Uniting perspectives from operations and IT to drive toward a shared reality.
Last winter, I was brought in to help a client whose operations were feeling the strain from technology challenges and process inefficiencies. The symptoms were visible, but the causes weren’t — different teams saw different realities, and no one had a single, consistent picture to work from.
Because I work hands-on with my clients — often right alongside their teams on the floor — I already understood what a “normal” day looked like. That meant I could design measurements that reflected their real work, not just what the system reports said. I pulled together counts of various daily activities from multiple sources, focusing less on perfect numbers and more on consistent, trustworthy trends.
Some of the most useful data came from unexpected places. For example, overrides — process deviations that stalled work — weren’t easy to extract from any system. But I found that each one generated an email to subscribed end-users. By analyzing thousands of these emails with AI, we could categorize them, identify root causes, and prioritize fixes.
This opened up a second opportunity: while the business waited for technology improvements, some identified root causes could be addressed immediately through targeted training or operational adjustments — freeing supervisors and keeping work moving.
Once we layered business activity data, override patterns, technology error logs, and key project milestones into one visual view, people across both operations and IT could see the same story. We could connect spikes or drops in performance directly to changes in technology or process and measure the impact of each improvement.
The takeaway: You don’t always need perfect data to solve complex problems — you need consistent data that reflects reality. When you combine that with a willingness to dig into unconventional sources, you can uncover solutions that make a difference right now, not just after the “big fix” is deployed.