Using Data to Drive Better Decisions: A Developer's Guide to Applied Analytics
Data-driven decisions are only as good as the data and the reasoning behind them. Here is how I think about analytics in software products — from collection to insight.
Using Data to Drive Better Decisions: A Developer's Guide to Applied Analytics
Every product generates data. The question is whether you use it. I have seen products where engineers instrument everything meticulously and product decisions are made with precision. I have seen others where data is collected, stored, and never looked at. The difference in outcomes is dramatic.
Here is how I think about data-driven decision making from a software engineering perspective.
Instrument First, Analyse Second
You cannot analyse data you did not collect. Instrumentation should be built into your product from the start, not added retroactively.
What to instrument in a web application:
- Page views and session duration
- Feature usage (which features are actually used)
- User flows (where do users drop off in your onboarding?)
- Error encounters (which errors do users hit most?)
- API response times by endpoint (p50, p95, p99)
- Database query times
- Cache hit rates
- Queue depths and processing times
- Signups, conversions, retention
- Feature adoption rates
- Revenue events (if applicable)
In Work Log Pro, I instrument every meaningful user action — creating a work log, completing a project milestone, generating a report. This gives me a clear picture of which features drive engagement and which are ignored.
Structured Logging Over Print Statements
The difference between useful logs and useless logs is structure. Unstructured logs are hard to search, filter, and aggregate:
# Useless
console.log("User logged in: " + userId);Useful
Structured logs can be queried like a database. You can ask: "How many failed login attempts from IP x.x.x.x in the last hour?" or "What is the average login time for users in West Africa?"