People data and people analytics is at a cross-roads. Whilst many professions and functions are data-rich and are maximizing how they use data, new research coming in June from the CIPD seems to show that HR continues to miss the opportunities that people data presents to the function. And this isn’t just a problem for HR.
In my view organisations in which data of all types is centralised, analysed and reported risk losing the unique perspective that data literate HR professionals may provide. The use of people data without the perspective of the “people function” is potentially harmful to employees.
Who is responsible for people data matters because if HR professionals aren’t the ones enabling and informing workforce related decisions (and enabling senior leaders by sharing their expertise) then the value HR is lost.
This risks organisations making poor or even damaging work related decisions. So should HR take control of people analytics and make it a core part of its expertise, or should it cede control to centralized data-science functions and instead become a “learned customer” of analytics outputs? Which route is best for the profession?
In academia the emerging view seems to be that HR should maximize outsourcing from the function and create integrated data-sets, of people, finance, operation and customer data. Some leading organisations are combining data functions centrally, building cross-functional relationships through people and finance data, and even utilising AI and machine learning to automate people data reporting – and these are all expected to be steps in evolution of analytics practice.
But many HR teams aren’t even off the starting blocks with their practice – there are still many barriers preventing practice from having an impact.
Some of the barriers above have been known for some time – CIPD research has shown that technology, skills and investment in analytics have all previously been highlighted as preventing progress in HR towards being data savvy. And now with high-profile data-related scandals, such as that of Facebook and Cambridge Analytica, have highlighted to us all why debates on data and its use are critical.
Facebook has shown some significant issues customers and consumers face, but what about employees? Are there hidden risks associated with the use of people data that HR has yet to face up to?
Thanks to Google, the days of excessive interviews are over.
Finding the right candidate for the job can be a challenge. Sometimes, it can feel as though you are looking for a needle in a haystack — and inevitably, the decision needs to be made quickly.
When the interviewing process drags on, work is left incomplete, other employees are stressed and overextended, hiring managers are impatient, and top talent is flying off “the shelf.” But, this is not a decision that you want to make out of haste. Getting it wrong can be not only extremely expensive, but also a drain on your whole organization — especially if you hire a toxic employee.
It’s no wonder why Google, in an effort to avoid toxic hires, would subject candidates to a grueling 12-interview process.
A Staff.com articlerevealed that Google receives more than two million job applications each year. Based on the ratio of applicants to hires, landing a job at Google is roughly 10 times more difficult than getting into Harvard.
While putting these candidates through the ringer might have made Google feel better about its hiring decisions, the exhausting process didn’t help its already overworked employees or its candidate experience.
Hit fast forward, and Google found that after four interviews, the statistical likelihood that an additional interview would improve a candidate’s chances of getting an offer dropped. In other words, “four interviews were enough to predict whether someone should be hired at Google with 86 percent confidence.” For every extra interview, the law of diminishing returns set in.
This research, in addition to a couple of other experiments, led Google to implement the “Rule of Four.” This change resulted in a two-week reduction in the average time-to-hire, saved Google employees thousands of hours in interviewing time, and helped reduce the already tedious process for candidates.
Let’s explore the significance of these results further.
Time-to-hire is calculated by taking the total days positions are open and dividing them by the number of positions filled.
For every extra day a position goes unfilled, your organization is spending unnecessary money on recruiting fees, marketing/advertising costs, and on resources (consultants, technology, or other employees) — not to mention the opportunity cost.
When you have as many open positions as Google, it adds up.
Freeing up employees.
Interviewing is a considerable time commitment. It takes a lot of effort to prepare, participate, and provide feedback. That’s valuable time employees could be spending elsewhere.
As one of the only non-renewable resources, time is something that every organization needs to be respectful of, especially since those on your interviewing panels are more than likely your organizations top performers.
Click here to continue reading Michael Schneider’s article.
There’s a lot of confusion about the difference between HR metrics and analytics. In this article, we will explain both concepts, how they relate, and how both can add value to the practice of Human Resource Management.
