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Analytics on the Project: The Pivot Point for Development!

#analytics #team #development #app
Wed, May 28, 2025 - 3 min read
analytics on project

📊 Analytics on the Project: The Pivot Point for Development

Archimedes once said: “Give me a pivot point, and I will turn the world upside down.” In large part, feature analytics and documentation are this very pivot point for the development of any project.

Analytics on the project is very important. Even if there is no formal analytics in the project, it’s very important to write it yourself, especially for developers, for the code and features they create.

Why Feature Analytics Matters

1. Understanding How Features Work

When you write analytics for a feature, you’re forced to think about why it’s needed, how it works, and why it’s implemented this way.

Example feature analytics for “Auto-save drafts”:

Goal:

  • Prevent data loss when accidentally closing the tab
  • Improve UX through transparency of the process

How it works:

  1. When typing in the input field, a 2-second timer starts
  2. After input ends, data is saved to localStorage
  3. When page reloads, data is restored

Why we chose this approach:

  • Debounce to save browser resources
  • localStorage for instant saving without server
  • Restore on component mount

2. Helping Future Developers

In the future, people will look at how a feature works, and if it doesn’t work, some won’t be able to figure it out without analytics.

3. Your Own Protection

When in six months you don’t remember why you implemented a feature this way, analytics will save you from rewriting a working solution.

Modern Tools for Writing Analytics

Modern realities make it very easy to write feature analytics. It can be saved in any format:

1. Simple Text Files

The easiest way is to keep documentation in Markdown format in a docs folder in the project.

Example documentation structure:

Feature: Notifications for task completion

Goal: Increase user engagement and improve task completion.

Implementation:

  • WebSocket connection for instant notifications
  • Local storage of notification settings
  • Sound alert for new notifications

Alternatives:

  1. Polling every 30 seconds (rejected due to load)
  2. Push notifications (rejected due to setup complexity)

2. Integration with AI Tools

In the modern world, writing analytics has become incredibly easy. AI tools can automatically generate analytics based on code:

  • Feed code to AI tools
  • Get ready analytics 3 times faster
  • No need to think what to write — AI does it for you
  • Analytics quality constantly improves

3. Integration with Project Management Systems

Analytics can be:

  • Saved in Confluence/Jira
  • Made part of the task description
  • Added to the project Wiki
  • Integrated with Notion or other systems

What to Describe in Feature Analytics?

1. Business Goal

Why is the feature needed? What problem does it solve?

Feature: Export reports to Excel Business goal: Allow managers to work with data in a familiar format User value: Time savings on manual data transfer (~15 min/day)

2. Technical Implementation

How is the feature implemented? Why this approach?

Feature: Lazy loading of images Implementation: Intersection Observer API Why not just scroll events:

  • Better performance
  • Less browser load
  • Built-in throttling support

3. Alternative Approaches

What alternatives were considered? Why was the current one chosen?

Feature: Social network authentication Considered options:

  1. OAuth 2.0 (chosen) — standard, security, support
  2. SAML — complex setup, overkill for the project
  3. Simple iframe — security issues

Rules for Good Analytics

1. Write for Your Future Self

Imagine that you’ll be figuring out this feature in 6 months, remembering nothing, and you need to fix a bug urgently.

2. Explain “Why”, Not Just “What”

Bad example: Using Redux

Good example: Using Redux for form state management because:

  • Complex validation with multiple dependencies
  • Need to preserve state when navigating between steps
  • Easy to test and debug via Redux DevTools

3. Update Analytics Along with the Feature

Analytics is part of the feature. If you change the logic, change the analytics too.

Personal Experience: How I Understood the Importance of Analytics

I used to think that if a feature works, analytics isn’t needed. Then I had to figure out my own feature after 3 months.

I spent 6 hours trying to understand why I implemented a complex caching system that seemed excessive. If I had left analytics, 10 minutes would have been enough.

Since then I:

  1. Always write analytics before implementing a feature
  2. Describe business goals and technical solutions
  3. Document alternatives and reasons for choice
  4. Update analytics with every change

Result: less time spent understanding other people’s features, fewer bugs, faster onboarding for new developers.

Conclusion: Analytics Is an Investment

Feature analytics on a project are not time expenses, but investments in the future. Modern tools, including AI, make it very easy to create and maintain analytics in any convenient format.

Analytics is a love letter to your future self.

Next time you want to skip analytics for a feature or not describe an architectural decision, remember: you’re saving 15 minutes now, but you might spend 15 hours later. Write analytics. Your team and future you will thank you.