11
Generally speaking, measuring and reviewing the performance of employees is done poorly by many companies. Traditional methods such as annual performance reviews are often disliked by both the employees being reviewed and the managers conducting the reviews, and can be a huge waste of time. Intelligent, data-driven HR teams, however, take advantage of data and analytics to better monitor actual performance on a more regular basis (even in real time) and provide feedback to employees in a more constructive, continual and consistent (ie without bias) way. As we have seen throughout this book, it is now possible to measure pretty much everything an employee does in the course of their daily working life. That is not to say you would necessarily measure absolutely everything, as most companies simply do not have the budget and data capabilities to measure everything an employee does, and the risk of alienating your workforce in that scenario would be high. But, with well-chosen metrics, it is certainly possible now to gather an accurate picture of how your people are actually performing and use that information to provide recognition and feedback to help employees grow.
A word of warning before we start
Clearly, measuring people performance with data and analytics can offer a great deal, but it must be applied carefully. There is a fine line between performance improvement and employee surveillance, and companies that have overstepped this mark have faced huge backlashes. Most people do not want their boss to monitor their every move and, in fact, this can be hugely demoralizing for staff, particularly the most self-motivated members of the workforce. To avoid your company coming across as some sort of Orwellian tyrant, you will need to tread a fine line, gathering the data that you really need to give people genuinely useful feedback, without upsetting your workforce or damaging your employer brand. Undoubtedly, this is a difficult balance to achieve and maintain, and my concern is that many employers will not get this delicate balance right. I hope this chapter will help you to chart a course that is appropriate for your organization.
So what can you expect from this chapter? I start by exploring a few key developments in the world of sport, which may give us some hints about where data-driven employee performance may be heading in the future. I then look at two key strands of data-driven employee performance: intelligently measuring employee performance and intelligently reviewing how employees are performing. Finally, I look at the potential backlash concerning using performance data and look at two in-depth case studies: one that demonstrates how not to handle employee performance, and one where the company managed to strike the right balance.
Lessons from the world of sport
Sport is often at the cutting edge of data and analytics, and it provides a useful glimpse of how data can be used to drive very real performance improvements. Coaches across a whole range of sports, from cycling to football, are using data to assess and improve individual performance.
Measuring physical performance and sleep
A number of US National Football League (NFL) teams use an athlete tracking system called OptimEye, developed by Catapult Sports.1 A lightweight wearable device (worn in a small top that looks a bit like a sports bra) tracks metrics such as players’ speed, motion and heart rate, and calculates player exertion. Having these data helps coaches and support staff to identify which players are working hardest in practice and who could work harder, as well as preventing illness or injuries from players pushing themselves too hard. It also means workouts and practice drills can be tailored to each individual on the team. Plus, if a player does get injured, the historical data will help to ensure the player does not reinjure themselves during recovery. OptimEye devices are also used in UK football by many Premier League teams during practice sessions to monitor and design individual training routines, and spot early warning signs of injury. Looking to the future, devices are being developed to monitor things like adrenaline and cortisol (the stress hormone) levels, as well as perspiration levels.
But sport performance is not just about physical exertion; good quality sleep is another critical factor in getting athletes to perform at their best. A Stanford University study found that basketball players who slept for an extra 90 minutes improved both the accuracy of their shots and how fast they could run.2 In football, many clubs give players wristbands to assess sleep quality, in order to assess any potential problems and find solutions that will help to boost the players’ performance.
Moving to real-time analysis
When it comes to analysing player performance in matches, most analysis traditionally was done post-match using video analytics. That is starting to change though as, in 2015, the International Football Association Board agreed to change the rules governing the use of wearable devices, opening up the potential for league and competition organizers to allow players to wear such devices during matches themselves.3 This provides coaching teams with a wealth of new possibilities to track actual player performance during a match and potentially make changes at half-time based on what the data are telling them. The hope is that the use of tracking devices would also help to reduce the number of deaths from cardiac arrest. While in-depth monitoring like this clearly goes way beyond what the average company is capable of, the use of cheap and readily available fitness trackers could change this situation. For example, it is not inconceivable for employers to use sleep data to understand who may be too tired for a certain job, especially if it is a dangerous task. Even in a typical office setting, a critical sales pitch or meeting could be allocated to the employee who is the most rested.