Definitions of HR metrics and Analytics
HR metrics are measurements used to determine the effectiveness and efficiency of HR policies.
Metrics help compare different data points. For example, if turnover was 5% last year and is now 7.5%, it has increased by 50%. The former are data points, the latter is the metric.
Metrics don´t say anything about a cause, they just measure the difference between numbers.
HR analytics, also called people analytics, is the quantification of people drivers on business outcomes. Analytics measures why something is happening and what the impact is of what’s happening.
Consider the following example:
This example starts with an opinion: “I feel like a lot of people are ill this month!” This opinion, however, is a gut feeling, it’s not a fact. The person could be mistaken.
Data helps to factualize the opinion. When we look at the data, we see that absence levels are at 12% this month. With this data we still can’t make a value judgement. We don’t know if 12% is high or low.
To understand this, we need a norm (a.k.a. compare with a second data point). For example, when we know that the company average is 8.5%, and the national average is 4%, we know that this month is a really bad month – and that there’s a potential problem in the company! This is where the metrics come in.
Another metric could be ‘cost of lost productivity due to absence’. For this, we need the following numbers:
Assuming that the organization has 60,000 employees with an average annual labor cost of $45,000, the formula for this month alone is:
Analytics helps identify causes and how it impacts the business. For example, the number of people who report flu-like symptoms has significantly increased, or the number of flu symptoms reported in the company increased at similar rates as in the country. This information helps to identify a cause.
The final step is insight. What will we do with these numbers? First of all, cost of absence is far above the market average. It is so high that it threatens the competitive position of the company. Second, we should try to reduce the absence of employees in a flexible manner in case a new flu epidemic sweeps the land. This can be done by fighting the cause or by fighting the symptoms: sponsoring flu vaccinations or enabling flexible deployment of on-call workers.
This example shows how HR metrics and analytics relate.
Analytics tracks the effects of metrics on business performance
To properly explain the relationship between HR metrics and analytics, we’ll use the HR value chain. The value chain shows different kinds of HR metrics and how they impact business performance.
On the left, we have the so-called efficiency metrics. They show how efficient HR is in its work. Examples include cost of training, cost of hiring, number of applications, average years until promotion, et cetera.
In the middle, we have effectiveness metrics. They tell us how well HR is performing its role. Outcomes include employee retention, employee engagement, employee performance, et cetera.
The difference between efficiency and effectiveness can be described as HR input and HR output. However, the third category, the impact metrics, are the business impact of everything HR is doing. These are the results that count and that influence the (long-term) viability of the company.
Everything we do in HR needs to serve these business goals – which can differ between organizations.
Now where does analytics fit in? Analytics tracks the effectiveness of HR metrics on HR and business outcomes. It helps to answer the following example questions:
How does learning & development investment impact sales performance for my account managers?
Will quicker promotions help us retain our top talent?
What can we do to retain employees and thus save money?
How can we best improve customer satisfaction through smart people processes?
All these questions can be answered using analytics and the aforementioned HR value chain.
How to get from metrics to analytics
Now you have a basic understanding of the difference between metrics and analytics, we’ll finish with how to get from metrics to analytics.
Start with your data: As you know now, metrics are the relations between data points. In order to start with metrics, you need to have your data right. Smart HR system design and high data quality are key components to improve before you invest into getting your metrics ready for HR reporting.
Getting the metrics right: This step sounds easier than it is. Measuring basic data is easy but keeping track of more complicated metrics, like the % of unwanted turnover, is something a lot of companies are struggling with, as it requires them to combine multiple systems (their main HRIS and their performance system in this case).
Select the relevant KPIs: The second step is to select the HR Key Performance Indicators that matter most for your business. These KPIs should be connected to business goals. For each KPI a target score should be specified.
Identify areas where analytics adds value: You can leverage the data and metrics to add value using analytics. This starts by identifying a business case that, when solved, would add value to the business. This means that your outcomes need to be actionable.
