machine learning

On Manual Process & Reporting

Data automation has been popular business buzzword jargon as of late, and to some extent, rightfully so!

Data automation has been popular business buzzword jargon as of late, and to some extent, rightfully so! It saves time, creates seamless integration between applications, and can provide always-on, real-time information for making appropriate decisions. But data is only as good as the story it tells, and automation often hinders the storytellers that it aims to serve. Until machine learning surpasses human capabilities, there are key aspects of manual, hands-on project management that can't be effectively replaced by automation (though I'm sure some will try).

Un-Automate Your Data

Let's step back and talk about Metal Toad's project managers and the project status snapshot. Once a week, the team goes through and updates a simple high-level spreadsheet with each project's pertinent data that our leadership team cares about. They supplement that with a short narrative-form email highlighting each project's health. The goal is that the task of updating the snapshot itself is quick and doesn't feel like too much of a grind, though there may be some work involved coming up with the information to populate the snapshot. Our PMs are busy people, so time spent compiling reports needs to be truly valuable. This seems like the perfect situation for some data automation, right? Pull some budget data from our time tracking tool, check our client satisfaction app for unhappy customers, integrate Jira and Trello for backlog sizing and velocity, and spit it all out with the push of a button...

Check Your Work

Yet the project snapshot remains a very intentional manual labor of love for two primary reasons. One is that the data presented to leadership may look the same, but the context behind the numbers can only be fully understood and the narrative effectively communicated when the process of reporting is completed with some old-fashioned brainpower. The other reason is that through the manual compilation of project data, each project manager is forced to double-check their team's work and examine the fidelity of their data. It's a great exercise in proactive project management and preemtive risk identification rather than the burnout-inducing practice of relying on PM firefighting mode, and it triggers actions like sending invoices, checking in with clients on their satisfaction, updating burndown charts, creating budget projections, completing stage gates, and other basic practices that are essential to agency health.

Context is king in communication, and the only way to create context is to understand the data you're communicating. The best way to fully understand that data is to do the work of compiling it yourself!

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