Intelligently measuring employee performance
There is clear evidence that measuring performance delivers real operational and financial improvements, as the UPS example later in this chapter shows; however, ‘performance’ is the critical word in that sentence and companies need to make it clear they are monitoring performance and not individual behaviour. Think about what motivates you as an employee and what would send your satisfaction and engagement plummeting. Personally, I am very self-motivated: give me a goal and I will achieve it, without needing to be hit with a big stick. If I felt my boss was watching my every move to make sure I achieved that goal, I would hardly thrive.
It is a tough balance to strike
If we think of the publisher of this book as my boss for a second, I know I would not be happy if my boss was monitoring how many minutes I spent typing at my computer. On paper, those minutes spent ‘idling’ and not typing would look unproductive, and yet those minutes are critically important for research and organizing my thoughts. If my every keystroke was monitored, I would feel extremely demotivated and disengaged. Not only that, while such monitoring may lead me (at least in the short term) to produce more words a day, the quality of my output would be likely to go down, not up. This is the danger with monitoring employees: self-motivated employees could well be put off and you could end up with the opposite effect to what you intended.
We know that it is now possible for HR teams to understand more about their employees – how they think, what they are feeling, who they interact with, how productive they are etc – than ever before. The opportunities are endless, in fact. So, to improve people performance in a meaningful way without alienating your workforce, it is important to drill down to the right metrics that will drive performance while maintaining employee engagement. Intelligent HR is likely to involve looking at metrics such as what motivates employees, what stops them performing at their best, where people are dissatisfied in the organization etc, rather than metrics like how many hours employees spend at their desk or how long they spend in the bathroom. In this way, data-driven, intelligent employee performance is about finding a more grown-up way of measuring performance, where intelligent people understand exactly what they need to do to help the company succeed, and data are used to see how this is going in reality. Crucially, it is not, and should never be, about punishing individuals.
The IoT and how happy, connected employees are more productive
The rise of Internet of Things (IoT)-enabled devices, particularly wearables, plays a huge role in HR’s ability to effectively measure performance. This can mean measuring physical movements, such as how staff are coping in challenging physical conditions (there is more on employee safety and wellbeing in Chapter 9), or how people are interacting with each other, such as the IoT-enabled badges we have discussed already in this book. Using technology to drive efficiency is not a new thing. In the 1990s, telecommunications company Bell Canada gave phone technicians devices to wear on their wrists that let them enter repair data without having to go back to the computer in their vehicle. And this reportedly saved each technician almost an hour a day.4 But this technology has leapt forward with modern wearables. Research from Tractica predicts more than 75 million wearable devices will be used in the work environment by 2020.5
Creating happier, more productive employees
What particularly interests me is how wearables can help to measure and improve both productivity and wellbeing. As we saw in Chapter 9, happy employees are more productive, and connectivity plays an important role in this – one standout statistic from Chapter 9 being that connected workers are typically 8–9 per cent more productive. One innovative example of this link between connectivity, wellbeing and, ultimately, productivity comes from video-game publisher Ubisoft. The company has trialled measuring employees’ stress levels with a finger-clamp sensor that is linked to a gaming interface. During the test period, the stress levels for one group of users decreased by over 50 per cent.4 Hitachi employees wear badges that house sensors to effectively measure employee happiness.6 The badges gather data on employee movements, such as time spent sitting, talking and nodding, and the company has used these metrics to develop an algorithm to measure happiness. Bank of America used similar technology to identify that call-centre workers who took breaks together were happier and, after instituting a group break policy, saw a double-digit increase in productivity.7
It is about working smarter, not necessarily faster
Wearables are also helping employees to work smarter, and one example of this in action comes from Tesco.8 Workers at the retail giant’s distribution centre in Ireland wear armbands that track the products they are picking, saving employees time having to check those goods off a list, thereby improving their productivity over the course of a day. Wearables also allocate tasks to Tesco staff, telling them what they need to pick next, and prompting staff when an order is short, with the idea being to help employees work smarter, not just faster.
Helping workers complete tasks in a smarter, quicker and safer way is a critical pillar of data-driven people performance management. Digital consulting company Accenture has written about how its collaboration with Airbus helped to boost productivity in one specific area by a whopping 500 per cent.9 The company deployed the latest wearable technology (devices that it calls ‘heads-up displays’) to help component assembly workers access assembly information quickly, whenever they needed it. As a result, workers were able to assemble more components, more quickly, and with dramatically fewer errors.