Implementation of results: Once you’ve completed your first analytics project, you can implement the results in the organization. At this point, you’ve leveraged your HR data to create value for the organization and you’ve added to the organization’s strategic goals.
Technology is opening up a new world of opportunities for HR professionals, speakers told the CIPD HR Analytics Conference yesterday.
But beyond the oft-discussed need to become more cognisant with data, what are the more profound ways HR departments can use the multitude of analytics available to them to effect real change inside their workforces? Here are six key ideas that emerged.
Get to the root of employee engagement
Analytics can help HR link employee engagement to changes in the business, according to Jonathan Ferrar, co-founder and chief executive of analytics firm Insight222.
He explained that by using natural language processing to look at the actual words in freeform answers on surveys, one telecoms organisation discovered that its workers had not been equipped with rain gear.
“By providing rain gear, the company increased the value for the customer by £1.8m and saw a decrease in attrition,” said Ferrar.
Encourage HR to build an inquisitive company culture
Colin Strong, head of behavioural science at Ipsos Mori, warned that there is a “power in data that makes it sort of unarguable”.
“We all need to manage a bit and say that data should not be the final word,” Strong said. “It’s there to guide you. It’s a way of supporting decisions. Some people will start deferring to it and forget all the judgement and values that have gone into it along the way.”
He suggested that businesses work towards a richer model of ethical decision-making by seeking out human perspectives on data.
Enable HR to work closely with other departments
Luk Smeyers, co-founder of iNostix by Deloitte, said HR professionals would benefit from taking a leaf out of marketing colleagues’ books when dissecting data.
Smeyers brought in marketeers to work on his HR projects and said the practice allowed HR to look at data through techniques that, while new to the function, had been used by other departments for years. However, he admitted that some practices had been difficult to push as it “goes against what a lot of HR does”.
Boost productivity in a human way
Policing employees to boost productivity is not what HR should focus on, according to Victoria Pile, vice president and group head of HR systems at Capgemini.
Pile said she was more interested in creating a better workplace than measuring individual employees, and would openly ask people for their input and gather anecdotal evidence.
By presenting this data back to leaders, Pile said she can show them what they can do to create a better workplace instead of focusing on where individuals need to be more productive.
Click here to continue reading Maggie Baska’s article
Since many HR leaders don’t have a good sense of how to get value from analytics, they move it off their plate by passing it to the HR reporting team or to some newly hired data scientist. Not surprisingly, HR leaders are not familiar with the role the average HR professional plays in analytics so they don’t involve them at all.
Here are why these mistakes are detrimental to analytics success:
Mistake 1: Asking HR Reporting to do analytics without sufficient resources
When organizations are unsure how best to approach analytics, the accountability for analytics is put on the people already handling data — the HR reporting team. It might make sense to combine analytics and reporting; however, the reality is that most HR reporting teams don’t have the mandate, tools, or skill set to do analytics.
Hoping that a team already up to their necks in producing routine reports can suddenly start doing sophisticated analytics, such as predicting turnover or using machine learning, is unrealistic.
Mistake 2: Hiring data scientists and asking them to “do people analytics”
Data scientists will be happy to work with the IT team on integrating data sets and playing around with a data warehouse in the hope they’ll bump into something interesting. This kind of wandering around in data is both expensive and unlikely to yield helpful results.
Analytics needs to be closely tied to business issues. This means the people leading analytics must be business people who understand the goals and key performance indicators of the organization – rather than just analytics wizards.
In fact, it makes sense to have analytics as part of the HR strategy group because this ensures it focuses on the most important issues.
Mistake 3: Overlooking the role of the average HR professional
Even companies with effective analytics teams and effective HR reporting teams sometimes make slow progress because these groups are not closely connected to the average HR professional.
The analytics team can only do so much and they’ll quickly be overwhelmed if the average HR professional cannot handle everyday analytics on their own.
Welcome to the 5th edition of our ‘Most Trending Articles’ of 2018! There have been a lot of great articles in the past month, including articles on ONA, the role of the HR Analyst, red flags in analytics programs, and more. I loved reading these articles, and I’m sure you will too. Happy reading!
