Data-Driven Design for Workplace Productivity

Database design has its roots in accounting; ledgers became spreadsheets, which became applications, which then became data platforms. As a result, we track performance through transactions and deliverables, because they can be counted. So instead of tracking HOW work is done (and how effectively things are done), we track WHAT work has been done, making it very hard to improve. This 'original sin' of database design leads to so much cultural dysfunction because the people doing the work are not necessarily the people credited with the work. It doesn't have to be that way; we can design dashboards that demonstrate 'self-evident' value - but only if we can show how people work; behaviors, the sequence of behaviors, and the cycle times between them. This would allow us to understand systems the way an economist would and in ways we could systemically improve the entire system - with actionable insight that gives managers specific issues and opportunities to address. Behaviors are the fractals of culture and we are starting to better understand the impact of culture on organizational performance. https://lnkd.in/ejKmSNDt

Data ≠ Insight Dashboards ≠ Decisions Tracking ≠ Understanding   Every time I see another company proudly say “We’re data-driven,” I want to ask: Driven where?   Collecting data is easy now. Everyone has it. You still have to sort it, arrange it, build something with it. And ideally, tell a story someone can actually understand and act on.   A story that shows where users get stuck. A story that reveals where productivity leaks out. A story that drives real improvements because it connects data to the people it impacts.   You need structure. Context. Story. Relevance. That’s been a core obsession of mine with Germain UX.   Turning raw data into narratives that help teams spot friction, recover lost productivity, and improve experience. Not just for “users,” but for teams doing real work.   Because metrics are only useful if they lead to better decisions. And stories help us get there.   #AI #UX #DataDriven

Output per man/woman hour of work - the classic productivity definition. It works well on assembly lines but not for knowledge workers. How can we fix this? Matthew Boyle and Alexandre Tanzi at Bloomberg write a good summary of the issues on productivity measurement for knowledge workers. But what's missing is how to correct this problem so employers can adjust their operating processes and working environments based on objective measures, not subjective surveys that are everywhere in knowledge work. See the entire article with this link; paywall removed: https://lnkd.in/gk79a6un Note the chart in the article on US Productivity - Short-Term Noise. I presume it is based on the non-farm sector of BLS reporting (approximately 160 million people). But 40% +/- of non-farm labor (approximately) includes people who almost always need to be on-site with their employers and will be performing work that cannot primarily be classified as knowledge workers. Building maintenance workers, retail clerks in stores, bus and taxi drivers aren't doing knowledge work. What's needed is real, objective data generated by data collected from the workplace, integrated and supported by AI, and designed uniquely for each organization based on its mission and values. A case in point is Ericsson Ericsson is a global leader in telecommunications and IT. They use a data-driven approach to continuously transform their operations and improve their customer satisfaction, quality, and efficiency.  Ericsson discovered the positive results of distributed work during the Pandemic and built them into their current workplace programs: - Their knowledge workers were able to work remotely and flexibly, using digital tools and platforms to collaborate and communicate. - Knowledge workers were more productive, satisfied, and engaged when they had more autonomy and choice over their work environment, schedule, and tasks. - Ericsson adopted a hybrid work model, where their knowledge workers can choose to work from home, office, or other locations, depending on their preferences and needs. Results of Ericsson's overall continuous operational improvement and workplace strategy in a recent evaluation: - Improved customer satisfaction by 15 percent and reduced customer complaints by 50 percent - Increased quality by 25 percent and reduced rework by 30 percent - Enhanced efficiency by 20 percent and reduced lead time by 40 percent - Saved costs by 15 percent and increased revenue by 10 percent