Other data-driven performance measurement systems
Aside from wearable devices, there are a number of different technologies to help you measure performance. Once again, I want to stress that the idea behind this should be to help individuals and the company as a whole perform better, not to punish individuals who are not performing well. If someone is not performing a task as well as expected, there may be a very good reason for this, ranging from fatigue or stress to systems not working properly or impeding what the employee is trying to do. Data and analysis should help to get at the why of performance metrics, as well as the what.
Tracking computer usage
It is now possible to measure virtually everything an employee does on their computer. Software from Veriato logs web browsing, document use, e-mail use, chat applications and keystrokes, and takes regular screen grabs that are stored for a certain period of time. It also has the potential to alert managers when certain thresholds are met. Personally, I think this is edging very close to the line between what is acceptable for boosting performance and what is infringing on individual privacy. But, speaking to the Wall Street Journal, one Veriato client said the system delivered real benefits.10 Celeste O’Keefe, CEO of Dancel Multimedia, uses the system to measure a team of 16, made up of animators, artists, administrators and salespeople. O’Keefe felt the system allows her team to be more streamlined and focused, and she finds it useful for guiding her people in the right direction. O’Keefe uses the system to skim graphs and screen grabs to spot problems with employee productivity. Often these are the result of someone not being familiar with certain software or systems, thereby identifying opportunities for training and guidance; however, O’Keefe also acknowledged that her using the system had led to at least one firing.10
Productivity tools and apps
Other productivity-related tools include Basecamp, which allows staff to add their upcoming tasks for the day, week or month and tick them off as and when they are completed. This allows managers to easily see what people are working on and how much they are able to get done. Similarly, the Asana app allows managers to assign tasks and track their progress in real time. For sales teams, Salesforce details how many sales calls and e-mails were made in a day and how much revenue has been generated from that activity. Tools like these can drastically help to cut down the amount of time managers and staff need to spend e-mailing each other with updates on projects or holding team meetings.
AI tools that predict performance
In addition, artificial intelligence (AI) lends a predictive quality to employee performance. AI capabilities mean it is now possible to identify characteristics and activities that are linked to high and low performance, and predict relationships between factors like employee characteristics, training investment, employee engagement and performance. For example, predictive analytics company iNostix provides predictive systems that it claims can lead to faster time to contribution, predict organizational effectiveness, accurately assess employee engagement and predict absenteeism or the risk of workplace accidents.
Intelligently reviewing employee performance
The way in which many companies manage employee performance is through traditional annual reviews that evaluate employees against certain key performance indicators (KPIs). Yet, in today’s fast-paced, technology-driven workplaces, annual performance appraisals simply are not working any more. Business moves so much faster these days. It is no surprise then that one study indicated that only 6 per cent of companies thought their performance management processes were working.11 To me, the traditional performance review model is a perfect example of how not to review performance because, by its very nature, the process involves looking backwards far more than looking forwards. Plus, employees dislike annual reviews because they usually have to fill out lengthy questionnaires, and managers dislike them because they are incredibly time-consuming. In fact, an organization’s productivity can dip as much as 40 per cent during the annual review period.12 Now, companies are starting to move away from annual reviews, generating more regular discussions and looking to the future more. Data-driven performance reviewing should be about creating an ongoing dialogue between employees and management, all based on and facilitated by data and evidence. This may include using AI-driven systems, and conducting much more regular (but shorter) reviews, as we will see throughout this section.
A word on linking incentives to performance
Before we get into that, although designing incentive schemes is beyond the scope of this book, I think it is important to note that data-driven employee performance is not about simply hardwiring KPIs and performance reviews to the incentive system. So many companies design narrow metrics that drive all the wrong employee behaviours; when people know they are being evaluated on certain metrics only, those are the activities they focus on, sometimes to the detriment of other value-building activities. Say, for example, I ask my kids to tidy their room and promise a cinema trip in return, but they know I only evaluate how tidy the floor is and never look under the bed or in the cupboard. Which areas do you think they will tidy and which will they ignore? The answer is obvious. And yet employee reviews and incentives are often designed in the same way, which is why, in my mind, it is better to focus on outcomes rather than narrow metrics, ie if the company is performing well and individuals are contributing to that success, then they should be rewarded accordingly.
