Monitoring Individual Employees Isn’t the Way to Boost Productivity

Monitoring Individual Employees Isn’t the Way to Boost Productivity

Ever since workers moved from offices to work-from-home setups, companies have worried about how they’re spending their time. Many have bought invasive “productivity monitoring” software to keep tabs on people — logging keystrokes, timing how long they’re away from their computers, even watching them through cameras. But these tools are about control more than productivity. They reflect a “squeeze ’em” view of work that uses superficial metrics that measure busyness, and employees know it — and often resent it. There is, however, a better and more empathic way to use data to improve productivity: look at where the company can improve, not individuals. By using anonymized data about how people work — where they encounter friction, where broken processes make their jobs harder — companies can reveal what about their systems isn’t working. In other words, this data should be used as a mirror, not a microscope. To do this effectively, in a way that aligns company and employee interests, employers should: make individuals anonymous, collect data at the team level, make participation opt-in, share the data with workers, invite collective problem solving, and empower teams to use this data on the local level.

Ever since the pandemic moved workers to home offices and away from their bosses’ watchful eyes, companies have worried are people really working as much as they’re supposed to? Despite surging productivity in the work from home era, this anxiety has persisted. For relief, companies have increasingly turned to new digital tools that can apply a level of oversight that even the most hovering of managers couldn’t achieve. These tools, marketed as “productivity” measurement applications, identify employees by name and, track how they spend their time — logging keystrokes, counting messages, recoding their screens, even logging when they step away from their desks for bathroom-breaks.

In theory, employers can use this information to optimize how people work. In practice, though, these tools are often a blunt instrument that communicates keep working because you’re being watched (quite literally in some cases: employers have used cameras in their employees’ homes to monitor their work). The logic of this approach is straightforward: hold people to account on exactly how they spend time and squeeze productivity from every second. Approaches like these personify a popular belief that management and teams are often at odds with each other.

There are two major problems with this “squeeze ‘em” approach.

First, it’s oppressive and will likely demoralize workers, who rightly feel their privacy is being violated and that they’re being intensely (if ambiently) micro-managed. It shouldn’t be hard to see how this environment could eventually drive people to quit.

Second, in-office work activity can be a poor proxy for actual productivity. By focusing just on the actions people take, these tools completely ignore the environment in which people are working. Managers are often attempting to juice productivity within fundamentally broken environments — full of fragmented or non-standardized processes and tasks, user-unfriendly IT applications, poor UX design, bottlenecks, and other factors that make work harder and slower. No single employee controls these variables and is instead subjected to them. Therefore, not only do these productivity tools fail to fix what is broken, they don’t even surface the real issues.

Looking at how work is done — even down to the granular details — can be valuable, and collecting employee data can be an essential piece of that. But a “squeeze ‘em” approach that focuses on individual productivity is incomplete, lacks nuance, cannot scale, and does not reveal the full truth of how people get their work done and what ails them.

Instead, companies need to turn the question around and ask whether their work environment supports their people in being productive. And the key, to doing this is empathy.

The Importance of Empathy
Identifying and fixing sources of friction in a work environment leads to more productive teams. Using empathy to understand work from the point of view of the people who are doing it can reveal what in the environment is broken and how those breakdowns affect people. This approach is also compatible with the motivations of the team — to be motivated, happy, and engaged at work. But understanding work from this angle necessarily means asking different questions.

Consider questions that productivity tracking software can answer:

How many minutes did Tim spend on the phone?
How long was Tim away from his desk? How many times?
How many keystrokes did Tim execute today, before lunch vs after lunch?

First off, all of the answers to these questions will be about Tim, not his team. Answering them strips away his privacy and the minute-by-minute audit sends a message that he’s always being watched, never just trusted to do his job. Moreover, these questions ignore the environment he’s working in and don’t surface how the work is being done, what’s broken in those processes, and how it might be improved.

Now, consider questions that focus more on the experience of the work:

Is inefficient process design causing the team to do the same work in multiple ways?
Where does the team need training / mentoring to serve customers better?
Is bad user interface or user experience frustrating and slowing the team’s response to customers?
How are technology problems slowing down my team? Is the team equipped with adequate tools for collaboration or problem solving?

These questions can be answered with aggregated — and anonymized — team data, so individual privacy is protected. They focus on the environment in which people work, and the common broken patterns of work that exist across the team. And it avoids the petty minute-by-minute accounting of time spent.

Broadly, empathy means focusing on a team, not an individual, and focusing on the environment in which people work and not on the specific actions of an individual. Hence, an empathetic approach inherently is bottom-up, inclusive, contextualized to each team’s local experiences at work, and protects the privacy of each user and limits the possibilities of what kinds of analyses are possible with the data.

In my recent research, my team found that the proliferation of apps in organizations is forcing users in Fortune 500 companies to toggle between apps as many as 3,600 times a day to get their work done. As a result, they are constantly required to distract themselves when they work.

There are two ways of looking at this situation.

The squeeze ‘em approach:
Accept that “this is how we work” and push workers to produce more in a broken system. This means creating individual IDs for employees, then monitoring the volume of activities they complete and how quickly they progress through them to ensure they don’t “waste” any time. In fact, get them to possibly toggle more! And if this proves to be too much of a headache outsource the work to another company and make it somebody else’s problem.

