Part 1. Introduction to HR analytics
The last decade has heralded a massive transformation of HR – there’s the digitization of human resources activities, the changing roles and expectations of HR professionals, and perhaps, most prominently, the phenomenal rise of data and HR analytics.
However, HR professionals were not trained to be data scientists, and it’s probably safe to say that few have any desire to be data scientists! But with the increasing emphasis on data – big data, people data, data analytics, data-driven decisions – sometimes it seems like a data scientist is just what many HR teams need (spoiler alert: it’s not).
In the past, it was enough for HR to measure attrition and celebrate when a 12% attrition rate decreased by a couple of percent. Now, we can’t hide from the fact that there’s more to the story. To tell that story we need to collect and mine more data, analyze more variables, and combine datasets to truly understand what’s driving that decrease in attrition.
For example, if all of the attrition in the business occurred within the first 12 months, or all the employees who resigned were female, then a 2% reduction doesn’t look so rosy anymore. But with data in its raw form and navigating a maze of excel spreadsheets, getting these answers is no easy feat.
In this guide, we’re going to take a deep (but accessible) dive into HR data and analytics, sharing some tips, techniques, and tools for doing best practice, data-driven HR – without the data science degree. We’ll cover:
- What is HR analytics and why it’s important for HR (like you need any convincing).
- The HR data cycle and best practices for collecting data
- Measurement, metrics and the 4 pillars of HR analytics
- Data visualization, application and avoiding bias
- How to stay on top of a rapidly evolving industry so you don’t get left behind.
In the past, measuring success was all about the balance sheet: profit and loss. Although this is typically the finance department’s remit, every business function has its own way of measuring success – legal uses time and cases resolved, marketing uses leads and engagement, and sales uses – well – sales.
So it makes sense for HR to measure success, too. Thankfully, organizations now realize that success is driven by much more than a dollar figure; that there’s value in people too, and that people, more often than not, are the reason behind that dollar figure. This is what HR analytics comes in.
HR analytics enables HR practitioners to evaluate and measure HR activities as well as human capabilities and behaviors.
Say you’ve just launched a new learning and development program for example, which has required a substantial investment both in time and hard costs. The best way to evaluate if the program is achieving what it set out to – i.e. if it’s impacted performance, engagement and turnover – is through HR analytics.
Key benefits of HR analytics:
- Make data driven-decisions across recruitment, engagement, performance and L&D.
- Link HR activities back to the business’s bottom line (and demonstrate the value and ROI in HR).
- Understand what’s working and what’s not, to assist with planning, budgeting and resource allocation.
- Ensure your staff are engaged, performing and growing.
People analytics, human capital analytics, workforce analytics, HR analytics. These terms are often used interchangeably, but what is the difference?
To help answer this question, we’ve delineated some broad definitions for each below, but it’s important to be aware that because these fields are still in relative infancy, they can often mean completely different things to different people.
Workforce analytics typically refers to workforce management processes and applications that surround people such as payroll, time, absenteeism, attendance.
People analytics is a broader term than workforce analytics, encompassing payroll, time and absenteeism as well as statistics and analysis of individual and team attributes, for example psychometrics, performance, wellbeing and stress.
Human capital analytics
Human capital is the knowledge, skills and experiences of employees that influence their work and the business. Although Investopedia says that human capital is an “intangible asset or quality not listed on a company’s balance sheet”, the purpose of human capital analytics is to quantify the value of this capital and add to and improve upon it. Human capital analytics takes a more financial, economic approach, calculating things like return on investment and the value of productivity.
HR analytics is a broader term that encompasses all of the above. It refers to the understanding of people data from a HR perspective.
There are four key stages to working with data: enablement, visualization, comprehension and application. These can be thought of as occurring in a cycle.
- Data enablement: how does information flow through your organization? How can you collect it so that you gain access to real-time information?
- Data analysis: how does your data get quantified, analyzed and augmented? Are you using the correct statistical methodology? Is there anything missing? (By choosing not to report on specific things we create bias).
- Data visualization: Is your data displayed in a clear, objective and effective way? How do you interpret and make meaning out of it? Can you use it to tell stories about your people and business?