How big employers are overhauling performance reviews
A number of big companies, such as Accenture and Deloitte, have announced that they are getting rid of the dreaded annual performance reviews and revamping their review processes. This is not unexpected. In a survey Deloitte itself conducted, it found that more than half of the executives surveyed did not believe their employee review systems drove employee performance or engagement.13
New approaches to reviewing performance
But what do these companies use in place of old annual reviews, rankings systems and 360-degree feedback models? The new systems generally focus on the employee in their own role, as opposed to ranking employees against one another or comparing performance to other employees. Many review systems in the past were designed to try to simplify employee performance down to a single number: a rating or ranking. This new breed is more about generating a richer, nuanced view of every employee to facilitate better performance. These new systems also provide feedback much more often. Rather than a single review once a year, they tend to conduct more frequent reviews, for example, at the end of each major project or every month. More frequent check-ins and reviews mean that a manager has more opportunities to steer an employee towards their best performance. These more regular reviews typically take far less time to complete. Deloitte, for example, is using only four questions, two of which require ‘yes’ or ‘no’ answers.14 There is also a new focus on looking to the future, instead of past performance. Rather than reviewing an entire year’s performance in one go, these shorter, more frequent reviews are designed to help employees move forward with their careers rather than looking back on past accomplishments or failures. This means people are no longer dwelling on what happened in the past, but instead focusing on how to improve in the future.
Making reviews more objective
One major problem with standard performance reviews is that a reviewer’s assessment of an employee’s skills says more about the reviewer than the employee. But these new ways of reviewing performance help to remove subjectivity from the process. For example, to combat potential bias, Deloitte has changed its questions to ask what a manager would do with a person (promote them, incentivise them etc) rather than what they think of that person.14
The use of AI in performance reviews
So, many companies have undergone a move away from traditional, metrics-based performance assessment in recent years. Sometimes this is because they have been found limiting, but sometimes it was found that employers and managers are too easily inclined to simply ignore them, if their findings do not line up with their personal ‘gut feeling’ on who they like or dislike.
Reducing biases
Much of the difficulty in assessing performance has been put down to difficulties caused by workplace biases. These are well-documented, conscious or unconscious behaviours that can unfairly influence an assessment of an individual’s contribution to an organization. Race and gender are perhaps two of the most obvious sources of individual bias. Fortunately, they are often quite easy to spot; however, others are more ephemeral, and it may not be so immediately obvious when they are taking place. One is known as contrast bias, meaning an assessor is inclined to compare an individual’s performance to that of their peers, rather than to defined standards of achievement. Another is recency bias, where actions in the recent past are given more weight, perhaps unfairly, than actions which happened further back in time (but still within the period where performance is being assessed). This is where AI can come in, as bias is a human failing that AI does not have to overcome. Kris Duggan, founder and CEO of BetterWorks, which provides an analytical goal-setting and performance assessment platform, believes the traditional annual performance review is behind the decline in usefulness of performance assessment. He argues that an ongoing feedback process is part of the solution, and intelligent, AI-driven systems can help us to achieve this. As Duggan told me: ‘We think that if you can make collecting feedback much more frequent and agile, and more lightweight … and it’s open and collaborative … those things really do drive performance’.15
Machines will not put off conducting performance reviews
One great thing about AI is that it will not treat the job of performance reviews as something to do ‘when I’ve got time’. Unlike many human managers, it will not put off assessments until the last minute; tell it you want an ongoing, 360-degree view of your workforce’s effectiveness and (in theory) that is what you will get. And because AI-driven assessment can happen in real time (with systems monitoring targets, quotas and how these are affected by people’s connections), incentives and praise for good performance can be dished out immediately. If targets are not being met or performance standards are slipping, then intervention can take place before the problem grows and becomes unmanageable.
Implementing more regular, or even continual, feedback loops
In Chapter 8 we saw how short, regular ‘pulse’ surveys from providers like Glint can be used to gather more regular feedback from employees. Technology like this will form a critical part of feedback loops within data-driven HR. But this process works both ways. As well as the company benefitting from regular employee feedback, employees themselves benefit from regular feedback. Regular feedback, be it from a line manager, peers or a mentor, helps employees to understand their performance, feel recognized for their contribution and feel more connected with the company, thereby boosting engagement.