#5: This is why psychological knowledge is essential to success with People Analytics
Starting off at our #5 spot, we have an excellent article written by Morten Kamp on the importance of psychological knowledge in People Analytics.
More and more people are emphasizing the importance of analytical skills within HR Analytics, but Morten Kamp actually rates psychological skills higher when looking at the core skills of an HR Analyst.
He discusses in-depth, with several examples, on why he believes this. You can read the entirety of his article here.
#4: 10 Trends in Workforce Analytics
At our #4 spot, we have an excellent article written by Tom Haak about the upcoming trends in Workforce Analytics. Workforce analytics is developing and maturing, but how?
Tom Haak mentions several interesting trends, such as the focus on productivity and transparency. He also covers the cracks in the top-down approach, expectation management, and more.
#3 The role of Organisational Network Analysis in People Analytics
In February, we hosted a great webinar on Organizational Network Analysis (ONA) by Michal Gradshtein. There was quite some buzz surrounding this webinar, which comes as no surprise as ONA is becoming more and more mainstream. It’s a great time to learn more about this topic, as it will only grow even more important over the next few years.
In this article, David Green explores the role of ONA in People Analytics. He answers practical, yet important questions such as:
What is ONA?
Why is ONA growing in importance?
What is ‘Active’ and ‘Passive’ ONA? Which one should we use? Can we use both?
What can we use ONA for?
What case studies on ONA are available?
Where can I find out more about ONA?
If you want to learn more about the practical application of ONA, read the rest of his insightful article here.
#2: What is the role of the HR Analyst?
At our #2 spot, we have an excellent article written by our very own Erik van Vulpen, about the role of the HR Analyst. In our HR Analytics Academy, we have a course specifically made for the budding HR Analyst, but what exactly is an HR Analyst?
In his article, Erik describes the different facets of an HR Analyst. He describes in-depth the multiple tasks and competencies an HR Analyst needs to possess to succeed in his field. Furthermore, he talks about the differences between an HR analyst and an HR business partner.
More and more people want to become an HR Analyst, but they are not necessarily aware of how to become one, what the salary range is, and how a possible career path would look like. All these topics, and more are discussed at length by Erik in his article here.
#1 Ten red flags signaling your analytics program will fail
Coming in at #1, we have an article written by McKinsey Analytics about the ‘ten red flags signaling your analytics program will fail’.
These days, it’s rare that a CEO wouldn’t know that business must become analytics-driven. Many business leaders have been charging ahead with investments in analytics resources- and teams.
However, far too often, CEO’s believe that the main challenges to becoming analytics-driven are behind them due to these investments. They fail to realize that many of their investments will not pay off due to ineffective management of the analytical capabilities of their company.
In their article, McKinsey Analytics have gathered 10 red flags that showcase what, and how, things can go wrong despite best intentions.
I loved reading this article, and I think you will too. You can read the entirety of the article here.
More companies than ever are using workforce data to analyze, predict, and improve performance. But as organizations start to use people analytics in earnest, new risks are also taking shape.
People analytics has been advancing steadily over the past few years, and today it appears to have hit the mainstream. Eighty-four percent of respondents to Deloitte’s 2018 Global Human Capital Trends survey now view it as important or very important to their organizations, making it the second-highest-ranked trend this year.1
Sixty-nine percent of surveyed organizations are building integrated systems to use worker-related data to analyze, predict, and improve organizational performance; 17 percent have real-time dashboards in place to crunch the avalanche of numbers in new and useful ways.
Leading organizations are mining a rich variety of sources to create a comprehensive architecture for listening to employees, thereby providing new insights about the entire employee experience, job progression, career mobility, and performance.
But while such tools offer tremendous opportunities, there are also significant potential risks. The European Union’s new General Data Protection Regulation (GDPR), for one, can make the mere existence of people data in a company’s systems a risk.