- Data application: Does your data help make informed decisions and inspire action? What have you learned and are you feeding your learnings back into Stage 1 to inform the data you collect in the future?
Data enablement and the importance of real-time information
In smaller businesses, HR has full visibility and can directly see everyone and everything in real-time. If someone isn’t feeling well, they’re offered support. If another staff member is performing well, they’re recognized. Equally, issues are managed as they happen.
Beyond small teams and offices, how can HR achieve the same level of visibility? How can great performance be recognized and replicated across the business if it can’t be seen? How can managers intervene in poor performance before it’s too late? What’s the risk of decisions being made based on second-hand or incomplete info, or long after an event has occurred?
There are millions of pieces of “people” data running through every organization, every single day, as a result of existing people management practices and tools. HR has a unique opportunity to leverage this information to gain visibility over their people and enhance responsiveness, no matter how big or small the organization.
But without a clearly articulated framework for data enablement, it will be difficult to collect, prioritize and organize your data, rendering it meaningless.
Taking a design thinking approach
Harvesting the information collected from your people management practices and tools is where real-time analytics comes from. The first steps are to:
- Consider your business objectives and strategic priorities, e.g. culture and values, performance management, learning and development, health and safety, and leadership.
- Understand how information flows through your organization.
- Identify opportunities to capture it – in real-time, e.g. are you measuring multiple aspects of your people’ performance? Do you invite contribution by asking staff to provide feedback? Do you do this in an engaging, meaningful and easy way, or do you struggle to get everyone to complete your annual engagement survey?
You’ll need to ensure your data collection processes are practical, usable and seen by staff members and their leaders as value adding – otherwise they will not be used and will become a hindrance to getting the job done.
There is also a great opportunity to harness non-compliance (e.g., non-completion of surveys) to understand where and how to improve a process or a framework to better align people with business strategy, at the same time enhancing the real-time data collection framework behind it.
Organizations that find convenient ways of embedding data capture opportunities into everyday business practices make better strategic decisions, ones that are based on real-time empirical evidence and analysis, not assumption or guesswork.
You also need to consider the quality and nature of your data. Quality refers to the data’s completeness (i.e. is there any missing data?), validity (does it measure what it sets out to?), accuracy (can we be sure the data is correct?) and representativeness (is it free from bias?).
If your data isn’t clean and high quality, your results and insights won’t be either.
When collecting data, there are a few key principles to follow to ensure you get good, clean, and most importantly, usable data:
- Be transparent – what are you using the data for? What are the benefits to the respondent?
- Set goals – what is the purpose of collecting the data? This will help you decide if you really need to ask a certain question.
- Speak in plain language – avoid clever copy and technical jargon (or if you have to, be sure to explain terms and spell out acronyms).
- Use data validation – e.g. use number-only fields if you want to capture a number (rather than a text field) or maximum values.
- Be consistent – for your data to be valid and comparable over time, it needs to be collected in the same way. For example, if one week you ask “how happy are you in your role?” and then change it to “how happy are you at work” the following week, you’re technically asking a different question and thus the responses are not comparable.
- Use tools to support you – the more data is handled, the more likely you are to pick up errors and inaccuracies along the way. Use data collection tools like intelliHR to do the heavy lifting.
To start leveraging the real-time data flowing through your organization in a structured way, you might be wondering what data to collect, or which HR metrics to measure.
Like fashion, HR analytics and metrics seem to go through cycles and trends of what’s in vogue.
“In the early 2000s it was all about costs and retention, then the focus moved to employee engagement and performance. Diversity has had a recent resurgence, as well as wellbeing which has come under the spotlight as a result of COVID and working from home,” says Glenn Donaldson, intelliHR’s President of the Americas and analytics expert.
But none of these are more or less important and they all influence each other, which is why, Glenn says, you need to take a consistent, continuous approach to measurement.
Choose the metrics that are most meaningful to your organization, ensuring that you represent each of the different functional areas of HR, and measure each metric continuously every month, not just as a flavor of the month.
The 4 Pillars of HR analytics
Here at intelliHR, we categorize HR metrics into four key areas. These include:
- Culture, community and wellbeing
- Performance and productivity
- Risk and compliance
- Planning and financial drivers
Next, we’ll explain why each of these is important, the problems they can solve and dive deeper into the top 10 metrics for each pillar (which intellliHR automatically measures and analyzes for you!).
