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Does your tech roadmap equip the business with new performance metrics? According to recent Adecco Group research, knowledge workers overwhelmingly want their bosses to measure their performance based on results – not hours. But today's scattered workforce is changing how work gets done. Leaders are struggling to set the performance goals and metrics demanded by this new reality. Effective performance management for the hybrid workforce means flexible goals, data-based metrics, and borderless, organization-wide data ecosystems.
The traditional annual goal-setting process lags behind the nimble digital strategies needed to thrive in this fast-changing world. Today's teams need agile, cadenced, and outcomes-focused goals that flex in sync with market volatility. New hybrid working models, the gig economy, rising automation, and increasingly specialized knowledge work make employees feel more disconnected than ever. (Only 37% of the non-managers surveyed felt that their leaders instill a positive working environment and team culture.) Instead of counting hours at screens, more team-oriented goals can encourage your people to collaborate better while countering feelings of disconnectedness. And remember, today's 'great re-evaluation' means workers today expect more than just a paycheck. With 75% saying that a job with a clear sense of purpose is important, make sure you base goals on something more profound than just the bottom line.
Workers across the globe overwhelmingly want a post-pandemic hybrid working model. But the loss of physical proximity makes monitoring and managing employee performance metrics like work quality, quantity, and efficiency more important than ever. At the same time, digital transformation allows teams to 'work out loud,' improving visibility across domains while reducing the opportunity to hide poor performance. Leaders should integrate tried and tested performance metrics (think: product defects, error counts, net promoter scores, sales numbers) with digital personal scorecards and real-time dashboarding. Collaboration and workflow tools provide transparency around how employees spend their time, whom they contact, and which information they share. But they can also smack of employer surveillance and erode the employer-employee trust that's so vital in these tumultuous times. Employers must balance employee autonomy and accountability while upholding individual privacy and respecting boundaries between work and home life. Ask yourself: will this datapoint help me measure business outcomes, or is it a 'busy metric' that could drive workers to burnout?
People – and their leaders – need greater insight into how they're doing through analytics dashboards, especially now. Yet, in most businesses, information sits in siloes, presided over by the IT department or BI team. It's often inconsistent (marketing has different numbers from finance), invisible (data's collected way faster than analysis throughput), or mired in risk (think: GDPR and its many international cousins). Creating an organization-wide ecosystem of apps and information based on consistent standards is a fundamental prerequisite of effective performance management for the hybrid workforce. It also allows managers greater insight into where the skills gaps might lie, or which workflows might be best suited for automation. Getting your data house in order is a labor-intensive but essential undertaking. This requires a strategic partner with flexible options for defining what needs to be done, providing skilled staffing if you need help, and supporting ongoing success through upskilling and reskilling.
Data-based performance metrics can miss crucial nuances. It can be challenging to tie certain roles and individual performance to aggregated numbers, meaning frequent and regular conversation is more important than ever. Checking in with your employees often, and with an open mind, will help you iron out any kinks in your data, and even spot whether some goals might be too challenging and accelerate the risk of burnout. On the other hand, data and technology play a crucial role in mitigating human bias. Often, unintentional cognitive biases impact decision-making in crucial areas like promotion, the awarding of development opportunities, or compensation. AI and Machine Learning tools can nudge leaders’ in-the-moment decisions toward objectivity, from intelligent dashboards providing enterprise-wide bias analytics to semantic analysis tools that pinpoint gender-based language in employee reviews and feedback conversations.
So, how will your company address changing worker expectations? Do you have the smart systems in place to protect your employees' wellbeing and career growth needs?