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How teams can use data to enable flexible work from home policies

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apples new work policy, which requires employees to be in the office three days a week, has stripped their hybrid model of its flexibility. The announcement sparked strong opposition from employees, including the departure of a senior executive who: resigned in protest.

If flexible work policies can improve workplace satisfaction and employee retention, as well as a company’s ability to hire and retain the best talent, why are some companies still forcing them to go back to the office? in a open letter to Apple, one disgruntled employee said the decision was made because of “fear of the future of work, fear of employee autonomy, fear of losing control.”

Bringing employees back to the office is a step back. “Offices are a artifact of the industrial age,‘ said Brian Elliott, executive leader of Future Forum. Rather than forcing a return to offices to regain control of employee output, companies should embrace new ways to improve productivity with technology. With the rise of digital tools, such as Slack and Zoom, it has become easier not only to centralize virtual work, removing the reliance on shared physical spaces and rigid 9-to-5 schedules, but also to track output to measure and improve.

While some jobs clearly cannot be done from home (including construction, transportation, and film production), most fall into a spectrum of flexibility. According to an study by McKinseyFinance and insurance have the greatest potential for remote working, with the ability to do 75% of the time spent on activities remotely without losing productivity.

Effective companies are data-driven

Adjusting work policies for teams and departments can be an obscure, time-consuming task. The companies most likely to succeed in creating flexible work policies are data-driven, continuously collecting data and feeding it back into the system to drive continuous learning.

So how can leaders effectively use data to enable agility? Software engineering teams provide an interesting example. A developer day consists of many feedback loops. Some loops are large, such as validating that a function meets the requirements; while others are minor, such as checking if a code change works in a local development environment. When developers face complexity and friction (such as too many meetings, lack of automation, and linked architectures), the time it takes to complete a single feedback loop increases.

Effective teams optimize feedback loops to get more done in less time, improving overall system throughput. For example, if a team sees a spike in the time it takes to ship a new feature, the team may wonder if the code reviews are being completed in a timely manner? If not, the team can set reminders for code reviewers when work falters, or set a special time for reviews.

The same methodology can be applied to almost every department. Just as engineering teams strive to release software quickly in small batches (elite teams release software) multiple times a day), finance departments strive for continuous close processes to avoid peaks at the end of the quarter. Accounting teams can measure the time it takes to close the books each month and then experiment with processes to bring them closer to continuous close, such as setting up change management, reducing the number of data feeds, and using machine learning to detect anomalies. to detect and signal.

Every team and department is different, so it’s important to collect data that helps teams answer these kinds of questions (and, as a result, understand the right policies and processes that enable flexible working).

Data visibility enables flexibility

As a co-founder of a company that has studied developer productivity among 300,000 developers, I’ve had a unique point of view into the work policy constraints many companies face. For example, we found that: developers worldwide code less than an hour a daywhich may indicate that there is a general need in the industry to protect more focus time for developers.

There are several lessons learned from studying software development that can generally be applied across all departments in determining a strategy to empower individuals and teams with data: protecting privacy, sizing data, and changes over time. measure the time.

Individual data should always be private unless it is shared by that person. Spying on employees is self-sabotage; in striving to answer a question about productivity, it destroys psychological safety and creates a culture of fear. Instead of using data as an arbiter of individual productivity, teams should use data as fuel for positive change.

Even with good intentions, making data visible to improve collaboration can be met with individuals’ fears that it will be used to micromanage it. Managers can communicate the benefits of data collection and measurement by taking a “data for one, data for all” approach. With this approach individual data is never shared with a manager; instead everyone sees the same aggregated and anonymized team data in one dashboard.

In some cases, making data visible can also drive positive change across multiple departments. For example, product analytics have traditionally been locked into product teams. “By making customer data visible to all teams, including product, marketing, sales and customer success, everyone can work within the same context,” said James Gross, co-founder of customer growth platform Variance. Revenue teams can determine metrics such as the conversion rate between product milestones and the average time to complete each milestone. By setting up team alerts when milestones are reached and using those alerts to guide customers through a product funnel, teams can optimize the time to convert product qualified leads.

Context matters, which is why teams need to dimension their data as well. Microsoft’s SPACE frame is based on the idea that software engineering productivity cannot be reduced to ‘one metric that matters’, but should instead be spread across at least three dimensions. Engineering teams can weigh quantitative measures of performance and efficiency, such as code review speed, turnaround time, and implementation frequency, against satisfaction measures compiled from surveys and one-on-one. “Making feedback a regular, cultural practice is essential not only to improve employee happiness and retention, but also to build a high-performing team,” said Andrew Zhou, co-founder of Kona, which conducts daily checks. on mental health for remote teams. .

In a company update last year, Amazon CEO Andy Jassy shared his perspective about the future of work, and it was refreshing: “We’re going to experiment, learn and adapt for a while as we get out of this pandemic.” Teams are their own best reference classes, which is why it’s important to measure changes over time.

By establishing systems to observe short- and long-term trends, teams can determine whether feedback loops are improving and run experiments to measure the effectiveness of flexible work policies. For example, engineering teams investing in better documentation can reduce the time it takes for new developers to get on board, which will be an important factor in how well companies can retain technical talent in the future.

There are many solutions, practices, processes and tools that can help teams thrive in a flexible work environment. Rather than forcing employees to work from an office, leaders should focus on building the digital infrastructure and processes that improve visibility and support teams to make their own decisions.


Brett Stevens is the co-founder of software.coma data platform that allows engineering teams to measure and improve performance.

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