Culture is a key driver of employee engagement and wellbeing, satisfaction, retention and performance, as well as overall business performance. Although culture is tricky to define and near impossible to measure, to understand the culture of your organization, there are two things we really want to know the answer to:
- Are we a great place to work?
- Are our staff happy?
Although you could easily just ask staff to answer these questions, to get a more comprehensive picture of culture there are a number of other key related and predictive variables that are worth exploring:
- What is the general level of employee wellbeing? Are employees mentally healthy and well? Or are they struggling to cope?
- Are staff happy in their roles? How does happiness change over time and what factors influence it?
- Are staff loyal to our company? Do they feel a sense of belonging, affinity and pride?
- What is the overall sentiment of my business? Is it trending up or down?
- How diverse is our organization? How is diversity impacting performance and teams?
- How collaborative are we or how siloed are we? Are goals shared across the organization and teams?
- What does our organization look like across key cultural impact dimensions? E.g. values, diversity, organizational networks, recognition and learning, sentiment and happiness.
Let’s check out a few of the metrics you can use to measure culture, community and wellbeing.
1. Employee wellbeing
How to measure it
Staff won’t be able to perform at their best if their wellbeing is compromised, and this can impact the culture and performance of the broader team. But unless you proactively check in on your team and ask how they’re doing, many of them won’t offer that information without being asked.
That’s why we recommend tracking employee wellbeing on an ongoing basis, so that you can identify any issues as they happen and provide appropriate support.
- Use short, regular pulse surveys that ask employees how they are coping and if they need any support (see example questions below).
- In the survey, provide the option for staff to give additional feedback with some open-ended questions.
- Use a rating scale for wellbeing and make it meaningful. Numbers can sometimes remove the human from the data, which is why at intelliHR we use a three-point emoji scale, giving employees the option to respond with a thumbs up, thumbs down, or okay sign (the intelliHR HR software also sends notifications alerting managers to the number of employees who “aren’t coping” (thumbs down) so nothing slips through the cracks).
What to do with the data
- Have a conversation with those who aren’t coping and find out how you can support them. This sounds simple but it can be daunting! If you find these conversations challenging, be sure to check out our step-by-step guide to having a mental health conversation with your staff).
- Look further than individual wellbeing. Are there particular teams, locations or business units with higher or lower wellbeing than others? Which ones? Why might this be? Do junior employees have lower levels of wellbeing than more senior staff?
- Look for themes by creating a word cloud with the text that will display any common issues or patterns. You can do this for free using tools like WordItOut, or intelliHR generates one automatically, allowing you to click into the individual words to get additional information like the teams and business units that the particular issue is occurring in, or drill down to see exactly who has said what.
- Analyze shifts in wellbeing over time and compare this with other events and activities across your business. For example, does wellbeing drop at the end of the financial year and rise before the holidays? If so, what could you do to reduce stress at this time?
- Follow up on feedback! This helps to build trust by demonstrating that you’ve listened and you care.
Through intelliHR’s inbuilt automation and analytics, we’re able to quickly ascertain the ongoing mental health and wellbeing of our staff [during a COVID-19 lockdown]. Particularly relevant now more than ever, we’ve been able to use intelli to identify who in our team may need additional support, and act accordingly. In this era of remote work it’s critical that we can maintain a view of how our people are faring, especially during times of uncertainty.
Put your wellbeing checks on autopilot by scheduling recurring employee pulse surveys.
2. Employee satisfaction
How to measure it
Employee satisfaction is another key indicator of employee engagement and culture, but with more of a role/job focus.
The key with employee satisfaction is to measure it regularly, so that you can build up a picture of each employee’s baseline “happiness” and identify any shifts or changes over time.
- Use short, regular pulse surveys (once a month or every couple of months).
- Use a rating scale for employees to rate their happiness in their role (we recommend 1-10 as this will provide a granular rating that will be more sensitive to subtle changes, versus 1-5).
- Provide the option for staff to give additional feedback with open-ended questions.
What to do with the data
- Compare relative movement in satisfaction of an individual over time instead of absolute scores between staff. For example, if an employee who is typically a 10 out of 10 drops to a 7, that might mean something very different to one that usually sits at an 8, and drops to a 7.
