortfalls, successes); evidence relating to outcomes (tangible/intangible); evidence relating to new or unmet requirements (needs analysis, change analysis, consultation outcomes); opinion and testimony of affected parties; outcomes of critical path analysis.
2.4 Provide a rationale for your decision based on evaluation of the benefits, risks and financial implications of potential solutions.Benefits (for exampleachievement of objectives, enhanced worker productivity, customer engagement, improved organisational culture, enhanced metrics and business awareness, increased capabilities, perception of fair policy and processes, legal compliance). Risks (for examplelegal, health and safety,financial, reputational, capability, impact on worker or customer engagement).Financial implications: direct costs (costs of implementing the solution) and indirect costs (for exampleloss of working time, need for skills upgrade in relation to the solution); costs in relation to the short and longterm benefits (costbenefit analysis, return on investment); costs in relation to budget limitations and feasibility of solutions.
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Decision-making approaches
https://www.managementcentre.co.uk/blog/action-learning-sets-group-problem-solving/
https://peopleprofession.cipd.org/profession-map/core-behaviours/situational-decision-making#gref
https://www.linkedin.com/pulse/how-does-future-pacing-work-bhavana-nissima
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Action learning approaches
De Bono (six thinking hats)
best fit
future pacing
problem-outcome frame
White: The White Hat calls for information known or needed. "The facts, just the facts."
Red: The Red Hat signifies feelings, hunches and intuition. When using this hat you can express emotions and feelings and share fears, likes, dislikes, loves, and hates.
Black: The Black Hat is judgment. Spot the difficulties and dangers; where things might go wrong.
Yellow: The Yellow Hat symbolizes brightness and optimism. Under this hat you explore the positives and probe for value and benefit.
Green: The Green Hat focuses on creativity; the possibilities, alternatives, and new ideas. It's an opportunity to express new concepts and new perceptions.
Blue: The Blue Hat is used to manage the thinking process. It's the control mechanism that ensures the Six Thinking Hats guidelines are observed.
This order organise your discussion:
Blue to start with approach and process
White: review the facts
Green Generate new ideas without judgement
Yellow focus on benefits
Red Consider emotional responses to any ideas
Black Apply critical thinking after the benefits have been explored to test the viability of the new ideas
Any hat can make a reappearance in the discussion. For example after facts (white) are laid out, more process (blue) may be applied or after pros (yellow) and cons (black) are discussed, new ideas (green) may surface
Coloured paper for paper hats, red, blue, black, yellow, green & white. Ask students to write on each hat what the colour represents
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Action Learning Set (ALS)
Reg Revans developed action learning in the 1940s at the National Coal Board. He was a scientist, university professor and management consultant. He was also a very practical man, keen to develop new ways for managers to learn and develop together. Revans was aware that, although it is a valuable starting point, training alone did not always bring better results in the workplace
Today action learning is used in a number of organisations across the not for profit sector
There is no single definition of what is an Action Learning Set though there are some general principles…
What is an Action Learning Set?
In simple terms…Action learning sets are small groups of people who are willing to offer as well as seek help from one another in a supportive and confidential learning environment.
The group agrees to meet regularly over a fixed period- from as little as 6 weeks to as long as 18 months. They come together find practical ways of addressing the ‘real life’ challenges they face, and to support their own learning and development.
Assuming the set has a facilitator their job is to help shape the work of the group. They ensure that the ground-rules are followed and that the learning is clarified. They may intervene a lot at the start of the group and much less as the group grows in confidence and competence.
A typical set meeting might last 2-3 hours and might have a structure something like this:
So an ‘Action Learning Set’ is a group of between 5-7 people. These are usually peers or at a similar level of responsibility and experience. They can be from one organisation or from a range of organisations.
The group agrees to meet regularly over a fixed period- from as little as 6 weeks to as long as 18 months. They come together find practical ways of addressing the ‘real life’ challenges they face, and to support their own learning and development.
The set normally has a trained facilitator who guides the process, though it is possible to run without this support if participants are experienced and disciplined.
Essentially set members are encouraged to find their own solutions to challenges and issues through a structured process of insightful questioning combined with a balance of support and challenge from the group.
Set members normally agree some ground rules at the beginning of the process and review them throughout the period they’re working together. Over time, and as trust builds, the group learns together.