Increasing the frequency of feedback
BetterWorks’ AI-driven tool is again leading the charge in increasing the frequency of feedback. BetterWorks’ implementation of AI is powered by what it calls its ‘work graph’. This is a map of all the connections within a workforce, not just in terms of which employees’ jobs are intertwined, but also where goals and targets are shared. The work graphs’ AI algorithms can be used to track employee goals and progress, and provide comments, nudges and recognition where needed. The system then prompts feedback from the relevant people, such as a line manager. Importantly, the system also recognizes an individual’s preferences for feedback and interactions, such as real-time feedback notifications or batches of notifications. This type of instant or very regular feedback could provide the ideal solution to the problem of annual reviews based on data that are already out of date (like the annual review). Managers can evaluate performance and deliver feedback based on real-time data, and employees can get helpful feedback and recognition also in real time.
Peer reviews in the workplace
Peer feedback is another growing aspect of performance reviews. Start-up company Zugata has developed a software solution that delivers continuous, anonymous peer feedback to employees, alongside mentor recommendations to help them improve their performance.16 The system figures out who individuals work with and asks for anonymous feedback from those peers every week. Tools like this allow team members to communicate openly and regularly with each other, and help employees identify their strengths and opportunities for growth and improvement. And for managers and HR teams, Zugata’s system provides information that helps them to understand wider skills, strengths and areas for improvement, to help them design more effective learning and development programmes. But, as we will see later in the chapter, employee peer review systems need to be approached with caution. When used as part of an employee ranking system, which pits employees against each other, they can be open to abuse and attempts to rig the rankings by delivering negative feedback on peers. But, used carefully, it is easy to see how open, supportive feedback from peers could help individuals to improve their performance, grow as employees and achieve their potential.
Looking at the potential backlash
Of course, there are legitimate privacy concerns about monitoring employee performance. Employees have a right to privacy, and that must be carefully managed alongside the need to better understand their performance. Improper use of data not only can tarnish your employer brand, but also could potentially land you in legal trouble.
Troubling examples
Say, for example, that employees wear badges that track their interactions with customers and other staff. A manager could potentially use this information to identify which member of their team went to HR with a complaint about their conduct. If the manager then fired that employee based only on what the data told them, and with no other grounds, the employee would have an excellent case for unfair dismissal. Or, if employees are wearing fitness tracking devices, for example, there is a danger that health data could be used to discriminate against those who are less physically healthy, regardless of how well they perform in their job. One real-life example of employee monitoring backfiring came from the Daily Telegraph in 2016. Journalists reportedly arrived at work one morning to find motion sensors had been installed under their desks, without any warning or explanation whatsoever.17 The employees’ union got involved and the newspaper’s management quickly removed the devices. I find it pretty shocking that this measure was not communicated to staff prior to the installation, if it had been handled better, all the uproar could have been avoided.
Output is not the same as performance
It is also important to remember that improved performance and increased output are not necessarily the same things. If all you are doing is trying to increase output, with no consideration of employee wellbeing and engagement, the strategy is likely to backfire. And that is especially true in today’s working environment, where 80 per cent of HR directors are worried about losing their best employees to burnout.18
Lessons from Amazon: how not to handle people monitoring and reviews
In the United Kingdom, working conditions at Amazon’s distribution centres have made national news in the past, with stories of workers reportedly walking up to 15 miles during a shift, having their every move monitored by global positioning system (GPS) tracking tags and having just 30 minutes to walk the equivalent of nine football pitches to get to the canteen, eat lunch and get back to the warehouse.19 The company reportedly had the ability to monitor staff during every minute they were on site, including their precise location in the warehouse, exactly how many items they picked or packed, and even how many bathroom breaks they took and for how long. This delivered huge efficiencies for Amazon, but no doubt harmed their employer brand in the United Kingdom.
Huge workloads and secret feedback
In addition, the retailer’s feedback culture came in for significant criticism in a 2015 New York Times article, which focused on the company’s headquarters in Seattle. The article, which featured interviews with many ex-Amazon employees, describes a ‘bruising’ feedback culture that encourages employees to criticize colleagues’ ideas and send secret feedback to their managers.20 According to the article, every new employee has to subscribe to 14 leadership principles, ranging from the not so unusual like ‘think big’ to the slightly ominous sounding ‘disagree and commit’ and ‘frugality’. Amazon clearly wants to push each person as far as possible to get maximum value, and many employees talk positively of how this has helped them to excel. Other interviewees, however, describe a culture where huge workloads and pressure are commonplace, with one saying she did not sleep for four straight days and others reporting working nights, weekends and holidays. According to the article, the harsh performance review culture includes weekly or monthly reviews where individual employees are given lengthy reports (sometimes 50 or 60 pages) on the various metrics that they are being held accountable for. They are then quizzed on various aspects of the report.