Organizations are approaching a tipping point in their use of this data; those that tilt too far could suffer severe employee, customer, and public backlash, with the potential for lasting brand damage.
Risks New and Old
This year’s Global Human Capital Trends survey yields some important insights about the data-related risks facing organizations today.
For instance, 64 percent of respondents report they are actively managing legal liability related to their organizations’ people data; six out of 10 say they are concerned about employee perceptions of how their data is being used.
But only a quarter report their organizations are managing the impact of these risks on their consumer brand.
Fears over employee privacy appear to be justified. One employer, for instance, installed body heat detectors at desks to track how many hours people spent in the office.
Employees reacted with outrage, swamping managers with complaints and leaking unflattering stories to the media. Many employees also fear sensitive data may be vulnerable to high-profile cyberattacks—again with good reason.
While 75 percent of surveyed companies understand the need for data security, only 22 percent report having excellent safeguards in place to keep employee data safe.
Other types of risk are more insidious. For example, some experts worry algorithms and machine-based decisions could perpetuate bias due to flaws in the underlying data or the algorithm itself.
Click here to continue reading Josh Bersin, Erica Volini, and Jeff Schwartz’s article.
That is, you invest in workforce programmes to increase employee competencies (“the how”) which in turn delivers increased employee and organisational performance (“the what”).
The role of HR analytics is to calculate whether your HR programmes do in fact raise employee competencies and performance. If the analytics shows that your programmes are not improving performance, it provides guidelines on how to fine-tune them so that they do.
Faulty competency and performance management frameworks
Over the past 15 years, I’ve asked many conference and workshop audiences the following questions about their performance management and competency frameworks:
1. Performance management: To what extent do you believe in your organisation’s performance ratings as measured say by your annual performance review? Do they objectively reflect your real behaviour, and are they a fair unbiased basis for your next promotion and salary increase (as opposed to your manager promoting whoever they feel like promoting)?
2. Competency management: To what extent do you believe that your organisation’s competency framework accurately captures the competencies required for high employee performance in your organisation?
By far the vast majority of audiences tell me that they believe in neither their competency nor performance management frameworks because, for example:
1. Competency frameworks: In most cases, the competency framework was brought in from outside and not created by managers who understand the real competencies required for high performance in their particular organisational culture. Thus their managers don’t believe in their competency framework and use it as a tick-box exercise.
Furthermore, since the framework covers multiple job families, it is unlikely to predict performance across different job families e.g. is it really likely that salespeople and accountants require the same competencies for high performance? Research shows that these lists should contain very different competencies; yet most organisations use a “one size fits all” possibly with minor adjustments.
2. Performance management: Performance ratings are ultimately the subjective view of an all too human line manager. What chance then do employees have of an unbiased performance rating? (Vodafone is a notable exception here where evidence for performance ratings are verified by multiple people).
Furthermore, many organisations use forced performance distributions meaning that only so many people can be high performers. Who can blame high performers for not believing in their performance management system or their chances of promotion when the forced distribution says “sorry but the top bucket is already full”?
GIGO: Garbage In, Garbage Out
So here’s the problem: if like most people you don’t believe in your organisation’s competency and performance management frameworks, then you certainly aren’t in a position to believe in the results of statistical analysis based on data generated by these frameworks. As the old acronym, GIGO says, Garbage In, Garbage Out.
Click here to continue reading Max Blumberg’s article.
A couple of years ago I was working as a HR controller for a large organization. We decided it was time to start working on strategic personnel planning, so I built a model. The model turned out to be terrible. In this article, I’ll explain why it failed and how we should have fixed it.
My model failed because I was building it on missing, unreliable, outdated data records. In the HR administration there were faults in overhead categories, productivity data was missing, the headcount was incomplete, et cetera.
Building assumption upon assumption led to a model that was as reliable as a slot machine.
Unfortunately, a lot of other organizations suffer from the same problem. Research by Experian Data Quality has shown 75% of the organizations believe that incorrect data is obstructing an excellent customer experience.
Yet 65% of the organizations will wait until problems in data quality arise before action is taken.