- Examine satisfaction alongside other data points. Is lower satisfaction linked with lower goal completion and wellbeing? Are there particular teams, locations or job roles where satisfaction is higher?
- For those teams with higher satisfaction, how can you replicate what’s working well across the rest of the organization?
How to measure it
eNPS or employee net promoter score measures loyalty, which is basically a combination of how a staff member is feeling in their role and whether they feel the company is looking after them.
- Ask “How likely are you to recommend [company name] as a place to work to your family and friends?”.
- Ask a follow-up question to give employees the opportunity to provide additional feedback e.g. “What’s one thing we could do to make you happier at work?”
- Conduct eNPS pulses twice a year at a minimum so that you can be responsive to trends and issues.
What to do with the data?
Often eNPS is treated as a standalone score, but if you collect quality data across lots of different variables in your organization, then some further analysis will allow you to get a lot more out of it.
- Consider scores across age demographics, gender, department and location Look at eNPS over time – are there particular times of the year that eNPS is lower? Does this look the same every year? Are there other things occurring in the business that coincide with drops in loyalty?
- Follow up on feedback. If there are common issues make sure you do something about them and if there are things you need to confirm or clarify, ask!
- Check out our eNPS explained guide for more.
Happiness scores change day-to-day, whereas overall loyalty to a business stays fairly stable regardless of whether you’ve had a couple of bad days or a temporarily large workload. But if bad days or a big workload continue over a long period of time, then there would be a trend that eNPS will identify.
People performance directly relates to organizational performance, and being able to understand the first one will help you identify what time and resources you need to allocate to optimize both.
Here are the key variables and questions to explore.
- Overall, how is the business performing along the indicators or dimensions we deem as important (e.g., safety incidents in a mine, sales outcome, budget)?
- Are goals being set and met? Do goals align with overall organizational strategy? Do they contribute to business success or objectives?
- Are there people we should recognize and reward? Are there staff or managers that might need support?
- What performance issues are occurring? Are there common themes or areas that require more attention or training? What would the cost-benefit of this be?
- How are leaders performing on key dimensions (e.g. leadership, team performance, team engagement, team development, team compliance?). Are there areas that need coaching, investment or improvement?
- What skills gaps do we have in the business and where?
4. Business performance
How to measure it
Measuring performance is not only useful because it gives you granular information about how to support or develop an individual employee, but it also gives you aggregate insight into overall business performance.
- Use a continuous performance management framework to conduct regular check-ins and reviews.
- Ask your employees to rate themselves on key indicators such as productivity, teamwork, quality, compliance and values.
- Ask supervisors to rate employees across the same variables so that you can compare the two.
What to do with the data
- Filter by business unit, supervisor, pay grade and location to identify if there are any patterns or trends in performance across entities.
- Explore how performance trends over time. Are there particular times of year when it drops?
- Look at performance by tenure. Are there particular milestones or times in the employee lifecycle when performance drops? How can you enhance the employee experience to protect against disengagement?
How to measure it
Tracking and measuring employee goals or OKRs in your organization is a key indicator of how engaged staff are, their level of productivity or performance and any issues with workload or expectations.
- Have staff set goals at a regular cadence (i.e. quarterly), or cascade goals down from a team or organizational level.
- Break goals into bite size components – what individual tasks need to be completed to achieve the larger goal? For a tender for example, that might include competitor research, drafting the tender, getting input from legal, review, and submission.
- Define success. Is it simply completion or is there a meaningful metric or KPI that could help define the measure of success?
What to do with the data
- Use goals to understand your employee’s career aspirations and personal development goals.
- Identify if any employees need training or support. Are there commonalities or trends across particular roles or teams that could be solved with a training course?
- Are goals being set? If not, this might indicate that staff are too busy, overwhelmed or not invested in the long-term success of the organization.
- Are goals being completed on time? If not, is there an issue with the frequency of check-ins, or workload?
- Are goals aligned with organizational strategy?
6. Skills gaps
How to measure it
Identify which skills are critical to your organization, who has them, and where there are gaps that need to be filled through employee development, training or hiring.