Amazon’s internal feedback tool perhaps raised the most eyebrows. Called the ‘Anytime Feedback Tool’, this system allows employees to send positive or negative feedback about their colleagues to management. And while managers would know who sent the feedback, individual employees would not know who had given feedback on them and do not have a chance to see their own feedback for themselves; it is always passed on by a manager. This system is open to abuse. Team members are ranked, and those scoring lowest are reportedly eliminated each year, which means employees are effectively competing against each other for their jobs and everyone feels they have to outperform against everyone else.
Average employee tenure is just one year
It is perhaps no wonder that a PayScale survey ranks Amazon second on a list of companies with high staff turnover.21 According to the data, Amazon employees stick around on average for just one year – one of the very briefest tenures in the Fortune 500. Amazon founder and CEO Jeff Bezos responded to the article by writing a memo to Amazon staff stating that the article did not reflect the culture he knew, and asking staff to report any unfair practices to HR.22 But the high staff turnover indicates that the internal feedback system is having a negative effect on employee satisfaction. For me, one of the main problems with the Amazon feedback system is not only that employees feel driven to outperform against each other, but also that their access to feedback filters down only from their managers. To really help employees grow and improve, they should be able to ask for or access feedback when they need it, using systems like the continuous performance reviews discussed earlier in the chapter.
Lessons from UPS: how to drive performance without alienating people
With vehicle sensors and GPS data, it is possible to know exactly where delivery drivers are, which route they are taking or how fast they are driving, and many companies are routinely using these sorts of data to improve driver behaviour and optimize delivery routes. UPS, however, has taken the use of data and analytics to a whole new level. For example, the hand-held computer that drivers have been carrying for years (those electronic boxes that you sign to say you have received your parcel) is actually a sophisticated device that helps drivers make better decisions, such as which order to deliver parcels in for the most efficient route.23 But it is the delivery trucks themselves that provide a wealth of data about driver performance. UPS trucks are fitted with more than 200 sensors that gather data on everything from whether the driver is wearing a seatbelt, or when the back doors are open, to how long the vehicle spends idling as opposed to in motion and how many times the driver has to reverse or make a U-turn. The company has almost 100,000 vehicles on the roads, delivering nearly 17 million packages to more than 9 million customers every day, with drivers making an average of 120 stops each day.
Big benefits from big data
With this many drivers on the road, improving driver performance so they drive as efficiently as possible means big savings. The company has said that shaving just one minute off the time each driver spends idling as opposed to in motion saves over US $500,000 in fuel across the whole fleet. UPS has also said that same minute adds up to operational savings of US $14.6 million a year. One insight gained from the sensor data was that drivers opening the truck door with a key was slowing them down and eating up valuable time. So the company gave drivers a key fob with a simple push-button to open the doors much more quickly. Tiny time savings like this make a huge difference across a fleet the size of UPS. And the savings are clear. By monitoring their drivers and providing feedback and training where needed, UPS has achieved a reduction of 8.5 million gallons of fuel and 85 million miles per year.24 Plus, while drivers now make an average of 120 stops a day, that number used to be less than 100, meaning the same drivers with the same trucks are now able to deliver significantly more packages than they used to.
Protecting and rewarding employees
This increased performance has been reflected in increased wages, with UPS drivers now earning around twice what they did in the mid-1990s.23 The company is widely regarded as the biggest and most efficient parcel shipper in the world – largely thanks to its innovative use of data – and its drivers are among the best paid in the industry. That no doubt helps to support employee buy-in for monitoring so much of what drivers do. But the company has also had to take other steps to ensure it does not face a huge backlash from drivers; for example, under the terms of drivers’ contracts, UPS cannot collect data without informing drivers of what it is gathering. And neither can it discipline a driver based only on what the data have told them. Sensible safeguards like this would work for almost any type of performance data in any industry. When implemented and properly followed, such safeguards help to facilitate employee buy-in, ensure transparency and minimize the risk of damage to morale or the employer brand.
