The first instinct when setting up time tracking is usually to build a detailed category list that mirrors every kind of work that actually happens — a dozen or more entries covering every project, every task type, every client. This almost always fails within a few weeks, not because the categories are wrong but because choosing between a dozen similar-looking options, several times a day, is itself a tax on attention that quickly makes people stop logging altogether.
Fewer categories, chosen for the decision they inform
A useful taxonomy starts from the question 'what decision will this data help me make,' not from 'what kinds of work do I do.' If the goal is understanding the split between deep, focused work and reactive, interrupt-driven work, two or three broad categories answer that question perfectly well — adding twelve project-specific sub-categories doesn't make the answer more accurate, it just makes the logging slower and the categorization more arbitrary in the moment.
A common working structure uses five to eight categories total: something like focused work, meetings, communication/admin, learning, and a genuinely honest 'other' or 'unclear' bucket. The unclear bucket is not a failure of the taxonomy — it's a necessary release valve. Forcing every few minutes into a tidy predefined category produces exactly the false precision described elsewhere on this shelf; an honest 'unclear' entry is more useful than a confidently mislabeled one. For a practical software example related to employee attendance tracking software, see the details here.
When more granularity is actually worth the cost
Fine-grained, per-project or per-client categories earn their complexity in specific situations: billing, where the category directly determines an invoice line, or budget tracking, where a project's hours need to be defensible against a specific line item. Outside those cases, granularity mostly serves a feeling of thoroughness rather than an actual downstream decision, and it's worth being honest about which situation applies before building an elaborate category tree.
- Start with 5–8 categories maximum for personal, non-billing tracking; expand only if a specific recurring decision genuinely requires the extra detail.
- Include an honest 'unclear' or 'mixed' category from day one — its absence doesn't produce more accurate data, it produces more mislabeled data.
- Revisit the taxonomy every few months, not every week — categories that shift constantly make trend comparison across weeks meaningless.
- If a category's total keeps growing and you can't explain why, that's more informative than any single day's log — investigate the category, not just the day.
The taxonomy is a lens, not an inventory
The goal of a category system isn't to fully describe every minute of a day — that's an impossible standard that no taxonomy, however detailed, actually achieves. It's to make one or two specific questions answerable at a glance, weeks or months later, without having to re-read the raw log. A taxonomy built for a question is smaller, more stable, and more useful than one built to be exhaustive.
Most of the value in time-tracking data comes from consistent, low-friction categorization sustained over months, not from a single perfectly detailed week — which is the strongest argument for keeping the category list smaller than it initially feels like it should be. For additional background on this subject, consult RescueTime.