The empathic approach:
Ask: Is this how we want employees spending time? Will our environment motivate them to do their best? To fix the above problem, look for where work is poorly structured, applications are fragmented, and data is scattered across multiple systems. Fixing these problems involves a range of solutions including automation, investing in fixing IT systems, or even changing how future versions of enterprise software are built.

There’s a cost to forcing people to do pointlessly hard and unproductive work. For one, it often demotivates people, especially if they don’t have opportunities to learn new skillsets. But on top of that, in the example above badly designed work causes a 9-10% loss of annual productivity. Auditing how quickly employees can finish a transaction might lead to incremental improvements — for a while anyway — but it won’t address what’s really wrong with the system.

Using Data to Find Better Ways of Working
Data and technology have created powerful new ways to understand how work gets done. Monitoring tools have focused on individuals, but the real opportunity is for companies to use them as a mirror to understand their own systems. While monitoring creates an oppositional relationship, analytics to understand work environments (or how people experience work) can also be used in a way that aligns employees and the company: more efficient, less frustrating work environment is a boon to both worker experience and productivity.

Most companies already have a precedent for how to do this. Retail companies invest millions of dollars to map customer journeys. Previously, this was done through surveys and interviews. But to do this at scale, they use technology and data to understand customers’ experiences and make those experiences efficient, intuitive, and pleasant. Just like customers, digital workers create volumes of data everyday as they work, and employers can use this to build a work graph, or a digital map of how teams experience work, to see where processes are broken and make them better.

To do this responsibly and empathically, employers must adhere to the following rules when building a work graph.

Anonymize every user. Make it so you can’t pin-point or name a user, and don’t use screenshots or videos of a user’s work. Identifying a user by name violates their privacy and reduces their trust in the system.

Aggregate data from users to a team. In aggregation lies anonymity. The focus of this process should be on identifying patterns that are common within a team — not auditing an individual’s use of time — with the goal of improving work for the entire team.

Make opt-in the default mode for all users. Participation in providing data to improve their teams’ work patterns should be totally voluntary. This ensures that users will participate only if they perceive value in the exercise.

Eliminate information asymmetry. Open up your analysis/data/insights to end-users from whom you collect data. Let them see what this data says about the team, their friction points, and how you’re going to help the team, and talk about this work in townhalls. Demonstrate to your people that the work graph data is about changing the experience at work for teams and is not meant to identify and harm any individual.

Shift towards collective problem solving. Involve your employees in deciding what action to take to reduce friction in the team’s daily experiences. For example, if your data suggests high variability in the work patterns, discuss with your teams whether they need additional training or if this work has to be streamlined. Empower each team to build and access its own work graph to incentivize each team to solve its own local problems.

Localize patterns and actions to each team. Understand challenges local to each team and adapt solutions specific to the team. For example, a top-down one-size-fits-all diktat of “all teams save 10% costs by automation” may not work. Instead, there may be teams where training may be the better option.

These rules espouse an empathy-first approach, which differs from previous tools because the source of the data is democratic i.e., users voluntarily participate in providing data, and the diagnosis and corrective actions are aimed at how teams experience work rather than how an individual does work. Moreover, it’s aligns the interests of employees and the company, both of whom benefit from fixing what’s broken workers’ jobs.

How the C-Suite Can Scale Empathy
Whether its enhancing growth, retaining talent, managing costs, or managing organizational change, empathy lies at the heart of every CxO’s mandate. Typically, members of the c-suite build an understanding of employees’ experience by interviewing users — which is prohibitively hard to do at scale — or through the use of monitoring tools that invade employee privacy. The work graph, on the other hand, is an organization-wide digital map that can help CxOs understand how teams experience work at scale without violating employee privacy. As such, it offers a new source of data for CxOs to manage and drive change within their organizations.

There are a few extra steps that can be taken to make this tool really work for building an understanding of how employees experience work across the organization — in other words, to scale empathy.

First, it has to be deployed organization-wide, and getting good data about the environment in which people work has to be a first-class concern. Empowering teams to build and access their own local work graph can help, because it lets teams see the utility of this tool and empowers them to use its insights. But the important point here is that reaping the full benefits of this approach — being able to act on its insights to make the work environment better — requires deploying it both widely and well.

Second, use work graph data as a point of conversation in both executive meetings and employee townhalls. Soliciting employee feedback can provide important insight and context, deepening the c-suite’s understanding. The point here is to demonstrate that the work graph data is about changing the experience at work for teams and is not meant to identify and harm any individual.

Finally, use these insights to make more empathic management decisions that involve your teams. For example, when promising earnings-per-share increases to shareholders, use the work graph data to test your hypothesis whether the productivity promises you make are actually viable and whether they address issues that teams on the ground face today.

Too many companies jump to trying to squeeze employees without considering how they might be making their work harder. But trying to squeeze more productivity from a broken system can only get you so far, and at a high cost. The instinct to use data to improve productivity isn’t wrong, but done poorly it can quickly erode trust and morale. For companies to realize the potential gains this data can really offer, they need to be willing to use it to take a hard look at themselves. In the end, that produces better results for everyone.

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