If you really want to start making an impact in HR, forget about complicated analysis and predictive modeling for now. Start with building a solid administration that is complete, accurate and topical. And yes, we can use algorithms for that, so it doesn’t have to be boring at all.
Why (HR) data is often flawed
Let’s start with why (HR) data is often flawed. In my experience, everything that is not continuously cleaned will result in a mess at some point.
It is no secret that organizations administrate masses and masses of records. In the end, the bulk of those records get typed in manually at some point in the process.
People that have to administrate a lot of records make mistakes, typos, forget things and take shortcuts (for example by filling in a dash or a zero in a required field because it will help them submit a form quicker).
Figure 1: Top reasons for flawed data according to Experian Data Quality Research (multiple answers possible)
In practice organizations generally notice this when they really, really need the information for a report, analysis, and so on. Usually, that’s when they take action to clean up their data.
In my example: when we noticed the overhead categories were flawed in the administration we did what most organizations would do.
We made an analysis of the records that were flawed, we instructed our administrative team to correct the flawed records, we ran a check to see if the records were corrected and we even instructed the administrative team to pay closer attention in the future.
And we left it at that.
A comparison that springs to mind is that of shoplifting. In practice, most organizations hire a ‘security guard’. Like the security guard that does his incidental round, they employ a controller or business analyst that performs an analysis on a specific field, creates a list of flawed or missing records, instructs the administrative team and goes on to perform the next analysis of a different field.
The risk is that the second the security guard takes a smoke break, the shoplifters return, and so does the flawed or incomplete data. The next step is to advance from ‘Ad-Hoc Improvement’ (the security guard) to ‘Continuous Improvement’ (24/7 security cameras).
Use case: Continuous Improvement at Leiden University
One of the leading organizations in Continuous Improvement is Leiden University in the Netherlands. Rob van den Wijngaard and his team from the Financial Shared Service Center have built a series of algorithms to continuously monitor and improve on flawed or missing records.
An example: in Finance double invoices can bring unnecessary costs to an organization. One of the algorithms they use continuously scans the administration for potential double invoices.
It checks the reference, vendor, and amount of the invoice. Each element can result in either no match, a hard match (the element is exactly equal to another invoice) or a fuzzy match (the element is very similar to another invoice).
Based on the number of matches a potential double invoice automatically gets flagged by the system (called an ‘exception’). Leiden University uses dozens of algorithms like this (checking on matches, empty fields, incorrect data, et cetera).
The fact that the scans are performed automatically means that a lot of different aspects of the administration can be examined on a continuous basis, without taking up precious resources.
Next to the algorithms Leiden University is currently also implementing a digital workflow to handle the exceptions. The exceptions will get sent to the administrative team automatically.
For each exception the system logs who has done what and why (laying the foundation for Continuous Auditing). That way not only the exceptions are being monitored, but also the follow-up.
This not only ensures exceptions are being handled but it also offers valuable feedback for the algorithms and process used. If the exception in the example above was in fact not a double invoice the administrative team flags the exception as a so-called ‘false positive’.
The input of the false positives ‘trains’ the algorithms to be more precise. If the same exceptions happen over and over again, it could mean the processes are not clear or need adjustment.
The trefoil model
It is important to recognize that the success of the example above is not only caused by IT. Leiden University is a firm believer of the so-called trefoil model. Continuous Improvement can only be a success when the pillars IT, People & Culture, Management & Organization, and Processes are closely working together.
For example, a motivated administrative team and management that focuses on fixing the flagged of exceptions are equally as important as their cutting-edge software that detects these exceptions.
Figure 2: the trefoil model
Continuous Improvement in HR
The use case above focusses on improving the Finance administration. In practice the quality of the administration tends to get more attention in Finance than in HR. I suspect this has two main causes.
The first cause is that the business case is less obvious in HR. Paying, for example, a double invoice leads to clear and direct unnecessary costs. To some this negative effect seems less apparent in HR.