- Create a library of all skills and competencies in your organization with proficiency levels (i.e. beginner, intermediate, expert, want to learn).
- Assign skills to staff when they’re hired, when they upskill or let them do it themselves.
What to do with the data
- Create a skills matrix to understand where your core business skills are distributed, how many people have them and at what level.
- Use skills profiling to identify potential internal candidates for a job.
- Use the skills matrix to identify skills gaps, learning and development opportunities and to plan your L&D budget.
- Use your skills matrix and gap analysis to inform succession planning, to ensure key skills don’t leave your organization when employees do.
Measuring risk and compliance so that you can manage and minimize risk involves identifying and mapping out where in your organization there are potential risks.
Key variables and questions to ask:
- Is there a risk that someone might get hurt in or by our organization?
- Has someone reported an incident or something important that we should do something about?
- Are people or leaders not completing important tasks such as policy sign-offs, setting goals, giving feedback, reviewing probation etc? Are their team not completing tasks?
- Are we breaching any laws, acting unethically, or not being responsible?
- Are we at risk of losing our top performers, key post holders or business critical staff?
- What attrition trends should we investigate to reduce the risk of future regrettable leavers?
- Do we have people in our business subject to disciplinary processes? How are we managing these and what are the costs to the business?
- Do we have people in our business not taking annual leave regularly?
How to measure it
Understanding attrition in hr, the rate at which people are leaving your organization and why they’re leaving, feeds into almost every single other aspect of HR – recruitment, performance, onboarding, engagement, etc.
There are multiple formulas that HR uses for calculating attrition or employee turnover (intelliHR lets you choose between the three most common ones below).
Employee attrition formula 1: Full employee monthly count
The denominator is based on the full employee count for any given month. Employee count is determined by the count of employees at the start of the month plus the count of all new employees started in that same month.
Employee attrition formula 2: Monthly average employee count
The denominator is based on the average employee count for any given month. An employee count is made at the beginning of the month and the end of the month. This is divided by two to determine the monthly average employee count.
Employee attrition formula 3. Period start date employee count
The denominator is simply based on the employee count at the beginning of the date range selected.
What to do with the data
This data is incredibly powerful if you layer it with other data sets and employee information, like your onboarding data, for example.
- Look at average tenure and when people are leaving. Is it in the first three months? The first 12? If your early turnover rate is high, then you may need to invest more in your onboarding and training.
- Explore turnover by leader, location, business unit, pay grade and more. Does a particular supervisor have a far higher retention rate than others? Why might that be?
- What are the common reasons for turnover? Is it voluntary or involuntary?
- Look at time trends – does turnover happen at a particular time of year (i.e. just after bonuses or around times of year with particularly high goals or targets)?
- What’s your average cost of turnover? This can be quite a complex calculation once you consider all of the hard and soft costs, but we’ve created a free Employee turnover cost calculator to make it easy.
- Survival analysis – how likely is someone to stay in the business and what are the deciding factors that might determine whether they leave or stay?
Be aware! Although data can help remove bias, it can also introduce bias when it implicates someone as a “problem”. For example, if there are lots of staff leaving under one leader, that doesn’t necessarily mean they are a “bad” leader. Perhaps they haven’t been trained effectively, or maybe the team dynamic is the problem.
One of the principal parts of HR is planning – people, roles, time, budget, and being able to demonstrate a return on investment of your people and activities.
Key variables and questions to ask:
- What is recruitment costing per person? Where are we spending and are we getting value?
- What is the true cost of recruitment by source, leader, and over time (cost/tenure)?
- What are we investing into training and what is the impact? Are we getting value for our dollar spend? What budget should be set per person and for the business?
- Where are our human capital resources? Do we have headcount gaps now or coming up in the future? What is our succession plan?
- How are our overall wages trending? Are they increasing per person and by how much? How does this look across roles, teams, business units and location?
- How is unplanned leave impacting our organization? What are the reasons and financial cost?
- How much has attrition actually cost the organization?
8. Training and development investment
How to measure it
When employees are engaged, challenged and growing, your organization will grow too. Measuring training investment helps to put a figure on both the monetary and time costs involved in employee development.
- Quantify all of your training (even internally conducted) to assign a dollar amount by hour.