Six best-practice tips for your organization
Based on what has been discussed in this chapter, the following are six simple best-practice guidelines to help you walk the fine line between legitimately useful performance measurement and privacy-invading surveillance.
1 Be transparent
Be transparent with your employees about exactly which data you collect and how you intend to use them. Be specific on how this data collection will benefit them and help to improve company performance overall. Make it clear that it is about looking at performance, not watching over every little thing employees do, and that it would never be used to punish individuals.
2 Minimize the data you collect
Practice thoughtful data minimization and only collect the data you need to have a genuine impact on performance. You should always be able to justify exactly why you need certain data. If there is no good business case for collecting certain data, do not collect them.
3 Get consent
You must ask your employees for consent to use their performance data. And, once you have got consent, only use those data for the purpose for which employees have given consent.
4 Work with the union
If your employees are members of a union, like UPS drivers are, you will need to consult with the union and gain agreement on performance measurement practices before you implement any measures.
5 Maintain dialogue
Keep your employees informed when you make any changes as to which data are gathered and how they are used. Just because you got their buy-in once, does not give you carte blanche to monitor anything you like in future.
6 Demonstrate clear benefits from the data
Be vocal about successes and show how data-driven performance measuring and reviewing improves the bottom line and helps the company meet its goals, just as in the UPS example. When improved employee performance delivers better financial performance for the company as a whole, reward your people accordingly.
Key takeaways
Clearly, this is one of the trickier areas of data-driven HR, and a lot of careful thought is needed regarding what is right for your organization and what will best help your employees. The following is a summary of what has been covered in this chapter:
· There is a very fine line between performance improvement and employee surveillance, and companies that have overstepped this line have faced huge backlashes. Done badly, measuring performance can destroy employee motivation.
· You should be looking at metrics such as what motivates employees, what stops them performing at their best, where people are dissatisfied in the organization etc, rather than metrics like how long employees spend in the bathroom.
· The idea is to help individuals and the company as a whole to perform better, not to punish individuals who are not performing well. If someone is not performing a task as well as expected, there may be a very good reason for this, such as fatigue or stress.
· The IoT again has a huge role to play in driving employee performance. Connected workers are generally happier and more productive.
· In today’s fast-paced, technology-driven workplaces, annual performance appraisals are simply not working any more. Companies are starting to move away from annual reviews, instead generating regular discussions and looking to the future more.
· Improper use of data not only can tarnish your employer brand, but also potentially could result in legal action, so follow my six best-practice guidelines for using performance data in a fair, ethical way.
We have now reached the end of our journey into the world of data-driven HR. But, before you put this book down and start charting your own way forward, I would like to leave you with a look at where data-driven HR might be going in the future. In Chapter 12, I take a peek at key future trends in data and technology and explore how these might impact on the HR teams of tomorrow.
Endnotes
1 Catapult Sports [accessed 23 October 2017] Integrating Wearable Performance Data into AMS by Catapult [Online] http://www.catapultsports.com/uk/media/catapult-clearsky-wearable-athlete-tracking-applied-indoors
2 Singer, E (2011) [accessed 23 October 2017] Extra Sleep Boosts Basketball Players’ Prowess [Online] http://www.technologyreview.com/view/424608/extra-sleep-boosts-basketball-players-prowess
3 Alvarez, E (2017) [accessed 23 October 2017] FIFA Envisions a Future Where Players Wear In-game Fitness Trackers [Online] https://www.engadget.com/2017/08/03/fifa-epts-wearable-technology
4 Wilson, H J (2013) [accessed 23 October 2017] Wearables in the Workplace [Online] https://hbr.org/2013/09/wearables-in-the-workplace
5 Tractica [accessed 23 October 2017] Wearable Devices for Enterprise and Industrial Markets [Online] https://www.tractica.com/research/wearable-devices-for-enterprise-and-industrial-markets
6 Lee, T (2015) [accessed 23 October 2017] Hitachi Creates Wearable Sensor to Measure Employee Happiness [Online] http://www.ubergizmo.com/2015/02/hitachi-creates-wearable-sensor-to-measure-employee-happiness
7 Frankel, S (2016) [accessed 23 October 2017] Employers Are Using Workplace Wearables to Find Out How Happy and Productive We Are [Online] https://qz.com/754989/employers-are-using-workplace-wearables-to-find-out-how-happy-and-productive-we-are
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