Additionally, Finance generally pays a lot more attention to data quality because of Audit risks and obligations. However, to say that Continuous Improvement adds more value in Finance than in HR would be a mistake.
An example to illustrate this: Research by SD Worx shows that in Europe 44% of 4.000 respondents have experienced late payment of salary. In 48% of those cases payment was not only late, but the payslip also contained errors. In 79% of these cases the error was discovered by the employee himself.
This example raises a couple of points. The first point is that there is still a lot of room for improving data quality in HR. In my experience the payroll process is the HR process where data quality receives the most attention.
Errors in the payroll process obviously cost money and can raise compliance issues, so generally data quality in this field will receive more attention than for example registration of overhead categories. Just imagine how error-prone other HR data is that receives less attention. It also demonstrates that HR data can result in unnecessary direct costs.
There are other examples. One is the incorrect use of third-party hiring documents. Using the wrong third party hiring document leads to tax fines. Other examples are salary conformity with collective agreements, possible fraud in overtime premiums, et cetera.
Apart from direct costs, flawed HR data will lead to substantial indirect costs. Faults in overhead categories might seem insignificant, but if the result is that the organization is not able to reliably steer on overhead ratios they might prove to be costly mistakes.
We’ve seen that administrations are error-prone. Not just in Finance but also in HR (maybe even more so given a lower sense of urgency).
Flawed data is costly, either directly or indirectly. Luckily nowadays there are examples of organizations that successfully rely on ‘Continuous Improvement’ rather than on ‘Ad-Hoc Improvement’.
So next time you’re building a model based on HR analytics ask yourself the following question: “Is the data that I need for my model accurate and complete at this moment and will it be in the future?” If the answer is “No”, you might want to consider investing in Continuous Improvement first.
Whether you are examining your sales pipeline, the success of a marketing campaign, or employee retention, data and analytics are essential for business decision-making. Despite this, many companies still overlook people analytics when it comes to solving complex business problems.
Yet, despite this clear advantage, only 2% of organizations have reached a high level of people analytics maturity. One reason is that reaching this level of maturity requires connecting thousands of different data points across disparate systems, from HR and payroll systems to financial performance and operations systems.
By organizing multiple data points into one source of truth, people analytics make it easier to elevate HR to the forefront of business strategy.
But before you can reap the rewards of people analytics, you first need to consider how you can shift HR from a siloed vertical within your organization to an outward-looking function that informs decision-making across the enterprise.
Let’s look at one of the examples from the High-Impact People Analytics study.
Jetblue: Harnessing The Power Of Data To Challenge Assumptions
American airline carrier JetBlue is an example of an enterprise that has found the sweet spot for people analytics in business strategy.
The company’s leadership team began question the quality of new hires amid a perceived lack of dependability in terms of absences and delayed flights.
The company’s people analytics team decided to investigate the problem by taking into account as many data sources as possible from the previous 10 years to identify trends. This included:
Customer survey feedback
Unsolicited compliments and complaints from customer emails that identify
individual crew members
Engagement survey feedback
HRIS data (including the experience levels of new hires, transfer rates, attrition rates, and promotion rates)
Applicant tracking system (ATS) data
To review trends on dependability and absences, the team looked at new hires’ individual flight records, including the number of flights scheduled to fly, number of hours in the sky, number of days on duty, delay statuses of those flights and completion factors.
By comparing data for employees with less than two years tenure against those with more than two years of experience, the people analytics team made some surprising findings.
Perhaps most significantly, they debunked established corporate myths, including the commonly held view that new hire attrition was increasing and new crew members were less dependable.
The people analytics team found instead that new-hire attrition had been stable for the past decade and dependability had actually improved.
Rather, absences and attrition had increased across the board because the company had experienced significant growth–but the rate for new hires was stable or better than before.
Bringing together data from multiple systems is essential to getting the full picture.
For JetBlue, combining qualitative data with quantitative HR and business data was essential to understanding this people-related business challenge–and potentially saving the business significant funds in re-training or re-hiring staff.