- Track training completion in a tool like intelliHR (you can even link online training products like Go1 to automatically feed into intelli).
- Ask staff what training they want/need.
What to do with the data
- Calculate your total training investment – how much have we invested in training in terms of indirect and direct costs?
- Calculate the average training cost per employee. Do certain roles or seniority levels require more training than others?
- Calculate the average number of training hours per employee. Which types of employees are receiving the most training? Is it fair and equitable?
- Compare training metrics with goal achievement. What effect has the training had on goal achievement and/or performance?
- Analyze training against attrition. Are employees who’ve received training more likely to stay? Were there training needs not met for exiting employees? What can you implement to address this in the future?
9. Remuneration trends
How to measure it
Analyzing your remuneration data can provide valuable insight to inform hiring, promotion, training and more. In addition to actual remuneration, other data points that are valuable to track/capture include employment type (e.g. contract, full time, part time) time since last remuneration change, tenure, as well as job and demographic data.
What to do with the data
- Analyze remuneration changes. Are there particular teams or business units that are increasing salaries more than others?
- Forecast expected remuneration spend for the future. Does our remuneration budget reflect expected growth in the business?
- Examine remuneration by recruitment source and attrition. Is one recruitment source consistently costing us more money? Do these employees typically turn over faster than others, thus delivering lower ROI?
- Track time since the last remuneration change. Are there any roles or business units with longer duration between pay rises
- Calculate the normalized impact of a pay rise – this tells you how a pay rise might feel to them. e.g. 10% pay rise might sound great, but if it’s the first one an employee has had in 10 years, it might only feel like a 1% increase to them. (intelliHR calculates this automatically, see how).
10. Workforce distribution and organizational growth
How to measure it
Can you easily see how many staff you have and how they’re distributed? What about how your organization is changing and growing?
To analyze your workforce distribution and organizational growth doesn’t require any specific calculations, you simply need accurate, real-time data in your HR software that automatically updates when employees join, exit or change roles.
What to do with the data?
- Track how many people are joining your organization and how many people are leaving.
- Examine what demographics are coming in (e.g., sex, age, culture, work type). Are your processes biased toward a particular demographic? What’s the gender representation around leadership?
- How diverse is our workforce (gender, gender in leadership roles, race, sexual orientation)? How diverse is your organization?
Part 4: Visualization, application and avoiding bias
Data visualization and HR software
Most HR platforms provide some level of HR reporting; however, many are still providing only operational, basic reports and dashboards. In fact, Fosway Group reports that over 85% of HR professionals indicated adopting HR analytics was a challenge because of their current solution.
What you really want to be looking for is HR analytics tools (like intelliHR!) that provide not only the tools for collecting quality data, but also have the capability for data augmentation – to analyze and integrate your data across the 4 pillars of analytics.
intelliHR does this, and takes it a step further, using AI and machine learning to provide predictive analytics and insights from the data to inform strategic, unbiased, data-driven decision-making.
How machine learning can help reduce biases
There are a number of unconscious biases that operate in workplaces and HR, and many of them are so deeply ingrained that they often exist outside of our conscious awareness.
Types of bias in HR and people management
- Compassionate bias: when feeling pity for someone impacts our perception of other aspects of their skills and abilities.
- Recency bias: evaluating situations or performance based on what’s happened most recently (a big danger with annual performance reviews!).
- Proximity/distance bias: tendency to favour those closer in time and distance to us.
- Similarity/ingroup bias: tendency to favour those who are more similar to us.
- Confirmation bias: looking for information to confirm a pre-formed opinion and overlooking information to the contrary.
- Halo effect: the impact of a single negative or positive characteristic or event on the entire evaluation.
- Negativity bias: humans are more alert and impacted by negative events and information than positive.
It can be challenging to fully remove biases, and the danger of this is that leaders might not be fairly and equitably treating staff (even if they think they are). This is where machine learning and automatic insights (like those generated in intelliHR) can help.
Machine learning is where we feed data and information in the form of observations and real-world interactions to a computer and it improves its learning over time. Essentially, it gets smarter the more data it receives.
“The beauty of machine learning is that it can provide unbiased insights into your data, which is essentially the wisdom of its learning,” says Glenn.
These insights are tangible, actionable takeaways that help you to correctly understand, interpret and problem solve based on your data.
