Sleuth vs. minware
Sleuth is a recent upstart offering a fast and easy way to track your DORA metrics.
But, they haven’t adopted modern data methods.
If you want to actually get better at planning, quality, and process efficiency with actionable metrics, Sleuth’s first-generation platform is inadequate.
The problem with first-gen metrics
Sleuth’s first-gen metrics rely on existing data fields (PR cycle time, deployment count, etc.) They will get in you in the right ballpark, but are:
Lagging, unactionable
They tell you what happened, but not what caused it. Why are cycle times slow – is it interruptions, bad estimates, large tickets? How do I get better?
Disconnected from impact
Sleuth’s metrics use arbitrary unit counts (tickets, PRs, etc.), which don’t tell you what matters.
Cumbersome and manual
If you want additional visibility (work type, tech debt, active dev effort, etc.), you have to painstakingly label tickets or log time by hand.
“Instead of AI replacing engineers, maybe it should replace tedious non-engineering work that wastes their time.”
Kevin Borders
Founder & CEO, minware
Next-gen data models offer insights without effort
Next-gen data models use modern methods to derive metrics with higher-level meaning (e.g., active development time per ticket) that are:
Actionable
See exactly where problems lie so you know where to improve.
Impact-Focused
Measure the effect on available engineering time instead of arbitrary unit counts.
Automatic
Compute high-level properties without having to log time, impose mandatory fields, or change the way you work.
Next-gen data models answer real questions
CEOs don't ask about Sleuth metrics like cycle times. Next-gen data models answer real questions like these (links go to live demo):
minQL and BI report builder let you customize anything
Sleuth has nice metric visualizations and lets you configure alerts. However, the customization is only surface level and the reports themselves are locked in place.
All minware reports are built on top of the minQL query language and fully editable. Access any field from any data source to create custom metrics with powerful formulas, including custom event cycle times.
Say goodbye to spreadsheets and SQL.
Zero-effort setup
Sleuth makes it easy to get started, but configuring everything to link and name different aliases can still be a lot of work.
We’ve invested heavily in making minware work out-of-the box with fully automated configuration and no process changes. All you need to do is hook up version control and ticketing systems with a few clicks.
No story points? No sprints? No tickets in PRs? No Org Chart? Different usernames in Git/Jira? Crazy ticket statuses? No problem, we’ll figure it out.
Setup Difficulty
minware
Sleuth
Intensive (Weeks)
Self-Service (Hours)
Zero Effort
Why choose Sleuth over minware?
This question may be the opposite of what you were expecting.
Sleuth does one thing well – DORA metrics – and is an easy solution if your boss is just asking for that, or if you are only responsible for DevOps.
If that’s all you want and you are not interested in deeper, more actionable metrics, then minware might be overkill.
We evaluated similar first-gen DORA metrics tools a few years ago ourselves, but saw how they just don’t make it easy to get better.
If you want to unlock your team’s full potential...