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Action learning set
At the start of the meeting each member ‘checks in’- feeding back on progress or changes since the group’s last meeting. They may well be feeding back on commitments they made at the previous set meeting.
One or more members then seeks permission from the others to share/present- an issue they’re dealing with at work they’d like to explore. This should be a concrete project and not one with a simple solvable answer.
The set agrees an initial person to focus on and the issue to be addressed- this is sometimes called ‘claiming airspace.’ The presenter outlines the issue or challenge they’d like to consider. They may use statements like: ‘I’d like to explore’, I’m wondering whether’, ‘I’m not sure if’, or ‘I’m puzzled by.’
Set members ask questions designed to help the presenter analyse the concern they have, clarify what the challenge is and why they’re struggling to deal with it. The do this by asking open questions moderated by the facilitator.
These questions can take a number of forms: e’. g. clarification – ‘Are you saying that…?’, understanding – ‘Could you explain this issue a bit more…?’, checking implications – ‘You said before that.. so If that’s so then what would happen if…?’, to explore possibilities – ‘Have you thought of…?’ or ‘Would x,y,z … be useful’
It’s important that set members don’t offer advice, or opinions. They also need to avoid using the airspace for telling their own stories or discussing their issues. The focus must be on the on the presenter and on the issue they’re working to resolve.
At the end of a period – 15 minutes or so – the presenter reviews their thinking and selects one or more courses of action which they then commit to. In dong so they are committing to take action and to be held accountable for action at the next meeting. Then another group member presents.
The group might then typically reflect on the quality of the group process, and reflecting on what was successful and less successful and how they might improve for next time. The facilitator may take a leading role in this and offer the group feedback on their process.
Once back at work the presenter applies the insights they gained to their work issue. They will consciously choose to note what worked and not in order to report back to the group on effectiveness. And they bring that learning back to the next meeting.
Provide a rationale for your decision based on evaluation of the benefits, risks and financial implications of potential solutions (2.4)
Provide a justification for your decision based on evaluation of the benefits, risks and financial implications of potential solutions.
Benefits for example achievement of objectives, enhanced worker productivity, customer engagement, improved organisational culture, enhanced metrics and business awareness, increased capabilities, perception of fair policy and processes, legal compliance.
Risks for example legal, health and safety,financial, reputational, capability, impact on worker or customer engagement.
Financial implications: direct costs (costs of implementing the solution) and indirect costs (for example loss of working time, need for skills upgrade in relation to the solution); costs in relation to the short and long term benefits (cost benefit analysis, return on investment); costs in relation to budget limitations and feasibility of solutions.
What are the key challenges of knowledge management?
The three most common challenges of knowledge management relate to:
Obsolete technology;
Employee motivation; and
Making information easy to find.
What are the benefits of knowledge management?
improved organisational agility.
better and faster decision making.
quicker problem-solving.
increased rate of innovation.
supported employee growth and development.
sharing of specialist expertise.
better communication.
improved business processes.
Influence that data has on organisational culture, performance and internal and external perceptions
3.2 Task 2
22
Basically, streamlined analytics will force your company to shed inefficient practices and focus only on what brings in customers and makes you money.
Data culture is important for growth as it enables organizations to make more robust decisions at a much faster pace.
With the help of big data, companies aim at offering improved customer services, which can help increase profit.
They help businesses analyse information and improve decision-making.
Qualitative data
3.2 Task 2
23
Describes the qualities observed by someone and is subjective.
It is useful for understanding the ‘what’, ‘why’ and ‘how’ of something. Employee engagement, performance appraisals and exit interview notes are examples of qualitative data.
Qualitative data can be turned into quantitative data. For example, a performance appraisal can be summarised as a performance rating.
Quantitative data
Is quantifiable and objective.
It can be described in numbers. The number of employees, average age and salary range are examples.
3.2 Task 2
24
Quantitative data
Types of information to enable informed decision making
Resourcing
Absence
Performance and reward
Turnover
Dismissals
L & D skills and competencies
Employee voice
Engagement
Communications
Wellbeing
Change agendas
Policies and practices
HOMEWORK Case studies…The CIPD has conducted case study research with a number of organisations to understand how they are using people analytics to deal with a variety of business challenges. Their experiences and insight can help you in your own context to embrace people analytics and adopt good, data-supported actions to improve organisational practice and performance
Next week
Take a look at Task 2…
Be able to measure the impact and value of people practice to the organisation (LO3)
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