“It’s the “ah-ha” light bulb moment that helps you to turn numbers into comprehension and tools for problem-solving.”
Take a 250 headcount business, for example, with turnover that was roughly 10% of their staff (a score that HR was proud of).
- First analysis of attrition data showed turnover was attributed to mostly one particular location. For that location, they were turning over roughly 60% of the team, mostly sitting under one supervisor.
- When layered with offboarding survey data from both the leader of the team and employee showed high levels of negative sentiment analysis with the language being used as to why they’ve left as well as quantitative responses around the key motivator.
- Comparing this against costs, revealed $150,000 cost of turnover (and when including time investment this figure increases to $300K+)>
- Trends and insights showed lack of training, turnover in first 1 2 months and ‘bullying’ flagged in engagement surveys of those that turned over
You can never remove the human element in business, but adding in machine learning and data tools delivers power, rich, unbiased insights.
Inspiring action through storytelling: A case study
Part of the challenge of numbers – and analytics – is making them meaningful. So that you can convince C-suite to uptake your new initiative, allocate more budget or allow you to hire more staff.
This is where storytelling and the power of (good) data visualization is absolutely essential.
To illustrate this point, take this nation-wide corporation that used to report on lost time due to injury.
Each month they would present the numerical figure in hours in their HR report and to the board. Some months it might be 50 hours, others it could be hundreds, and it didn’t go much further than that.
But then one manager flipped the way they were reporting the metric to tell a story. Instead of lost time due to injury, they reported “we hurt X number of people at work this month”, which made everyone start listening. They made the data meaningful, and put the human at the centre of the picture.
Along similar lines, here at intelliHR we don’t just report on staff’s wellbeing at a numerical level, we translate that into the number of staff not coping, which, while confronting, is more likely to get people’s attention so that you can do something about it.
If you want to inspire action, don’t just report the numbers, use the data to tell a story.
Amplifying intelligence in your organization
HR analytics and data shouldn’t just be kept for HR, the benefits and learnings should be available to be leveraged throughout your organization, in particular by C-Suite.
- Opens up access to attrition, team engagement, happiness, skills development, remuneration spread, training needs.
- Helps allow an additional data point of context for HR when interpreting the data, directly from the leader/s.
- Self-diagnosis and correction of internal biases when presented with your own data insights and patterns of behavior.
Although the approach to HR and HR data has been through a major transformation, it’s not over yet.
Glenn says that the next evolution of HR data will be decentralizing it out from HR.
“In the next few years I think we’ll see the access to and visualization of people data available to all leaders in an organization – both upper management and C-suite as well as line managers.”
This will open up opportunities for leaders to analyze their own activities, self-check biases and diagnose issues.
“If a manager can see that two people in their team have consistently lower satisfaction scores, or that they’ve given everyone raises except two people, they can start to ask why.”
“Decentralized HR data will not only empower leaders to act on insights about their team, but to proactively improve their leadership capability.”
But don’t worry, HR won’t be out of a job! HR’s role will be to facilitate data collection, educate around the comprehension of the data, and most importantly, to support strategic action. With management more invested in the outcomes, it follows that they’ll be more invested in the data collection too, so all of sudden the sole responsibility of sending performance check-ins and employee feedback pulse surveys won’t lie with HR alone.
Another thing that Glenn sees changing is the way HR uses their software tools, and the unification of data across these tools to get a big picture view.
“Right now, many HR professionals see “all in one” as the ultimate solution, because, at first glance, it appears to simplify things. But two very effective tools are better than one that’s less effective and does everything.
“So I think we’re going to see HR start to choose best-of-breed tools that talk to each other and build their own HR ecosystem so that they can get a complete, rather than fragmented view of their people.
“The more we improve the tools and processes we use to collect data, the more accurate and valuable it becomes because you’ll have a larger, more powerful data set.
Good data in equals good data out. Great data in equals great data out.
Taking the next step
Now that you understand the importance of HR analytics, how HR metrics fit into not just HR strategy, but overall business strategy, as well as the questions you can answer and problems you can solve, it’s time to start using them! Although it might seem like a lot at first, the key is to start with one or two analytics and build from there (and having the right tools to support you).